esa-sceva/satcom-chunk-collection huggingface dataset:
# CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network
## 5 Conclusions  
In this paper, we have developed a new algorithm, named CCAIB, under the framework of AQM to handle the congestion problem for wireless networks. CCAIB takes multiple bottleneck routers into account. The wireless packet loss ratios are estimated by adaptive method and the AQM problem is solved by backstepping procedure. Compared to another congestion control method for wireless networks called H\(\infty\) algorithm, CCAIB is better in queue length convergence time, queue packet loss ratio and window size. Besides, the effectiveness and feasibility of CCAIB in different network environments are validated.  
The relationship of adjacent routers is considered only for simple series topology in this research. Since different topologies have different relationships between adjacent routers, designing the control strategy for more realistic and complex network topologies, such as mesh networks, is still an open problem. Besides, this work only considers the congestion after the router, the case of congestion before router can be explored in the future work.  
**Author Contributions:** Conceptualization, X.D. and L.M.; methodology, X.D.; software, X.D.; validation, X.D. and L.M.; formal analysis, X.L.; investigation, X.D.; resources, X.L.; data curation, L.M.; writing--original draft preparation, X.D.; writing--review and editing, L.M.; visualization, X.D.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Simulation results of multiple routers using CCAIB for different routers. Columns from left to right show the performance of CCAIB in (**a**) queue length stability, (**b**) window size and (**c**) packet loss ratio in the queue.  
**Funding:** This research was supported by the Key R&D Plan of Shandong Province for Major Scientific and Technological Innovation Project (2021 CXGC011205), Major Scientific and Technological Innovation Project of Shandong Province (2019 JZZY010115, 2019 JZZY010120), Technology SMEs Innovation Capability Improvement Project of Shandong Province (2021 TSGC1053) and the NSERC (in Canada).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Applications of Capacitive Imaging in Human Skin Texture and Hair Analysis
## 2 Materials and Methods
### Analysis of Capacitive Image
#### 2.3.3 Hair Water Content  
In previous work [7], the skin water content percentage and solvent concentration are calculated from capacitive images using Equation (4). In that study, a normalized cross-correlation algorithm detects the same skin area across measurements taken before and after the solvent application on the skin, which allows measurement of the solvent concentration in skin more accurately. In order to apply the same equation on hair samples, the repositioning algorithm is replaced with a range threshold [36]. In this way, only the pixels in contact with hair samples are isolated increasing the accuracy of water percentage calculation. Figure 6 demonstrates how this range threshold excludes pixels with bad contact on a single frame of hair sample.  
\[\text{Water Content}[\%]=100\frac{\epsilon_{m}-\epsilon_{dry}}{\epsilon_{ water}-\epsilon_{dry}} \tag{4}\]  
where \(\epsilon_{m}\), \(\epsilon_{dry}\) and \(\epsilon_{water}\) the dielectric permittivity of the sample, the dry sample and this of deionized water correspondingly.
## 4 Correlation Analysis of Shield Tunneling Parameters  
### Correlation Analysis Methods  
Correlation analysis serves not only to identify whether there is a mutual influence between the research objects but also to quantify the extent of that influence. In statistics, it is a method used to study the relationships between random variables, employing the correlation coefficient as a numerical measure [38]. Typically denoted as \"r\", the correlation coefficient ranges from \(-1\) to \(1\). A negative value indicates a negative correlation, a positive value indicates a positive correlation, and zero suggests no correlation. The absolute value of the correlation coefficient, denoted as \"r1\", is used to assess the degree of correlation between variables. When \"r1\" is within (0, 0.2), it signifies an extremely weak correlation; within (0.2, 0.4), a weak correlation; within (0.4, 0.6), a moderate correlation; within (0.6, 0.8), a strong correlation; and within (0.8, 1.0), an extremely strong correlation.  
Correlation coefficient analysis employs methods such as the Spearman correlation coefficient and Pearson correlation coefficient. Pearson correlation is commonly used in statistics but comes with prerequisites: the data must be continuous, follow a normal distribution, and exhibit a linear relationship. The Spearman correlation coefficient, on the other hand, is a rank-based measure of linear correlation that does not require specific distributional assumptions, thus offering a wide range of applications. However, its statistical efficiency is lower compared to the Pearson correlation. Consequently, Pearson correlation is employed for normally distributed data, while Spearman correlation is utilized for other cases.
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 3 Neural Network (NN) Based Elasticity
### Representative Numerical Examples
#### 3.2.2 A-Notched Bar in Tension  
Next, a tensile test of the A-notched bar as depicted in Figure 8 is conducted. The bar is clamped at the left end and a prescribed displacement with an amplitude of \(u=0.02\) mm is imposed on the right end with \(L=1\) mm and \(r=0.25\) mm. The geometric domain of the structure is discretized by unstructured meshes with a total of 306 triangular T1 elements, leading to 189 nodes.  
Figure 8: A-notched bar in tension. Geometry and boundary conditions.  
Figure 6: Compression test of a plate. (**a**) Geometry, boundary conditions, and FEM discretization. (**b**) Load–deflection curve using finite element formulation.  
Figure 7: Compression test of a plate. Contour plot of the stresses \(\sigma_{yy}\) [N/mm\({}^{2}\)] with (**a**) finite element method, and (**b**) neural network formulation.  
Similarly, the A-notched bar stresses computed with the neural network model are compared with those of the purely finite element method. It can be seen from the contour plot of both FE and NN formulations that FFNN predicts the stresses very accurately, as shown in Figure 9.
# Impact of Road Traffic Tendency in Europe on Fatigue Assessment of Bridges
## 2 Real Traffic Measurements
### European Traffic Measurements in Background Studies of Eurocodes  
Besides Auxerre, the most important long-distance motorway traffics considered in the development of the Eurocode were recorded in France, on the A1 motorway between Paris and the Roissy Airport in Garonor; in Germany, on the A61 motorway near the Brohltal bridge; and in Italy, on the A1 motorway, in three locations: Fiano Romano, near Roma, Sasso Marconi, near Bologna, and Piacenza.  
The measured flows on the slow lane in these locations resulted in the range 8500-20, 000 lorries per day, but measured flows on main roads or secondary roads were markedly lower: 600-2000 lorries per day on main roads; and 100-500 lorries per day on secondary roads.  
Representative parameters of the aforementioned traffics: daily flow; mean value; maximum value and standard deviation of axle loads and total lorry loads are summarized in Tables 1 and 2, respectively. Moreover, the study of the available data proved that:  
* HGV long-distance continental European traffic is satisfactorily homogeneous;
* over time, in long-distance European traffic:
* the percentage of articulated lorries tends to increase, opposite to a significant reduction of the percentage of lorries with trailers, which are commercially less profitable;
* due to more rational management of lorry fleets, the percentage of empty lorry trips descends, thus raising the mean vehicle loads;
* the percentage of single lorries, increasingly used on local routes declines;
* as expected, long-distance traffics are much heavier than local traffics;* daily maxima of axle-loads and total weight of vehicles largely exceed the values legally admitted; to all appearances, the ceiling on overloads depends only on the mechanical strength of the vehicles;
* statistical distribution of the axle-load is generally unimodal, the mode being around 60 kN;
* statistical distribution of the total weight is often bimodal, with the first mode around 150 kN and the second mode around 400 kN;
* mean values of axle-load and total weight of heavy vehicles are strongly dependent on the traffic typology, i.e., on the road classification, and are very scattered;
* daily maxima values are much less sensitive to traffic typology: they are in the range 130-210 kN for single axles, in the range 240-340 kN for two axles in tandem, in the range 220-390 kN for three axles in tridem, and in the range 400-690 kN for the total lorry weight.  
It must be also underlined that, as consequence of specific industrial choices of lorry manufacturers, HGV geometries adopted in 1980s practically have remained unchanged until now and the sole remarkable innovation concerns the introduction of LHVs.
# Development of a GPS Forest Signal Absorption Coefficient Index
## 2 Materials and Methods  
### Study Site  
The data used in this study were collected in 15 different forests throughout the United States. Figure 1 depicts the location of each forest and Table 1 provides forest details, including the date of each data collection, the average and standard deviation of both the diameter at breast height (DBH) and tree height, and a brief description.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ID** & **City Vicinity** & **Tree Type** & **Date (ddmmyy)** & **HT/STD (m)** & **DBH/STD (m)** & **Notes** \\ \hline
1 & West Point, NY & Oak/Hickory & 110515 & 23.4/3.4 & 0.30/0.18 & Military \\  & & 100\% Deciduous & 100815 & & & Reservation \\  & & & 241015 & & & \\
2 & IMPAC & 100\% Pine & & & & Managed Forest \\  & Gainesville, FL & Control Plot & 110215/250815 & 5.77/1.4 & 0.09/0.05 & Fertilization \\  & Gainesville, FL & Weeded Plot & 110215/250815 & 8.21/0.64 & 0.12/0.04 & Research plots \\  & & Fertilized and Weeded & 110215/250815 & 8.21/0.64 & 0.13/0.06 & \\
3 & Hogtown Forest & 80\% Deciduous & 050216 & 20.4/2.47 & 0.46/0.08 & Uplands Natural Mixed Forest \\  & Gainesville, FL & 20\% Coniferous & & & Loblobly Woods Nature Park \\
4 & Charleston, SC & 90\% Pine, US deciduous & 230516 & 24.0/3.1 & 0.36/0.05 & Francis Martin National Forest \\
5 & Alexandria, LA & 90\% Pine, 10\% Deciduous & 190616 & 23.2/4.1 & 0.56/0.11 & Kisatchie National Forest \\ \hline \hline \end{tabular}
\end{table}
Table 1: Forest study sites and description.  
The vast majority of data were collected during the summer of 2015 (Table 1). Due to personnel availability constraints, weather conditions varied between each location. In each case, best efforts were made to collect data in the morning or during times with mostly-cloudy conditions to avoid sun glare on the images. During this data collection period, California had a lack of winter precipitation and was in drought conditions. In contrast, the gulf coast region had higher precipitation than usual.
###### Abstract  
In this study, the hull form optimization process to minimize resistance of KCS (KRISO containership) at \(Fn=0.26\) is described. The bow hull form of KCS was modified by varying such design parameters as sectional area curve (SAC), section shape, bulb breadth, and bulb height using multiple parametric modification curves devised by the authors. The resistance performances of modified hull forms were analysed by the viscous flow Reynolds-Averaged Navier-Stokes (RANS) solver of WAVIS ver2.2. With a view to saving computational time during iterative analyses in the optimization process, the sinkage and trim were set to the fixed values which had been obtained for the original hull form with free condition. The validity of such constant sinkage/trim was then verified by conducting analysis for the optimal hull form with free condition. Optimization to minimize the cost function of the total resistance coefficient of model \(\mathrm{C}_{TM}\) was performed by sequential quadratic programming (SQP), which is one of the gradient-based local optimization methods. Utilization of parallel computing led to the simultaneous calculation of the gradient, thereby speeding up the whole optimization process. At the design speed of 24 knots, the optimal hull yielded \(\mathrm{C}_{TM}\) reduction by 1.8%, which is extrapolated to 3.1% reduction of effective power \(P_{E}\) in full scale.  
hull form optimization; parametric modification function; sequential quadratic programming; simulation-based design; KRISO containership (KCS)
# Influence of Category and Sex in Game Structure Variables in Young Elite Tennis
## 4 Discussion
### Mean Set and Game Time  
Similarly, the mean set time showed consistency between sex, reflecting what has been reported at the U-18 international level (34.6 min for males and 34.2 min for females) and among professionals (41.2 min for males and 43.5 min for females) [18]. In terms of mean game time, our results were comparable to previous findings in professional tennis  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **U-12** & **U-12** & **U-14** & **U-14** & **Sex** & **Category** \\
**Variables** & **Male (a)** & **Female (b)** & **Male (c)** & **Female (d)** & **Effect** & **Effect** \\  & _Mean (SD)_ & _Mean (SD)_ & _Mean (SD)_ & _Mean (SD)_ & \(p\) & \(p\) \\ \hline Total Match Time (min) & 97.38 (23.44) & 94.01 (36.60) & 75.85 (19.17) & 77.90 (31.06) & 0.922 & 0.091 \\ Total Set Time (min) & 37.41 (12.72) & 35.67 (11.98) & 33.76 (7.81) & 31.92 (8.10) & 0.503 & 0.205 \\ Total Game Time (s) & 189.66 (91.89) & 171.27 (98.68) & 163.36 (95.45) & 162.07 (94.73) & 0.326 & 0.041 \\ Point Duration (s) & 9.48 (6.98) c & 8.76 (6.57) a,d & 6.13 (6.49) a & 6.41 (7.06) b & 0.790 & \textless{}0.001 \\ Percentage of Active Time & 26.33 (3.99) & 25.03 (2.86) & 18.59 (4.52) & 20.15 (4.52) & 0.688 & \textless{}0.001 \\ Number of Sets per Match & 2.50 (0.55) & 2.50 (0.55) & 2.17 (0.41) & 2.33 (0.52) & 0.583 & 0.149 \\ Number of Games per Set & 9.53 (2.10) & 9.67 (2.32) & 9.54 (2.11) & 8.93 (1.69) & 0.877 & 0.345 \\ Number of Points per Game & 6.77 (2.64) & 6.66 (2.80) & 6.25 (2.70) & 6.54 (2.87) & 0.719 & 0.105 \\ Number of Strokes per Point & 6.75 (5.15) b,c & 6.14 (4.42) a,d & 4.94 (3.67) a & 5.12 (3.87) b & 0.037 & \textless{}0.001 \\ Stroke Frequency per Match & 42.20 (2.12) c & 42.09 (0.91) & 46.44 (2.02) a & 45.35 (2.67) & 0.459 & \textless{}0.001 \\ (strokes/min) & & & & & & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect of sex and category on the temporal and formal structure of the game.  
(174.2-178.6 s for males and 183.8 s for females) [25; 26]. Higher values were found in U-12, and slightly lower ones in U-14, possibly due to differences in point dynamics between the two stages.
## 3 Dataset Explication  
### Data Accumulation  
The robustness of our federated learning model is underpinned by two distinct datasets: The eICU Collaborative Research Database [34] and the Generative Adversarial Networks (GANs) synthetic dataset. The eICU Collaborative Research Database is a large database of de-identified health information from several hospitals, including demographic information as well as patients' vital signs, laboratory data, treatments, and clinical results. This dataset is a complete real-life data source with a wide coverage of illnesses and therapies that occur in intensive care units (ICUS). The data generated using the GANs to mimic real-world patient data in terms of statistical distribution and medical features guarantee the privacy of the patients while at the same time providing a diverse and rich dataset needed for this study. To generalize the model across multiple healthcare settings, the synthetic dataset emulates patient records with different disease states, laboratory tests,and treatment strategies. This augmentation makes the model more versatile, especially in healthcare where the occurrence of many situations may be limited in real-life data.
## 4 Results and Discussions
### Performance Analysis
#### 4.1.1 Bit Error Rate (BER)  
In characterizing data channel performance, it is used as an essential parameter. At the remote end, errors in critical parameters appear while transmitting data from one to another point in wireless link/radio or wired telecommunication link.  
\[BER=\frac{N_{b}(e)}{T_{N}(t_{b})} \tag{15}\]  
where \(N_{b}(e)\) is the number of bits in error, and \(T_{N}(t_{b})\) is the total number of transmitted bits.
## 3 Results  
### Data Extraction  
The process through which the studies were included in the systematic review is described in Figure 1. The initial searches provided 230 papers potentially relevant to the present study. Of these, 117 were identified from Scopus, 87 from Web of Science, 13 from PubMed, and 13 from EBSCOhost. No articles were identified from screening the reference lists of the identified studies.  
The duplicates were removed (N = 97) and 133 entries were screened. After screening the titles and abstracts, 123 articles were removed, leaving a total of 10 articles for assessment of the eligibility criteria. As shown in Figure 1, 123 articles were excluded for the following reasons: (1) 11 for the publication type; (2) 46 for the outcomes (neuropsychological functions, mathematical skills, socio-emotional skills, or life skills); (3) 10 for using an intervention based on other technological tools (particularly biofeedback, virtual reality, augmented reality, qwerty keyboards, robots, musical interventions, video games, brain-computer interfaces, indirect sensing surfaces, and SG-design rules); (4) 9 for the use of SGs for screening (for dyslexia and dysgraphia) and not for rehabilitative purposes; (5) 24 for the population examined (ADHD, pre-school age, dyscalculia, media-associated disorders, cognitive disability, or autism); (6) 1 for language; and (7) 22 for the absence of a control group. After reviewing the full texts, a further three articles were excluded due to not meeting the inclusion criterion concerning the presence of a control group. In addition, two articles were excluded because the first and second researchers contacted the authors three times, but never received a response. We obtained a Fleiss K of 0.77 (substantial agreement level), an almost perfect agreement according to the reference values of [31].  
This selection procedure supplied six articles suitable for inclusion in this systematic review (see Figure 1 for a synthesis of the studies included).
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
###### Abstract  
This paper addresses robust link adaptation for a precoded downlink multiple input single output (MISO) system, for guaranteeing ultra-reliable (99.999%) transmissions to mobile users served by a small cell network (e.g. slowly moving machines in a factory). Effects of inaccurate channel state information (CSI) caused by user mobility and varying precoders in neighboring cells are mitigated. Both of these impairments translate to changes of received signal-to-noise plus interference ratios (SINRs), leading to CSI mispredictions and potentially erroneous transmissions. Knowing the statistics of the propagation channels and the precoder variation, backoff values can be selected to guarantee robust link adaptation. Combining this with information on the current channel state, transmissions can be adapted to have a desired reliability. Theoretical analysis accompanied by simulation results show that the proposed approach is suitable for attaining 5G ultra-reliability targets in realistic settings.  
5G; link adaptation; mobility; precoder; SINR variability; ultra-reliable communication +
Footnote †: journal: Information
# Integrating Knowledge Graphs into Distribution Grid Decision Support Systems +
## 6 Analysis of Outages  
Generally, power utilities are interested in analysing of outages due to adverse weather conditions at a specific location. Quite often, the location refers to a per single cell on a predetermined spatial grid. However, a more appropriate way to determine the effect of outages should consider the number of affected customers [33]. This would provide the ability to estimate the effect of adverse weather conditions on the reliability of the power system by calculating the average system interruption frequency index (SAIFI), defined as the average number of interruptions that a customer would experience.  
Estimation of the impact of power outages can be performed using GridKG. The graph provides easy access to the topology of the power system it represents. This alone allows consideration of differences in locations between power outages and customers. An extreme weather event that leads to a power outage in one grid cell can affect customers in various grid cells positioned downstream to the power outage or protective equipment locations. Therefore, the information stored in GridKG allows for estimation of the number of affected customers, regardless of their physical location.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Iron Sulfide Films  
The SILAR-deposited FeS films were prepared at 0.15 M of the complexing agent (TEA), showing the best nano-crystal structure [74]. The obtained films could be used for solar cell applications due to their excellent transmittance properties and high absorption coefficients (more than 5 \(\times\) 10\({}^{5}\) cm\({}^{-1}\), wavelength less than 700 nm). In addition, these films showed p-type behaviors and had hole mobilities of about 100 cm\({}^{2}\)/Vs.