esa-sceva/satcom-chunk-collection huggingface dataset:
# A PEKS-Based NDN Strategy for Name Privacy
## 5 Experiments and Results
### Measuring Cache Hit Rate and Download Time  
We examine the relationship between the cache-hit ratio versus download time to check whether the in-network caching mechanism is preserved in the proposed scheme. For that purpose, we use the experimental setup shown in Figure 10.  
In the experiment, all consumers, Con1 to Con4, randomly request using 25 content out of \(\mathbb{N}\), which contains 100 names. Each consumer selects a random name and retrieves 100 data content from the producer by using PEKS_Strategy. The cache hit rate for each router is measured and also download time for each consumer is recorded. Then the results are then observed. Figure 12 shows the download time with respect to the cache hit ratio at router R4 which is the nearest to consumer Con1. In the figure, when the percentage of hit ratio increases at Router R4, the average download time drops. This clearly shows that even though the PEKS-based NDN strategy is applied to NDN, in-network caching, which is one of the NDN properties as described in Section 2.1.3, the mechanism is maintained.  
Figure 11: Comparison for number of tests counts and download time. \(d\) is the divisor to \(\#N_{j}\) in the plaintext name to get the number of input to the PEKS scheme.
## 4 Simulation Results and Discussion
### Experimental Setup  
Our simulation was conducted using a personal computer equipped with an Intel(r) Core(TM) i7-10750H CPU with a 2.6 GHz frequency and 16 GB RAM capacity running on the Windows 10 Home 64-bit platform and preinstalled with Python for development. A multi-user with a multi-UAV environment is considered in which an area of \(100\,\times\,100\) m\({}^{2}\) with five UAVs cooperating and associated with a single cloud server through a core network that is also equipped with an edge computing server is assumed. The height for the UAVs is set at 20 m. In addition, we have 30 mobile device users distributed across UAVs, and each user device has an intensive task that needs to be completed. The size of data is distributed uniformly within a range of [0, 10] MB, whereas the CPU cycles required to process each data bit are set to 1000 cycles/bit. The cloud server's CPU computational capabilities are set to 20 GHz and for UAV servers is distributed uniformly within a range of [3.5, 5] GHz, whereas the CPU capability of computation for each device is distributed uniformly within a range of \(\{0.5,0.6,\ldots,1.0\}\). The energy consumption for each mobile device is uniformly distributed within a range of \((0.20\times 10^{-11})\) J/cycle [47]. The remaining settings are summarized in Table 3. The optimization is solved using the GEKKO Python-based package [48].
# Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform
## 5 Results and Analysis
Taking the experimental results of one step as an example, the MAE of LSTM was decreased by 2.71% and 12.24%, respectively, compared with RNN and BP; the MAPE of LSTM was decreased by 7.16% and 13.20%, respectively, compared to RNN and BP; the RMSE of LSTM was decreased by 3.06% and 5.74%, respectively, compared to RNN and BP. The experimental results demonstrate that LSTM is more suitable for time series wind power prediction problems than RNN and BP neural networks because the unique structure of LSTM can learn the temporal relationship contained in input sequences better. Analysis of Tables 2 and 3 shows the same conclusions as Table 1. However, for the brevity of this paper, they were not demonstrated.
# EffShuffNet: An Efficient Neural Architecture for Adopting a Multi-Model
## 4 Experimental Results
### Ablation Study  
An ablation study was conducted to evaluate the effect of the scale of the model on its performance (Figure 10). The scale of the model is determined by the number of parameters, where a scale of one denotes the original model complexity. To reduce the complexity, we scale down the model to a 0.5 scale, while a scale of \(x(x>1)\) represents an increase in the number of parameters by a factor of \(x\). The scale of the model is proportional to the number of channels in each layer, with the width of the model being scaled while maintaining the depth constant. It should be noted that the complexity of our proposed EffShuffNet at all scales is simpler than that of ShuffleNetv2, as we follow the architecture of ShuffleNetv2.  
To compare the performance of our proposed models, EffShuff and EffShuffDense Net, with the corresponding ShuffleNetv2, we conducted experiments on two tasks: age  
Figure 10: Performance (accuracy) comparison of our proposed EffShuffNet and ShuffleNetv2 with respect to the model’s complexity. (**a**) The Audience dataset: age prediction; (**b**) the NA Birds dataset: class prediction.  
Figure 9: Loss comparison of EffShuffNet, EffShuffDenseNet and other models in validation scheme. (**a**) The Audience dataset: age prediction; (**b**) the Audience dataset: gender prediction; (**c**) the Butterfly & Moths dataset: class prediction; (**d**) the NA Birds dataset: class prediction.  
prediction and NA Birds classification. For the age prediction task, we evaluated the performance of the models at five levels of scale. The experimental results showed that the EffShuffDenseNet outperformed the ShuffleNet at small scales (from \(\times\)0.5 to \(\times\)2), while the ShuffleNetv2 performed better at larger scales (\(\times\)3 and \(\times\)4). The results also demonstrated that the EffShuffDenseNet consistently outperformed the EffShuffNet, highlighting the importance of the Dense connection in our building block. Similar experiments were conducted on the NA-Birds dataset, and the results suggest that ShuffleNetv2 is better suited for scaling on large datasets, consistent with the rule that a \"complex\" dataset requires a \"bigger\" model. It should be noted that the EffShuffNet utilizes Pooling within a building block, which is a \"free parameters\" operator that compresses features. Therefore, increasing the number of channels may introduce difficulties for training due to the lack of adjusted weights.
## 1 Introduction
### The Study Site  
This study focuses on the Levant basin seafloor off the base of the southeastern Mediterranean Sea continental margin, offshore Israel (Figure 1). This presently passive, continental margin was formed during the Mesozoic to Neogene eras [47]. Constrained connectivity with the Atlantic Ocean during the Messinian Salinity Crisis resulted in the accumulation of a \(\sim\)2 km-thick layer of evaporites, primarily salt, within the southeastern Mediterranean basin [48]. The subsequent development of the Nile and its deltaic system, as well as the transportation of clastic sediments along the northeastern shore, resulted in the rapid accumulation of an up to 1.5 km-thick sedimentary wedge that forms the present-day continental margin [49]. In the measurement area, the Messinian evaporites are overlain by a \(\sim\)1 km-thick compacting section of primarily fine-grained siliciclastic (clay and silt) sediments [50,51]. Water-mass stratification of the southeastern Mediterranean Sea undergoes pronounced seasonal changes, with the development of a warm and salty surface layer through the summer and autumn and the mixing with deeper water in the winter [52] (Figure 2).  
The objective of this paper is to present two complementary methodologies, and the corresponding results, for an acoustic characterization of a compacting passive-margin basin setting using standard commercial multichannel seismic data. The estimated parameters are the seafloor compressional sound speed \(c_{0}\), the linear positive sound-speed gradient \(K\) below the seafloor, and the thickness of the relevant sub-bottom sedimentary section \(H_{2}\). A basic description of one of these methods was given by [PERSON] et al. [53], and its application is expanded in this paper.  
Figure 1: A bathymetric map of the studied area, overlaid with the outlines of the seismic lines (yellow lines). The analysis was carried out for areas with simple geological structures (red dashed lines). The location of source #2290 is depicted as a white star.
## 3 Experimental Measurements
### Dynamic Vapour Sorption Measurements  
The result obtained by the DVS device is shown in Table 9, including the total duration of the individual measurements for the sorption and desorption cycle. An example of the time course of the sorption measurement using the DVS device is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Density**} & \multirow{3}{*}{**Samples Set**} & \multirow{3}{*}{**Mass**} & \multicolumn{6}{c}{**Environmental Chamber**} \\ \cline{4-10}  & & & \multicolumn{6}{c}{**Description**} \\ \cline{4-10}  & & & \multicolumn{6}{c}{**Sorption**} \\ \cline{4-10}  & & & **50** & **75** & **85** & **95** & **85** & **75** & **50** \\ \hline \multirow{4}{*}{430} & 1 & 22.939 & 0.998 & 1.447 & 2.093 & 5.728 & 2.838 & 2.485 & 1.944 \\  & 2 & 21.242 & 0.829 & 1.257 & 1.93 & 5.513 & 2.665 & 2.368 & 1.94 \\  & a \({}^{1}\) & & 0.913 & 1.352 & 2.011 & 5.62 & 2.751 & 2.426 & 1.942 \\  & d \({}^{2}\) & & 0.085 & 0.095 & 0.081 & 0.108 & 0.087 & 0.058 & 0.002 \\ \hline \multirow{4}{*}{520} & 1 & 26.687 & 0.794 & 1.173 & 1.641 & 5.268 & 2.496 & 2.256 & 1.885 \\  & 2 & 26.082 & 0.978 & 1.346 & 1.791 & 5.582 & 2.557 & 2.285 & 1.883 \\  & a \({}^{1}\) & & 0.886 & 1.259 & 1.716 & 5.425 & 2.526 & 2.27 & 1.884 \\  & d \({}^{2}\) & & 0.092 & 0.086 & 0.075 & 0.157 & 0.031 & 0.015 & 0.001 \\ \hline \multirow{4}{*}{630} & 1 & 33.247 & 1.194 & 1.627 & 1.97 & 6.013 & 2.496 & 2.184 & 1.739 \\  & 2 & 34.249 & 1.183 & 1.609 & 1.942 & 5.927 & 2.435 & 2.134 & 1.79 \\  & a \({}^{1}\) & & 1.188 & 1.618 & 1.956 & 5.97 & 2.466 & 2.159 & 1.764 \\  & d \({}^{2}\) & & 0.006 & 0.009 & 0.014 & 0.043 & 0.031 & 0.025 & 0.026 \\ \hline \multicolumn{4}{c}{duration (days)} & 8 & 10 & 10 & 24 & 14 & 2 & 6 \\ \hline \multicolumn{4}{c}{\({}^{1}\) Average; \({}^{2}\) standard deviation.} \\ \hline \multicolumn{4}{c}{} & & & & & & & & & & \\ \multicolumn{4}{c}{} & & & & & & & & \\ \multicolumn{4}{c}{} & & & & & & & & \\ \multicolumn{4}{c}{} & & & & & & & & \\ \multicolumn{4}{c}{} & & & & & & & & \\ \multicolumn{4}{c}{} & & & & & & & & \\ \multicolumn{4}{c}{} & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 8: Results of environmental chamber tests.  
Figure 6: Moisture content of the samples in the environmental chambers.  
Figure 7: Sorption kinetic of the AC-4 sample at the first and second cycle.
## 3 Results
### Results of Bone Histomorphometry
#### 3.2.4 Eroded Surface/Bone Surface (%)  
Mean eroded surface/bone surface \(\pm\)SD (%) at Week 12 was 24.19 \(\pm\) 3.03% in Group C vs. 18.94 \(\pm\) 1.98% in Group F (\(p\)! 0.05), with a significantly greater decrease in Group F.  
2.5 Number of Osteoblasts/Bone Surface Length (N/mm), Osteoid Surface/Bone Surface (%), Mineral Apposition Rate (\(\upmu\)m/day), and Bone Formation Rate/Tissue Volume (%/year)  
For Figure 8e-g, no significant differences were noted between the two groups at any time point. For Figure 8h, at 8 weeks the ratio of bone formation rate/tissue volume was sig  
\begin{table}
\begin{tabular}{c c c c} \hline  & **4 W (Groups** & **8 W (Groups** & **12 W (Groups** \\  & **C vs. F)** & **C vs. F)** \\ \hline a: Bone volume/Tissue & 19.89 \(\pm\) 4.51 vs. 21.51 \(\pm\) 14.1 & 35.37 \(\pm\) 8.70 vs. \\ volume (\%) & 57.53 \(\pm\) 12.4 * & 61.17 \(\pm\) 2.12 vs. 75.77 \(\pm\) 5.88 ** \\ \hline b: Trabecular thickness (\(\upmu\)m) & 60.71 \(\pm\) 9.10 vs. 68.92 \(\pm\) 18.8 & 103.71 \(\pm\) 8.8 vs. 120.87 \(\pm\) 10.5 * & 121.19 \(\pm\) 11.1 \\  & & 158.11 \(\pm\) 24.2 * \\ \hline c: No. osteoclasts/Bone & & 1.39 \(\pm\) 0.48 vs. \\ surface (N/mm) & 0.85 \(\pm\) 0.30 vs. 0.65 \(\pm\) 0.34 & 0.98 \(\pm\) 0.22 & 1.78 \(\pm\) 0.35 vs. 1.28 \(\pm\) 0.13 * \\ \hline d: Eroded surface/Bone & 9.47 \(\pm\) 1.87 vs. 5.88 \(\pm\) 4.89 & 19.27 \(\pm\) 3.23 vs. 16.08 \(\pm\) 2.36 & 24.19 \(\pm\) 3.03 vs. 18.94 \(\pm\) 1.98 * \\ surface (N/mm) & 43.10 \(\pm\) 5.78 vs. 44.68 \(\pm\) 11.11 & 25.62 \(\pm\) 8.51 vs. 34.15 \(\pm\) 5.34 & 24.66 \(\pm\) 6.35 vs. \\  & & 31.08 \(\pm\) 4.90 \\ \hline f: Osteoid surface/bone & 73.10 \(\pm\) 3.40 vs. 76.00 \(\pm\) 13.50 & 47.60 \(\pm\) 9.84 vs. 55.31 \(\pm\) 7.14 & 47.10 \(\pm\) 9.90 vs. 56.17 \(\pm\) 5.14 \\  & & 3.03 \(\pm\) 0.21 & 2.41 \(\pm\) 0.52 vs. 2.08 \(\pm\) 0.69 \\ \hline h: Bone formation rate/bone & 225.50 \(\pm\) 145.50 vs. 261.67 \(\pm\) 211.94 & 220.91 \(\pm\) 119.08 vs. 405.47 \(\pm\) 92.29 * & 247.09 \(\pm\) 73.64 vs. 254.80 \(\pm\) 142.83 \\ volume (\%/year) & & & \\ \hline \end{tabular} * \(p\)! 0.05; ** \(p\)! 0.05.  
\end{table}
Table 1: Results of bone histomorphometry.  
nificantly greater in Group F (220.1 \(\pm\) 119.08%/year in Group C vs. 405.47 \(\pm\) 92.29%/year in Group F (\(p<\) 0.05)).
###### Abstract
[50] developed a multilabel, multilevel sound classification method using a short-time Fourier transform and convolutional neural network to recognize the activities of multiple types of heavy construction equipment simultaneously. [PERSON] et al. [51] proposed an inner pipeline method for detecting the support conditions of buried gas subsea pipelines using forced vibration signal analysis, with tests demonstrating the method's applicability. In practical applications of vibration signal analysis, real-time signal recognition may produce erroneous results due to factors such as signal noise, feature selection, and classifier choice. Therefore, noise reduction processes [52; 53], time-domain and frequency-domain feature selection [54; 55], and the choice of appropriate classifiers are crucial steps influencing the final recognition accuracy. For large structures like the HBM, minimizing sensor deployment for state recognition is economically advantageous. In such cases, vibration signal monitoring presents a more favorable solution. Most of the vibrations in the HBM exist in the forms of structural response, self-excited vibration, or the vibration response caused by environmental factors. These include vibrations from construction personnel operations, asynchronous vibrations of hydraulic cylinder telescopic cylinders, vibrations in response to encountered climbing obstacles, vibrations due to mechanical equipment operation, local structural vibrations, and vibrations in response to overhead wind loads. The monitoring and diagnosis of vibration signals plays a crucial role in enhancing the stabilization of an HBM. Developing a monitoring and diagnosis system for the HBM working state provides a scientific basis for accident early warning and precise control, thereby improving the safety of the HBM and its associated equipment. As a result, we propose a real-time recognition method for the working state of the HBM. Triaxial accelerometers collect vibration signals from the HBM's main force components in real time. The time-domain and frequency-domain characteristics of these signals are analyzed, and ML techniques are employed to monitor and identify the HBM's critical operating conditions during the construction of the concrete core tube. Drawing on the working characteristics of the HBM, the recognition results of real-time vibration signals are further optimized and classified, enhancing the accuracy of the final output state. This will enable management personnel to accurately comprehend the current state of construction equipment, achieve a comprehensive evaluation of high-rise building construction progress, and ensure the safety and efficiency of the construction process.
# An Efficient Opportunistic Routing Based on Prediction for Nautical Wireless Ad Hoc Networks
###### Abstract  
Nautical wireless ad hoc networks are becoming increasingly popular in oceans due to their easy deployment and self-curing capability. They may alternate frequently between connected mobile ad hoc networks and partitioned opportunistic networks due to mobility in large spaces. Traditional mobile ad hoc network routing is used to find the shortest route for connected networks. However, for opportunistic networks, routing schemes with a broadcast nature mainly exploit the reduction in message duplication and the local relaying technologies described in the literature, which may lead to unnecessary resource waste and low packet delivery ratios. To solve the problem, we propose an efficient opportunistic routing scheme based on prediction for nautical wireless ad hoc networks. The scheme first develops an effective candidate intermediate region to recognize the unavailability of some apparently qualified intermediate nodes, and then takes into account the packet reception ratio between nodes and relay advancement prediction, to improve packet delivery. The proposed scheme achieves performance improvements regarding packet loss ratio and throughput with a tolerable latency increase, compared to other schemes.  
opportunistic routing; nautical wireless ad hoc network; packet reception ratio; relay advancement prediction +
Footnote †: journal: Marine Science and Engineering  
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Footnote 0: _J. Mar. Sci. Eng._**2022**, 10, 789. [[https://doi.org/10.3390/jmse1006789](https://doi.org/10.3390/jmse1006789)]([https://doi.org/10.3390/jmse1006789](https://doi.org/10.3390/jmse1006789))
# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
## 1 Introduction
This last aspect allows for obtaining more robust results based on a larger amount of data, and gives more significance and meaning to the analysis of the intercomparative statistics as a function of time. Unlike the cited validation studies of satellite products, in this work the datasets are not compared with ground-based rain gauge data. In this analysis, two reasons justified the exploitation of direct pairwise comparisons among the satellite products. Rain gauge network density, availability, and reliability is not guaranteed in Africa [20]. Station coverage is not uniform, and there are areas completely lacking rain gauges (e.g., Somalia). Even where ground stations were dense, there is a continuous decrease in their number due to maintenance costs. Moreover, other studies have focused on limited validations over sparsely-gauged African watersheds [21]. Moreover, all the evaluated precipitation products contain a contribution from rain gauge data sets according to their own specific methodologies. This fact makes it very difficult to identify which rain gauge data has not already been used in the satellite products being used for reference. This paper is organized as follows: In Section 2, the precipitation products and adopted methodologies for the analysis are described. Section 3 focuses on the investigated regions and their precipitation climatology. Section 4 presents the results of cluster analysis and seasonality, statistics from product intercomparisons at daily scales, and the temporal evolution of rain detection and estimation capabilities. Discussion and conclusions are proposed in Sections 5 and 6, respectively.
# Predicting Football Match Results Using a Poisson Regression Model
## 2 Related Work  
A plethora of studies related to the subject of either predicting scores or outcomes have been conducted throughout the last few decades and have influenced the current study.  
Most notably, [PERSON] [1] tried to predict the number of goals scored and the outcome in a football match with the Poisson distribution and a Poisson regression model. The study was based on the English Premier League for the 1995-1996 season. The home effect was constant across all teams in the league, while the offensive and defensive parameters varied between teams, based on their past performance--i.e., goals scored in previous matches, but constant between home and away matches. The results of that simplistic modeling approach were rather satisfactory, and the estimation by the model and the observed/actual results were close.  
[PERSON] [2] using data from three seasons (1973-1975) for the Four Top Divisions of the English Football League, resulting in a total of 12 league campaigns, tested a number of models--based on the principle that the goals scored follow a Poisson distribution. The results revealed that a single parameter is sufficient to describe the quality of attack and a single one for the quality of defense of a team, whether the team is playing at home or away, and a differentiation between home and away attacking or defensive quality for each team is not necessary, as it does not have any significant impact on the results produced.  
In the work by [PERSON] and [PERSON] [3], based on the 1997-1998 seasons of the English Premier League and Italian Serie A, the results indicated that the best fit model was the one with a constant home effect across all teams and, similar to [PERSON]'s results, that one parameter is adequate to demonstrate a team's attacking and defensive quality, whether playing home or away.  
Other attempts with the purpose of estimating football match goals and final outcomes are presented in [4; 5; 6; 7; 8]. Notably, [PERSON] and [PERSON] [9] made predictions in the realm of national teams, where the number of matches is considerably fewer within a season compared to a national league.
## 1 Introduction
Importantly, the main objective of the implementation of various techniques is to improve flexural capacity and stiffness for the prevention of brittle failure of the over-reinforced beams. Additionally, the use of external strengthening by bolted/glued steel plates system, which is a low-cost system with high stiffness, has been proven to be an effective technique for the improvement of the reinforced concrete structures. The system is glued to the beams and attached to the compression/tension zones of the beam to increase its flexural capacity, ductility, and stiffness [21; 22; 23]. According to [PERSON] and [PERSON] (1990), RC beams that are strengthened with tension-face plates may undergo brittle failure as a result of the disconnection between steel plates and concrete surfaces. Therefore, overcoming premature failure is key in the design of beams with externally glued steel [24]. [PERSON] et al., (1998), also highlighted that the use of web bonded steel plates could prevent premature failure of reinforced concrete members with flexural strengthening [25]. Based on [PERSON] and [PERSON]'s (2011), study on the flexural performance of bolted steel plates BSP beams, it was found that omitting the partial interaction between the beams and the steel plates would lead to up to 30% of overestimation of the enhancement [11; 26]. However, various techniques and materials to strengthen structural has received significant attention among researchers in the recent decades. To date, a few studies have investigated the behavior of concrete beams that are over-reinforced using the bolts compression steel plates (BCSP) system. Therefore, this study aims to investigate the effects of bolts compression steel plates (BCSP) on the behavior of over-RC beams. It attempts to change the behavior failure from brittle to ductile. Furthermore, achieving an inelastic mechanism with appropriate deformation capacity will reduce the crushing by the BCSP-system. This was followed by the improvement in strength, strain, and energy absorption (EA) for the over-reinforced section. In this research, simply supported beams and steel plates of different thicknesses were used in the development sections.