esa-sceva/satcom-chunk-collection huggingface dataset:
# Goal Shot Analysis in Elite Water Polo--World Cup Final 2018 in Berlin
## 3 Results
### Biomechanical Analysis of the Goal Shot Behavior on Collective Level
#### 3.3.2 Collective Tactical Goal Shot Analysis  
From a total team perspective--evaluating all \(N=1482\) goal throws--the hit rates differed considerably with regard to the four different attack types: fast break attack, position attack, man-up attack and penalty shot (see Figure 4b). As expected, the observed national teams scored about twice as much in the attack initiation via a fast break attack (\"counterattack\") with about 48% goals than in the position attack with equal numbers (6-vs.-6), which generally follows an unsuccessfully broken fast break attack and is played out until the end of the (in this tournament) regular attacking time of 20 s. Even if only a quarter of these positional attacks have resulted in a goal, the exclusion of an opponent due to an infringement of the rules often results in a far more promising man-up attack (6-vs.-5), which is restarted with a further attack time of 20 s. Such an man-up attack is usually played by means of a power play set-up and in the 2018 World Cup Final it brought on average almost a doubling of the scoring rate compared to the positional attack.  
Comparing the hit rate of the prototypically unimpeded and maximally fast penalty throwss (\(n=66\)) in the 2018 World Cup final, for example, with that at the 2007 Men's World Cup in Melbourne, where the penalty throw distance was increased from 4 m to 5 m for the first time at a World Cup, the first result that stands out is the significantly reduced hit rate from 74.1% at the time [33] to the current 67.0%. Since the throwing speed increased significantly during the same historical period, the main causes might be an improved goalkeeping performance on the one hand, and a lower goal shot accuracy due to the greater shooting distance on the other hand.  
The final analysis of the relationship between the overall team offensive and defensive behavior--based on the positional data (position sampling) of all six offensive and defensive players and the goalkeeper-, i.e., specifically, collective space control, is again based on the \(n=395\) goal successful shots of all eight teams (Figure 4b).  
Focusing on the offensively and defensively controlled partial field areas (\"controlled space\") generally the probability to throw a goal is significant lower (\(p<0.05\)) when the defending team controls larger space in front of their own goal (during man-up attack; Figure 9a) or is significant higher (\(p<0.05\)) when the attacking team controls larger space in front of the opponent's goal (during positional as well as man-up attack; Figure 9b).  
Simplified, in the case of successful attacking completions, the attacking team in the sum of its six players controlled more space in front of the opponent's goal compared to the six defensive players together with their goalkeeper (Figure 10a) and--in contrast--for unsuccessful attacking attempts, this area ratio was often exactly reversed, i.e., the defensive team dominated the controlled space (Figure 10b).
# Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification
## 4 Numerical Results and Analysis
is used to apply the EWM, and the corresponding diagram is presented in Figure 10:9 Comparing both diagrams, it is concluded that the damage located at 75% of L was identified with the WEAM with 99.68% precision (Figure 12), requiring a processing time for the signals provided by the FEM simulations of 4 min and 49 s; while, on the other hand, the EWM identified the damage with 99.79% accuracy, requiring a computation time of 51 s. In this way, the important benefits of the EWM are verified and justified, since, for the same case of the damage analyzed, the EWM identified the damage with 0.11% greater precision, obtaining results 5.67 times faster.
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 5 Conclusions  
In this survey, we provided a brief overview of the current proposals related to WBANs and their possible application in BBNs. Four design challenges have been tackled: energy efficiency, mobility prediction, QoS, and security. The existing WBAN and BBN-related works have been discussed and compared in order to highlight the key features for BBN design. The energy issue is definitely a hot topic for WBANs and more so for BBNs. In addition to the single WBAN sensing activity, further energy is consumed for the sensor data relaying among coexisting WBANs. Then, the BBN energy profile should take into account the sink-forwarding-to-sink energy component, and should also support the WBAN topology changes and the mobility of WBANs in the BBN vicinity. The most appealing QoS requirement for BBNs is the QoS mapping, in case of heterogeneous BBN devices and traffic, apart from the ultimate delay and reliability constraints, that could have a vital impact on the health-based applications. Accurate mobility management is also mandatory to ensure seamless transfer of sensor data in dynamic scenarios. The main mobility features to take up are: the need of a handover mechanism for the WBAN roaming to handle channel switching and prevent link failures. Also, the WBAN mobility patterns could take advantage from the regular aspects of the human body movements, and should consider differently the indoor and outdoor scenarios, since the wireless channel propagation characteristics are not the same. Above all the aforementioned issues, tough security policies are required in BBNs, first to prevent the energy-constrained resources from malicious attacks, and second to secure the BBN activity itself, especially in health-oriented applications, since human lives are directly implicated. Finally, a number of candidate protocols have been identified, and further research issues have been also discussed. Hence, by enabling inter-WBAN communications, BBNs could extend the existing infrastructures, bringing innovative services with great social benefits, ranging from gaming and entertaining to ubiquitous healthcare, rescue teams in hazardous situations or military applications, etc. In fulfilling these goals, our ongoing work is about compiling the aforementioned proposals to implement an inter-WBAN routing protocol in order to boost the research incentives of BBN effective deployment.  
[PERSON], [PERSON], and [PERSON] conceived the presented topic. [PERSON] and [PERSON] developed the original draft. [PERSON] took the lead in the study, investigation and writing of the manuscript with close participation of [PERSON]. [PERSON] and [PERSON] supervised the findings and theoretical syntheses of this work. All authors provided critical review and editing of the paper. This research received no external funding. The authors declare no conflict of interest.
Geological Characteristics and Exploration Practices of Multilayer Shale Oil and Gas in the Yanchang Formation, Fuxian-Ganquan Area, Ordos Basin  
[PERSON]  
1 Natural Gas Research Institute of Shaanxi Yanchang Petroleum (Group) Co., Ltd., Xi'an 710065, China; [EMAIL_ADDRESS] (P.S.); [EMAIL_ADDRESS] (J.Z.)1 Natural Gas Research Institute of Shaanxi Yanchang Petroleum (Group) Co., Ltd., Xi'an 710065, China; [EMAIL_ADDRESS] (P.S.); y  
oilfield, the Qingcheng Oilfield, in the Chang 7 member of the Yanchang Formation in the Longdong area of the Ordos Basin [5,6]. In recent years, the Shaanxi Yanchang Petroleum Group has also made significant theoretical advances in lacustrine shale gas exploration in the Ordos Basin, with several wells in the Chang 7 shale yielding shale gas flows [7, 8, 9].  
In addition to the Chang 7 shale in the Yanchang Formation of the Ordos Basin, shale layers are also developed in the Chang 8 and Chang 9 members. However, current shale oil and gas exploration and geological research are mainly concentrated on the Chang 7 member [10,11]. Due to the limited distribution range and depositional thickness of Chang 8 and Chang 9 shales, only a few studies on source rock evaluation have been conducted [12, 13, 14]; thus, the systematic assessments of shale reservoirs are very rare. Furthermore, current geological research and exploration practices often tend to only focus on shale oil or shale gas. However, the Yanchang Formation shales exhibit the characteristics of oil and gas symbiosis and sequential accumulation [15,16]. The state of occurrence of shale oil and gas, and their respective processes of enrichment and accumulation, influence each other. Therefore, conducting a comprehensive evaluation of the geological characteristics of multilayered shale oil and gas in the Yanchang Formation is of great significance to the later comprehensive exploration of shale oil and gas.  
In this study, three sets of shales in the Yanchang Formation in the Fuxian-Ganquan area of the Ordos Basin were investigated. Cores and outcrop samples were utilized for X-ray diffraction mineral analysis, organic geochemical analysis, field-emission scanning electron microscope analysis, and hydrocarbon generation thermal simulation experiments. By integrating logging data and core test data, a comprehensive examination was conducted on the shale distribution characteristics, mineral petrology characteristics, organic geochemical characteristics, reservoir characteristics, and oil and gas contents of the Chang 7, Chang 8, and Chang 9 shales. Based on the experimental results and the exploration practice of multiple vertical wells, the feasibility of the integrated exploration and development of multilayer shale oil and gas formations was evaluated to provide a valuable reference for the exploration of similar continental shale oil and gas formations.
## 5 Results and Discussion
### Experiment I: Charging of TES Tank
#### 5.1.2 Variation of TES Capacity and EER  
Figure 9 shows the changes in TES capacity and effective TES capacity with charging time. It can be seen from the figure that the TES capacity continued to increase, and the effective TES capacity was zero in the first 4 h. After 4 h of charging, the effective TES capacity began to increase linearly. After 12 h of charging, the effective TES capacity reached \(1.25\times 10^{8}\) J. The TES capacity was \(2.47\times 10^{8}\) J, which was twice that of the effective TES capacity. It can be seen from Section 2.4. that the design TES capacity of the device was \(2.4\times 10^{8}\) J, and the test value meets the design requirements. The remaining 1/2 TES capacity that cannot be utilized was used to reduce the water temperature from its initial state to 7 \({}^{\circ}\)C. If the remaining 1/2 TES capacity for reducing the water temperature of the TES tank can be reused, the power consumed by charging will be greatly reduced.  
As shown in Figure 10, the 30 min EER in charging was stable, and the maximum was 3.56 in one hour. It decreased rapidly after 1.5 h and reached 1.24 at 6.5 h. Then, followed by fluctuation, it reached the minimum value of 1.12 at 12 h. This showed that the efficiency of charging was high at the beginning, decreased rapidly, and remained stable after 6 h. The fluctuation of EER during the charging process was due to the measurement error of TES capacity, but the fluctuation cannot influence the overall trend of EER during charging. However, the average EER was 3.5 at the beginning of the charging process and gradually decreased after 1 h, reaching the lowest value of 2.0 at 12 h. From Figure 10, we can know that the longer the charging time is, the lower the EER of TES is.  
Figure 8: Plots showing variation of (**a**) water temperature and (**b**) ice thickness of different layers with charging.
# Effects of Decision Variables Selection on Sewer Optimization Problem
## 2 Methods
### Selection of Decision Variables  
Decision variables are variables that correspond to the minimum or maximum value of the objective function according to the problem and whose values are aimed at being determined by solving the optimization problem. It is stated that the objective function  
Figure 2: Flowchart of APO algorithm. of the sewer network hydraulic optimization problem consists of the pipe cost, manhole cost, and excavation cost. The pipe cost is calculated depending on the pipe length, pipe material, and pipe diameter. For a network with a pre-defined layout and pipe type, the pipe cost will vary only depending on the pipe diameter, since the pipe lengths and pipe type are fixed. The manhole cost is calculated depending on the number of manholes, type of the manhole, and manhole depths. For a sewer network with a pre-defined layout and manhole type, the manhole cost will vary only depending on the manhole depth since the number of manholes and the manhole type are fixed. Finally, excavation cost is calculated depending on the excavation volume, the soil structure, and the excavation technique. For a sewer network with a uniform soil structure, pre-defined layout, excavation width, and excavation technique, the excavation cost will vary only depending on the excavation depth. In this specific problem, these variables can be directly selected as decision variables, or these variables can be indirectly calculated with each other to create different decision variable sets. In literature, three different cases are encountered in this sense. In the first case, the pipe diameter, in the second case, the pipe slopes, and in the third case, the pipe diameter and pipe slopes can be selected together as decision variables. If the pipe diameter is selected as the decision variable, the pipe slope, and therefore the excavation depths, can be determined by hydraulic calculations. If the decision variable is selected as the slope or depths, the pipe diameter can be obtained by hydraulic calculations. If the pipe diameter and the slope or depths are selected as the decision variables, there is no need for hydraulic calculations. In this context, 10 different alternative sets of decision variables are created in this study. The alternative sets are obtained by allowing or not allowing drops in the network, using cover depths, the nodal elevation values or slope values, and the diameter. A summary of information about alternative sets is given in Table 1.
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 2 BDCOA Based Optimum Control Signal
### DM Model  
In SLAO, the DM uses 32 actuators as the wavefront compensator. Figure 3 shows the normalized layout of 32 actuators. The Gaussian Model expressed in Equation (2) is used to define the DM's impact function. Next, the phase compensation \(c(x,\ y)\) is conducted and it is computed using 32 actuators, as expressed in Equation (3).  
\[G_{j}(x,y)=exp\left\{\ln\phi\left[\frac{1}{n}\sqrt{\left(x-x_{j}\right)^{2}+ \left(y-y_{j}\right)^{2}}\right]^{\beta}\right\} \tag{2}\]  
\[c(x,y)=\sum_{j=1}^{32}v_{j}G_{j}(x,y) \tag{3}\]  
where, the coupling coefficient is evaluated by the DM sizes and electrode actuators, denoted as \(\phi\), the center coordinate of actuator \(j\) is denoted as \(\left(x_{j},y_{j}\right)\), the normalized distance among the nearby actuators is denoted as \(n\), the Gaussian index is specified as \(\beta\)  
Figure 2: Working module of the SLAO of FSO.  
and voltage of \(j\)th actuator is denoted as \(v_{j}\). According to Equation (3), it is found that the \(c(x,y)\) is linear with \(v_{j}\).  
The optimization of actuator voltages is ensured by reducing the mean square error in \(c(x,y)\). BDCOA is used for optimizing the control signals or voltages. The WA compensation using BDCOA is detailed in the following section.
## 3 Results and Discussion
### Method Validation
#### 3.2.3 Precision  
Precision was determined by intra-day and inter-day analysis. The intra-day and inter-day RSDs of the four compounds ranged from 0.111% to 2.857% and from 0.946% to 3.593%, respectively. Accuracy values ranged from 96.74% to 102.53% and from 97.95% to 100.83%, respectively (Table 2). The intra-day and inter-day RSDs of berberine, palmitine, geniposide, and paeoniflorin were all \(<\)4%. Thus, the developed method was found to be both reliable and reproducible.
## 3 System Overview
### Data Storage and Visualization  
Our solution uses the Influx DB platform as a database. Influx DB is a time series database (TSDB). The TSDB is used to store data that change over the time. This storage is easy to integrate with other components such as a Grafana or Prometheus dashboard. Users can retrieve data for later analysis. For the visualization, there are many open source and closed source dashboard tools. The key highlights of the Grafana dashboard are that it connects easily and flexibly to the databases, with many options to adjust the dashboard and configurable alert functions when values rise or fall beyond set points. Grafana runs on a default port of 3000. The two parameters, the amount of tissue and the battery voltage in the bathroom or kitchen, are monitored using the Grafana dashboard.
###### Abstract  
This paper investigates the response characteristics of a stow net under wave and current excitations, as well as the feasibility of its monitoring system to check net functionality and prevent loss of fishing gears. The stow-net model is based on one of existing types used in southwest coast of South Korea. The measured wave and current data there are acquired and inputted as environmental loads for numerical simulations. The Morison equation for a moving object with equivalent net model is utilized as the external-force estimator, which has been validated by many researchers regarding fish-cage studies. Since the modelling of all the net elements is inefficient in terms of computational time in time-domain simulations, cruder equivalent-drag net elements are devised with equivalent wet mass, projected area, and axial stiffness. The performance of stow net is highly influenced by current velocity. Stow nets submerge more in stronger currents, which results in less wave forces. The proposed monitoring system can provide useful information, such as net functionality and loss of stow net, using the minimum number of monitoring sensors.  
Time-domain simulation; fishing gear; stow net; dynamic analysis; global performance; wave/current; equivalent net model; monitoring system
# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 3 Proposed Method  
This paper presents a novel NR-IQA method called MT-IRN that utilizes a multitask image restoration network. MT-IRN is comprised of a multitask image restoration sub-network and a score prediction sub-network, as shown in Figure 1. The multitask image restoration sub-network is used to generate pseudo-reference images and SSIM maps between the reference images and distorted images and to extract quality restoration features. This enables the model to not only use the differences between the pseudo-reference and distorted images but also leverage rich semantic information from the quality restoration features. The score prediction sub-network extracts high-level and multi-scale features from the distorted images and difference features between the distorted images and pseudo-reference images.
## 4 Experiments and Results
### Baseline Methods
#### 4.3.4 Discussion  
The results show that the DNN-MHAT model outperformed the other five models in terms of both F1 measures and accuracy with Twitter datasets. However, the improvements are less compared to utilizing the review datasets. The main reason for this is that the Twitter datasets contain a small number of words. As mentioned above, the DNN-MHAT model does not provide important improvements when utilizing short comments. The first feature extraction layer in this model is an RNN-based network, which is evolved to capture long dependencies.  
Due to the ability of BiLSTM to access both the previous and the following context, the information obtained by BiLSTM can be considered two different representations of the text. Moreover, employing a multi-head attention mechanism for each text representation can better focus on the significant related information and avoid the reciprocal intervention in the various representations. Thus, the multi-head attention mechanism in our model makes the determination of text semantics more accurate. Hence, our model effectually improves the accuracy of text classification and sentiment analysis.  
However, our study was limited to document-level sentiment analysis. In this study, we did not consider the aspect-level sentiment. We leave this part for future work.
# Optimal Investment Timing and Scale Choice of Overseas Oil Projects: A Real Option Approach
## 3 Model Description
### Value of Oil Projects
#### 3.1.5 Choice of Optimal Investment Scale  
Due to the importance of irreversible capital investment of oil projects, this part uses the constructed functions, which contain oilfield production, revenue, operation cost, and investment cost, to obtain the analytical solutions of optimal investment scale and the corresponding value of oil projects. The investment scale, which determines the oilfield development, is influenced by the volume of oilfield, the selected recovery factor, and the extraction rate. The volume of oilfield, which means the volume of in situ oil reservoir, is exogenous. This model sets both volume of oilfield and extraction rate as constants. Choosing recovery factor, which determines the degree of oilfield development, is the key point for investors to select the investment scale and investment cost of oil projects. The theoretical two-stage economic model established by [PERSON] and [PERSON] [50] also proves that choosing the optimal mining rate can significantly increase the mine's profit. To sum up, the choice of investment scale can be transformed to the choice of recovery factor in this model.  
When the optimal investment scale, which means the optimal recovery factor, is selected, the project's marginal revenue should be equal to the marginal cost, and the project's net present value could be maximized. Based on Sections 3.1.2 to 3.1.4, the optimal recoverable factor can be calculated from the revenue function, operation cost function, and investment cost function.  
\[\max[R(s;\theta)-TC(s)-I(s)]\Rightarrow\frac{\partial}{\partial s}R(s;\theta)= \frac{\partial}{\partial s}TC(s)+\frac{\partial}{\partial s}I(s) \tag{15}\]  
Therefore, the analytical solution of the optimal recovery factor (\(s_{optimal}\)) can be written as follows:  
\[s_{optimal}=\left[\frac{\partial^{\zeta}_{\zeta}-c\kappa}{B\cdot OIP\cdot \gamma}\right]^{\frac{1}{\gamma-1}} \tag{16}\]  
The corresponding project value expression can be written as follows:  
\[V(\theta;s_{optimal})=\left[\left(\frac{1}{B\cdot OIP\cdot\gamma}\right)^{ \frac{1}{\gamma-1}}-B\cdot OIP\cdot\left(\frac{1}{B\cdot OIP\cdot\gamma} \right)^{\frac{\gamma}{\gamma-1}}\right]\cdot\left(\theta\zeta-c\kappa\right)^ {\frac{\gamma}{\gamma-1}} \tag{17}\]  
It is true that the analytical solution of the optimal recovery factor could give guidance to create balance between irreversible capital investment and project revenue.