esa-sceva/satcom-chunk-collection huggingface dataset:
# CFD Simulation of Dry Pressure Drop in a Cross-Flow Rotating Packed Bed
## 4 Results and Discussion
### Model Verification  
The dry pressure drop at different gas flow rates and rotating speeds is calculated using the three turbulence models and compared with that obtained by experiment, as shown in Figure 3. Note that the average pressure of the cross section is used. The average error at the gas flow rate of 200 m\({}^{3}\).h\({}^{-1}\) is 5.2%, 6.3% and 182.9% for RNG k-\(\epsilon\), RSM and Realizable k-\(\epsilon\) model, while that at 800 m\({}^{3}\).h\({}^{-1}\) is 1.7%, 4.7% and 102.3%, respectively. Thus, both RNG k-\(\epsilon\) model and RSM can well describe the turbulence behavior in cross-flow RPB. It may be attributed to the unique structure of RPB, where gas suddenly expands at the inlet of RPB, then changes the direction at the bottom, and then suddenly narrows at the outlet. Figure 4 shows the pressure drop in cavity zone A. The realizable k-\(\epsilon\) model shows the largest error, implying that it is unsuitable to describe the turbulence in cross-flow RPB due to suddenly expansion of packing. The difference between RNG k-\(\epsilon\) and RSM becomes more pronounced with increasing rotating speed because of gas reflux. In case of no gas reflux, the gas-phase velocity and pressure loss would be kept constant even if the rotating speed is changed. Thus, energy loss caused by friction shows no significant change in this area, but the calculated pressure drop decreases. It is suggested that some gases in the packing area are sheared and the kinetic energy is returned to cavity zone A, resulting in a decrease of pressure drop. Thus, from the perspective of total energy, the pressure drop in the cavity zone A should decrease with increasing rotating speed. In conclusion, RNG k-\(\epsilon\) is more suitable to reveal the flow pattern in cross-flow RPB and, thus, it is used in the following calculation.  
Figure 3: Comparison of the experimental total pressure drop with calculated pressure drop for three turbulence models at different gas flow rates and rotating speeds. (**a**) Gas flow rate of 200 m\({}^{3}\).h\({}^{-1}\); (**b**) gas flow rate of 800 m\({}^{3}\).h\({}^{-1}\).
# Time Optimization of Unmanned Aerial Vehicles Using an Augmented Path
## 3 Proposed Augmented Path-Time Architecture
### GPS (Global Positioning System)  
The GPS is used for the location of the initial and final destination of the journey and also the tracking of the UAVs. The drone has to be tracked through a remote computer and all the data is stored and manipulated by an AI; however, the tracking can be easily done through the GPS while also monitoring the progress and the path of the drone. Additionally,  
Figure 1: Augmented path.  
in case of failure, the GPS can be tracked to locate the drone for its retrieval. Figure 2 represents the architecture of the proposed augmented path-time model.
## 2 Methods
### Preprocessing  
The sixth-order low-pass Butterworth filter with a cut-off frequency of 30 Hz was used to filter the trajectory data. The shoulder-center is taken as the origin. To eliminate offsets and scaling during data acquisition, all the joints coordinates were normalized by the equations as follows.  
\[\chi^{\prime}_{\mathrm{k}}=\frac{\chi_{0}-\mathrm{SC}}{|\mathrm{D}|} \tag{2}\]  
\[\mu=\frac{1}{\mathrm{m}}\sum_{\mathrm{k=1}}^{\mathrm{m}}\chi^{\prime}_{\mathrm{ k}} \tag{3}\]  
\[\sigma^{2}=\frac{1}{\mathrm{m}}\sum_{\mathrm{k=1}}^{\mathrm{m}}\left(\chi^{ \prime 2}_{\mathrm{k}}-\mu^{2}\right) \tag{4}\]  
\[\mathrm{X_{k}}=\frac{\chi^{\prime}_{\mathrm{k}}-\mu}{\sigma^{2}} \tag{5}\]  
where \(\chi_{0}\) (\(\mathrm{x}_{0}\), \(\mathrm{y}_{0}\), \(\mathrm{z}_{0}\)) are the original coordinates, SC (\(\mathrm{x}_{\mathrm{sc}}\), \(\mathrm{y}_{\mathrm{sc}}\), \(\mathrm{z}_{\mathrm{sc}}\)) are the shoulder center coordinates, \(\mathrm{X}_{\mathrm{k}}\) (\(\mathrm{x}_{\mathrm{k}}\), \(\mathrm{y}_{\mathrm{k}}\), \(\mathrm{z}_{\mathrm{k}}\)) are the coordinates after normalization, \(\mu\) is the mean, and \(\sigma\) is the standard deviation.  
Figure 5: Five movements: (**a**) Shoulder abduction (90\({}^{\circ}\)), (**b**) shoulder adduction, (**c**) shoulder flexion (0–180\({}^{\circ}\)), (**d**) elbow flexion (hand to lumbar spine), (**e**) hand to head.
## 4 Battery Cell-Module-Pack Level Modelling  
The battery modelling methodology can be divided into cell, module, and pack levels, and the interactions between these levels are depicted in Figure 7. It can be seen that there are two simulation paths. The first one is the backward path at step \(j\), where the state parameters of all individual cells are lumped together to form the state parameters of the battery  
Figure 6: Schematic of radiator modelling method.  
pack as a whole, which then receives pack-level inputs from the outside. The second one is the forward path at step \(j+1\), where the pack-level inputs are converted and distributed to each individual cell and the state parameters of each cell are updated accordingly. At step \(j\), each individual cell is represented by an equivalent circuit characterised by states such as open circuit voltage (\(OCV\)), internal resistance (\(R\)), state of charge (\(SOC\)), state of power (\(SOP\)), and temperature (T). Cells positioned along the coolant flow path are subject to temperature gradients due to their different upstream and downstream locations in a module. Generally, the coolant temperature downstream is higher compared to upstream. This leads to a gradient in heat dissipation capacity, with downstream cells experiencing less cooling than those upstream. Consequently, the temperature of the downstream cells is higher, resulting in a thermal gradient that induces performance variations among the cells. Then, all the cells within a module can be equivalently represented as a lumped, single equivalent circuit at the module level. Furthermore, the equivalent circuits of all modules are further combined to form a lumped, single equivalent circuit at the pack level, which will then interact with external inputs.  
At step \(j+1\), the pack-level model from step \(j\) will receive external inputs such as power demand (\(P\)), coolant temperature (\(T_{liq}\)), and coolant flow rate (\(q_{liq}\)). These pack-level inputs are then converted into current demand (\(I\)), coolant temperature, and coolant flow rate for each module based on the states of each module. Each module further distributes the inputs to individual cells based on their respective states. Then, each cell updates its state parameters at step \(j+1\) using the state parameters from step j and the inputs from step \(j+1\), which will be used for the next calculation. This modelling approach, based on circuit equivalence and utilising dual paths for step differencing, can greatly reduce computational complexity and enable the simulation of each individual cell, providing thermally induced cell-to-cell performance variations. The following section elaborates on the governing equations for each level from the perspectives of electrical and thermal aspects.
## 1 Introduction
Therefore we have implemented our protocols on TelosB motes in order to obtain the execution time and communication. Note that we used TelosB motes as a means for comparing the different protocols. We would have obtained better overall performance if we used motes with more computation capacities such as Tmotesky.The overhead of protection analysis for WSN cryptographic protocols is almost impossible to perform manually. This increases the difficulty to design secure and efficient protocols at the same time. Using our real implementation on TelosB motes, we have measured the execution time of every cryptographic operation and every step in each protocol. These measurements were obtained from a testbed of the minimal number of nodes. In addition, using the overhead evaluation results, we were able to put in place an attack detection process. This paper does not aim to show that the proposed protocols are faster than existing protocols. Instead, the main objective is to present secure solutions for node authentication and key establishment using standard cryptographic algorithms and primitives available on the Internet. The chosen primitives can be replaced by more optimized primitives for better performance. The evaluation we presented is essentially a proof of concept and shows that the steps of each protocol can be implemented on low cost motes such as TelosB motes that are known for their low capacities in computation speed. This work is an extension of the conference paper [10] and the book chapter [11]. The original authenticated join protocol was proposed in [11] and evaluated in different versions according to the level of protection in [10]. It is the only intersection with the current paper. In the next section, we summarize the related work. In Section 3, we present the cryptographic primitives we use to design our protocols. Then in Section 4, we propose two protocols for establishing secure multi-hop communications. In Section 5, we give two key establishment protocols that use the sink as a trusted third party and in Section 6 we propose four protocols that establish a new key without passing by the sink. Then in Section 8, we use Scyther to formally prove the security of our solutions. In Section 9, we evaluate the overhead of our eight protocols. Based on the overhead evaluation results, we explain in Section 10 how a node is able to detect attack attempts launched by other nodes in the network. We conclude the paper in the last section.
# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 1 Introduction
Moreover, since channel attention mechanisms are unaffected by spatial dimensions, they might be more stable when handling targets with different spatial distributions [39]. This can enhance robustness when dealing with targets of varying sizes, positions, and orientations. To this end, in this study, we introduce the SE (Squeeze-and-Excitation) channel attention mechanism into the one-stage detection network YOLOv7, namely SE-YOLO, enhancing the small target detection performance in complex backgrounds through the fusion of different features of small targets at different scales. The SE-YOLO model achieves the best performance about 0.983 on mAP, 0.969 on recall, and 0.978 on precision, respectively. In particular, the recognition performance for minimal targets with 20-50 pixel points achieves 0.926, 0.894, and 0.929 in terms of mAP, recall, and precision, respectively. The results demonstrate that SE-YOLO is suitable for wildlife detection in drone scenarios. Moreover, the WAID dataset is public at [[https://github.com/xiaohuucui/WAID](https://github.com/xiaohuucui/WAID)]([https://github.com/xiaohuucui/WAID](https://github.com/xiaohuucui/WAID)) (accessed on 12 September 2023). In summary, the contribution of this study is threefold:  
1. By incorporating the SE modules into the YOLO model, we propose a model called SE-YOLO suitable for wildlife detection in aerial imagery, which improves the accuracy of small wildlife targets in natural environments. 2. The SE-YOLO model achieves a mAP of 0.983 on the WAID dataset, and in particular, the recognition performance for minimal targets with 20-50 pixel points reaches 0.926, 0.894, and 0.929 in terms of mAP, recall, and precision, respectively, indicating that SE-YOLO is particularly suitable for the problem of wildlife monitoring in the wild perspective of aerial photography from a UAV. 3. To address the scarcity of aerial wildlife datasets in photography, the first valuable open multi-category aerial wildlife image dataset, WAID, has been made available to all researchers for free download from [[https://github.com/xiaohuucui/WAID](https://github.com/xiaohuucui/WAID)]([https://github.com/xiaohuucui/WAID](https://github.com/xiaohuucui/WAID)) (accessed on 12 September 2023). This contributes to the advancement of the academic community of unmanned aerial wildlife monitoring.
## 6 Discussion
### Practical Applications  
The current methodology allows decision-makers to comprehend the role of ML applications in marketing decision optimization based on a series of real-world applications. This study has shown how marketing campaigns and user engagement metrics can be optimized in favor of marketers and consumers [4,8].  
Following this, a series of scenarios involving ML practices based on the scientific literature are demonstrated.  
* Regarding the case of conversion rate optimization through user segmentation, a fashion e-commerce application divided the users based on their engagement level into low, medium, and high classes, incorporating DTs and organic user engagement metrics such as event count and bounce rate. Based on each class, the platform differentiated the marketing strategy [15,19].
* Bounce rate increases are a serious liability for electronic businesses. The use of an optimized user interface (UI) can affect an increase in session duration. Based on that, a technology company used ML techniques to analyze high bounce rate values on product pages and concluded that unclear navigation led online users to exit the platform [4,8].
* Marketing campaigns for user personalization based on metrics refer to a scenario of an online travel agency applying machine learning to classify users based on their possibility of making a booking reservation for the trip, using metrics like session count and event engagement [9,20].
* Keeping the clientele intact is one of the major priorities for companies. A case of customer churn prediction using DTs allows the users classification based on engagement metrics like event counts and bounce rate. A churn rate decrease could lead to a customer retention increase.
* Content personalization can lead to user engagement improvement. The case of an online cosmetics brand used organic user engagement to adapt the website content according to the users' needs. Personalized content can increase the average conversion rates and revenue, including likes, follows, subscriptions, and purchases [10,16].
# Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis
## 1 Introduction  
Eye blinking is a semi-voluntary action of fast closing and reopening of the eyelid. It occurs as a result of the co-inhibition of eyelid's protractors and retractors muscles. Blinking serves the spreading of the corneal tear film across the frontal surface of the cornea [1]. The average duration of a single eye blink is 0.1-0.4 s [2]. The blink rate (BR), measured in blinks/min, is influenced by environmental factors (humidity, temperature, brightness), and physical activity [3].  
Stress is considered to affect eye behaviour which is reflected in eye blinks patterns [4]. There is research evidence suggesting that eye-related features are associated with the emotional state such as trait anxiety [5]. Eye blink rate may be related to emotional and cognitive processing, especially with attentional engagement and mental workload [6]. The relevant literature has investigated the relationship between eye blinks and stress, reporting that during stress conditions blink rate is significantly increased [7,8]. In addition, there are blink parameters [4] such as blink duration, eye closing/reopening time, peak amplitude, inter-blink interval (IBI) which have not been investigated considerably in the literature, but they may provide useful information about eye functioning [9].  
IBI was studied for spontaneous eye blink rate in healthy adults in order to formulate the normal blink rate and IBI patterns [7]. It was reported that there were no gender IBI differentiations, but there was great intra-subject variability either in blink rates or in IBI. In our previous study [8], it was reported that there are 4 types of eye blink patterns categorized according to the IBI behaviour they follow. Additionally, in [8], it was observed that eye blinks appear to be significantly increased in specific stressful tasks. Thus, the research question was placed whether the IBI time-series can enhance information instead of just measuring blinks number and whether this information can reveal blinks patterns associated with stress conditions.  
The aim of this study was to investigate whether machine learning-based techniques can predict the experimental task under which the eye blinks took place and, further, reveal the dynamics of blinks generation in each task using Markov models. Accordingly, a task-based Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are trained and tested on the IBI sequence, in an effort to investigate the mechanisms related to each task, neutral and stressed, per cognitive process. The existence of memory in the process of eye blinks generation is analyzed with Markov models, revealing that strong \"attractors\" in eye blinks production differentiate under different cognitive tasks.
## 3 Principles of Operation
### Energy Flow in the System  
In order to optimise the power flow in the system, an energy flow chart is used to demonstrate the contribution of each component to the system. Based on the connection and conversion between them, the variables for the optimization process are derived and explained in the following section.
## 1 Introduction
It is worth mentioning that ANN and ELM have been widely developed and applied for different applications; for instance, ANN and ELM models are used in the fields of sustainability [48] speech recognition [49] medical [50] and load monitoring [51] environment [52] and hardware [53] respectively. The two models considered the dust accumulation and the ambient temperature as the independent variables and PV conversion efficiency as the dependent variable. A detailed comparison between the two models in terms of their features and accuracy is presented. Moreover, an estimation of the losses due to dust accumulation along with cleaning frequency is shown. The authors believe that this work will serve as a powerful tool for investors and stakeholders in the MENA region that predicts system performance based on the ambient temperature and dust accumulation. Moreover, this paper will provide the optimum cleaning frequency for PV systems. The aforementioned introduction discussed the effect of dust accumulation and the ambient temperature on PV system performance, along with cleaning techniques and frequencies for such systems. The remainder of the paper is divided as follows: Section 2 describes the experimental set-up and data collection for an experimental study in Zarqa, Jordan. Section 3 presents the models developed in this paper. This is followed by the results and a discussion of these models in Section 4. Section 5 will address the cleaning of the PV system. Finally, a summary of major findings and conclusions about the importance of this research is reported in Section 6.
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 2 Basic Principles
### Sensitivity and Ambiguity  
The SAR complex image can only record the main phase value within \([-\pi,\pi]\). When the phase difference caused by the sea surface velocity is greater than \(2\pi\), there is a difference of \(2\mathrm{k}\pi(\mathrm{k}=\pm 1,\pm 2\ldots)\) between the recorded phase and the true phase value.  
Figure 1: Schematic of along-track interferometry geometry.  
Substituting \(2\pi\) into (4)-(7), the ambiguous velocity \(v_{amb}\) and ambiguous height \(h_{amb}\) can be calculated as (8) and (9), respectively:  
\[v_{amb}=\frac{\lambda v_{p}}{B_{AT}\cdot\sin\theta}. \tag{8}\]  
\[h_{amb}=\frac{\lambda}{2}\cdot\frac{r\cdot\sin\theta}{B_{XT}}. \tag{9}\]  
For LuTan-1, \(v_{amb}\) = 5.8 m/s is obtained using Equation (8). This value exceeds the upper limit of the speed of most ocean currents worldwide. Regarding height ambiguity, \(h_{amb}\) is 45.64 m, which exceeds almost all sea surface heights worldwide. Therefore, it is likely that the experimental data will not produce velocity and height ambiguities.
## 3 Results and Discussion
### Sequential Batch Photobioreactor Experimentation with Duckweed and microalgae
#### 3.3.1 Bioremediation Potential of Duckweed in RW-Based Media
Additionally, the consumption and removal efficiency of COD, nitrate, phosphate, and sulfate (Figure 5A-D) can be attributed to the duckweed metabolism, which assimilates these nutrients during growth. Regarding nitrogen removal, it is important to note that the initial high concentration of nitrates observed in the RW (see Table 1) is not due to nitrification of ammonium nitrogen but rather reflects the native composition of the RW used in this study. Nitrates can be present in dairy products from both endogenous and exogenous sources [51]. In traditional wastewater treatment processes, nitrate removal can be challenging, often requiring specific conditions for denitrification. However, the reduction in the nitrate concentration observed in Figure 5 is primarily due to the high uptake by _Lemma minor_, which efficiently assimilates nitrates and other nutrients, incorporating them into its growth. Obviously, the organic nitrogen is assimilated alongside the organic carbon sources that contain it. [PERSON] and [PERSON] [52] observed a high uptake of organic carbon, nitrates, and phosphates by duckweed in dairy wastewater, with simultaneous biomass growth. Duckweed absorbs nitrogen through its leaves and roots, incorporating it into its biomass, and stores absorbed phosphorus endogenously, as it is essential for energy metabolism and nucleic acids [53]. Over the six-day pretreatment period, duckweed significantly reduced the concentration of pollutants in the culture medium. Interestingly, although the highest pollutant concentrations were observed in the medium with the greatest proportion of RW (RW-30%), it exhibited the best removal efficiencies (Figure 5E): COD 60.69%, nitrate 90.93%, phosphate 40.52%, and sulfate 22.58%. It can be seen in Figure 5 that the efficiency of pollutant removal could have been increased with more days of duckweed cultivation,  
Figure 4: Evaluation from the two-way ANOVA of the effect of (**A**) the proportion of RW in the culture medium (RW%), (**B**) the initial surface density of _Lemma minor_ wet biomass, and (**C**) the interaction between RW%-surface density on the percentage production of duckweed biomass of _L. minor_ (\(Y_{\text{\emph{PBR-1}}}\)) in the 4 L photobioreactor. Each RW% (**A**) and surface density (**B**) compile all the results obtained for the three surface density levels and three RW% levels, respectively. Bars around points represent 95.0% confidence intervals. Overlapping bars indicate no significant difference. (**D**) Photograph showing part of the _L. minor_ PBR-1 cultures carried out in the cultivation system used. but it is unlikely that complete bioremediation could have been achieved as nitrate was the limiting nutrient, as it was either exhausted in the RW-10% and RW-20% cultures, or its concentration was very low in the RW-30% culture.