esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Scenario 1 (S1)
#### 3.1.2 Cod  
In the calculation of COD environmental capacity, after the implementation of scheme B (HZ1), the COD environmental capacity reduced by 1.48% (Table 4). This indicated that reclamation condition HZ1, which covered an area of 18 km\({}^{2}\), had a small impact  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Redamation Scheme** & **Working Conditions** & **Reclamation Area/km\({}^{2}\)** & **Calculation Area of EC/km\({}^{2}\)** \\ \hline A & Current situation (no working condition) & 0.00 & 876.39 \\ B & HZ1 & 18 & 858.39 \\ C & HZ1+HZ2 & 40 & 836.39 \\ D & HZ1+HZ2+HZ3 & 86 & 790.39 \\ E & HZ1+HZ2+HZ3+HZ4 & 95 & 781.39 \\ F & HZ1+HZ2+HZ3+HZ4+HZ5 & 114 & 762.39 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Changes in the sea area of Haizhou Bay (scenario 1).  
Figure 7: Computed region of marine EC (scenario 1). Computed region of plan A; computed region of plan B; computed region of plan C; computed region of plan D; computed region of plan E; computed region of plan F.  
on the diffusion movement of pollutants, so it had little impact on the change of EC. The implementation of scheme C (HZ1+HZ2) caused the change of COD environmental capacity to reach 21.19%. The position of working condition HZ2 was located almost in the middle of the shoreline of Haizhou Bay, which was equivalent to separating Haizhou Bay across the middle after implementation, and may directly affect the diffusion of pollutants in the northern and southern parts of Haizhou Bay, thus, leading to large changes in EC. Meanwhile, implementation of schemes D, E and F had a huge impact on COD environmental capacity, especially D and E, each of whose impact exceeded 20% (Table 5). This showed that the effect of superposition implementation of working conditions on EC increased gradually. However, scheme F reduced the EC to some extent, probably because after the implementation of all working conditions, the concentration of pollutants in the north decreased, while the diffusion movement of pollutants in the south accelerated, leading to a moderate increase in EC.  
The feasibility evaluation results of schemes B to F are shown in Table 4. The results showed that the evaluation index \(R_{1}\) of scheme B was 10, so scheme B was feasible. The evaluation index \(R_{1}\) of schemes C, D, E and F was 0, so schemes C, D, E and F were not feasible, that is, working conditions HZ2, HZ3, HZ4 and HZ5 were prohibited.
# A Sign of Things to Come: Predicting the Perception of Above-the-Fold Time in Web Browsing
## 4 Evaluation
### The Influence of PCT on SpeedIndex (SI)  
It has been illustrated that SI may not estimate an accurate loading speed while using objective ATF time for website with animations (see the Introduction section). In this section, PCT is integrated with SI and demonstrates how PCT can improve the robustness of SI for websites with and without animations.  
Figure 8 presents a comparison of SI computed using perceived ATF against the objective metrics. The data is plotted with different shapes representing the content (animated/non-animated) and colors to represent the network speed. For most network conditions and page types tested, the computed SI using PCT and perceived ATF yield similar results. This can been seen by the large number of test points on the diagonal line of the left subplot of Figure 8. However, for websites with animated contents, marked with x markers in the right subplot of Figure 8, a wider distribution of SI is computed using the objective ATF. In most cases, SI is overestimating the speed of loading for websites with animated contents. While the overestimation of SI using objective ATF for the websites with and without animated content is more apparent for the lower bandwidth levels (3 Mbps and 1 Mbps), SI computed using PCT has a more consistent behavior for different content types and bandwidth levels (see the regression line nearly aligned with the diagonal line).  
Figure 8: The influence of PCT on SpeedIndex (SI). The (**left**) subplot represents the estimation of SI using PCT (\(y\)-axis) and its tight positive correlation with the corresponding SI computed based on ground truth perceived ATF (\(x\)-axis). The (**right**) subplot shows the relationship between SI estimated based on the objective ATF (\(y\)-axis) and perceived ATF (\(x\)-axis).  
Figure 7: The relationship on estimation of ATF time. The left subplot shows the relationship between PCT (\(y\)-axis) and perceived ATF (\(x\)-axis). The middle subplot illustrates the relationship between objective ATF (\(y\)-axis) and perceived ATF (\(x\)-axis). The right subplot demonstrates the relationship between PCT (\(y\)-axis) and the Objective ATF (\(x\)-axis).  
The coefficient of determination analysis (known as \"R-squared\") is used to quantify how close the SI PCT and SI perceived ATF time are to the fitted regression line. The computed R-squared is 0.93, meaning that 93% of the variation in SI computed using perceived ATF can be explained by the SI computed based on PCT. A similar analysis on the SI objective ATF and perceived ATF returns an R-squared of 0.74. The higher R-squared value for the SI computed using PCT shows that the known issue of the SI overestimation of SI was seen when animated contents exists is significantly reduced [10].  
Correlation analysis reveals a stronger relationship between SI PCT and SI perceived ATF time (\(SROCC=0.90\)) versus SI objective ATF and SI perceived ATF (\(SROCC=0.84\)).
## 3 Results and Discussion  
### Characterization Results  
#### 3.1.1 Microscopic Analysis  
Saudi low-grade pyrophyllite ore is generally metamorphic rock that is fine-grained, compact, and massive. Microscopic examination showed that pyrophyllite occurs as fine-grained anhedral flakes arranged in parallel with other constituents and exhibits a schistose texture (Figure 1a). Additionally, pyrophyllite was observed wrapping around quartz and feldspar crystals, creating an \"Augen\" texture. Associated minerals such as quartz and feldspars and rare amounts of muscovite, chlorite, and sercite were observed. Quartz was identified as finely grained, anhedral-strained crystals with wavy extinction and sutured edges. These crystals were often aligned in parallel and had a schistose texture (Figure 1b). Other associated minerals, such as feldspars and muscovite, are fine-grained anhedral crystals intercalated with rock constituents and arranged in parallel with other constituents to display the foliation texture. The observations showed that feldspars undergo partial alteration to form the minerals sercite, chlorite, and clay.  
The results of reflected light microscopy shown in Figure 2 demonstrate that impurities are present in rare amounts and occur as very fine-grained anhedral crystals disseminated in the rocks following foliation. Moreover, these impurities are evident in altered pyrite and hematite forms [21; 32; 33]. The results also indicated that pyrite was completely and partially dissolved into iron oxide (hematite). Partial alteration occurs in the pyrite rim and within it. Figure 2 shows high pyrite reflectance with a white-gray color, dissolved at the rim and within it to hematite (brown).
# RARZ: Ring-Zone Based Routing Protocol for Wireless Sensor Networks
## 5 Conclusions  
We presented an energy-efficient protocol using the ring-zone model, RARZ, for a WSN. RARZ is a stateless protocol that is completely table-less and non-position-based. Energy-rich nodes in the network are used for data relaying purpose. This results in even distribution of energy consumption and avoids depletion of a node's energy on a particular path. It also reduces the average hop count and end-to-end delay in the system and increases the throughput. In the future, we will assess the performance of RARZ on mobile sensors.  
RARZ achieved 32% energy efficiency against AODV, 19% against AIMRP, and 39% against Flooding. Simulation results have shown that RARZ increases the network lifetime and minimizes end-to-end delay in the system. RARZ achieved a 51% lower end-to-end delay against AODV, 33% against AIMRP, and 59% against Flooding. RARZ performs better and is suitable for applications in which the data must reach the required destination in time. As it is proven from simulation results that the RARZ lifetime is higher as compared to other protocols, more messages are received at the BS. RARZ achieved 49-70% improvements in terms of the data messages successfully received at BS. Simulation results show that RARZ reduces the average hop count in the system by 17-23%.  
[PERSON] and [PERSON] conceived and designed the experiments; R.N.J. performed the simulations; [PERSON] and [PERSON] analyzed the results; [PERSON] wrote the paper, [PERSON], [PERSON] and [PERSON] technically reviewed the paper.  
This research was funded by National Natural Science Foundation of China (Grant No. 61461136002).  
We are thankful to CAS-TWAS for supporting this work (CAS-TWAS No. 45, 2016). We thank the reviewers and the editor for numerous critical comments that largely improved our manuscript. We are also thanks to Dr. [PERSON] from COMSATS University Abbottabad Campus for his technical help during this research work.  
The authors declare no conflict of interest.
## 4 Algorithm Design
### Hybrid Ant Colony Optimization Algorithm
#### 4.2.1 Algorithmic Framework  
The overall HACO algorithmic framework is depicted in Figure 6, with the key steps outlined as follows:  
Step 1. Initialize the parameters of HACO, including the maximum iteration count \(maxIter\), objective value \(T\), ant set \(Ant\), neighborhood counter \(l=1\), initial iteration \(iter=0\), and the set of unserved customers \(UC=\{1,2,\ldots,n\}\).  
Step 2. Each ant constructs a solution in two stages. The first stage resolves charging conflicts using the dynamic rescheduling mechanism, while paths unable to defer charging proceed to the second stage for supplementary construction, yielding a feasible initial solution \(x_{ant}^{iter}(ant\in Ant\,l)\) that adheres to the capacity constraints of CI.  
Step 3. Optimize the solution constructed by the ants using the \(l\)-th local search operator, obtaining \(x_{ant}^{iter}(ant\in Ant)\).  
Step 4. If \(T\big{(}x_{ant}^{iter}\big{)}<T\big{(}x_{ant}^{iter}\big{)}\), update \(x_{ant}^{iter}=x_{ant}^{iter}\), keep the neighborhood counter \(l\) unchanged, and proceed to Step 5. Otherwise, update \(l=(lmodl_{max})+1\) and directly proceed to Step 5.  
Step 5. Update the pheromone using the global pheromone update strategy of the ant-cycle model.  
Step 6. Check if the termination condition is met. If satisfied, output the optimal solution and terminate. Otherwise, increment iter by 1 and return to Step 2.
# Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation
## 4 Experiment Results
### Sea Clutter Augmentation Using GCC-FLOW
The histogram was plotted for 10,000 consecutive samples of the generated data at the three different time points (beginning 1-(a), middle 1-(b), end 1-(c)), and the autocorrelation was plotted for 512 samples corresponding to each time point (beginning 2-(a), middle 2-(b), end 2-(c)). As illustrated in Figure 8, the proposed GCC-FLOW demonstrates the capacityto effectively learn the statistical characteristics from the Grimsby data, generating data that closely resemble the training data. In Figure 9, the proposed GCC-FLOW is compared with the existing block-by-block deep generative approach [25]. Sub-figure 1-(a) depicts the autocorrelation of the original high sea state data, 1-(b) illustrates the autocorrelation of data generated by the block-by-block GAN [25], and 1-(c) portrays the autocorrelation of data generated by the proposed GCC-FLOW. Whereas 1-(c) illustrates a similar autocorrelation pattern to 1-(a), the autocorrelation in 1-(b) is corrupted by the independent nature of the block-by-block generation (highlighted by the dashed box). Similarly, 2-(a), 2-(b), and 2-(c) plot the autocorrelation of the original low sea state data, the data generated by the block-by-block GAN [25], and the data generated by the proposed GCC-FLOW, respectively. It is notable that the same corruption in autocorrelation is observed in 2-(b). Figure 4: Magnitude distribution of the generated data by GCC-FLOW in comparison with the true data: high sea state (**a**), low sea state (**b**). Figure 5: Autocorrelation of the generated data by GCC-FLOW in comparison with the true data: high sea state (**a**), low sea state (**b**). Figure 6: Distribution (**1**) and autocorrelation (**2**) of the generated data trained on high sea state data: beginning (**a**), middle (**b**), end (**c**).
## 1 Introduction
From the radius obtained for the PA-shell, they could study the extension of the PA sequence according to the PA degree of ionization. In all these cases in which the star architecture is obtained through a micellization, the aggregation number and the radius of the core of the micelle have to be considered as additional parameters. On the opposite side, NaPA and CsPA stars synthesized and studied by SAXS a few years after by [PERSON] _et al._ had a negligible core since they were made of four arms [16]. As a result, they mostly showed a scattering behavior relatively close to that of linear PEs. In reference [6], the number of arms was substantially larger (with an average functionality <f> = 12), and the core could still be considered as negligible in size. For salt-free aqueous solutions, whatever the concentration, the total scattering function measured by SAXS displayed two maxima. The first, at \(q_{1}\)*, is related to a position order between star cores. It could be interpreted in terms of a privileged distance due to electrostatic repulsion, or close packing above the critical overlap concentration, \(c^{*}\). The second, at higher \(q\) value, \(q_{2}\)*, is similar to the broad halo observed in the scattering pattern of a semi-dilute salt-free aqueous solution of linear [17; 18; 19; 20; 21] associated with a correlation hole of electrostatic origin [22; 23; 24]. The distinct concentration dependence of \(q_{2}\)* in the dilute, where it stays constant, and in the semi-dilute regimes, where it increases with \(c\) due to the interpenetration between arms of all vicinal stars, provides a way to estimate the critical overlap concentration \(c\)*. This simple and direct picture was however complicated by the fact that in reference [6], the arms of the NaPSS stars were not completely sulfonated (sulfonation degrees in the range 0.63 < \(\tau_{\rm S}\) < 0.78). As a consequence PSS arms were actually copolymers, poly(styrene-\(\infty\)-styrene sulfonate) that have an amphiphilic character which modifies the \(q\)* values in linear chain solutions [25; 26; 27; 28]. This is due to the fact that they tend to adopt a so-called pearl necklace conformation, _i.e.,_ a combination of string-like and pearl-like parts all along the chemical sequence, of sizes depending on \(\tau_{\rm S}\), as predicted by theory [29; 30] and confirmed by scattering and simulations [25; 26; 27; 28; 31]. Incomplete sulfonation of stars made some conclusions [6] less refined as they could have been in a pure hydrophilic PE case, in particular about the arm length and the effect of counterion spatial distribution. In the present paper, we consider the same type of stars, but with PSS arms fully sulfonated (\(\tau_{\rm S}\) close to 100%). Though this may appear only a slight improvement, we show that it is more than that: we have now ideal model systems. They enable small angle scattering experiments quite simple to interpret via very well defined geometric information, to yield a solid ground to test a theoretical description, which would then be a support for understanding more complex systems found in applications.
## 2 Materials and Methods
### Evaluation  
In this study, we evaluate the performance of a drowsiness detection model using various metrics and scenarios. The primary goal of the evaluation is to determine the model's effectiveness in accurately predicting drowsiness, which is crucial for real-life implementation.  
The evaluation metrics used are shown in Equations (1)-(4), as follows:  
* _Accuracy_: This measures the proportion of correct predictions among all the predictions. It is calculated as the sum of true positives (_tp_) and true negatives (_tn_) divided by the sum of true positives, true negatives, false positives (_fp_), and false negatives (_fn_). \[Accuracy=\frac{tp+tn}{tp+tn+fp+fn}\] (1)
* _Recall_: Also known as sensitivity, this measures the ability of the model to identify all relevant instances (true positives) among the actual positives. It is calculated as the ratio of _tp_ to the sum of _tp_ and _fn_. \[Recall=\frac{tp}{tp+fn}\] (2)
* _Precision_: This measures the proportion of _tp_ predictions among all positive predictions. It is calculated as the ratio of _tp_ to the sum of _tp_ and _fp_. \[Precision=\frac{tp}{tp+fp}\] (3)
* \(F\)1-_Score_: The harmonic mean of precision and recall provides a balance between the two metrics. It is particularly useful in scenarios with imbalanced data distributions. \[F1-Score=2\times\frac{precision\times recall}{precision+recall}\] (4)* CPU Usage: percentage of CPU resources consumed by the model during inference.
* RAM Usage: amount of memory (in MB) consumed by the model during inference.
* GPU Usage: percentage of GPU acceleration and memory (in MB) utilized for faster inference (relevant for models deployed on devices with GPU support).  
To ensure a balanced assessment of the model's classification ability, we used macro-averaged values for precision, recall, and F1-score, representing the average scores across all classes. In addition to the aforementioned metrics, the computational efficiency of the drowsiness detection model in terms of CPU, RAM, and GPU usage during inference was also evaluated. The evaluation scenarios involved splitting the NTHU-DDD dataset into training and testing sets to assess the model's performance on unseen data. This approach helps validate the model's generalizability and robustness in detecting drowsiness. For the hardware used in development along with model training, the setup included a Ryzen 7 5800X 8-Core 3.80 GHz processor, a Nvidia GTX1050 Ti 4 GB GPU, and 32 GB DDR4 RAM. The training process utilizes pretrained models for head pose estimation and facial landmark detection to enhance the accuracy and efficiency of the drowsiness detection model.
# Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios
## Appendix A
#### Rigid-Body Dynamics Model
\begin{array}{cccccccc}0&0&0&0&0&0&0\\ -c(\alpha)&-c(\alpha)&c(\alpha)&c(\alpha)&-c(\alpha)&-c(\alpha)&c(\alpha)&c( \alpha)\\ -c(\alpha)&c(\alpha)&c(\alpha)&-c(\alpha)&-c(\alpha)&c(\alpha)&c(\alpha)&-c( \alpha)\\ -d&d&-d&d&-d&d&-d&d\\ D_{Fc}(\alpha)&-D_{Fc}(\alpha)&-D_{Fc}(\alpha)&D_{Fc}(\alpha)&-D_{RC}(\alpha) &D_{Rc}(\alpha)&D_{Rc}(\alpha)&-D_{Rc}(\alpha)\\ -D_{Fc}(\alpha)&-D_{Fc}(\alpha)&D_{Fc}(\alpha)&D_{Fc}(\alpha)&D_{Rc}(\alpha)&D _{Rc}(\alpha)&-D_{Rc}(\alpha)&-D_{Rc}(\alpha)\end{array} \right)\left[ \begin{array} []{c}F_{F1}\\ F_{F2}\\ F_{F3}\\ F_{F4}\\ F_{R1}\\ F_{R2}\\ F_{R3}\\ F_{R4}\end{array} \right] \tag{105}\]  
with \(\tau_{ST}=[X_{ST}\), \(Y_{ST}\), \(Z_{ST}\), \(N_{ST}\), \(M_{ST}\), \(N_{ST}]^{T}\), where \(c(\alpha)=\cos(\alpha)\) and \(\alpha=45\) degrees is the angular position of thrusters (for \(\alpha=45\) degrees \(\sin(\alpha)=\cos(\alpha)\)), \(\tau_{ST}\) is the vector of forces and moments provided by the side thrusters in the body-fixed reference frame. The vector of forces provided by each thruster is computed according to the nonlinear characteristic of this actuator, converting the PWM duty-cycle (provided by the controller) to the thruster effort. These thrusters also include a saturation on the maximum force, and a symmetric dead-band cantered on the zero speed (equivalent to 1500 \(\upmu\)s PWM input signal). The side thruster model is affected by a delay 0.01875 s and has the following dynamics model:  
\[A_{T}=\left[ \begin{array}{cc}-10^{3}&-25\times 10^{4}\\ 1&0\end{array} \right];\ B_{T}=\left[ \begin{array}{c}1\\ 0\end{array} \right];\ C_{T}=\left[ \begin{array}{cc}0&25\times 10^{4}\end{array} \right] \tag{106}\]The thrust \(y_{T}=T\left[\mathbb{N}\right]\) provided by the actuator is defined in terms of the control input signal \(u_{T}=PWM\left[\text{ms}\right]\) and \(y_{T}(k)=a_{2}(k)u_{T}^{2}(k)+a_{1}(k)u_{T}(k)+a_{0}(k)\). The characteristic is defined by the following:  
* If \(u_{T}(k)>1.5381\) then \(a_{2}(k)=54.262\), \(a_{1}(k)=-108.231\) and \(a_{0}(k)=38.101\);
* If \(u_{T}(k)<1.4492\) then \(a_{2}(k)=-28.058\), \(a_{1}(k)=134.08\) and \(a_{0}(k)=-135.38\);
* If \(u_{T}(k)\in[1.4492,1.5381]\) then \(a_{2}(k)=0\), \(a_{1}(k)=0\) and \(a_{0}(k)=0\).
## 3 Overview of Information Systems
### Components of an Information System  
From a socio-technical perspective, information systems are composed of several components presented in Figure 2.  
* Human Resources--encompassing end-users and anyone else interacting with the system, providing inputs, utilizing system outcomes, and making decisions based on the provided information.
* Equipment--physical devices used for inputting, processing, storing, and extracting data within the system, such as computers, servers, network equipment, storage devices, and peripherals.
* Software--programs, applications, and operating systems enabling users to interact with and manipulate data.
* Data--information collected, processed, and stored by the computer system, which can be structured (organized in a specific format like a database) or unstructured (such as text documents or images).
* Procedures--methodologies and rules governing system usage, management, and information processing, including rules, policies, workflows, and processes dictating activities within an organization.
* Networks--connecting different system components, enabling data transmission between computers and devices, either locally (LAN) or globally (WAN).
* Security--protecting the system against unauthorized access, including authentication, authorization, encryption, firewalls, and other security measures ensuring data confidentiality, integrity, and availability.  
Analysis--mechanisms gathering information about system performance, usage, and effectiveness, aiding in identifying improvement opportunities and adapting the organization to evolving technological needs.
* Control--mechanisms ensuring system functionality efficiency, including monitoring, error detection and resolution, and data consistency maintenance.
* Environment--the context in which the information system operates, influenced by external factors like economic conditions, industry trends, and social factors.  
All these components collaborate to create a coherent and functional information system supporting the objectives and goals of an organization. The design and integration of these components are crucial for the system's success in providing accurate, timely, and relevant information to support decision making and operational processes [22].