esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Pupil Center Localization Detection Algorithm with Improved YOLOv8
## 1 Introduction
However, this approach demands significant computational resources, extensive training data, and tackling issues such as overfitting. To enhance pupil positioning accuracy, this paper introduces a pupil center localization algorithm grounded in an enhanced YOLOv8n network. This algorithm integrates a dual attention mechanism into the YOLOv8n backbone network, effectively aggregating and disseminating informative global features across the spatiotemporal space of images. This approach facilitates access to spatial features in subsequent convolutional layers. While emphasizing global contextual information from input data, it mitigates dependence on specific regions, thereby enhancing model robustness. Furthermore, the incorporation of dilated convolutions, replacing standard convolutions in the original network, lightens the model and accelerates the detection speed. The employment of the Focaler-IoU loss function, focusing on diverse regression samples, strengthens the detector's performance across various detection tasks. The application of this algorithm in a custom-developed multi-parameter eyeball measurement system significantly reduces measurement time and elevates the accuracy, real-time performance, and robustness of optical eyeball biometric measurements, thereby contributing to the intelligent evolution of ophthalmic instrumentation. The structure of this paper mainly includes the following: Firstly, the YOLOv8 model is introduced and compared with the YOLOv5 model; secondly, the improved YOLOv8n algorithm is proposed, which mainly includes the integration of a dual attention mechanism in the YOLOv8n backbone network to enhance the ability to extract human eye pupil features, analysis of the impact of difficult samples and easy-to-handle sample distribution on regression results, and the proposal of a new method, Focaler-IoU, which can improve the detector's performance in different detection tasks by focusing on different regression samples, introducing the void convolution to not only reduce the network model but also improve the detection speed of the model; thirdly, our experimental results and analysis are presented, where the experimental dataset, experimental configuration, and evaluation index are introduced, and through the comparative experiments we verify the functional indicators of the proposed improved algorithm, including different attention mechanisms and different models for comparison. Finally, the research contents are summarized.
# A Trusted Multi-Cloud Brokerage System for Validating Cloud Services Using Ranking Heuristics
## 2 Literature Review
### Extraction of Service Trust Factors from Multi-Cloud Environment
#### 3.1.2 Service Trust Prediction Process  
In Algorithm 1, the inputs are available services and newly arrived cloud services with the parameter set. The SR value for each cloud services with feedback from service set(ss) is calculated by  
\[\text{compute SR}_{\text{n}}=\frac{p_{1}+p_{2}+\ldots+p_{13}}{13} \tag{15}\]  
where 'n' is the number of cloud services (cs).  
The newly arrived services represent a mix of IaaS, PaaS, and SaaS offerings, including compute and storage providers, email and collaboration tools, container-based platforms, helpdesk systems, and enterprise backup solutions. The SR value for newly arrived cloud services (without feedback) has been assigned by finding the equivalent available cloud services from the Service Collection Repository (SCR). Finally, the cloud services from the service set (ss) are ordered based on their SR values and formed into clusters, namely the 'Complete trustworthy cluster group', 'Trustworthy cluster group', 'Partially trustworthy cluster group', and 'Untrustworthy cluster group'. The validation and verification process of the proposed model is illustrated in Algorithm 1.  
```
Service Ranking (SR) algorithm Inputs: Service set (ss) = {cs\({}_{1}\), cs\({}_{2}\),..., cs\({}_{n}\)}  Parameter set (ps) = {p\({}_{1}\), p\({}_{2}\),..., p\({}_{13}\)} Outputs: Clusters with ordered services
```  
**Algorithm 1**Service ranking and cloud service cluster formation.  
```
1. Compute the SR value for each cloud services(cs) for each cloud services from ss if (each cloud services with customer feedback) compute SR\({}_{n}=\frac{p_{1}+p_{2}+...+p_{13}}{13}\) else //Newly arrived cloud services without feedback Finding the equivalent cloud services which is existing for the newly arrived cloud services from Service Collection Repository (SCR). Assign the SR of matched services to the newly arrived services. \(\text{Mapping(cs${}_{x}$,$n$cs${}_{y}$)}=\sum_{x=1}^{n}\sum_{y=1}^{m}max(cs${}_{x} \cap ncs${}_{y}$)\) \(\text{NSR}_{y}=\text{SR}_{x}\) end if
2. Clustering the cloud services based on SR values if (SR value between 0.750 and 1.0) Move cs to \"Complete trust worthy cluster group\" elseif (SR value between 0.500 and 0.749) Move cs to \"Trust worthy cluster group\" elseif (SR value between 0.300 and 0.499) Move cs to \"Partially trust worthy cluster group\" else (SR value less than 0.299) Move cs to \"Untrustworthy cluster group\" end if end for
```  
**Algorithm 2**Service Ranking and cloud service cluster formation.
## 3 Results
### Identification of Phytochemical Compounds of Artichoke By-Products by HPLC-ESI-TOF-MS
#### 3.1.2. Flavonoids  
With regard to flavonoids and its several sub-classes, only flavones and their derivatives were detected in arithokes by-products. Thereby, peak 6, with _m/z_ at 593.1512, was tentatively identified as luteolin-rutinoside [18], while peak 7 with _m/z_ at 447.0933 was proposed as luteolin-glucoside (cyanoside) according to bibliographic data [10; 21]. Furthermore, the compound eluting at 8.58 min (peak 9) and displaying _m/z_ 577.1563 was considered as apigenin-rutinoside (isorhofolin) [21]. In the same way, peak 10, detected at 8.98 min and _m/z_ 431.0984, was tentatively assigned to apigenin-glucosidase according to the comparison of the molecular formula provided by the detector and literature data [21]. Similarly, peak 13 at _m/z_ 285.0405 and molecular formula C\({}_{15}\)H\({}_{10}\)O\({}_{6}\) along with peak 14 at 15.44 min and _m/z_ 269.0455 were proposed according to the literature as luteolin [10; 21] and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Peak** & **RT** **(min)** & **Proposed Compound** & **Theoretical** _m/z_ & **Experimental** _m/z_ & **Molecular** _Formula_ & **Error** **(ppm)** & **PLE** **Experiment** \\ \hline
19 & 26.92 &
\begin{tabular}{c} Cynarasaponin A/H \\ isomer \\ \end{tabular} & 925.4802 & 925.478 & C\({}_{47}\)H\({}_{24}\)O\({}_{18}\) & 2.4 & 1,6,7,9 \\ \hline
20 & 30.05 & UK 4 & 205.1598 & 205.1606 & C\({}_{14}\)H\({}_{22}\)O & \(-\)3.8 & 2,3,4,5,6,7,8,9 \\ \hline
21 & 31.10 &
\begin{tabular}{c} Hydroxy- \\ octadecatrienoic \\ acid \\ \end{tabular} & 293.2122 & 293.2111 & C\({}_{18}\)H\({}_{30}\)O\({}_{3}\) & 3.9 & 1,2,4,6,7,9 \\ \hline
22 & 31.27 &
\begin{tabular}{c} Hydroxy- \\ octadecatrienoic \\ acid \\ \end{tabular} & 295.2279 & 295.2286 & C\({}_{18}\)H\({}_{32}\)O\({}_{3}\) & \(-\)2.6 & * \\ \hline
23 & 33.19 & Linolenic acid & 277.2173 & 277.2170 & C\({}_{18}\)H\({}_{30}\)O\({}_{2}\) & 1.1 & * \\ \hline \hline \end{tabular} (*) Indicate that these compounds were identified in all the PLE extracts. For those compounds that were not identified in all the extracts, the number of the PLE experiment in which they were detected was annotated. UK, unknown.  
\end{table}
Table 2: _Cont._  
Figure 1: Base peak chromatogram (BPC) of representative pressurized liquid extraction (PLE) extracts of _Cymara scolymus_ L. by-products.  
apigenin [10; 21], respectively. Finally, peak 17 with _m/z_ 283.0612 was tentatively assigned to methylapigenin [22].
# Using Fluorescence Spectroscopy to Detect Rot in Fruit and Vegetable Crops
## 3 Results and Discussion
### Experiments with a 527 nm Pulsed Laser
Figure 9: Pulsed fluorescence spectra of healthy and rotten green apples: (**a**) original spectra; (**b**) normalized spectra. Pulp spectra also demonstrate differences depending on cultivar (Figure 11). The fluorescence spectra of the central \"vein\" (point 1) and the main volume (point 2) differ for \"Udacha\" potatoes. The spectra are almost identical for the \"Zhukovsky\" potato. Since the rotten potatoes were damaged in various ways, the fluorescence spectra were recorded for each type of defect (Figure 12). Points 1 and 2 were taken at the places of rot on the peel. Point 3 corresponds to the thickened part of the peel, and point 4 corresponds to the thin part. Point 5 corresponds to the potato eye. The spectra vary for different defects. The following pros and cons of using pulsed and continuous lasers for detecting the fluorescence spectra of vegetables and fruits can be noted. Fluorescence spectroscopy with continuous laser pumping has several positive features, including low-cost equipment, and low power density preventing possible sample damage. Among the negative aspects, a single one can be highlighted; over time, the sample \"burns out\"--the method is sensitive to external light and geometry, and as a result method is not very suitable for field conditions. Alternatively, fluorescence spectroscopy measurements with the pulsed laser have its own advantages, including that external light influence can be easily skipped by gated measurements including remote sensing with the appropriate gating so distant targets can be sensed. For example, in vivo pulsed fluorescence spectroscopy measurements of plants  
Figure 11: (**a**) Pulsed fluorescence spectra of pulp of “Udacha” (conditionally healthy) potato; (**b**) fluorescence spectra of pulp of “Zhukovsky” (conditionally rotten) potato. Figure 12: Pulsed fluorescent spectra of different surface locations of “Zhukovsky” potato (see sampling spot in the insets). (**a**) Original spectra; (**b**) normalized spectra. can be done onsite in the field during different daylight conditions; additionally, long time period measurements can be conducted. The negative aspects of a pulsed laser-induced fluorescence spectroscopy include the potential damage of biological structures induced by high peak power density, and the high cost of equipment.
## 4 SINS/GPS Ultra-Tightly Coupling Structure Based on a Double Loop
### Pre-Filter Tracking Modeling  
The above pointed out that the pre-filter tracking algorithm is one of the key points of ultra-tightly coupled systems. Thus, this section gives the nonlinear pre-filter tracking loop model of the satellite signal.  
1. Pre-filter state equation establishment State variables take: \[\mathbf{X}\ =\ \left[\begin{array}{cccc}A&\delta\tau&\delta\phi&\delta f&\delta a \end{array}\right]^{\mathrm{T}}\] (49) where \(A\) denotes the normalized amplitude of the received signal, the code phase error; \(\delta f\) the carrier wave frequency error, and \(\delta a\) the carrier phase acceleration error. The equation of state of the system is: \[\dot{\mathbf{X}} =\ \left[\begin{array}{cccc}\dot{A}&\delta\dot{\tau}&\delta\dot{ \phi}_{0}&\delta\dot{f}_{0}&\delta a_{0}\end{array}\right]^{\mathrm{T}}\ =\ \mathbf{F}\mathbf{X}+\mathbf{G}\mathbf{w}\] (50) \[=\ \left(\begin{array}{cccc}0&0&0&0&0\\ 0&0&0&\beta&0\\ 0&0&0&1&0\\ 0&0&0&0&1\\ 0&0&0&0&0\end{array}\right)\left(\begin{array}{cccc}A\\ \delta\tau\\ \delta b_{0}\\ \delta a_{0}\end{array}\right)+\left(\begin{array}{cccc}1&0&0&0&0\\ 0&1&\beta&0&0\\ 0&0&1&0&0\\ 0&0&0&0&1\\ 0&0&0&0&1\end{array}\right)\left(\begin{array}{c}w_{A}\\ w_{\tau}\\ w_{\phi_{0}}\\ w_{f_{0}}\\ w_{a_{0}}\end{array}\right)\] where \(\beta\) is the transformation coefficient that converts the radian to the chip, and the components of \(\mathbf{w}\) are the amount of the process noise corresponding to the state variables.
2. Pre-filter measurement equation establishment The system's observation is obtained by in-phase signal \(I\) and quadrature signal \(Q\) outputting by the correlator. \(I\) and \(Q\) include the early (\(E\)) signal, the present (\(P\)) signal and the later (\(L\)) signal. The expression of the measurement equation is as follows: \[\left[\begin{array}{c}I_{P}\\ I_{E}\\ I_{L}\\ Q_{P}\\ Q_{E}\\ Q_{L}\end{array}\right]\ =\ \left[\begin{array}{c}ATD(t_{k}-\tau)R(\delta\tau) \sin c(\delta fT/2)\cos(\delta fT/2+\delta\phi)\\ ATD(t_{k}-\tau)R(\delta\tau-05\Delta)\sin c(\delta fT/2)\cos(\delta fT/2+ \delta\phi)\\ ATD(t_{k}-\tau)R(\delta\tau+05\Delta)\sin c(\delta fT/2)\cos(\delta fT/2+ \delta\phi)\\ ATD(t_{k}-\tau)R(\delta\tau)\sin c(\delta fT/2)\sin(\delta fT/2+\delta\phi)\\ ATD(t_{k}-\tau)R(\delta\tau-05\Delta)\sin c(\delta fT/2)\sin(\delta fT/2+ \delta\phi)\\ ATD(t_{k}-\tau)R(\delta\tau+05\Delta)\sin c(\delta fT/2)\sin(\delta fT/2+ \delta\phi)\end{array}\right]\ +\left[\begin{array}{c}n_{I_{P}}\\ n_{I_{E}}\\ n_{I_{L}}\\ n_{Q_{2}}\\ n_{Q_{L}}\end{array}\right]\] (51) where \(T\) is the cumulative sum of time; \(D(t_{k}-\tau)\) is the navigation message modulation; \(\Delta\) is the difference number of code between the early and the later codes, \(R(*)\) respectively correspond with the results of the received C/A code sequence and the locally-generated code in E code, P code, and L code after being relative to each other.
## 2 The MCVSK and MVSK Portfolio Optimization Model
### The MCVSK and MVSK Portfolio Optimization Model  
Above all, in this paper, we mainly study the following MCVSK portfolio optimization model:  
\[min f\left(x,\zeta\right) \tag{16}\] \[s.t. g\left(x,\zeta\right)\succeq_{\left(2\right)}g\left(y,\xi\right)\] \[S\left(x\right)>S\left(y\right)\] \[K\left(x\right)>K\left(y\right)\] \[\frac{CVaR_{a}\left(x\right)}{g\left(x,\xi\right)}>\frac{CVaR_{a} \left(y\right)}{g\left(y,\xi\right)}\] \[x\in X\]  
where \(y\) is benchmark investment, such as FTSE100 index portfolio. Towards the objective function \(f\left(x,\xi\right)\), we mainly consider the following two forms [31]:  
\[f\left(x,\xi\right)=-g\left(x,\xi\right) \tag{17}\]  
\[f\left(x,\xi\right)=-g\left(x,\xi\right)-\sum_{i=1}^{n}x_{i}^{2} \tag{18}\]  
where \(x\) is the investment proportion vector and \(\xi\) is a vector represents the return of assets. Thereinto, the Equation (17) is relatively simple and the Equation (18) is more complicated. For Equation (18) takes the impact on transaction cost into consideration, which prefer the big amounts investment in portfolio. Besides, the first three constraints on the MCVSK model represent the SSD, skewness and kurtosis constraints respectively. It is worth mentioning that we combine the CVaR with the return to portfolio, which is introduced into the MCVSK model as a new constraint. This constraint is mainly inspired by behavioral economics. We think the risk which investors can bear should not a fixed value. It is obvious that the more return of portfolio, the higher risk we should bear. Moreover, we don't consider the short-selling, so \(X\) can be expressed as follows:  
\[X=\left\{\left(x_{1},\ldots,x_{n}\right)\mid\sum_{j=1}^{n}x_{j}=1,x_{j}\geq 0,\forall j\in 1,\ldots,n\right\} \tag{19}\]In practical application, in order to ensure the diversity of portfolio, the upper bound on the fraction of capital invested in each asset is set to a relatively low vaule, and the specific value is adjusted by the number of assets available for investment. Meanwhile, as a comparison, we change the risk measure in MCVSK model into VaR and propose the following MVSK portfolio optimization model:  
\[min f\left(x,\xi\right)\] \[s.t. g\left(x,\xi\right)\succeq_{\left(2\right)}g\left(y,\xi\right)\] \[S\left(x\right)>S\left(y\right)\] \[K\left(x\right)>K\left(y\right) \tag{20}\] \[\frac{VaR_{\alpha}\left(x\right)}{g\left(x,\xi\right)}>\frac{VaR_ {\alpha}\left(y\right)}{g\left(y,\xi\right)}\] \[x\in X\]
# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 1 Introduction  
Human-induced climate change is an unprecedented threat with far-reaching consequences for the environment, societies, and economies [1]. The resulting rise in global temperatures contributes to severe weather events, disrupting ecosystems and endangering communities globally [2]. These effects are multifaceted and can manifest in various ways, affecting social, economic, and environmental aspects of communities [3].  
Addressing the impacts of climate change on the community level requires a holistic and collaborative approach, involving mitigation efforts to reduce greenhouse gas emissions as well as adaptation strategies to build adaptive and resilience capacity in socially and geographically vulnerable communities [4]. Adaptation becomes crucial in these contexts, providing proactive strategies to minimise vulnerability and enhance resilience [5]. Recognising communities as active agents in shaping their resilience, community-based adaptation (CBA) to climate change empowers concerned persons to co-design strategies, foster ownership, social cohesion, and sustainability [6]. CBA interventions address specific community needs and establish a robust foundation for long-term climate resilience [7]. Agent-based modelling (ABM) has the potential to capture the particular CBA features as it allows for the simulation of complex community-environment dynamics and offers insights into factors influencing adaptation decisions by the communities. Such thorough and critical understanding allows for identifying cost-effective and impactful interventions. Reviewing and assessing ABM tools for CBA is vital, considering that ABM tools have varying degrees of freedom in their elements, features, and capabilities [8]. Evaluations can guide researchers and practitioners to apply the right tool, optimise the application, and contribute to the continuous improvement of tools in CBA [9].
# Late Fusion-Based Video Transformer for Facial Micro-Expression Recognition
## 4 Experimental Results
### Evaluation Protocol  
Each person has a different form of expression on their face. Therefore, to avoid person-dependent issues, the performance of the model is evaluated with leave-one-subject-out (LOSO) cross-validation. LOSO measures the performance of the model using one of the subjects as a validation set and the rest as a training set. Then, we repeat the training and validation process as many times as the number of subjects in the dataset.
# Wind-Induced Response Control of High-Rise Buildings Using Inerter-Based Vibration Absorbers
## 4 Wind-Induced Response Mitigation Using Inerter-Based Vibration Absorbers
### Effects of the Inerter-Based Vibration Absorbers on ESWLs  
ESWLs are important parameters used by structural engineers for limit-state design as well as for assessing the bearing capacity of structures. Displacement Gust Loads Factor (DGLF) method [59], Moment-Based Gust Loads Factor (MGLF) method [66; 67], Load-Response Correlation (LRC) method [68], and Weighted Combination of Modal Inertial Load Component (WCMILC) method [69] have been proposed to calculate ESWLs of high-rise buildings. Among them, the DGLF method is widely used in practical projects owing to its simplicity and for this reason it is adopted in this paper.  
After the calculation of mean and extreme values of wind-induced profiles of displacements along the building height corresponding to 24 wind directions, ESWLs can be obtained from Equations (9) and (10). We here describe variation of ESWLs along the building height at a wind speed of 42.02 m/s (100-years return period).  
Figure 19a,b show the profiles of ESWLs of original structure, structure with TMD, TMDI and TID along the building height, corresponding to wind directions of 0\({}^{\circ}\) and 45\({}^{\circ}\), respectively. It is noted that the ESWLs of the original structure are larger than those of structure with vibration absorbers for every story. In this case, the performance of the three vibration absorbers is more or less comparable. As the ESWL is calculated by Equations (9) and (10) based on the extreme displacement, the mitigation effects of three vibration absorbers on EWL are similar to that on extreme displacement. Above the 60 th floor, the mean wind loads become smaller because floors above 60 th floor gradually draw back in plane as already illustrated in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Selected** & **Wind** & \(\hat{u}_{\text{OS}}^{top}\) & \(\hat{u}_{\text{TMDI}}^{top}\) & \(\hat{u}_{\text{TMDI}}^{top}\) & \(\hat{u}_{\text{TIDI}}^{top}\) & \(F_{\text{vu}}^{\text{(TMDI)}}\) & \(F_{\text{vu}}^{\text{(TMDI)}}\) & \(F_{\text{vu}}^{\text{(TID)}}\) \\
**Condition** & **Direction (\({}^{\circ}\))** & **(m/s\({}^{\circ}\))** & **(m/s\({}^{\circ}\))** & **(m/s\({}^{\circ}\))** & **(m/s\({}^{\circ}\))** & **(m/s\({}^{\circ}\))** & **(m/s\({}^{\circ}\))** & **(m/s\({}^{\circ}\))** \\ \hline Minimum \(F_{\text{vu}}\) & 180 & 0.0667 & 0.0508 & 0.0469 & 0.0413 & 23.88 & 29.78 & 38.09 \\ (TMDI) & 45 & 0.0687 & 0.0353 & 0.0350 & 0.0307 & 48.63 & 49.06 & 55.26 \\ Minimum \(F_{\text{vu}}\) & 180 & 0.0667 & 0.0508 & 0.0469 & 0.0413 & 23.88 & 29.78 & 38.09 \\ Maximum \(F_{\text{vu}}\) & 45 & 0.0687 & 0.0353 & 0.0350 & 0.0307 & 48.63 & 49.06 & 55.26 \\ (TID) & 75 & 0.1232 & 0.0701 & 0.0685 & 0.0602 & 43.04 & 44.40 & 51.12 \\ Maximum acceleration OS & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Vibration-mitigation effects in terms of top-floor acceleration response for some loading configurations.
# Method for Generating Panoramic Textures for 3D Face Reconstruction Based on the 3D Morphable Model
## 2 Related Works
### Texture Restoration in 3D Face Reconstruction  
In recent years, numerous studies have focused on 3D face reconstruction. This section introduces and highlights the recent advancements in this regard. Most studies in this field focus on the reconstruction of the facial structure and have neglected the restoration of facial texture [14; 24]. SADRNet [14] presents an end-to-end framework for face reconstruction using large-angle input facial poses to predict pose-dependent and pose-independent faces and combines them through occlusion aware self-alignment to build 3D faces. Although the network structure is lightweight, the absence of texture information restricts its applicability.  
Other methods use various 3 DMM-based face models to reconstruct facial texture and correspond to the default previous texture space of 3 DMM for face textures. DECA [13] and flame-fitting [22] restore the texture information of the input face by regressing the face texture coefficient. However, the texture is restricted to the default texture space expression capability of the 3 DMM and its performance in terms of personality features is unsatisfactory.  
PRNet [15], 3 DDFA [16] and 3 DDFA_v2 [17] used BFM as the prior 3 DMM model. Although they provide realistic texture information, they can only recover the face texture information of the visible portion of the input image and the occlusion-related texture part is randomly filled with stretched stripes.  
Several researchers [25; 26] have attempted to address this issue by recovering texture from multi-view inputs. MVFNet [25] predicts face models from various perspectives and the face texture is sampled based on the results of the face textures produced from multiple angles. FML [26] estimates facial models from videos. Although it is possible to integrate texture information from many angles, it generates a complicated network structure and incurs high training costs.  
As stated previously, the textures of 3D faces generated using existing approaches exhibit artifacts owing to the self-occlusion of the input image, or they are insufficiently realistic because of the expressiveness constraint of the default texture space. However, expensive scanning equipment or multiple input photos are required to achieve a genuine complete texture. Therefore, this study adopts a low-cost method to produce a panoramic texture dataset of faces and trains a panoramic face texture translator that can be used in conjunction with other 3 DMM-based face 3D reconstruction networks to offer a panoramic face texture. Finally, we obtain a realistic panoramic facial texture. With a 3D face mesh, it is possible to create a 3D face model with realistic texturing from various viewing angles.