esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### YOLO-Chili Network  
YOLO-chili is shown in Figure 9, which uses YOLOv5's backbone network to facilitate porting to different devices. YOLOv5's backbone network is CSPDarknet53, which consists of CBL, BottleneckCSP/C3, and SPP/SPPF. For ease of deployment, YOLOv5 removes the Focus module. The CBS module consists of a constant combination of Conv+BatchNorm+SiLU used to obtain the depth of the feature map. C3, on the other hand, draws on the residual idea for cross-stage connectivity, which is used to improve the feature transfer efficiency and information utilization. It consists of multiple convolutional layers and residual connections for extracting features from the input image. Compared with CSPDarknet53-tiny in YOLOv4, YOLOv5 has a deeper network structure and stronger feature extraction capability. Meanwhile, for the problem that YOLOv4 cannot handle multi-scale feature maps better, YOLOv5 uses FPN for fusion, which improves the model's ability to detect targets of different sizes. In terms of prediction, YOLO-chili uses the prediction module of YOLOv5, but uses ASFF-Detect instead of the original detection layer. It also employs the K-Means algorithm to cluster the anchor frames generated from the dataset, the non-great suppression and confidence threshold filtering to select the prediction frames, and Alpha-IoU instead of CIOU. IoU is computed as the ratio of the area of intersection of the target detection frames (which are usually the frames predicted by the model) with the true labeling frames (Ground Truth) and their concatenation area. The value of IoU ranges from 0 to 1. The value ranges from 0 to 1, with larger values indicating a higher degree of overlap between the detected frames and the true labeled frames, and more accurate detection results. \(\alpha\)-IoU introduces a parameter \(\alpha\) to regulate the calculation of IoU. Specifically, \(\alpha\)-IoU is calculated as follows: If the IoU between the detected frame and the real labeled frame is greater than or equal to \(\alpha\), the IoU is directly used as the final evaluation index. If the IoU is less than \(\alpha\), the IoU is multiplied by a factor less than 1 to reduce its influence on the final evaluation index.  
Figure 8: Resolution Adaptive Feature Fusion Network Module.
## 3 Results and Discussions
### Antioxidant Properties
#### 3.2.2 Reducing Power Activity  
Reducing capacity of plant extract is reflected by the electron-donation ability, which is considered to be an important strategy to behave as an antioxidant. The reducing power was investigated through spectrophotometric detection of Fe\({}^{3+}\)-Fe\({}^{2+}\) transformation in the presence of _Centaurea_ extract.  
Figure 2: DPPH radical scavenging activity (IC\({}_{50}\), mg/mL) determined in extracts obtained from aerial part of two _Centaurea_ species (_C. kroumirensis_ and _C. sicula_) with different extraction techniques (Different letters indicate significant difference (\(p<0.05\))).  
The ferrous ion (Fe\({}^{2+}\)) can be controlled by measuring the optical density of the formed Perl's Prussian blue at 700 nm. Figure 3 illustrates the results of ferric reducing potency. EC\({}_{50}\) value of each sample was calculated and presented in Table 1. The EC\({}_{50}\) value is inversely proportional to reducing power.  
In this study, noticeable variation was observed in EC\({}_{50}\) values within and among the _Centaurea_ extracts. Based on the obtained results, the UE and H extracts of _C. Kroumirensis_ showed the lowest EC\({}_{50}\) (5.15 \(\pm\) 0.09 and 6.28 \(\pm\) 0.77 mg/mL, respectively) and, hence, highest reducing power ability among studied _Centaurea_ extracts. Whilst concerning _C. sciula_ the lowest EC\({}_{50}\) (7.62 \(\pm\) 0.15 mg/mL) was registered for the C extract followed by UE extract with EC\({}_{50}\) = 11.75 \(\pm\) 0.58 mg/mL. The reducing power abilities in the studied extracts were ranked in the following order: _C. sciula_ hexane \(<\)_C. sciula_ methanol \(<\)_C. sciula_ ethanol \(<\)_C. sciula_ chloroform, for the C. sciula extracts, and _C. Kroumirensis_ chloroform \(<\)_C. kroumirensis_ methanol \(<\)_C. kroumirensis_ hexane \(<\)_C. kroumirensis_ ethanol, for _C. kroumirensis_ extracts.  
Similar to the DPPH assay, the EO and SFE (I-II) extracts for both _Centaurea_ species did not show a reducing power at least for the concentration used in this study. The SFE (I) of _C. kroumirensis_ exhibited the lowest TPC (4.79 \(\pm\) 0.15 mg GAE/g) and the highest IC\({}_{50}\) (30.93 \(\pm\) 1.24 mg GAE/g) and EC\({}_{50}\) (65.17 \(\pm\) 2.93 mg GAE/g). These results could be explained by the fact that the supercritical extraction (I) carried out under mild conditions of pressure and temperature (T = 40 \({}^{\circ}\)C, P = 10 MP) and for a short time (30 min.), does not allow for obtaining extracts rich in antioxidants.
# Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features
## 2 Material and Methods
### Classification
#### 2.4.2 Deep Convolution Neural Network  
Deep Convolutional Neural Network (DCNN) is referred to as a fully connected network that uses numerous layers to extract nonlinear features. The principled estimation is to extract the features by directly applying several convolutions and kernel parameters on raw image [36]. However, the HSI system belongs to an advanced spectroscopy technique and constructs more than hundred of images. These images processed through 2D-CNN can be prone to over-fitting due to a large number of kernels parameters and computational cost [26].  
To counter these issues [37], a deeper complex convolutional model of deep learning is required (3D-CNN) [28]. The 3D-CNN comprehends the HSI cube by using 3D kernels and 3D convolutional operations to extract the significant spatial-spectral information automatically as shown in Figure 5, which is further illustrated by the following equation;  
\[V_{Pij}^{xyz}=f(\sum_{l=0}^{L_{p}-1}\sum_{m=0}^{M_{p}-1}\sum_{n=0}^{N_{p}-1}Q_ {Pj}^{lmn}\ V_{(P-1)i}^{(x+l)(y+m)(z+n)}+b_{Pi}) \tag{4}\]  
where \(P\) indicates the convolution layer, \(i\) indicates the features learned from the previous layer, \(j\) represents the number of kernels, \(L_{P}\) represents the height, \(M_{P}\) represents the width, \(N_{P}\) represents the depth of spectral cube, \(b_{Pi}\) represents the bias, \(V_{Pij}^{xyz}\) gives the output of the feature learned on the \(i_{th}\) cube of \((x,y,z)\) through convolution and kernel implementation and \(Q_{Pj}^{lmn}\) represents kernel by \((l,m,n)th\) values to the \(ith\) feature cube. Further, \(f\) represents the activation function rectified linear units (ReLUs) \(f=max(0,v)\) and optimizer 'Adam' is used to optimize the loss function (categorically cross entropy); \(E(\theta)=-\frac{1}{p}\sum_{i=1}^{p}\sum_{j=1}^{m}{(j=Z^{(i)})log(g_{j}^{(i)})}\) where \(p\) represents number of samples and \(m\) represents number of classes. Conversely, \(Z\), \(g\) represent the model vectors and \(\theta\) represents the model biases and weights.
# On Multidimensional Congestion Games +
## 5 Bounds for the Price of Stability  
In order to bound the price of stability with respect to the social cost function \(\mathsf{Pres}\), we consider a pure Nash equilibrium that minimizes the potential function \(\Phi\) defined in (1), which leads to the following upper bound.  
Theorem 2: _For each \(d\)-dimensional weighted affine congestion game \((\mathcal{G},\mathcal{C})\), \(PoS(\mathcal{G},\mathcal{C})\leq 2\) under the social cost function \(\mathsf{Pres}\)._  
Proof: Let \(\sigma\) and \(\sigma^{*}\) be a pure Nash equilibrium minimizing the potential function \(\Phi\) defined in (1), and let \(\sigma^{*}\) be a social optimum. We have that  
\[\mathsf{Pres}(\sigma) = \sum_{r\in R}\left(\alpha_{r}\left(\sum_{j=0}^{d}k_{r,j}^{2}+2k_{ r,0}\sum_{j=1}^{d}k_{r,j}\right)+\beta_{r}\sum_{j=0}^{d}k_{r,j}\right) \tag{16}\] \[\leq \sum_{r\in R}\left(\alpha_{r}\left(\sum_{j=0}^{d}k_{r,j}^{2}+\sum _{i\in[n]:r\in\sigma_{i}}w_{i}^{2}+2k_{r,0}\sum_{j=1}^{d}k_{r,j}\right)+2\beta _{r}\sum_{j=0}^{d}k_{r,j}\right)\] \[= 2\cdot\Phi(\sigma)\] \[\leq 2\cdot\Phi(\sigma^{*})\] \[= \sum_{r\in R}\left(\alpha_{r}\left(\sum_{j=0}^{d}l_{r,j}^{2}+\sum _{i\in[n]:r\in\sigma_{i}^{*}}w_{i}^{2}+2l_{r,0}\sum_{j=1}^{d}l_{r,j}\right)+2 \beta_{r}\sum_{j=0}^{d}l_{r,j}\right)\] \[\leq \sum_{r\in R}\left(\alpha_{r}\left(2\sum_{j=0}^{d}l_{r,j}^{2}+2l_ {r,0}\sum_{j=1}^{d}l_{r,j}\right)+2\beta_{r}\sum_{j=0}^{d}l_{r,j}\right)\] \[\leq 2\sum_{r\in R}\left(\alpha_{r}\left(\sum_{j=0}^{d}l_{r,j}^{2}+2l _{r,0}\sum_{j=1}^{d}l_{r,j}\right)+\beta_{r}\sum_{j=0}^{d}l_{r,j}\right)\] \[= 2\cdot\mathsf{Pres}(\sigma^{*}),\]where (17) holds since \(\sigma\) minimizes \(\Phi\), and (16) and (18) hold by exploiting (1). By (19), we get \(\mathit{PoS}(\mathcal{G},\mathcal{C})\leq\frac{\mathsf{Pres}(\sigma)}{\mathsf{ Pres}(\sigma^{*})}\leq 2\), and the claim follows.  
Relatively to the social cost function \(\mathsf{Perc}\), the following upper bound is derived as a corollary of a result in Reference [25].  
Corollary 2: _For each \(d\)-dimensional affine congestion game \((\mathcal{G},\mathcal{C})\), \(\mathit{PoS}(\mathcal{G},\mathcal{C})\leq 2d\) under the social cost function \(\mathsf{Perc}\)._  
Proof: Theorem 6 of Reference [25] states that \(2\delta(G)\) is an upper bound for the price of stability of any graphical congestion game having independence number \(\delta(G)\). As the graphical congestion game equivalent to \((\mathcal{G},\mathcal{C})\) has independence number equal to \(d\), the claim follows.
## 3 Geopolymer Concrete (GPC)
### Fresh Properties of Geopolymer Concrete
#### 3.1.2 Geopolymer Concrete Setting Time  
Setting time refers to the time that concrete can be cast, compacted, and transported [14]. As per ASTM C807 and the British Standards Institution (2009), the Vicat needle device determines the concrete setting time. [PERSON] et al. [32] observed that NaOH concentration influences the curing time of geopolymer concrete. They noticed that reducing the molarity of NaOH could effectively delay the setting of GPC. Because of the slower reaction rate at a low ambient temperature of 20-24 \({}^{\circ}\)C, FA-based geopolymer paste takes more than 24 h to set. However, slag blending considerably reduces both the initial and final setting times. When 10% slag is added to the binder, the initial setting time is lowered to 290 min, further reduced to 95 min, and 40 min when the slag concentration is increased to 20 and 30%. The discrepancy between the beginning and final setting times are reduced by increasing the slag concentration in GPP. As shown in Figure 16, the higher the proportion of slag, the faster the setting. With the rise in slag content, Figure 16 depicts the change in initial and final setting times and the difference in the period between the two. S00, S10, S20, and S30 indicate the addition of 0, 10, 20, and 30% GGBFS blends, respectively [16].  
[PERSON] et al. [60] produced quick-setting geopolymer concrete with fumed silica at ambient curing conditions. Waste management can be effectively performed by utilising fumed silica in GPC [61]. The addition of 20-30% fumed silica to geopolymer concrete can improve its setting time [62].
## 2 Materials and Methods
### Data
#### 2.2.3 Wind Conditions  
Wind velocity and direction were obtained from the ERA5-Land reanalysis dataset [44]. ERA5-Land provides hourly high-resolution gridded climate variable estimations from 1950 to present with a resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) (i.e., about 11.1 km in the N-S direction and 8.3 km in the E-W direction at 41.1\({}^{\circ}\) N latitude), with the closest available location for the study region being at 41.1\({}^{\circ}\) N, 8.6\({}^{\circ}\) W. Hourly wind velocities (in m/s) were extracted and wind directions were computed from the respective estimates of the horizontal speed of air moving towards the east (u-component) and toward the north (v-component) at a height of ten metres above the surface of the Earth (Figure 2). Analogously to the processing of the wave data, wind roses were produced to show the wind direction distribution depending on the wind velocity, using 16 cardinal directions and frequencies for 3 wind-speed classes: more than 6.5 m/s, between 5 m/s and 6.5 m/s, and less than 5 m/s (Figures 3-5). The classes were based on the 85 th and 95 th percentiles of the hourly wind velocities recorded during the total study period, which were 5.2 m/s and 6.6 m/s, respectively. Notice that these data were model simulations and, although ERA5-Land wind data may be generally acceptable for coastal locations with moderate variability in topography, they should be used with caution for coastal zones because the ocean-land discontinuity affects model stability conditions [45].  
Figure 4: Distribution of 3-hourly wave directions for three significant-wave-height classes (**left plots**) and of hourly wind directions for three velocity classes (**right plots**) for the three yearly periods.
## 3 Results and Discussion
### Modeling of Dynamic Errors in the Partial System of Motion of Robot Links
Calculations were made for the conditions of the gripper holding a longitudinal object weighing 200 N and the center of gravity of the object being placed at a distance of 0.5 m in the horizontal direction from the pole of the gripper. Their results showed that due to the loads, the gripper shifted to the right by \(0.48\cdot 10^{-3}\) m (or 0.48 mm) and down by \(2\cdot 10^{-3}\) m (or 2 mm), and also tilted at an angle of \(0.8\cdot 10^{-3}\) rad clockwise around the \(x_{0}\) axis. \[\tau_{1}\tau_{\text{M}}\frac{d^{2}v_{1}}{dt^{2}}+\tau_{\text{M}}\frac{dv_{1}} {dt}+v_{1}=-\tau_{1}\tau_{\text{M}}\frac{d^{3}q_{\text{p1}}}{dt^{3}}-\tau_{ \text{M}}\frac{d^{2}q_{\text{p1}}}{dt^{2}}. \tag{68}\]1 Graphs of dynamic errors of the robot in the partial motion system: (**a**) acceleration; (**b**) velocity; (**c**) positioning.
## 4 Conclusions  
In this work, the multiscale lamellar-structure WS\({}_{2}\) particles with sizes of 2 \(\upmu\)m and 500 nm were successfully synthesized by solid-phase reaction method. Then, the tribological properties of multiscale 2H-WS\({}_{2}\) and ZDDP as additives in PAO at four sliding velocities were investigated at room temperature. The conclusions are as follows.  
The tribological properties of microscale/nanoscale 2H-WS\({}_{2}\) in PAO were tested first. The results show that the friction coefficient can be reduced by approximately 30%, and the wear scars are relatively shallow compared to the results of the base oil. The friction-reduction and anti-wear properties are improved due to a layer of oxide film formed on the rubbing surface and the 2H-WS\({}_{2}\) trapped in the surface grooves. Therefore, the multiscale 2H-WS\({}_{2}\) particles can play a role in friction-reduction and anti-wear when used as a single additive, according to the experimental results.  
The friction-reduction and anti-wear properties of the base oil are enhanced when the 2H-WS\({}_{2}\) particles and ZDDP work as additives at the same time. A strong reduction of the friction coefficient and slight wear of the surfaces were observed from the results. The friction coefficient can be reduced by up to 47.2%. The wear-scar width is narrower, the wear scar is shallower, and the rubbing surface is relatively smooth. The better results found with the use of ZDDP are explained by its antioxidant properties and a tribofilm composed of zinc phosphate compounds, which protects metal surfaces from oxidation and contact. Therefore, the positive synergistic effect between multiscale 2H-WS\({}_{2}\) and ZDDP can be verified from the above conclusions.  
Data curation, N.W. and N.H.; formal analysis, N.W. and J.W.; investigation, J.W.; methodology, N.H.; resources, N.H. and G.Z.; software, N.W.; supervision, N.H.; project administration, N.H.; writing--original draft, N.W.; writing--review and editing, N.H. and G.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51875271 and No. 51705223) and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
The authors declare no conflicts of interest.
# Components of the Fracture Response of Alkali-Activated Slag Composites with Steel Microfibers
## Materials and Methods
### Evaluation of \(F-\)Cmod Diagrams  
After the previously mentioned diagram adjustment, the double-\(K\) fracture model was employed for subsequent evaluation of the \(F\)-\(CMOD\) diagrams. The benefit of the double-\(K\) fracture model lies in its ability to predict the different phases that occur during crack propagation in quasi-brittle material: crack initiation and both stable and unstable crack propagation. The different phases of the fracture process in quasi-brittle material can be connected with two size-independent parameters: the initiation fracture toughness \(K_{\rm{Ic}}\)ini and unstable fracture toughness \(K_{\rm{Ic}}\)ini. The determination of double-\(K\) model parameters is based on an approach involving the action of cohesive forces on the faces of the fictitious (effective) crack increment combined with the stress intensity factor criterion (for details refer to (for example) [PERSON] and [PERSON] [37]).  
In the instance of the present research, as the first step the unstable fracture toughness \(K_{\rm{Ic}}\)ini was determined. In the second step, the cohesive fracture toughness \(K_{\rm{Ic}}\)i was determined. As the last step, the following formula based on the formerly obtained parameters was utilized to calculate the initiation fracture toughness \(K_{\rm{Ic}}\)ini:  
\[K_{\rm{Ic}}^{\rm{ini}}=K_{\rm{Ic}}^{\rm{un}}-K_{\rm{Ic}}^{\rm{c}} \tag{9}\]  
The exact procedure concerning the determination of cohesive and unstable fracture toughness values can be found in many published works, e.g., [37; 44].  
In general, the relation between the cohesive stress and the effective crack opening displacement is given by the cohesive stress function in the cohesive crack model. The cohesive stress at the tip of an initial notch at the critical state can be gained from the softening function. In this study, a non-linearsoftening function (1) (as stipulated in Hordijk [41]) was used. The parameters of the softening function were considered to be as follows: the tensile strength\(f_{t}\) was considered based on identification described in Section 2.4, the fracture energy was determined according to Equation (8), and the material constants were considered according to [41] as being \(c_{1}=3\) and \(c_{2}=6.93\).  
Finally, the load \(F_{\text{ini}}\), which expresses the load at the outset of stable crack propagation from the initial crack/notch, was determined according to this relation:  
\[F_{\text{ini}}=\frac{4\cdot W\cdot K_{\text{lc}}^{\text{ini}}}{S\cdot F_{1}( \alpha_{0})\cdot\sqrt{\alpha_{0}}} \tag{10}\]  
where \(W\) is the section modulus (calculated as \(W=1/6\cdot B\cdot D^{2}\)), \(S\) is span length, \(F_{1}(\alpha_{0})\) is the geometry function for a three-point bending configuration [14] and \(\alpha_{0}\) is the \(a_{0}/D\) ratio.
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 7 Conclusions and Future Works  
In this paper, the procedure of cooperative tasks execution for an MEC-assisted platoon within the execution deadline is studied. The task offloading decision problem is transformed into the shortest path problem in a directed acyclic graph. We employ the \"LARAC\" algorithm to obtain the optimal decision strategy for task offloading. The results show that there exists more than one migration between the platoon members and the MEC server, and all the members have the opportunity to participate in the tasks execution. Moreover, the proposed collaborative task execution strategy can greatly reduce task execution cost and execution time.  
For our future work, we will consider tasks offloading for a more general case of vehicular network, such as a cluster of vehicles in a cellular network. In addition, tasks offloading of crossing cell--which depends on the deployments of the MEC severs--will be achieved in future discussion.  
Conceptualization, X.F.; Funding acquisition, T.C. and Q.C.; Project administration, T.C.; Supervision, T.C.; Validation, X.F.; Writing--original draft, X.F. and C.C.; Writing--review & editing, Q.C. and K.S.K.  
This work was supported in part by the National Natural Science Foundation of China (6157073), the National Science and Technology Specific Project of China (2016 ZX03001010-004) and the special fund of Chongqing key laboratory (CSTC), the Program for Changjiang Scholars and Innovative Research Team in University (IRT_16R72) and the Doctor Start-up Funding of CQIPT (A2016-83).  
Figure 9: Deadline vs. cost.
# ABACO: A New Model of Microalgae-Bacteria Consortia for Biological Treatment of Wastewaters
## 3 Results
### ABACO Model
#### 3.2.10 Phosphate Phosphorus  
Phosphorous is contained into the wastewater both as organic and inorganic. Organic phosphorous is transformed into inorganic during degradation of biodegradable organic matter then the phosphate phosphorous concentration corresponding to total phosphorous available. Phosphate phosphorous is introduced in the system with influent wastewater, it being produced during decay of all microbial populations. It is consumed during the growth of microalgae, heterotrophic bacteria and nitrifying bacteria. The phosphate phosphorus concentration is modelled by the following equation:  
\[\begin{split}\text{Q}_{\text{d}}\cdot\text{X}_{\text{PO}_{4}, \text{in}}=\text{Q}_{\text{h}}\cdot\text{X}_{\text{PO}_{4},\text{out}}+\text{V} \cdot\\ \cdot\Big{(}\text{X}_{\text{ALG}}\cdot\text{\mu}_{\text{alg}} \cdot\text{Y}_{\text{con}}\bigg{[}\frac{\text{PO}_{4}}{\text{alg}}\bigg{]}+ \text{X}_{\text{heat},\text{out}}\cdot\text{\mu}_{\text{het}}\cdot\text{Y}_{ \text{con}}\bigg{[}\frac{\text{PO}_{4}}{\text{het}}\bigg{]}+\text{X}_{\text{ nit},\text{out}}\cdot\text{\mu}_{\text{nit}}\cdot\text{Y}_{\text{con}}\bigg{[}\frac{ \text{PO}_{4}}{\text{nit}}\bigg{]}\Big{)}+\text{V}\cdot\frac{\text{d}\text{X}_{ \text{PO}_{4},\text{out}}}{\text{dt}}\end{split} \tag{40}\]  
where \(\text{X}_{\text{PO}_{4},\text{in}}\) [g m\({}^{-3}\)] is the inlet phosphate phosphorus concentration, \(\text{Y}_{\text{PO}_{4},\text{out}}\) [g m\({}^{-3}\)] is the outlet phosphate phosphorus concentration, \(\text{Y}_{\text{con}}\bigg{[}\frac{\text{PO}_{4}}{\text{alg}}\bigg{]}\) [-] represents the phosphate consumption rate from microalgae, \(\text{Y}_{\text{con}}\bigg{[}\frac{\text{PO}_{4}}{\text{het}}\bigg{]}\) [-] is the phosphate consumption rate from heterotrophic bacteria and \(\text{Y}_{\text{con}}\bigg{[}\frac{\text{PO}_{4}}{\text{nit}}\bigg{]}\) [-] is the phosphate consumption rate from nitrifying bacteria.  
Although growth rate models for the different microorganisms are well defined, the consumption and generation parameters of nutrients associated with each species present some uncertainty. The production of microalgae using wastewater as culture medium presents diverse variability in the model parameters. Depending on the type of wastewater and its components, the generation and consumption parameters associated with microalgae and bacteria may vary. This fact raises the need for a model that allows adapting its parameters for each situation. Therefore, a calibration method is presented using genetic algorithms that is capable of estimating the characteristic parameters of the model from experimental data measured in the reactor.