esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Spatio-Temporal Module
#### 3.1.4 Fusion of Spatio-Temporal Modules  
On top of the \(K\) residual units, we add a second CBAM module (i.e., CBAM2 shown in Figure 4) and a convolution layer (i.e., Conv2 in Figure 4), and the final output is \(X^{(k+4)}\). Following the same steps as above, we can construct three spatio-temporal modules of closeness, period, and trend in Figure 4. After processing through the model, the outputs of the three modules are \(X^{(k+4)}_{closeness}\), \(X^{(k+4)}_{period}\), and \(X^{(k+4)}_{trend}\), respectively.  
Figure 8: Architecture of SAM.  
Figure 9: Residual unit.  
As the influence of closeness, period, and trend varies across different sites, we adopt a parametric-matrix-based fusion method [39] to merge the three spatio-temporal modules and obtain the output \(X_{RAN}\):  
\[X_{RAN}=W_{c}\circ X_{closeness}^{(k+4)}+W_{p}\circ X_{period}^{(k+4)}+W_{q} \circ X_{trend}^{(k+4)} \tag{10}\]  
where \(W_{c}\), \(W_{p}\), and \(W_{q}\) are weight parameters representing the degree of influence of three modules, respectively, and \(\circ\) indicates the Hadamard product.
## 4 Discussion
### Atmospheric Influence and Instrument Resolution  
Retrievals of SiF in the oxygen absorption lines, from aircraft platforms hundreds of meters above the surface, have routinely been atmospherically corrected [48; 64; 87; 88]. However, there has been little consensus until recently as to whether this correction would be needed for SiF in a proximal sensing context, i.e., from a flux tower mounted system within 20 m from the canopy. Historically SiF retrievals were formulated to be applied to top of canopy data, with (1) the assumptions of atmospheric path length between target and sensor is short enough to be neglected, and (2) that the solar irradiance is measured at the same height as the target. When atmospheric path length increases, for example with an instrument mounted on a tower or where large solar zenith and/or view angles are common, these assumptions cannot be met. Oxygen absorption is proportional to air pressure [89] and thus at this lowest level of the atmosphere even a few meters difference can result in significant error in retrieved SiF. In this study, oxygen transmittance was computed by making use of the HITRAN molecular spectroscopic database, therefore modelling the oxygen transmittance according to the experimental site configuration, e.g., fixing the optical path between the canopy and the sensor, and taking into account variations in the oxygen transmittance caused by changes in the environmental conditions, mainly temperature and pressure. Accounting for variations in the environmental conditions as part of the SiF retrieval strategy guarantees, especially in experimental sites subjected to abrupt changes of temperature and pressure, an accurate observation of the SiF seasonal patterns, as pointed out in Reference [58].  
The complexity of the influences of instrument spectral resolution, signal-to-noise, atmospheric correction, canopy structure, leaf biochemical parameters and directional effects all play a critical role in shaping the reliability of SiF to quantify GPP [58; 90; 91]. Only recently have intercomparisons in SiF retrievals been carried out across optical instruments. [PERSON] [90] carried out tests on four spectrometers and investigated their ability to retrieve both red and far-red SiF. The work presented highlighted that an \"accurate\" far-red SiF could be retrieved from spectrometers with an ultra-fine resolution (less than 1 nm) with the red SiF estimation requiring a significantly higher resolution (less than 0.5 nm). The Ocean Optics (USB-2000) system used in this study had a 1 nm resolution, and would therefore fall at the upper end of the recommended resolution and thus may have hampered the SiF retrieval.
## 2 Materials and Methods  
The engine analyzed in the present work is the Wartsila 6 L 46. This is a four-stroke diesel engine with six cylinders. Each cylinder presents two intake and two exhaust valves. The fuel injector has 10 holes and is placed at the center of the cylinder head. The fuel is injected directly into the cylinder since it is a direct injection engine.  
The geometry and mesh were realized using SolidEdge and Gambit softwares, respectively. A grid independence study was carried out to provide its adequacy. The software OpenFOAM was employed for the CFD simulations. OpenFOAM was chosen because it is an open software which allows a complete manipulation of the code. The simulation started at 360\({}^{\circ}\) Crank Angle After Top Dead Center (CA ATDC) and the whole cycle was analyzed. As boundary conditions, the heat transfer from the cylinder was modelled as a combined convection-radiation. The RANS (Reynolds-averaged Navier-Stokes) equations of conservation of mass, momentum and energy were solved. The k-\(\varepsilon\) was employed as the turbulence model due to its robustness and reasonably accuracy for a wide range of turbulent flows. As fuel droplet breakup, the Kelvin-Helmholtz and Rayleigh-Taylor breakup models [46] were employed, and the Dukowicz model [47] for the heat-up and evaporation. Regarding the chemical kinetics, a reaction mechanism was programmed by combining the following three kinetic schemes:  
* [PERSON] and [PERSON]'s [48] kinetic scheme, based on 131 reactions and 41 species, for combustion.
* [PERSON] et al.'s [49] kinetic scheme, based on 20 species and 43 reactions, for \(\rm NO_{x}\) formation.
* [PERSON] and [PERSON]'s [50] kinetic scheme, based on 24 species and 131 reactions, for \(\rm NO_{x}\) reduction.  
This numerical model was validated using experimental measurements. For this purpose, experimental measurements obtained elsewhere [51; 52; 53; 54; 55] were employed. The emissions and SpecificFuel Consumption (SFC) obtained numerically and experimentally at several loads are indicated in Figure 1. This figure shows a reasonable correspondence between numerical and experimental results. The in-cylinder pressure obtained numerically and experimentally at 100% load is shown in Figure 2. This figure also shows a satisfactory correspondence between experimental and numerical results. A certain error was inevitable due to both numerical and experimental handicaps. On the one hand, the instruments employed to characterize experimental measurements have a certain tolerance and, on the other hand, numerical errors are introduced due to the discretization processes and the hypothesis assumed to simplify the governing equations.
## 3 Results and Discussion
### Effects of OM Treatment on Lipid Oxidation
#### 3.2.2 GO and MGO  
Since the colorimetric measurement of TBARS was not sensitive, to further investigate whether OM treatment promoted lipid oxidation, GO and MGO, the most toxic secondary products of the lipid oxidation [33], were selected and quantified using the UPLC-MS/MS method.  
In the fish samples, there was no significant difference in the level of GO between OM treatment and CW treatment, but MGO content in the OM group was significantly (\(p\) < 0.05) higher than in the CW group (Table 2), indicating that OM treatment promoted the oxidation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multicolumn{2}{c}{**Pork**} & \multicolumn{2}{c}{**Fish**} \\ \cline{2-5}  & **CW** & **OM** & **CW** & **OM** \\ \hline TBRAS value & ND & ND & ND & ND \\ GO & 23.21 \(\pm\) 1.90 & 17.13 \(\pm\) 2.73 \({}^{\star}\) & 27.60 \(\pm\) 0.37 & 26.93 \(\pm\) 0.55 \\ MGO & 72.23 \(\pm\) 4.77 & 55.23 \(\pm\) 7.58 \({}^{\star}\) & 100.55 \(\pm\) 4.18 & 118.93 \(\pm\) 3.99 \({}^{\star}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The amount of thiobarbituric acid reactive substances (TBARS) values (mg MDA/kg sample), GO (ug/kg sample), and MGO (ug/kg sample) in pork and fish samples under conventional water-washing (CW) treatment and ozone-microbubble-washing (OM) treatment.  
of lipid in fish, which is consistent with the pro-oxidation property of ozone [34]. However, in the pork samples, the levels of GO and MGO in the OM group were significantly (\(p<0.05\)) lower than in the CW group (Table 2). GO and MGO are water-soluble compounds, and ozone-microbubble treatment may cause more GO and MGO dissolving in water, leading to a decrease in the residual GO and MGO content in foods within a certain concentration range. More MGO and GO were produced in fish than in pork samples under the same treatment conditions, which may be due to the higher fat content in fish samples. Therefore, OM treatment may be more suitable for foods with relatively low fat content.
## 3 An Overview of Selected Chemical Crosslinkers
### Dehydrothermal Treatment  
DHT exploits [PERSON]'s principle of driving a reaction forward by removing the by-product of crosslinking, water, through the application of heat and vacuum, as seen in Figure 4. [PERSON] et al. investigated the effect of DHT held at a range of temperatures (110, 120, 140, 160, 160, 180 \({}^{\circ}\)C) at 30-50 Torr for 24 h after ramping for an hour on Type I bovine dermal collagen scaffolds. The compressive elastic modulus of DHT-treated films was shown to increase with the applied DHT temperature, in line with the crosslink density of the films [21]. The treated collagen films were also susceptible to denaturing during the process, with significant denaturing (57.84%), crumpling and embrittlement at higher temperatures (180 \({}^{\circ}\)C) [21]. The crosslink density of DHT-treated collagen extracted from steer hide was found to increase with temperature from 105 to 125 \({}^{\circ}\)C, but not with time after 3 days [22]. Although some denaturing of collagen is proposed to increase the crosslinking density by allowing access to hidden groups, beyond 145 \({}^{\circ}\)C or 5 days, significant denaturing and reduced mechanical properties were observed [22].  
[PERSON] et al. conducted an in vivo assessment of DHT-treated Type I microfibrillar collagen-chondroitin-6-sulfate scaffolds in transected rat peripheral nerves [23]. The conditions of study included treatments at 90 \({}^{\circ}\)C for both 24 and 48 h, and 120 \({}^{\circ}\)C for 48 h under a vacuum of 50 mTorr. In this study, nerve regeneration was supported more successfully by the treatments at higher times and temperatures, providing an optimal degradation time-frame to match the rate of regeneration of the tissue. Additionally, an investigation of the in vitro cell response of DHT-treated Type I collagen-choindroitin-6-sulfate collagen scaffolds was conducted by [PERSON] et al. using MC3T3-E1 mouse preosteoblasts at higher temperatures. These scaffolds were treated at four temperatures (105, 120, 150 and 180 \({}^{\circ}\)C) under a vacuum of 37.5 Torr, although the duration was not specified. DHT-treated scaffolds displayed an extreme seeding and proliferation of the preosteoblasts in comparison with EDC-NHS or GTA-crosslinked scaffolds in the same study, particularly at the higher temperature of 150 \({}^{\circ}\)C [13]. While the results of an in vivo and in vitro test cannot be directly compared, it is likely that  
Figure 3: Mechanism of integrin adhesion loss through EDC-NHS crosslinking. The I-domains of integrins coordinate to collagen through a divalent cation (Mg\({}^{2+}\)), which is supported by the glutamic acid residue in the GXOGER MIDAS motif (GFOGER variant illustrated here). The formation of the peptide bond between glutamic acids (E) and lysines (K) is suggested to be the origin of the observed loss of integrin-specific attachment to EDC-NHS crosslinked collagen. Adapted from [PERSON] et al. [7].  
the use of DHT-treatment to elicit high cellular activity may be limited to lower treatment temperatures where the risk of denaturing is also low.
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 2 Related Work
### Related Frameworks  
Concerning existing approaches that aim to integrate AI/ML mechanisms into frameworks to improve security, the authors in [35] propose a security framework to automatically mitigate different IoT-related threats. This work employs AI-based reactive agents, powered by ML models, to analyze monitored data and utilize this information to raise intrusion alerts. However, it does not contemplate network slicing or E2E mechanisms for the management or orchestration of infrastructure security. In parallel to that idea, the authors in [36] present a model architecture that combines a validation framework for applications in conjunction with a reactive AI-driven system to enhance the security of the applications and the infrastructure in real time. That model leverages ML/AI to dynamically monitor and respond to security threats; however, it solely presents a general framework and does not incorporate network slicing or Zero-touch Service Management (ZSM) techniques.  
An AI-based network slice management framework is proposed in [37], but the introduced AI mechanisms are exclusively oriented to enhance radio link performance. A similar situation occurs in [38], where AI mechanisms are integrated into 5G networks to improve their security, among other aspects; however, they are mainly focused on the radio link rather than improving the security of the infrastructure itself. In [39], the authors propose a Federated Network Intelligence Orchestration approach for anomaly detection in B5G networks using Federated Learning (FL). To achieve this, the work orchestrates network intelligence to detect and prevent cyberattacks, integrating the system within a Zero-touch Service Management (ZSM) security framework, including multi-domain and multi-tenant orchestration. However, they neither present nor contemplate the inclusion of network slices in their orchestration. Lastly, in [40], the incorporation of certain security controls at the network layer is proposed. These controls are classified by efficiency and suitability, aiming to allocate resources dynamically with enhanced security assurance and isolation capabilities, which are tested to mitigate DDoS attacks. Nevertheless, although the work suggests the suitability of zero-touch mechanisms for resource and reactive orchestration, the theoretical solution outlined in the paper has not been implemented.
## 3 The Improved Symmetric Zero-Area Conversion Method
### ISZAC Algorithm  
The algorithm flow of ISZAC is shown in Figure 1.  
The diagram of data conversion and fusion processing is shown in Figure 2.  
The N conversion functions selected are \(C_{1}\), \(C_{2}\),..., \(C_{N}\), and the corresponding conversion results are \(y_{1}^{*}\), \(y_{2}^{*}\),..., \(y_{N}^{*}\), the fusion result is \(y^{*}\), then  
\[y^{*}=\frac{\prod\limits_{i=1}^{N}\text{sgn}(y_{i}^{*})\cdot\prod\limits_{i=1} ^{N}\left\{\text{sgn}(y_{i}^{*})+1\right\}\cdot\sum\limits_{i=1}^{N}y_{i}^{*}} {N} \tag{15}\]  
When there are non-positive numbers in the result group, \(y^{*}\) is 0. When they are all positive, \(y^{*}\) is the average of the sum of conversion results.  
Figure 1: Flow chart of ISZAC algorithm.  
The failure of SZAC in a specific scenario is the defect of the method itself. The introduction of a comparison method to correct the judgment results improves one of the limitations of the method. Take \(m=\) 2,3,4,5,6, construct a conversion function including narrow window to wide window, and give full play to the ability of overlapping peak recognition and noise suppression. The result of fusion is only 0 and positive, and \(T_{0}=\) 0 can be effectively separated. \(H_{G}\) can be set according to the peak type of input spectral data. Therefore, all three parameters can be determined, which improves SZAC's limitation of relying on data and experience.  
In addition, the conversion function group can be adjusted to obtain higher recognition efficiency when searching for peaks of bulk homologous samples with strong similarity. If the FWHM of the target phase peak is small, the comparison function with smaller \(cmpN\) can be selected to reduce the calculation amount. If there are important peaks with poor recognition effect, a new conversion function can be designed according to the data characteristics of the address to achieve targeted recognition. If the intensity of peak \(I\) and its surrounding five address is \(y_{[i-2,\,i+2]}=\) [9.8, 10.1, 10.5, 10.2, 10.1], generate the standard Gaussian linear symmetric zero-area conversion function with \(m=\) 2, \(H_{G}=\) 4, then \(C=\) [\(-\)0.24, 0.11, 0.26, 0.11, \(-\)0.24], and the result is a small positive value of 0.187. Affected by noise interference or other processing links, the result may be negative, resulting in wrong judgment. Replace \(C\) with \(C^{*}=\) [\(-\)0.34, 0.11, 0.46, 0.11, \(-\)0.34], and the conversion result is 0.297, which has a large margin compared with the original one. ISZAC based on passive anti-counterfeiting only needs the conversion function to meet the characteristics that the conversion result at the peak is greater than 0, there are no strict requirements for symmetry or the sum of conversion function elements is 0. The conversion function group has more possible combinations, which makes ISZAC have good expansibility in engineering practice.
###### Abstract  
Magnetic solid-phase extraction is an effective and convenient sample pretreatment technique that has received considerable interest in recent years. A lot of research indicated that magnetic nanocarbon-material-based composites have good application prospects as adsorbents for magnetic solid-phase extraction of pesticides. Herein, a novel zeolitic imidazolate framework based on magnetic multiwalled carbon nanotubes (M-M-ZIF-67) has been prepared as an adsorbent for magnetic solid-phase extraction of nine organochlorine pesticides from agricultural irrigation water samples. The obtained M-M-ZIF-67 material possessed porous surfaces and super-paramagnetism due to the utilization of magnetic multiwalled carbon nanotubes as the magnetic kernel and support. To evaluate the extraction performance of the M-M-ZIF-67, the main parameters that affected the extraction efficiency were researched. Under the optimal conditions, a good linearity for the nine organochlorine pesticides was achieved with the determination coefficients (R\({}^{2}\)) higher than 0.9916. The limits of detection (signal/noise = 3:1) were in the range 0.07-1.03 ug L\({}^{-1}\). The recoveries of all analytes for the method at spiked levels of 10 and 100 ug L\({}^{-1}\) were 74.9-116.3% and 75.1-112.7%, respectively. The developed M-M-ZIF-67 based magnetic solid-phase extraction method has a potential application prospect for the monitoring of trace level of organochlorine pesticides in environmental water samples.  
zeolitic imidazolate framework; multi-walled carbon nanotubes; magnetic solid phase extraction; organochlorine pesticides; agricultural irrigation water +
Footnote †: journal: Applied Sci. 2018
## 2 Design of Radial Grouting Range  
### Calculation Model and Parameters  
In this paper, FLAC3D software is used for simulation, and the numerical method is based on the nodal finite difference equation of the fluid continuity equation.  
The numerical model sized \(92\times 127\times 1\) m is designed to study the support of radial grouting range on the water-rich tunnel under high water pressure. The distances from the left and right boundaries to the tunnel are both 40 m, and the distances from the upper and lower boundaries to the tunnel are 95 m and 23 m, respectively (Figure 4). The surrounding rock, grouting area, and initial support are all simulated by solid elements, and the secondary lining is simulated by shell elements. Mohr-Coulomb (M-C) was adopted to obtain the constitutive relationship between the surrounding rock and grouting area, and other materials are all elastic; the initial support includes shotcrete and steel arches, and the material parameters are obtained according to the equivalent stiffness method. In terms of mechanical boundaries, the front, rear, left, and right boundaries are applied with normal constraints, while the lower boundary is applied with a fixed constraint; as for the seepage boundary, its bottom is impermeable, and the other boundaries are permeable. The initial stress only considers self-weight stress in the calculation, and the fluid-solid coupling effect is considered in the construction simulation. The fluid-solid coupling model involves indirect coupling. In other words, the flow switch is turned off during static calculation, and the static switch is turned off during flow calculation.  
See Table 1 for the calculation parameters obtained from the test and literature [13].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Material** & **E/GPa** & \(\mu\) & \(\gamma\)/(**kg\(\cdot\)m\({}^{-3}\)**) & \(\varphi\)/(\({}^{\circ}\)) & **c/(MPa)** & **k** & **EI** & \(\mathbf{k_{0}}\) \\ \hline Surrounding rock & 1.5 & 0.3 & 2400 & 30 & 0.3 & \(4.3\times 10^{-4}\) & / & 0.43 \\ Grouting area \({}^{\star}\) & 2.2 & 0.3 & 2400 & 45 & 0.45 & \(1.0\times 10^{-5}\) & / & 0.45 \\ Initial support & 28 & 0.25 & 2400 & & & \(2.61\times 10^{-9}\) & \(7.84\times 10^{9}\) & / \\ Secondary lining & 32 & 0.20 & 2500 & & & & \(25.6\times 10^{9}\) & / \\ \hline \hline \end{tabular}  
* 1. The materials in the grouting area shall be increased by 1.5 times the surrounding rock materials, and the permeability coefficient shall be obtained according to the on-site sampling test.  
\end{table}
Table 1: Values of relevant parameters.  
Figure 4: Sketch map of radial grouting model.  
According to the design specifications, in this calculation, the thickness of the double-layer lining is 80 cm. During calculation, the double-layer initial support is used with an anti-seepage grade of P8. Models are established for the four operating conditions with grouting ranges of 0 m, 2 m, 4 m, and 6 m (Table 2).
# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
###### Abstract  
Recently, object detection has become a research hotspot in computer vision, which often detects regular images with small viewing angles. In order to obtain a field of view without blind spots, fisheye cameras, which have distortions and discontinuities, have come into use. The fisheye camera, which has a wide viewing angle, and an unmanned aerial vehicle equipped with a fisheye camera are used to obtain a field of view without blind spots. However, distorted and discontinuous objects appear in the captured fisheye images due to the unique viewing angle of fisheye cameras. It poses a significant challenge to some existing object detectors. To solve this problem, this paper proposes a PGDS-YOLOv8s model to solve the issue of detecting distorted and discontinuous objects in fisheye images. First, two novel downsampling modules are proposed. Among them, the Max Pooling and Ghost's Downsampling (MPGD) module effectively extracts the essential feature information of distorted and discontinuous objects. The Average Pooling and Ghost's Downsampling (APGD) module acquires rich global features and reduces the feature loss of distorted and discontinuous objects. In addition, the proposed Czfs module uses Squeeze-and-Excitation (SE) blocks to model the interdependence of the channels to acquire richer gradient flow information about the features. The Czfs module provides a better understanding of the contextual information in fisheye images. Subsequently, an SE block is added after the Spatial Pyramid Pooling Fast (SPPF), thus improving the model's ability to capture features of distorted, discontinuous objects. Moreover, the UAV-360 dataset is created for object detection in fisheye images. Finally, experiments show that the proposed PGDS-YOLOv8s model on the VOC-360 dataset improves [EMAIL_ADDRESS] by 19.8% and [EMAIL_ADDRESS] by 27.5% compared to the original YOLOv8s model. In addition, the improved model on the UAV-360 dataset achieves 89.0% for [EMAIL_ADDRESS] and 60.5% for [EMAIL_ADDRESS]:0.95. Furthermore, on the MS-COCO 2017 dataset, the PGDS-YOLOv8s model improved AP by 1.4%, AP\({}_{50}\) by 1.7%, and AP\({}_{75}\) by 1.2% compared with the original YOLOv8s model.  
fisheye image; object detection; pooling; ghost module; Squeeze-and-Excitation (SE) block