esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Exhaust Gas Temperature  
Figure 5 illustrates the exhaust gas temperature (EGT) under different engine settings, namely, using diesel fuel (D100), a blend of 5% hydrogen (DP20H5), a blend of 10% hydrogen (DP20H10), and a blend of 15% hydrogen (DP20H15). Typically, the exhaust gas temperature (EGT) has a positive correlation with the engine load [28; 29], wherein an increase in engine load results in a rise in EGT. This observation suggests that the engine exhibits enhanced performance when subjected to higher levels of workload. The experimental results indicate that the exhaust gas temperature (EGT) of the DP20H5, DP20H10, and DP20H15 engines is consistently greater than that of the D100 and DP20 engines, as seen in the graph. It has been observed that the lowest EGT for diesel fuel is 451 \({}^{\circ}\)C for diesel (D100), whereas the highest EGT for a DH15 sample is 602 \({}^{\circ}\)C. The hydrogen diesel fuel mix with a concentration of 15% exhibited the greatest exhaust gas temperature (EGT) at maximum load. Specifically, the EGT reached around 25.0% at 23.5 b TDC (before top dead centre) while the engine was operating at full load (10 kg load).  
Figure 4: FFC with injection timing for hydrogen blends.  
Figure 5: EGT (\({}^{\circ}\)C) with injection timing for hydrogen blends.
# Electron Beam Characterization of REBCO-Coated Conductors at Cryogenic Conditions
## 3 Results and Discussion
### Secondary Electron Yield
This suggests that electron irradiation, which also decays into electronic excitations, is unlikely to impair the HTS performance. Therefore, the SEY can also be expected to remain unaffected by the conducting state of the HTS. The experimental SEY curves can be fitted using a multi-component semi-empirical model, given in Equation (3), as implemented in PyECLOUD code [46] to facilitate further application. The three main components to fit the entire SEY curve, marked here as \(\delta_{TOT}\), are the following: The true secondary electrons (TSE) part \(\delta_{TSE}\) that dominate at primary energies above \(\sim\)20 eV and are fitted with a classical Furman-Pivi fit [43]. The elastically  
Figure 3: SEY energy dependence of RESCO samples held at 12 K measured in as-received and electron–conditioned states using 300 eV electrons at a few mC-mm\({}^{-2}\) doses, as indicated in the legend. The measured data points (not shown) are smoothed with a Savitzky-Golay filter [42] and plotted as a thick line, while the Furman–Pivi approximation [43] is the thin line. backscattered (EBS) part \(\delta_{EBS}\) that dominates at sub \(\sim\)20 eV energies is approximated by a fit reportedly derived by [PERSON] [47]. Meanwhile, the inelastic backscattered (IBS) part \(\delta_{IBS}\) starts at 0, but swiftly increases to the \(r\) value, reaching 63% at 10 eV. An exponential function was chosen here for simplicity, but the low-energy SEY region generally deserves greater care given its crucial role in EC build-up. \[\delta_{TOT} =\delta_{TSE}+\delta_{EBS}+\delta_{IBS} \tag{3}\] \[\delta_{TSE} =(\delta_{max}-r_{BSE})\cdot\frac{s(E/E_{max})}{s-1+(E/E_{max})^{s}}\] \[\delta_{EBS} =r_{0}\cdot\left(\frac{\sqrt{E_{0}}-\sqrt{E_{0}+E}}{\sqrt{E_{0}}+ \sqrt{E_{0}+E}}\right)^{2}\] \[\delta_{IBS} =r_{BSE}\cdot(1-e^{-E/10})\]  
The parameters that best approximate the measured data are listed in the legend of Figure 3 and correspond to those suitable for technical metals. The following parameters were used here to accurately represent the measured SEY curves: reflectivity at 0 eV \(r_{0}\) = 0.75, EBS cutoff at \(E_{0}\) = 150 eV, skewness parameter \(s\) = 1.65, and IBS fraction \(r\) = 0.3. Lastly, the values of \(\delta_{max}\) and \(E_{max}\) of the fitted curve have to match the measured values.
## 2 Description of the Classification Method
### Structure of the ResNet18  
The number of convolutional layers plays a key role in detecting high-level concepts [19]. Residual learning framework could decrease the difficulty of the training of deep neural networks with many layers [21]. Motivated by this, the architecture of ResNet18, which is a modified version of the general ResNet50, is shown in Figure 4. The details are listed as follows:  
* Stage 0: The input layer is zero-paddled of 3 \(\times\) 3. After the processing, the shape changes from 60 \(\times\) 41 \(\times\) 3 to 66 \(\times\) 47 \(\times\) 3.
* Stage 1: The first stage consists of a convolutional layer of 64 filters of the rectangular shape of 3 \(\times\) 3 and stride of 2 \(\times\) 2. The batch-normalization is applied, followed by a Rectified Linear Unit (ReLU) as the activation function and max-pooling of 3 \(\times\) 3 and a stride of 2 \(\times\) 2.
* Stage 2: The second stage consists of a convolutional block named \"block-2a,\" an identity block named \"block-2b,\" and an identity block named \"block-2c.\" The structure of the convolutional block and the identity block is shown as Conv_block and Id_block in Figure 4, respectively.
* Stage 3: The third stage consists of a convolutional block named \"block-3a,\" an identity block named \"block-3b,\" and the average pooling layer of 2 \(\times\) 2. After processing, the shape of the output of the flatten layer changes from 15 \(\times\) 11 \(\times\) 512 to 7 \(\times\) 5 \(\times\) 512.  
Figure 3: The depiction of the feature extraction process.  
* Stage 4: The stage contains the flatten layer and the designed module of center loss. The number of hidden units of the first fully connected layer is 17,920. As for the designed module of center loss, the second fully connected layer with 24 hidden units connects to the first layer, followed by the Parametric Rectified Linear Unit (PReLU) as the activation function. The last fully connected is with 5 hidden units.  
We should mention that for the identity block, nonlinear activation helps the designed networks to learn more complex features. The activation function is defined as  
\[PReLU(x_{i})=\max(0,x_{i})+\alpha_{i}\min(0,x_{i}), \tag{1}\]  
where \(x_{i}\) is the input of the nonlinear activation on the \(i\)th channel and \(\alpha_{i}\) is a learnable coefficient controlling the slope of the negative part.
## 4 New Routing Protocol for MWSNs
### Our Efficient Routing Strategy in MWSNs
#### 4.1.1 Design of the Neighbor Table  
Above of all, we design the neighbor table that is the key component of our protocol. It is illustrated in Table 1.  
self_ID means a node itself, neighbor_ID means its neighbor node, Dis is the distance between self_ID and neighbor_ID. isRelay is a Boolean flag indicating whether the node pair <self_ID\(\rightarrow\)neighbor_ID > within a routing path, isRelay = 1 means true and data packet is routed from self_ID to neighbor_ID, its initial value is set to 0. Table 1 is the neighbor table for node 7 and illustrates that node 7 has two neighbor nodes: node 3 and node 9, the distance between node 7 and node 3 is 20, the distance between node 7 and node 9 is 15, in addition, <7\(\rightarrow\)3> within a routing path.  
Notably, our neighbor table is a wonderful issue. Some advantages can be summarized as follows: Firstly, neighbor table is the basic component of our protocol. Its basic function is used to record neighbor information which is the key information used to build the optimal routing path. Secondly, neighbor table enhances the MWSN scalability and the protocol robustness. When some nodes are failed, they will be deleted from its neighbors' neighbor table on demand, and never be used to build the new optimal routing path. If the failed nodes are repaired or replaced, or new nodes are added in the MWSNs, corresponding neighbor information will be inserted into their neighbors' neighbor table, thus, they can be used to build the new optimal routing path. Beyond that, when the routing path is changed, it is only to reset the isRelay flag of the relevant nodes, and this feature is well suited to resolve the routing problem in MWSNs. Thirdly, the isRelay flag indicates the routing information. It serves two purposes: routing tree establishment and locating the failed nodes. The routing tree is established by resetting the isRelay flag. In addition, if a relay node has not received any packet from its previous relay node after a reasonable time interval, it implies that the previous relay node has failed. With the help of the isRelay flag, the relay node can quickly find failed node ID from its neighbor table.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 2 Materials and Methods
### Methods
#### 2.3.3 Regional Classification  
For a detailed analysis of GWS variations, Peru was categorized into three main climatic zones:  
1. _Northern Zone_: Tumbes, Piura, Lambayeque, Cajamarca, La Libertad, Amazonas, and San Martin regions.
2. _Central Zone_: Lima, Ancash, Huanuco, Cerro de Pasco, Junin, Huancavelica, Ucayali, Ayacucho, and Ica regions.
3. _Southern Zone_: Cusco, Puno, Arequipa, Moquequa, Tacna, Madre de Dios, and Apurimac regions. This regional classification captures climatic similarities and improves the interpretation of groundwater dynamics and their relationship to climate variability.  
Figure 2: Methodological flow of GWS analysis in Peru (2003–2023): objectives (purple), satellite and climate data processing (blue), estimation and validation methods (green), drought indicator calculation and mapping results (orange).
## 4 CFD Simulations
### Validation  
The field test values for air velocity and CO concentration in the Jinzhou oil storage cavern were used to validate the CFD results. The simulated values were compared to the measured values as shown in Figure 7. It can be seen that the air velocity in the main cavern and construction roadway obtained by the CFD simulation fit the field test well. The average errors in the main cavern and construction roadway are 7.4% and 8.3%, respectively. Moreover, the simulation results of the average CO concentration in 2 test sections are in good agreement with the measured values. The average relative errors of the CO concentration in Sections 2 and 4 are 9.5% and 13.2% respectively. The validation results indicate that the 3D steady RANS coupled with the RNG \(k\)-\(\epsilon\) turbulence model can give a satisfactory prediction of the flow ventilation in the Jinzhou oil storage caverns.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Section** & \(A\) **(m\({}^{2}\))** & \(G\) **(kg)** & \(L_{\mathrm{OT}}\) **(m)** & **The Amount of CO (m\({}^{3}\))** & \(C_{0}\) **(mg/m\({}^{3}\))** \\ \hline Upper layer & 135.1 & 320 & 79 & 12.8 & 1499.1 \\ Middle layer & 228 & 420 & 99 & 16.8 & 930.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The initial concentration of CO after cavern blasting.  
Figure 7: \(Cont\).
## 4 Proposed Scheme
### Login Phase  
Vehicle \(V_{i}\) should validate the driver before accepting the secure domain key calculation in 5G-enabled vehicular networks. The login phase follows these steps:  
* Driver inputs (\(PW_{i}\), \(DID_{i}\), \(ID_{i}\)) to vehicle \(V_{i}\).
* Vehicle \(V_{i}\) checks whether the equation \(B_{i}=H_{1}(PW_{i})\oplus A_{i}\) holds a given \(PW_{i}\), where TA preloads \(A_{i}\).
* If the driver inputs match the login password \(PW_{i}\), then vehicle \(V_{i}\) permits this login request; otherwise, vehicle \(V_{i}\) rejects this request.
## 4 Implementation Results and Comparisons
### Comparison to State of the Art
#### 4.3.3 Overall Summary  
Due to the use of a schoolbook multiplication method in our proposed digit-parallel multiplier architectures (i.e., dedicated and flexible/scalable), our method is successful in terms of FPGA slices as compared to architectures reported in [17; 18; 19; 20; 21; 22; 23; 24; 26; 27]. Moreover, our proposed dedicated and scalable multiplication designs consume less power as compared to multiplication architectures published in [17; 18; 19]. On the other hand, the architectures of [20; 21; 22; 23] are more power-efficient as compared to our multiplication architectures. In most state-of-the-art designs, the comparison in terms of timing parameters (i.e., clock cycles, frequency and latency) is not possible as the relevant information is not described. Consequently, our dedicated and scalable multiplication architectures report competitive results in comparison to state-of-the-art multiplication designs.
## 2 Materials and Methods
### Biophysical RBE Models  
#### 2.6.1 Theory of Dual Radiation Action (TDRA)  
A microdosimetric expression for RBE may be derived from the theory of dual radiation action (TDRA) [65]. According to the'site' version of TDRA, any lethal lesion that may lead to a biological effect is caused by the combination of two sub-lesions that are produced in the same site. The number of sub-lesions is proportional to the specific energy (\(z\)) in the site. A site refers to the average (sub) cellular volume in which a combination of sub-lesions (leading to lethal lesions) may occur. According to TDRA's site version, RBE may be expressed as follows [66]:  
\[\text{RBE}_{\text{TDRA}}=\frac{\sqrt{z_{D,R}^{2}+4\;D_{P}(z_{D,P}+D_{P})}-\;z_{ D,R}}{2D_{P}}, \tag{32}\]  
where \(z_{D}\) is the dose-averaged specific energy which is related to \(y_{\text{D}}\) by (assuming spherical site) [67]:  
\[z_{D}=\frac{\overline{l}}{m}\;y_{\text{D}}=\frac{y_{\text{D}}}{\pi\,\rho\;r^{ 2}}, \tag{33}\]  
with \(m\) being the mass of the site, \(\rho\) the density of the medium and \(r\) the radius of the site. If a density of \(1\) g/cm\({}^{3}\) is assumed for the subcellular domains, the above equation can be simplified as [67]:  
\[z_{D}=\frac{0.204}{4r^{2}}\;y_{\text{D}}, \tag{34}\]  
where \(y_{\text{D}}\) is expressed in \(\text{keV}/\upmu\)m and \(z_{D}\) in Gy.
## 4 Evaluation Method
### Comprehensive Evaluation and Decision Analysis
#### 4.2.5 Cloud Model (CM)  
The CM is based on the concepts of probability and statistics, providing a framework to tackle uncertainty and ambiguity by outlining the probability distribution of different states. When it comes to determining weights, the CM effectively mimics the fuzzy evaluations of experts regarding assessment indicators, highlighting the importance of each indicator through the computation of cloud parameters, like expectation, entropy, and super-entropy, thus aiding in weight determination. This method integrates the subjective insights and assessments of experts while also addressing the uncertainties and ambiguities that are intrinsic to the evaluation process. Additionally, the CM acts as a mathematical framework that captures complex systems marked by uncertainty and fuzziness. It converts qualitative descriptions into quantitative measures through the creation of membership functions, cloud functions, and cloud generation algorithms, facilitating the modeling and analysis of complicated systems. The CM is equipped with strong data processing and analytic functions, proficiently managing various complex data structures and associations, therefore laying a scientific groundwork for informed decision-making. Moreover, CM is capable of processing and analyzing extensive datasets to derive meaningful information and insights. In the field of supportive decision-making analysis, they provide a scientific foundation for decision-makers by simulating the probabilities and risks linked to different decision-making options.  
The CM offers significant benefits in GSEUUS, primarily through its ability to incorporate randomness and fuzziness. This allows for a seamless transition between qualitative linguistic values and quantitative measurements, thereby improving both the precision and impartiality of the evaluation process. Particularly noteworthy is the model's adeptness at handling the intrinsic fuzziness and uncertainty linked to the GSEUUS. Nonetheless, the CM has its drawbacks. Its efficacy can be affected by various factors, including data completeness and the accuracy of quantification and classification. Furthermore, the successful implementation and parameterization of the CM demand a certain level of specialized knowledge and expertise, which may influence the reliability and accuracy of the evaluation results (Figure 7).  
The CM is widely used for GSEUUS [7] and analyzing slope stability [136]. The combination of the AHP with the CM significantly improves the process of determining the weights and certainty degrees associated with evaluation metrics [67]. By applying a limited interval in the CM, the randomness and fuzziness present in geological indicators are effectively tackled, which enhances the evaluations' accuracy and objectivity [135]. Additionally, the genetic algorithm-based weighting method considers both subjective and objective weights, leading to evaluation results that are more rational and in better accordance with actual conditions [7] (Figure 8f and Table 1).
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 4 Results
### Validation of the Phenological Metrics  
Table 2 lists the SoS, EoS and PoS of different LAI products. The SoS extracted from coarse LAI products is 1-4 days (d) earlier than that of reference LAI except for EPS which is 6 d later. The date for EoS and PoS of coarse resolution LAI products were both later than that of the reference LAI except MODIS and C3S V2 LAI products. C3S V2 shows a systematic advance of \(-\)4 d for the SoS and \(-\)6 d for the PoS and EoS. EPS LAI product showed the highest differences as compared to the reference LAI with a delay in the timing of phenology metrics: +6d for the SoS, +20 d for the PoS and +24 d for the EoS.  
Figure 7: Comparison between (**a**) MODIS, (**b**) GEOV2, (**c**) GEOV3, (**d**) EPS, (**e**) GLASS and (**f**) C3S V2 with the reference LAI. The black dashed line indicates 1:1 line, and solid line indicates linear regressions between LAI products and reference LAI. The gray dashed lines show the Global Climate Observing System uncertainty requirements for LAI (max (0.5, 20%)).
## 6 Conclusions  
In this paper, Dynamic-DSO is proposed, which is a semantic monocular direct visual odometry using image semantic segmentation method in deep learning to improve the performance of positioning and mapping in dynamic environments. This is different from the most current systems, which combine the traditional indirect SLAM or VO with deep learning. The proposed system is completed based on direct method. Firstly, image semantic segmentation technology is used to obtain the pixel-wise semantic information of the dynamic objects in images. Secondly, a novel candidate points extraction method and a tracking model are proposed to eliminate the dynamic objects in an effective way by using the image semantic information. Finally, we apply the semantic information of dynamic objects into the sliding window optimization to avoid dynamic objects disturbing the optimization process. The proposed method was evaluated both on the public TUM dynamic dataset and the modified Euroc dynamic dataset, and we compared the pose estimation performance of our method with the state-of-the-art direct method. The results demonstrate the accuracy and robustness of the proposed method. The position accuracy of Dynamic-DSO is significantly higher than the state-of-the-art direct method in dynamic environments, and the semi-dense cloud map constructed by Dynamic-DSO is clearer and more detailed. In the future, a more real-time semantic segmentation framework should be used to improve the real-time performance of the entire system. Considering the semantic information and the Sparse-to-Dense model proposed by [PERSON] et al. [32], we might build a dense 3D semantic map in dynamic environments based on the semi-dense map constructed by Dynamic-DSO.  
Figure 17: 3D point cloud maps of indoor scene and partial enlarged maps constructed by Dynamic-DSO and DSO in V101_syn sequence. (**a**): Calibration board shown in V1_01 and V101_syn. (**b**): Room corner shown in V1_01 and V101_syn. (**c**): point cloud map constructed by DSO. (**d**): point cloud map constructed by Dynamic-DSO.  
**Author Contributions:** Conceptualization, C.S. and S.P.; methodology, C.S.; software, C.S. and Y.T.; validation, C.S., S.P. and W.G.; formal analysis, Y.T. and T.Z.; investigation, C.S. and Y.T.; resources, W.G.; data curation, T.Z.; writing--original draft preparation, C.S.; writing--review and editing, C.S. and S.P.; supervision, S.P.; project administration, S.P. and W.G.; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is partially supported by the National Natural Science Foundation of China (Grant No. 41774027, Grant No. 41904022 and Grant No. 41574026) and the National Key Technologies R&D Program (Grant No. 2016 YFB0502101).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effects of Nanoparticles on Algae: Adsorption, Distribution, Ecotoxicity and Fate
## 4 Ecotoxic Effects of NPs on Algae  
### Effect of NPs on the Cell Membrane  
NPs have a high reactivity in the liquid phase, and the toxicity of the NPs that aggregate through the cell wall to the cytoskeleton structure (mainly referring to the cell membrane) is significant. The interactions of the NPs with algae caused damage to the cell membrane and the release of lactate dehydrogenase into the test solution, which may be one of the toxicity mechanisms inducing cell death [20]. NPs mainly cause toxicity to the cell membrane by the oxidative stress reaction, resulting in  
Figure 1: Possible distribution and potential toxic outcomes of NPs in algal cell.  
membrane damage and the decreasing of membrane integrity [45]. In a study of the toxicity of TiO\({}_{2}\) NPs to _Anabaena variabilis_, the exposure of algae to NPs brought an increase in the content of reactive oxygen species (ROS) in the cell membrane and the production of membrane protein crystals. At the same time, the cell membrane was abnormal or even damaged [52]. In the case of cytotoxicity of NiO NPs on _Chlorella_ sp., algae cells appeared on the cell wall separation and cell membrane rupture in the presence of NiO NPs [53]. One study has shown that NPs may cause membrane changes and increase its porosity, thereby further facilitating NPs to enter cells [54]. It was deduced that contact with NPs induced the formation of new pores in the membrane through lipid peroxidation mechanisms, making it more permeable and less selective [51]. Furthermore, NPs can also accumulate in the cell membrane and lead to cell wall depression, which causes changes in cell membrane permeability, until cell apoptosis occurs [20]. Nanoparticles have been found to increase lipid peroxidation in cell membranes, resulting in membrane structure deformation [55]. In the cytotoxicity study of TiO\({}_{2}\) NPs toward _Scenedesmus obliquus_, plasmolysis and dentate cell membrane were observed. In addition, the cell membrane thickened with the presence of TiO\({}_{2}\) NPs, which can be attributed to the protective nature of the cells, preventing the internalization of NPs [22].