esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Evaluation Index and Analysis Method of Broken Cable
### Analytical Method  
The main methods used to study the sudden cable breakage of cable-stayed bridges are the pseudo-dynamic method, full dynamic method and semi-dynamic method. The pseudo-dynamic method [9; 10] refers to the direct removal of the cable and the application of an equivalent load at its anchorage, which is twice the static cable force of the cable, and the static analysis is carried out on this basis. The full dynamic method [21] refers to removing the cables that need to be broken, simulating the original structural state with the actual cable force, and then applying a pair of concentrated forces that change with time in the opposite direction at the cable anchorage position of the main beam and the cable tower. The whole analysis process is under dynamic conditions. The semi-dynamic method [21] refers to using the \"life and death element\" of ANSYS to simulate the sudden breaking of cables. The simulation process is a transient dynamic analysis starting with a static load step.  
The research shows that the amplification factor of 2.0 in the pseudo-dynamic method cannot accurately describe the dynamic response of the structure [9; 10]. Compared with the full dynamic method, the semi-dynamic method does not need to calculate the initial cable force separately or define the cable force change after the cable is broken. The semi-dynamic method can simulate the extreme situation of a cable breaking suddenly only by using \"EKILL\" to remove the cable unit at a certain moment. Therefore, a method was proposed to study the instantaneous cable breakage of a single-tower cable-stayed bridge, and the specific calculation process is as follows:  
1. Enter ANSYS and input the finite element model.
2. Set an appropriate static analysis step size. The static analysis of the structure is carried out to obtain the initial stress and deformation of the structural cable before breaking.
3. Set the step size of the dynamic analysis and perform the dynamic calculation. First, the cable element is broken in an instant, and the appropriate analysis step and integration time are specified. Second, in order to obtain the maximum dynamic response of the structure, the analysis sub-step is extended. Finally, the analysis step is extended again, and the integration time is increased.
4. Extract the dynamic response time history of the structure.
## 1 Introduction
Similarly, in _Streptomyces_ species, mutations in two ribosomal components, RpsL and RsmG, resulted in increased streptomycin resistance and antibiotic production [18]. Additionally, in _Salmonella enterica_, high-level resistance to streptomycin was associated with increased expression of _aadA_, which encodes an aminoglycoside adenyltransferase, and is controlled by a guanosine penta/tetraphosphate [(p)ppGpp]. The deletion of _zunA_, a gene that encodes a periplasmic zinc-binding protein, increased the transcriptional level of _aadA_[3; 19; 20]. The technology used for these studies was ribosome engineering, a process used for drug screening that has several advantages over simple selection. Similar to other bacteria, SmR mutants of _Thermus thermophilus_ were spontaneously selected through serial cultivation with gradual streptomycin treatment. Most mutants revealed point mutations in streptomycin-interacting residues of ribosomal protein S12 [11; 21]. Further, investigation of the mutation positions in RpsL and their effects on streptomycin interaction showed that these amino acid substitution mutants exhibited different phenotypes, such as SmR at P41S, K42R, K87R/E, and P90A/C/G, SmP at R85C/H, and SmD at P90E/M/R/W, in the S12 protein. Additionally, a mutation in the target gene _rsmG_ through transposition of the insertion sequence (IS) element _ISTth7_ resulted in streptomycin resistance in _T. thermophilus_ HB8 [22]. This IS element transposition was selected from a large collection of spontaneous SmR mutants. While searching for a new biomarker to track the potential transposition phenomenon of insertion sequence (IS) elements in the radiation-resistant bacterium _Deinococcus genthemalis_, it was discovered that an IS element was integrated into the _rsmG_ gene in a strain that showed streptomycin resistance [23; 24]. Further, some SmR mutations caused by point and frameshift mutations under chronic oxidative stress conditions resulted in two wild-type _dps_ gene (_dgeo_0257_ and _dgeo_0281_)-disrupted mutants and a putative LysR family regulator (_dgeo_2840_)-disrupted mutant [25, 26, 27]. In this study, we characterized the transposed IS element, transposition and mutation loci, and the mode of transposition with alternative SmR screening of various mutations in _rsnG_, _rpsL_, _mthA_, and other potential target genes involved in antibiotic uptake and modification. As a result, we elucidated the SmR phenotype caused by the oxidative stress response to H\({}_{2}\)O\({}_{2}\) treatment in an extreme gamma radiation-resistant bacterium. The phenotypic SmR frequency was 1.36 \(\times\) 10\({}^{-7}\) in the wild-type strain, which is slightly higher than non-pigment production by IS transposition under H\({}_{2}\)O\({}_{2}\) treatment [27]. Additionally, these findings contribute to a better understanding of the mechanism of streptomycin resistance and ribosomal structure resolution, in particular, the small ribosomal subunit structure for streptomycin binding.
# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 5 Results and Analysis
### Comparison with Current Work  
Compare OI-EleAttG-GRU with other current advanced models, including LCNN [23], DLFC [35], CNN-GRU [36], BiLSTM [37], as well as other effective models, such as SVM, IN, DCL on the two representative public datasets WISDM and UCI. The comparison is based on two common comprehensive performance metrics: Accuracy and F1-score. The comparison between existing models on the WISDM and UCI dataset is presented in Figure 10.  
According to the results shown in Figure 10a,b, the OEG method achieved impressive performance on the WISDM dataset. The accuracy reached 97.52%, and the F1-score reached 97.51%. When compared to the baseline method SVM, OEG exhibited significant improvements of 8.99% and 8.96% in accuracy and F1 score, respectively. Even when compared to current advanced methods, OEG still demonstrated noticeable accuracy improvements of 1.31% to 2.70% and F1-score improvements of 1.29% to 3.25%.  
Furthermore, OEG also performed well on the UCI dataset, surpassing the baseline method with an increase of 8.9% in accuracy and 9% in F1-score. Compared to the current advanced methods, OEG achieved improvements of 0.52% to 3.4% in accuracy and 0.48% to 2.96% in F1 score.  
Overall, due to its ability to capture the long-term dependencies in sensor data, OI-EleAttG-GRU demonstrates a performance advantage, displaying excellent temporal and spatial feature extraction capabilities, and generalizing well across different datasets when dealing with complex or noisy data.
## 4 Discussion
### Age Effects on GPP, ET, and WUE  
A strong correlation between GPP and ET with stand age in our study was consistent with other studies [78; 79; 80; 81]. The increasing trend in GPP with age in YP, that stabilized thereafter, was pointed out by [PERSON] et al., 2020 [46]. Productivity was expected to stabilize with minimal fluctuations with age in more mature forests, considering that nutrients are immobilized within the plant biomass [82].  
Our study showed that ET across the chronosequence increases with time commensurate with GPP increases. Therefore, the positive ET-stand age relationship implied that if plantations are maintained across the landscape in different age classes simultaneously, variations in depths of the water table would be reduced leading to small effects of drought on plant water availability. Meanwhile, WUE was not affected by age (\(p>0.05\)), which does not support our hypothesis of a strong age-dependency of WUE.
## 3 Results and Discussion
### Pristine Fullerite C\({}_{60}\) Study
According to works [62; 63; 64], the BE of O 1s electrons of a water molecule, regardless of whether it is adsorbed on a metal, or metal oxide, lies in the BE range between 533 and 535 eV. The energy position of the O 1s signal of a water molecule adsorbed on the surface of a solid usually shifts down the BE scale as compared to a free molecule. This shift is usually attributed to surface screening effects since an inner shell vacancy in a water molecule is more effectively screened by electrons on the surface than by those of a free water molecule. For a water molecule bound on a metal surface, two O 1s XPS peaks are commonly observed [64]. These peaks are associated with H\({}_{2}\)O (\(\sim\)532.5 eV) and hydroxyl groups (\(\sim\)531 eV) in the surface water layer, respectively. The presence of bound hydroxyl groups enhances water binding [61] because the H\({}_{2}\)O-OH H-bonding is stronger that the H\({}_{2}\)O-H\({}_{2}\)O H-bonding. For example, two O 1s XPS peaks at BE of 530.8 and 532.4 eV were observed for Cu(110) [61] and can clearly be resolved at 530.8-532.3 eV for Ru(0001) [65]. Moreover, the formation of a water bilayer was observed on the surface of metal [66], graphene [67] and fullerite C\({}_{60}\)[60] under ultra-high vacuum conditions. Thus, the comparison of our XPS results with those of other studies confirms the above interpretation that the low-energy component of the O 1s XPS spectrum of C\({}_{60}\) (532.2 eV) is associated with hydroxyl groups present in a surface water layer.
# Random Violation Risk Degree Based Service Channel Routing Mechanism in Smart Grid
## 5 Simulation and Results
### Monte Carlo Experiment Based on Four Nodes  
As shown in Figure 5, there exist two service channels from node N1 to node N3 named \(SC_{N1-N2-N3}\) and \(SC_{N1-N4-N3}\), whose availability are both 99.7%. Now from N1 to N3, there is a kind of power communication service \(S_{i}\) with availability requirement of 99.5%. The monthly failure number and the repair time of \(SC_{N1-N2-N3}\) and \(SC_{N1-N4-N3}\) are respectively generated according to the parameters \(\{\lambda_{1}=0.36,(\mu_{1}=1,\delta_{2}=0.5)\}\) and \(\{\lambda_{2}=0.18,(\mu_{2}=2,\delta_{2}=0.5)\}\).  
To reduce the impact on experiment results which is brought by our algorithm's random nature, we get the probability of service channel violation risk through repeating experiment 10,000 times and the method that the channel monthly statistical average failure times are divided by the experiment times. The statistical average data and the routing results of two algorithms are shown in Table 1. In the Table 1, the thick arrows denote the route selected by \(SCVRD\)-\(R\) algorithm, while the thin arrows denote the route selected by AAR-OS algorithm.  
From Table 1, it can be seen that the violation risk of the two channels are fluctuating. If AAR-OS algorithm is adopted, even when the violation risk of \(SC_{N1-N2-N3}\) is lower than \(SC_{N1-N4-N3}\) (for example on month 6 and 7), AAR-OS will always choose the channel \(SC_{N1-N4-N3}\) for the service \(S_{i}\) because the channel \(SC_{N1-N4-N3}\) has slightly higher statistical availability than \(SC_{N1-N2-N3}\). If \(SCVRD\)-\(R\) algorithm proposed in this paper is adopted, then the lower risk route will be selected for \(S_{i}\) according to the change of the violation risk value. Thus, the number of the service violation will be reduced and its \(A_{APA}(t)\) will be enhanced.
# Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data
## 4 Privacy-Preserving Vertical Data Mashup Protocol
### Setup Protocol
#### 4.1.1 Identify Data Providers  
To facilitate the identification of the data partitions that may be vertically integrated to satisfy the requirement of the data consumer, the data providers must: publish the data schemata, set the identifier attribute that may be used as a connector in the integration process, and define the quasi-identifying and confidential attribute sets. Since publishing the data schemata heavily depends on the DBMS technology used by each data provider,we suggest mapping to well-known technologies used for the Web of Data to publish the schemata of the involved datasets, as explained in the following subsection.
## 2 Materials and Methods
### Oil Spill Behavior and Calculation Principles  
#### 2.2.1 Transfer Process  
The transport of oil particles involves both expansion and drift processes, and the composition of oil particles does not affect these processes [18].  
Expansion movement: Oil film expansion is calculated using the modified Fay gravity-viscosity formula:  
\[\left(\frac{\mathrm{d}\mathrm{A}_{\mathrm{oil}}}{\mathrm{d}t}\right)=\mathrm{K}_{ \mathrm{a}}\cdot\mathrm{A}_{\mathrm{oil}}^{1/3}\cdot\left(\frac{\mathrm{V}_{ \mathrm{oil}}}{\mathrm{A}_{\mathrm{oil}}}\right)^{4/3} \tag{1}\]  
where \(\mathrm{A}_{\mathrm{oil}}\) is the oil film area, \(\mathrm{A}_{\mathrm{oil}}=\pi\mathrm{R}_{\mathrm{oil}}^{2}\); \(\mathrm{R}_{\mathrm{oil}}\) is the oil film diameter; \(\mathrm{K}_{\mathrm{a}}\) is the coefficient; and t is time.  
Drift motion: the forces driving oil particle drift are water flow and wind pulling forces, and the total drift speed of oil particles can be calculated as:  
\[\mathrm{U}_{\mathrm{tot}}=\mathrm{c}_{\mathrm{w}}(\mathrm{z})\cdot\mathrm{U}_{ \mathrm{w}}+\mathrm{U}_{\mathrm{s}} \tag{2}\]  
where \(\mathrm{U}_{\mathrm{w}}\) is the wind speed at 10 m above the water surface; \(\mathrm{U}_{\mathrm{s}}\) is the surface velocity; and \(\mathrm{c}_{\mathrm{w}}\) is the wind drift coefficient, generally 0.03\(-\)0.04.  
Turbulence spreading: assuming that horizontal diffusion is isotropic, the possible diffusion distance \(\mathrm{S}_{\alpha}\) in the \(\alpha\) direction within a time step can be expressed as:  
\[\mathrm{S}_{\alpha}=\left[\mathrm{R}\right]_{-1}^{1}\cdot\sqrt{6\cdot\mathrm{ D}_{\alpha}\cdot\Delta\mathrm{t}_{\mathrm{p}}} \tag{3}\]  
where \(\left[\mathrm{R}\right]_{-1}^{1}\) is a random number from \(-1\) to 1; \(\Delta\mathrm{t}_{\mathrm{p}}\) is the diffusion time; and \(\mathrm{D}_{\alpha}\) is the diffusion coefficient in the \(\alpha\) direction.
# GIS-Based Multi-Objective Routing Approach for Street-Based Sporting-Event Routing
###### Abstract  
This study proposes a decision-making framework that integrates a routing model based on the geographic information system (GIS) and a genetic algorithm into a building-information modeling (BIM) environment to overcoming the limitations of the planning process of traditional street-based sporting events. There is a lack of research on improving the manually conducted decision-making processes for street-based sporting events. Moreover, previous routing studies were limited to GIS environments, and proposals for decision-making models integrated with BIM environments are lacking. In this study, the applicability of the framework was verified by presenting the variables of the existing GIS-based routing model as environmental variables to consider the impact of street-based sports events on a city. The evaluation model for the route selection was parameterized independently, such that its priority could be changed according to the user's needs. Moreover, we integrated the data into BIM to create and analyze models that assess urban effects. This method is a decision-making system for policymakers and event planners to promptly conduct initial venue surveys through the technological integration of GIS-routing-BIM. Additionally, the GIS stipulated in this study can be applied to other cities. The Gwanghwamun area of Seoul, South Korea, was selected as the case study.  
GIS; routing; multi-objective problem; street-based event; GIS-to-BIM; optimization
## 3 Results of Complete Soft Galloping (SG) Responses and Complete Hard Galloping (HG) Responses
### Hard Galloping (HG)
#### 3.2.5 Summary  
Similar to SG, besides the above four typical response paths, there are lots of response paths in HG as well. In all HG response paths, the lowest \(U_{r}\) for the onset of galloping is 11.625. This value has been verified by numerous tests. When \(U_{r}\) is less than 11.625, the prism only exhibits characteristics ofVIV in all cases. When \(U_{r}\) exceeds 11.625 (including 11.625), the prism can be initiated to galloping by an appropriate instantaneous push on the prism. Therefore, \(U_{r}\) = 11.625 is the critical velocity needed to separate the galloping and VIV zones. In the galloping zone, galloping can be initiated by external excitation; while in the VIV zone, galloping cannot be initiated in any case.  
In conclusion, the complete response characteristics of HG can be summarized as follows: (a) The VIV branches are complete, but the VIV-galloping transition branch and galloping branch are incomplete; (b) The self-excited region disappears, and a critical velocity appears instead; (c) The prism cannot be self-initiated from VIV to a galloping in any case; (d) When \(U_{r}\) is beyond the critical velocity, galloping can be initiated by appropriate external excitation.
# ISAR Image Quality Assessment Based on Visual Attention Model
## 2 Proposed Method
### Loss Function  
\(F_{out}\) is input into the MLP block, which is composed of linear layer, relu, dropout, linear layer, and relu. The output \(F^{N\times H/8\times W/8}\) denotes the prediction score of each patch. The score token output by Gram-T passes through MLP to obtain an integral quality evaluation score. Then, for an image, its total prediction score is the sum of the prediction scores of each patch and the wholeness score. This method refines the evaluation of the image patches and makes the overall image quality evaluation more accurate. Due to the texture similarity and structure similarity among patches in the ISAR image, we propose structure and texture constraints to improve model performance on the ISAR image dataset. In addition, MSE loss is employed to measure the distance between quality score prediction and the ground truth. According to the characteristics of the ISAR image, the loss function is specially designed, which includes the following: (1) Square error loss (MSE loss): \(N\) is the number of samples, \(\widehat{y}_{i}\) is the prediction quality score, and \(y_{i}\) is the true labeling of the \(i\)-th ISAR image.  
\[MSE\ LOSS=\frac{1}{N}\sum_{i=1}^{N}\left(\widehat{y}_{i}-y_{i}\right)^{2} \tag{5}\]  
Figure 3: IRAB module.  
(2) Texture and structure similarity constraints: \(N\) is the number of samples, \(\mu_{ij}\) is the predicted quality score of the \(j\)th patch of the \(i\)th image, and \(\tilde{\mu}_{ij}\) is the average predicted quality score of all patches of the \(i\)-th ISAR image :  
\[TSSC\;LOSS=\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{L}\bigl{(}\mu_{ij}-\tilde{\mu} _{ij}\bigr{)}^{2}/L \tag{6}\]  
The final loss function is  
\[\mathcal{L}_{\text{Quality}}=\frac{1}{N}\sum_{i=1}^{N}(g_{i}-y_{i})^{2}+\frac{1 }{N}\sum_{i=1}^{N}\sum_{j=1}^{L}\bigl{(}\mu_{ij}-\tilde{\mu}_{ij}\bigr{)}^{2}/L \tag{7}\]  
where \(L\) is the total number of patches.
## 2 Materials and Methods
### Test Setup  
Isometric hip rotation was assessed in a standing position using the custom device and three 9-DoF inertial measurement units (IMU) sensors (Yost Labs, Portsmouth, OH, USA; orientation accuracy of \(\pm 1^{\circ}\) for dynamic conditions, 360\({}^{\circ}\) orientation range for each axis).  
Figure 1: Device for measuring rotational moments of the lower limbs and the ground reaction forces.  
Isometric external rotation of the lower limbs applied to the hip joints was tested--foot movement was blocked by part of the device. The experimental research was conducted in accordance with previously published methodology [20].  
Two IMUs were placed on the lateral knee ligaments to capture limb rotation; a third was mounted on the sacrum to detect pelvic tilt. Sensors were calibrated before use according to the manufacturer's guidelines.  
Prior to the commencement of measurements on the device, a standardized foot positioning procedure was implemented for each participant to ensure accurate measurement of pressure forces at the relevant anatomical points. Additionally, the foot was aligned in such a way that the limb's axis of rotation consistently coincided with the platform's axis of rotation.  
To enhance measurement precision, the foot was divided into four segments, ensuring that each part maintained contact with a separate measurement platform. The division between the forefoot and hindfoot was determined based on the location of the Chopart joint, which, during the examination, was positioned along the line separating the anterior and posterior support platforms of the tested limb. Meanwhile, the distinction between the medial and lateral parts of the foot was established with reference to the center of the ankle joint in the frontal plane. During the measurement this center was positioned along the line separating the two platforms located on the left and right sides of the tested limb.  
Each trial was preceded by a 10 s stabilization period in a habitual standing position. Participants were instructed to maintain an upright posture without altering the kinematics of the upper limbs, trunk, or head.  
The subjects were instructed to rotate with the maximum force they could achieve. During this study, three trials were recorded successively:  
* Maximum external isometric rotation of the right and left lower limbs simultaneously (RoRLLS, RoLLLS) applied in the hip joints for 10 s.
* Maximum external isometric rotation of the right lower limb (RoRLL) applied in the hip joint for 10 s.
* Maximum external isometric rotation of the left lower limb (RoLLL) applied in the hip joint for 10 s.