esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Chemical Composition and Physicochemical Properties of Sausages  
The proximate chemical composition of the three formulations of beef and fallow deer sausages is shown in Figure 1. As expected, all formulations showed a similar chemical composition in terms of moisture, protein, fat, collagen, and salt contents. Meat beef sausages were characterized by a higher (\(p\leq\) 0.05) fat content compared to sausages produced from fallow deer meat. The salt concentration in the products was similar due to the same amount added during production and a similar degree of drying of the sausages during processing.  
Statistical analysis showed that the process factors such as variant and type of meat used influenced (\(p\leq\) 0.05) the pH, water activity, and TBARS of the tested samples (Table 2). The elimination of nitrite and the addition of acid whey powder had a different effect on the pH of the sausages depending on the type of meat used. In the case of beef sausages, the sample without nitrite with the addition of whey was characterized by the highest pH, while in the case of fallow deer sausages, the same sample (SAW) showed the lowest pH among all the samples. Water activity did not differ statistically significantly between the formulations. The values ranged from 0.910 to 0.915 and from 0.915 to 0.918 for beef and fallow deer fermented sausages, respectively. Regarding lipid oxidation, TBARS values were significantly higher in beef fermented sausages compared to fallow deer sausages. Elimination of nitrite as well as the addition of acid whey powder did not lead to statistically significant changes in TBARS in fermented sausages at the end of production.  
Figure 1: Chemical composition of sausages. C—control sample, S—sample with sea salt, SAW—sample with the addition of sea salt and freeze-dried acid whey (equivalent to 10% of liquid acid whey).
## 3 Results and Discussions  
### Material Characterization  
The X-ray diffraction measurements for the adsorbents under examination were performed in a range of 20 from 5\({}^{\circ}\) to 40\({}^{\circ}\) and the relative patterns are presented in Figure 1a. For GO, the sharp (0 0 2) diffraction peak at 20 = 10.6\({}^{\circ}\) indicates a well-defined layered structure with an interlayer spacing, as determined by Bragg's law, of about 0.81 nm [37]. For RGO and NaGO adsorbents, the diffraction peak at 20 = 23.5\({}^{\circ}\) indicates stacked graphene layers. The Scherrer equation was used for the estimation of the average diameter of stacking layers. GO, as was estimated by the Scherrer equation, was found to consist of eight graphene layers while RGO consisted of 3-4 layers and NaGO of about three [38], with crystallite average size about 1.34 nm for RGO and 1.21 for NaGO. Moreover, the modification of graphene oxide with sodium hydroxide was confirmed by energy-dispersive X-ray spectroscopy (EDS) spectra where Na was found on the sample's surface (Figure 1c). From the scanning electron microscopy (SEM) micrographs (Figure 1b), it can be concluded that the NaGO sorbent displayed typical rippled and crumpled surfaces, which are indicative of graphene. Wrinkling and ridge formation were observed both in SEM (Figure 1b) and transmission electron microscopy (TEM) (Figure 1d) images and they could be the reason for the enhanced porosity of the NaGO sorbent.
## 4 The Proposed Hybrid Approach for Intrusion Detection
### Attack Recognition  
After completing the above steps, we obtain clusters of training datasets, which are divided into \(K\) disjoint subsets \(DC_{1},DC_{2},\cdots,DC_{K}\). These clusters are used to calculate the fuzzy membership matrix \(U=\left[\mu_{i,j}\right],i=1,2,\cdots,K;j=1,2,\cdots,N\) of the test samples associated with each cluster. For each test sample \(x_{j}\), we use the nearest neighbor sample \(c_{i}\) of the \(i\)th cluster to calculate the fuzzy membership degree \(\mu_{i,j}\) according to Equation (14). Once these sub-DBNs classifiers have been trained on their training subsets, the test data are applied to each trained classifier \(DBN_{i}\) (_where \(i=1,2,\cdots,K\)_) to detect and identify normal and intrusion network traffic. The predicted values of each sub-\(DBN_{i}\) classifier are aggregated according to the fuzzy membership degree \(\mu_{i,j}\). The output of test sample \(x_{j}\) in each sub-\(DBN_{i}\) classifier is defined as \(DBN_{i}(\vec{x}_{j})\). The fuzzy aggregate output of each test sample \(\vec{x}_{j}\) can be computed as follows:  
\[Out=\sum_{i=1}^{K}\mu_{i,j}\cdot DBN_{i}(\vec{x}_{j})\quad, \tag{28}\]  
where \(K\) is the number of clusters and represents the number of sub-DBNs classifiers.  
The predictions of these sub-DBNs classifiers are aggregated into the final output of the intrusion detection system and used to detect attack categories. The proposed hybrid approach for intrusion detection is detailed in Algorithm 3.  
```
0: Dataset \(S\), cluster number \(K\), Gaussian kernel parameter \(\sigma\).
0: the final classification results.
1: Data collection: a training dataset and a testing dataset.
2: Data preprocessing: feature mapping and data normalization.
3: According to Algorithm 1, MDPCA is used to divide the original training dataset into \(K\) subsets \(DC_{1},DC_{2},\cdots,DC_{K}\).
4: According to Algorithm 2, each training subset \(DC_{i}\) is used to train the corresponding classifier \(DBN_{i}\).
5: Calculate the fuzzy membership matrix U of test samples according to Equation (14).
6: Test sample \(x_{j}\) is tested on each trained \(DBN_{i}\) classifier. The predictions of these \(DBN_{i}\) classifiers are fuzzy aggregated according to Equation (28), and the final classification results are output.
7:return the final classification results.
```  
**Algorithm 3** MDPCA-DBN (Modified Density Peak Clustering Algorithm and Deep Belief Networks)
## 2 Modeling and Analysis of Wind Storage Systems
### Modeling of Stand-Alone Infinity Systems with Wind Storage  
There are two ways for wind storage to connect to the power grid: distributed and centralized. Distributed access refers to each wind turbine motor being equipped with an energy storage device, which requires a small energy storage capacity and flexible compensation. However, it requires multiple sets of control devices and is complex to install; Centralized access refers to the installation of a set of energy storage devices at the entry point, which requires a large energy storage capacity. However, only one set of control devices is needed, making it easier to centrally control. Moreover, the output of each wind power is not exactly the same, and there is partial complementarity. Centralized access has a higher utilization rate of energy storage. This article adopts a centralized access method, connecting the energy storage device to the grid-connected bus of wind power, which can effectively improve the acceptance capacity of wind turbine grid-connected power generation, and achieve power compensation and peak shaving and valley filling.  
Figure 5 shows the grid-connected model of the wind storage system, which mainly includes SG, wind turbine, ESS, and infinite grid.  
In the wind storage integrated power plant, battery ESS auxiliary wind power is connected to the grid, which is jointly determined by the wind power output and ESS Co-determination.  
\[P_{\rm PCC}=P_{\rm W}+P_{\rm ESS} \tag{16}\]  
The system power balance equation is:  
\[P_{A}=P_{e}+P_{\rm PCC}=P_{e}+P_{W}+P_{ESS} \tag{17}\]  
Figure 4: GSC Side Converter Control Block Diagram.  
In the equation, \(P_{\text{ESS}}\) the positive and negative represent the charging and discharging state of the energy storage, the positive represents the energy storage discharge, and the negative represents the energy storage charging.  
Analysis shows that the power balance of the system can be achieved through power control of wind turbines and ESS.  
This article analyzes the operation and related characteristics of the wind energy storage joint system. Based on the output characteristics of the doubly fed wind turbine power generation and the complementary operation characteristics between multiple types of hybrid ESS energy storage, the operation control strategy for the grid connection of the wind power hybrid energy storage joint system is analyzed.  
The active output power of synchronous generators are:  
\[P_{G}=\frac{E{U_{\text{A}}}}{{X_{\text{s}}}}\sin\theta \tag{18}\]
# Water Basin Effect of Cofferdam Foundation Pit
###### Abstract  
This study addresses the water basin effect in the underwater sand layer of steel pipe pile cofferdams by integrating the concept from building foundation pits to cofferdam foundation pit analysis. A theoretical derivation is presented for the deformation evolution of steel pipe piles and bottom seals within the cofferdam pit. The cofferdam construction dewatering process is divided into four stages: riverbed excavation for bottom sealing, dewatering to the second support, dewatering to the third support, and dewatering to final bottom sealing. The steel pipe piles are modeled as single-span or multi-span cantilever continuous beam structures. Using the superposition principle, deformation evolution equations for these statically indeterminate structures across the four stages are derived. The bottom seal is simplified to a single-span end-fixed beam, and its deflection curve equation under uniform load and end-fixed additional load is obtained via the same principle. A case study based on the 6# pier steel pipe pile cofferdam of Xi'an Metro Line 10 Jingwei Bridge rail-road project employs FLAC3D for hydrological-mechanical coupling analysis of the entire dewatering process to validate the water basin effect. Results reveal a unique water basin effect in cofferdam foundation pits. Consistent horizontal deformation patterns of steel pipe piles occur across all working conditions, with maximum horizontal displacement (20.72 mm) observed at 14 m below the pile top during main pier construction completion. Close agreements are found among theoretical, numerical, and monitored deformation results for both steel pipe piles and bottom seals. Proper utilization of the formed water basin effect can effectively enhance cofferdam stability. These findings offer insights for similar engineering applications.  
water basin effect; deformation evolution mechanism; steel pipe pile cofferdam; hydrological-mechanical coupling 2025157374
## 5 Conclusions  
In this study, a numerical analysis has been done on finding the response characteristics of a semisubmersible FWT when it is moored by an SPM system, based on the well-proven open-source software FAST. An extensive discussion has been made and useful conclusions have been obtained, which can be summarized into the following aspects:  
1. The free-decay test shows that SPM layout elongates the natural periods of the wind turbine in rotational modes, but this is not the case for the translational natural periods.
2. Further analysis of the force-displacement relationship based on the Python wrapper of MAP++ library shows that the diagonal rotational mooring restoring terms decrease due to the SPM layout, and the crossing stiffness terms are also changed.
3. RAO comparisons elucidate that the presence of wind significantly influences the sway, roll and yaw motions of the wind turbine moored by the SPM layout, in comparison to the one moored by the MPM layout.  
Figure 11: Yaw moment attributed to the shaft tilt: (**a**) side view of wind turbine; (**b**) velocity component analysis at the rotor plane.  
Figure 12: Yaw moment attributed to the cone angle of the blades.  
Figure 10: Yaw moment attributed to the wind shear: (**a**) front view of rotor; (**b**) top view of wind turbine.  
The weatheruse test shows that an asymmetry of the free-yaw motion response exists when the FWT is moored by an SPM system, attributed to the aerodynamic asymmetry of the rotor properties.  
These findings are valuable to the design and application of the SPM technology in moored FNTs.  
Conceptualization, Y.L. and S.Y.; methodology, Y.L.; software, Y.L.; validation, Y.L., H.Y. and A.T.; formal analysis, Y.L., S.Y., H.Y., A.T., G.H. and S.Y.; investigation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, H.Y., A.T., G.H. and S.Y.; supervision, S.Y.  
A small part of the APC (60 CHF) was funded by the Kyushu University Platform of Inter/Transdisciplinary Energy Research (Q-PIT) Support Program for Young Researchers (Grant Number 18112).  
The first author gratefully thanks the financial support from the Q-PIT Support Program for Young Researchers (Grant Number 18112), the Overseas Collaborative Research Program (Grant Number PJT-8) of the Japan Society of Naval Architects and Ocean Engineers (JSANAOE) and Grant-in-Aid for Early-Career Scientists (JSPS KAKENHI Grant Number JP18K13939) for his research. The authors are grateful to the guidance and help from Dr. [PERSON] of NREL and Dr. [PERSON] of Siemens Gamesa during the course of this study.  
No potential conflict of interest was reported by the authors.  
The Python subroutine (to be run together with the MAP++ solver) for analysis of the force-displacement relationship of an arbitrary mooring system, developed in the present study, is freely available on contacting the first author.
# Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing
## 6 Evaluation and Empirical Results
### Agent-Based VM Migration (ALBA) versus Centralized VM Migration
#### 6.1.2 Experimental Settings
The first aim of the experiment is to determine whether ALBA's consideration of CRA successfully mitigates the security risk of cloud computing. The second aim is to assess whether the additional costs of implementing CRA mitigation in ALBA are justifiable. 6.2.2. Experimental Settings  
To compare and validate the empirical results, we used MapLoad [66] as the reference method. MapLoad is an Agent-based VM migration method that considers both CPU and memory usage, and employs the same four frequently used load-balancing strategies of BAs (as discussed in Section 5.5). The testbed was designed with three different sets of parameters: (i) cloud computing parameters, (ii) load-related parameters (Table 1), and (iii) agent-based testbed settings (Table 2). 6.2.3. Performance Indicators  
The effectiveness of ALBA and MapLoad were evaluated using the performance metric Balance, calculated as shown in Equation (4). A lower Balance value indicates a more evenly distributed computing resource usage across (possibly heterogeneous) hosts. To compare the efficiency of ALBA's and MapLoad's CRA mitigation mechanisms, we used the security measures of Coverage and CVR. These measures were calculated using Equations (5) and (6) respectively. 6.2.4. Results and Analyses  
The experimental results are presented in Figure 6. Two conclusions can be drawn from the findings. Analysis 1  
As illustrated in Figure 6, MapLoad achieved a lower balance (1 st, 3 rd, 5 th, and 7 th columns) than ALBA using the four commonly used VM migration heuristics (2 nd, 4 th, 6 th, and 8 th columns). MapLoad distributes the load more evenly across heterogeneous hosts because it does not consider CRA, allowing low-load hosts to be fully utilized. Conversely, ALBA restricts the migration of load from high-load hosts to other hosts, even if the latter have low CPU and memory resource usage, because it cannot accept VMs that do not match the security level of the host.
# Selecting Remote Driving Locations for Latency Sensitive Reliable Tele-Operation
## 3 Selecting Driving Locations for Multiple Paths
### Selecting the Smallest Number of Remote Drivers under Latency Constraint
```
1:Input: \((P,\mathcal{F},L)\)
2:Output: \(D\)
3:\(P\): Src-Dst Path List
4:\(\mathcal{F}\): Set of candidate driving locations
5:\(L\): Latency Threshold
6:\(D\): Solution Set
7:\(B\leftarrow\) all the base stations in \(P\)
8:\(C\leftarrow\emptyset\)
9:for\(d\in\mathcal{F}\)do
10:if there is any base station, \(b\in B\), such that \(dist(d,b)\leq L\)then
11:\(C\gets C\cup\{d\}\)
12:\(D\leftarrow\) RDSMP \((P,C,L)\)
13:\(ad\leftarrow\mathit{Dist}_{avg}(D,B)\)
14:\(E\leftarrow\) the set of driving locations which cover at least one first base stations of \(P\)
15:for\(d\in E\)do
16: Let \(G\) be the set of the base stations along the paths, \(P\), that \(d\) covers
17:\(P^{\prime}\leftarrow\) Shortened paths in \(P\) by removing the base stations in \(G\) from \(P\)
18:\(D^{\prime}\gets\)\(RDSMP(P^{\prime},C,L)\)
19:if\(((|D^{\prime}|==|D|)\land(ad>\mathit{Dist}_{avg}(D^{\prime},B))\)then
20:\(ad\leftarrow\mathit{Dist}_{avg}(D^{\prime},G)\)
21:\(D\gets D^{\prime}\)
22:return D
```  
**Algorithm 2** ERDSMP.
## 3 Design and Analysis
### Dielectric Appraisal of FGIL Apropos of the Stranded Conductor
#### 3.3.1 Breakdown Voltage of the Proposed Configuration Regarding Streamer Breakdown Theory
The operational and design values of the electric field in the proposed pliable GIL could be evaluated through Equation (7) by considering the normal operating voltage and standard BIL voltages, respectively [41]. Further, Equation (7) can be rearranged by considering Equations (1), (5), and (6) for the evaluation of the critical electric field as shown in Equation (8) [41, 58]. In Equation (8), \(BV\) is the breakdown voltage and \(E_{C}\) is the critical electric field as evaluated on the basis of Equations (5) and (6). Figure 15  
Figure 14: Breakdown voltage (BV) appraisal for the proposed FGIL scheme. Figure 13: Electrode gap comparison between conventional and proposed flexible GIL (FGIL) schemes. shows the comparison regarding the operational, design, and critical electric fields for the proposed scheme. Critical perusal of Figure 15 shows that the operational and design values for the electric field were well within limits as specified by the standards for gas-insulated equipment, and both field magnitudes were much less than the critical field value. Thus, per the appraised dimensional specifications, the proposed 132-kV FGIL scheme exhibits good electrostatic stress withstand capability. \[E_{max}\ =\ \frac{U_{0}}{r\cdot\ln\left(\frac{\delta}{\delta}\right)} \tag{7}\]  
\[E_{c}\ =\ \frac{BV}{f\cdot\left(R-r\right)} \tag{8}\]
## 3 Results and Discussion
### Analysis of the Mechanical Properties of the Scaffolds  
Since scaffolds have to have sufficiently high mechanical properties and the compressive tensile capacity varies with the porous structure, compressive tension experiments were designed to evaluate the mechanical properties of scaffolds with different filled patterns. Figure 11a shows the pressure-strain curves of the scaffolds using the same porosity orthogonal and concave hexagon filled patterns, since the scaffold material is nonlinear, its compression characteristic curve does not have the characteristics of the compression characteristic curve of the general linear elastic material. And the analysis shows that the compression characteristic curves of the porous scaffolds of different patterns are significantly different, the slope can be used to indicate the compressive properties. The concave hexagon curve has a slope of 5.207 is higher than the orthogonal filling pattern which has a slope of 4.069 so that the compressive properties of the concave hexagon figure are higher than the orthogonal filled figure, which indicates that this pattern of the scaffold is more rigid [39]. The tensile load-displacement curves of the orthogonal and concave hexagon scaffolds are shown in Figure 11b, and neither curve has a significant yield phase, indicating that the composite material prepared is not linear. And under the same force,  
Figure 9: Velocity distribution of different filled structures at the same porosity (**a–c**) Pressure distribution (**d–f**).  
the displacement of the concave hexagon is larger than that of the orthogonal one, so the concave hexagon filled pattern has a higher elasticity. Therefore, the material can be suitable for elastic tissue-engineered scaffolds requiring certain stiffness in the vertical direction and certain flexibility in the horizontal direction.  
Figure 11: Pressure-displacement curves for orthogonal and hexagonal scaffold (**a**) and tension-displacement curves for orthogonal and hexagonal scaffold (**b**).  
Figure 10: Printed cube model using different fill patterns (**a**–**c**), printed cube model (**d**–**f**) and microscope photographs of the printed cube model (**g**–**i**).
## 3 Results and Discussion
### Statistical Reliability--Weibull Analysis
The CoCrMo alloy exhibits enhanced electrical conductivity (\(\eta\) = 41.13), rendering it well-suited for applications that demand such properties. The 17-4 PH alloy demonstrated the most consistent conductivity (\(\beta\) = 51.57), though at lower conductivity levels, which renders it predictable but limited in specific electrical biomedical contexts (Figure 22b). 17-4 PH alloys demonstrate higher corrosion rates (\(\eta\) = 1.96) with the greatest consistency (\(\beta\) = 3.58), suggesting predictable yet elevated corrosion rates. Ti-based alloys exhibit intermediate corrosion resistance, rendering them well-suited for environments characterized by moderate levels of corrosion (Figure 23a). The highest oxide resistance (\(\eta\) = 388.89) exhibited by Ti alloys is coupled with excellent consistency (\(\beta\) = 22.63), making them especially suited for applications in highly oxidative biological environments. CoCrMo alloys have also demonstrated noteworthy oxide resistance (Figure 23b). The Weibull parameters, which have been proven to be consistent and statistically significant, offer crucial insights into the reliability and application-specific suitability of these biomedical alloys. In these graphs, it was observed that as the \(\beta\)-value increased, the results became more consistent; as the \(\eta\)-value increased, the performance increased. The most consistent structure in terms of hardness was ascertained to be 17-4 PH (\(\beta\) = 26.09), and the highest average value was determined to be CoCrMo (\(\eta\) = 364.71) (see Figure 21a). In the wear analysis, the lowest average value was observed in the CoCrMo group, and the most variable structure was 17-4 PH (see Figure 21b). The highest recorded values of hardness in elastic modulus were determined in CoCrMo-B\({}_{4}\)C composites (see Figure 22a). The highest \(\eta\)-value in electrical conductivity values was observed in CoCrMo (\(\eta\) = 41.13), while the lowest variation was found in 17-4 PH (\(\beta\) = 51.57) (see Figure 22b). The composite with the highest average corrosion rate was identified as 17-4 PH-12B\({}_{4}\)C (see Figure 23a). These findings were corroborated by EIS analysis. In surface oxide film resistance, Ti-5B\({}_{4}\)C attracted attention with its high \(\eta\)-and \(\beta\)-values (see Figure 23b). These analyses quantitatively reveal both the stability of the production process and the statistical reliability of the obtained composites.
## 6 Optimization Scenarios for 6G MCNs
### Energy-Efficient Communication for UAVs and AUVs
The state observed in each DRL episode can include local channel conditions, residual battery level, and past transmission outcomes, whereas the action taken by each agent is the selection of transmission power level (or sleep/awake state) [96]. The received reward for a given action can be the negative consumed energy, penalized if QoS is violated, as reflected in the following formula: \[r_{t}=-\frac{P_{t}}{\eta}+\lambda\cdot I_{\{R_{t}\geq R_{min}\}}\] (5) where the reward \(r_{t}\) collected at time \(t\) is maximized when the power allocation \(P_{t}\) is minimum (first term). The second term reflects an extra constant reward \(\lambda\) that is given when the QoS is satisfied (i.e., \(R_{t}\geq R_{min}\)), as defined by the indicator function \(I_{\{R_{t}\geq R_{min}\}}\), which equals to 1 when the condition is true (otherwise it is 0). For large-scale systems, DRL or Proximal Policy Optimization (PPO) agents [97] can be used to learn power allocation strategies that achieve the lowest possible energy consumption with satisfied QoS.