esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Inoculation of Cheese Food Matrix and Homogenization Procedure  
In order to inoculate the whole food matrix, a suitable quantity of \(Lm\) _inoculum was prepared. The volume of inoculum introduced into each sample should not exceed 1% of the mass of each sample, which was 200 g. All three Batches of light cheese and all three Batches of full-fat cheese were inoculated with _Listeria monocytogenes_ (\(Lm\)) to a target concentration of 150 CFU/g. Based on the determined concentration of the working inoculum and employing the formula outlined in the EURL \(Lm\) Technical Guidance Document (C inoculum \(\times\) V inoculum = C whole matrix \(\times\) M matrix), the precise volume (in milliliters) required for the inoculation of samples within each Batch was calculated to achieve the target final concentration of 150 CFU/g of \(Lm\). To preserve the modified atmosphere packaging (MAP) within the experimental units, the inoculum was divided into four portions and introduced via four double septa using sterile syringes, as depicted in Figure 1b. The inoculation of each sample was performed both on the surface and within the depth of the cheese. Subsequently, the packages underwent vigorous agitation. This methodology aimed to ensure the comprehensive contamination of all regions of the cheese and the headspace environment within the package, resulting in a uniform distribution of the microbial load.  
Eight samples (\(n\) = 8) of each cheese type (light and full-fat), referred to as \"Test Units\", were inoculated with \(Lm\) strains through the double septum, upon the receipt of the cheese samples at the laboratory, on \"Day 0\".  
Four additional samples (\(n\) = 4) of each cheese type, referred to as \"Control Units\", were injected with physiological water through the double septum. The injected volume for the \"Control Units\" was identical to the volume of the \(Lm\) inoculum injected into the \"Test Units\", and the storage conditions for the \"Control Units\" were maintained identical to those of the \"Test Units\" (Figure 1).  
For the enumeration of \(Lm\), upon opening each package, the cheese slices were aseptically reduced to smaller fragments using a sterile, single-use scalpel. Subsequently, the comminuted cheese was transferred to a sterile Stomacher bag, and the contents were initially mixed manually to ensure homogeneity prior to mechanical homogenization using a Stomacher apparatus for a duration of three minutes.  
Figure 1: The cheese samples when delivered were packaged under MAP conditions. The net weight of each package was 200 g (**a**). Inoculation was performed on each studied sample using sterile syringes through the double septum (**b**).
## 10 Conclusions and Future Perspective
### Hybrid and Composite Membranes  
Hybrid and composite membranes represent a significant advancement in fuel cell technology, addressing the limitations of traditional single-polymer membranes by combining the strengths of different materials. Hybrid membranes are created by blending two distinct polymers, which helps to mitigate the deficiencies of the primary polymer while enhancing its overall physical and electrochemical properties. For example, combining a polymer with high proton conductivity but poor mechanical strength with another polymer that offers robust mechanical stability can result in a membrane that excels in both aspects. This synergistic effect improves the membrane's performance in fuel cells, making it more durable and efficient under varying operating conditions.  
In another approach, composite membranes are developed by incorporating hydrophilic nanoparticles or inorganic fillers, such as metal-organic frameworks (MOFs), silica, alumina, or titania, into a primary polymer matrix like styrene. These additives significantly enhance the thermal and mechanical properties of the membrane, making it more resistant to high temperatures and mechanical stresses. Additionally, the hydrophilic nature of these nanoparticles improves the membrane's water absorption capability, which is crucial for maintaining high ion conductivity, especially in proton exchange membranes (PEMs). The retained water molecules facilitate the transport of protons, thereby boosting the membrane's overall electrochemical performance.  
By integrating the advantages of both organic and inorganic components, hybrid and composite membranes offer a versatile and promising pathway for developing next-generation fuel cells. These membranes not only exhibit improved thermal stability, mechanical strength, and ion conductivity but also demonstrate enhanced durability and reduced fuel crossover, making them suitable for a wide range of applications, from automotive fuel cells to stationary power systems. The ability to tailor the properties of these membranes by adjusting the composition and concentration of the additives further expands their potential for optimizing fuel cell performance. As a result, hybrid and composite membranes are poised to play a critical role in advancing fuel cell technologies, contributing to the development of more efficient, cost-effective, and sustainable energy solutions.
# Mobility Prediction of Mobile Wireless Nodes
## 4 Related Works
This method was compared with recent works, and it proved the significance and importance of groups of users in a cluster manipulated with NOMA technology. In [34], the grounded multi-antenna was suggested to work as a green interference to improve the transmission security of the physical layer in the satellite network. Results showed high performance and attitude. As for [35], the authors suggested a strategy to reduce satellites' transmission power and BS while keeping users' rate requirements. This method was designed using singular value decomposition and uplink-downlink duality. Finally, the authors in [36] aimed to increase security and decrease the network's consumed power by using SLNR and SCA while applying a new iterative algorithm.
## 3 Finite Element Analysis of Loss Distribution
### Sensitivity Analysis of Loss to Design Parameters  
The sensitivity of electromagnetic losses to key design parameters exhibits pronounced nonlinear relationships that significantly impact the thermal behavior and efficiency of high-speed magnetic field-modulated motors in flywheel energy storage systems. As illustrated in Figure 6, parametric analysis reveals that air gap length demonstrates an inverse quadratic relationship with modulator losses but exhibits a more complex influence on permanent magnet eddy current losses, where initial reductions in air gap length decrease losses until reaching a critical threshold of approximately 0.5 mm, beyond which further reductions dramatically increase losses due to enhanced spatial harmonic penetration. As shown in Figure 7, permanent magnet segmentation emerge as a crucial design parameter, with circumferential segmentation providing substantially greater loss reduction (up to 73% with eight segments) compared to axial segmentation (42% reduction with equivalent segmentation) due to fundamental differences in eddy current path disruption mechanisms. A consolidated sensitivity profile is provided in Figure 8. Modulator pole geometry, quantified through the pole arc coefficient, exhibits a parabolic relationship with total losses, achieving minimum values at 0.72-0.78 due to the optimal balance between magnetic loading and harmonic content. Material selection analysis demonstrates that while higher grade silicon steel reduces core losses by up to 35%, this benefit diminishes at extreme speeds where proximity to saturation negates the advantage of improved B-H characteristics. Temperature sensitivity analysis reveals that permanent magnet losses increase by approximately 18% when operating temperature rises from 25 \({}^{\circ}\)C to 100 \({}^{\circ}\)C due to reduced material resistivity, creating a positive feedback mechanism that can precipitate thermal runaway under inadequate cooling conditions. Rotor skew angle demonstrates particular sensitivity for loss reduction, with an optimal value of 0.8 slot pitches reducing harmonic losses by 68% while maintaining 96% of the rated torque capability. These complex parametric relationships necessitate multi-objective optimization approaches that consider the interdependence of design parameters and their collective impact on both electromagnetic and thermal performance across diverse operating conditions.  
Figure 6: Sensitivity of losses to air gap length.  
Figure 8: Sensitivity of losses to modulator geometry, material properties, and operating conditions.  
Figure 7: Sensitivity of PM eddy current losses to segmentation strategy.
# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 3 Preliminary
### Preference Distributions  
The calibrated recommendations aim to provide unbiased results, where preferences reflected from historical behaviors and recommendation lists are consistent. We use symbols \(p(s)\) and \(q(s)\) which stand for the preference distributions from historical behaviors and recommendation lists, respectively. We follow previous work which applied item attributes to define preference distributions [17], which are introduced below in detail.  
* \(p(s)\) is the preference distribution from the sequence \(s\). For each attribute \(g\), the distribution value is computed as: \[p(g\mid s)=\frac{\sum_{x\in s}p(g\mid x)}{\mid s\mid}\] (4) where \(p(g\mid x)\) is the indicator function of item \(x\) and attribute \(g\), which satisfies \(\sum_{g\in C}p(g\mid x)=1\). If item \(x\) does not contain attribute \(g\), the value of \(p(g\mid x)\) is 0. If the item contains two attributes, the value of \(p(g\mid x)\) equals to 0.5 for each attribute \(g\). Finally, the preference distribution can be represented as a \(G\)-dimensional vector \(\{p(g=1\mid x),p(g=2\mid x),\ldots,p(g=G\mid x)\}\), where \(\mid G\mid\) is the total amount of item attributes.
* \(q(s)\) is the preference distribution of the recommendation list. For each attribute \(g\), the distribution value is computed as: \[q(g\mid RL_{s})=\frac{\sum_{x\in RL_{s}}p(g\mid x)}{K}\] (5) where \(RL_{s}\) is the recommendation list of the sequence \(s\), and \(K\) is the size of \(RL_{s}\). Similar to \(p(s)\), the distribution \(q(s)\) can also be represented as a \(G\)-dimensional vector \(\{q(g=1\mid x),q(g=2\mid x),\ldots,q(g=G\mid x)\}\).
## 1 Introduction
In another approach, the application of external perturbations involving simultaneous changes in curvature and external refractive index directly affects the MZFI succeeding the adjustment of the FSR, extending the multi-wavelength emission in the C band (from \(\sim\)1544.66 nm-1565.72 nm to \(\sim\)1530.62 nm-1565 nm) [12]. In works regarded with Ytterbium-doped fiber lasers, tunable dual-wavelength emission was performed with the aid of the MZFI and careful adjustments on the polarization states in the ring cavity [13], or applying tension or strain on the MZFI; spectral shifting was performed due to the adjustment of the birefringence within the Ytterbium ring cavity. This demonstrated a highly stable set of spacing tunable dual-wavelength emissions [14]. Alternatively, diverse setup configurations were designed to produce adjustable, tunable spacing dual-wavelength in EDFRLs, wherein the MZFI was driven by an acoustic transductor, as detailed in [15; 16; 17]. The versatility of integrating MZFI features with various wavelength-dependent fiber optic devices enhances the capability to control and manipulate laser wavelengths. Among the methods frequently utilized is the addition of a polarization controller (PC) to adjust the polarization state of light. This serves as an additional wavelength-dependent loss mechanism, enhancing the tunability and stability of the lasing wavelengths. In this aspect, experiments where the combination of the MZFI and the fine-tuning of the PC. were implemented, and they reported the different configurations allow up to triple lasing wavelength with high wavelength stability and amplitude [18; 19; 20; 21; 22], and also for up to quadruple lasing wavelengths in thulium fiber lasers [23]. This work demonstrates a tunable and switchable multi-wavelength EDFRL progressing sequentially from single-wavelength to sextuple-wavelength emission. This is achieved using an MZFI as an intra-cavity filter which is formed by a pair of concatenated fiber tapers. Applying curvature to the MZFI increases spectral fringe visibility, allowing precise control over the tunable and multi-wavelength emissions through adjustments to both the curvature and polarization state. The laser oscillation can be tuned from 1563.705 nm to 1558.05 nm, while four sets of switchable dual-wavelength emissions are attainable at 1543.21 nm and 1563.66 nm. Additionally, switchable emissions from single up to six wavelengths between 1559.59 and 1563.54 nm exhibit an optical signal-to-noise ratio of 38 dB. The system demonstrates stability, with amplitude variations of less than ~3 dB for most lasing wavelengths. This approach provides a versatile and cost-effective method to achieve tunable multi-wavelength laser oscillation by utilizing intra-cavity wavelength-dependent loss elements.
# Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications
## 3 Experiments
### Experiments in Staircase  
In order to determine the performance of SLAM algorithms in narrow spaces such as stairs, tests were carried out in the staircase environment with three different algorithms, and the results are given in Figure 3.  
The results given in Figure 3a,b show that the LOAM algorithm has a very high odometry drift in the staircase environment. This caused distortions in the 3D map. A minimal amount of odometry drift was detected for the staircase environment in the A-LOAM results given in Figure 3c,d. No odometry drift was observed in the HDL Graph SLAM results given in Figure 3e,f. Thus, the HDL Graph SLAM algorithm gives more accurate results than other algorithms in the staircase environment.  
Figure 2: Results of the SLAM algorithms for long corridor environments: (**a**) top view of LOAM results; (**b**) side view of LOAM results; (**c**) top view of A-LOAM results; (**d**) perspective view of A-LOAM results; (**e**) side view of A-LOAM results; (**f**) top view of HDL Graph SLAM results; (**g**) perspective view of HDL Graph SLAM results.
## 4 Security Attacks
### Application Layer
#### 4.5.1 Cryptojacking  
Cryptojacking [28; 31] is an attack targeting web and cloud-based services to illicitly perform Proof of Work (PoW) calculations for cryptocurrency mining without the user's consent. The most prevalent form of cryptojacking today is in-browser cryptojacking, where compromised websites turn visitors' devices into mining nodes. PoW requires intensive computational resources to solve complex mathematical problems. As the overall network hash rate increases, the mining difficulty also rises, leading to the use of specialized hardware such as GPUs and ASICs in professional mining operations. Cryptojacking can burden unsuspecting users by draining device resources and increasing energy consumption. To combat cryptojacking, it is essential to implement robust security measures on web platforms, such as script blocking, frequent security audits, and user awareness programs regarding unauthorized mining activities.
# PSO-Based Voltage Control Strategy for Loadability Enhancement in Smart Power Grids
## 2 Related Work  
Voltage control has been recognized as an important issue in power grid operation during the past few years. Secondary voltage control (SVC), the structure of which is shown in Figure 1, is able to reduce the possibility of voltage instability. Indeed, a lot of approaches for the design of secondary voltage controllers have been reported in the literature [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27], some of which have been proven to be effective methods to guarantee the stability and security operation in some electric power industries [6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. The basic operation principle of the SVC is summarized in the following: when voltage violations occur at certain key load buses which are designated as pilot nodes, the SVC will be enabled to keep the scheduled voltage profiles by optimal coordinated control of reactive power sources. Furthermore, the potential benefit that can be gained from the secondary voltage control is the improvement of system security and power quality.  
In recent years, some worst-case design based methods [24; 25; 26; 27], which ensure that the largest load voltage deviation remains within the specified limit after control, have been proposed to design SVC. In general, the reactive power resources should be adjusted based on their capabilities in order to gain more reactive power margin. However, without taking reactive power generation limits into account,  
Figure 1: Secondary voltage control (SVC) in a power grid.  
the control outputs provided by the methods [24; 25] have high risk to exceed the VAR capacities of the generating units. Recently, a method [26] considering generator reactive power limits for practical applications was proposed to design SVC. An extension of the method in [26] was proposed in [27]. Indeed, the work [27] is a two-stage method with two optimization problems in the control algorithm. In the first stage, system load disturbances are estimated by applying the least-norm technique. With the estimated system load disturbances, a set of optimal control actions can be determined by applying the worst-case design technique in the second stage. For a bulk power network, however, it is difficult and is a time-consuming task for approximating system load disturbances due to the requirement of matrix inversion in the computation. In other words, a two-stage method may require a large amount of computation time to determine optimal control actions. To address these difficulties, a more flexible method to voltage control using the PSO technique for the achievement of the stability and security operation in power grids is developed in this paper. The performance comparison between the proposed PSO-based method and some worst-case design based methods is illustrated and discussed in Section 6.2.2.
# Characterization of a COTS-Based RF Receiver for Cubesat Applications +
## 5 Test Results
### Measure of the Noise Figure  
The Noise Figure (NF) measurement was performed by adopting the same scheme depicted in Figure 8. In this case, a 50 \(\Omega\) coaxial load was applied at the 2050 MHz RF input connector. The receiver board was normally power supplied and the external LO signal was delivered through the PLL evaluation board.  
Figure 10(a) shows a photograph of the test setup. Figure 10(b) shows a screenshot of the noise power measured with the spectrum analyzer [30]. A marker was placed at the 390 MHz central frequency.  
Starting from the noise power evaluation, the noise figure has been calculated using Equation (2),  
\[\mathrm{NF=NOUT-10 log_{10}(RBW)+174-GS}, \tag{2}\]  
where \(\mathrm{N_{OUT}}\) is the noise power, RBW is the instrument resolution bandwidth, and \(\mathrm{G_{S}}\) is the system gain. The system gain has been calculated, starting from the theoretical gain obtained by system simulations (i.e., 112.4 dB when the input power level is \(-\)115 dBm). The total loss of the 50 \(\Omega\) impedance lines analyzed in [17] and of the RF test points has been subtracted from the gain value. Assuming a 0.05 dB loss for each RF test point [24], and as there are 20 RF test points on the signal path, the system gain has been assumed to be equal to \(112.4-0.73-(20\times 0.05)\), i.e., 110.67 dB. Using Figure 10(b) and Equation (2), the receiver noise figure has been estimated to be equal to 2.34 dB, which is smaller than the system requirement (3 dB).  
Figure 11: Photograph of the noise figure test setup (**a**) and output noise power screenshot at 390 MHz of the receiver board (**b**) with the RF input terminated with a 50 \(\Omega\) coaxial load.
# A Method for Synthesizing Ultra-Large-Scale Clock Trees
Specifically, by this optimization, the time complexity and space complexity have been effectively controlled, enhancing the performance of the algorithm in ultra-large-scale clock tree synthesis. Furthermore, this optimization is obviously more suitable for clock tree synthesis with clustering as the primary method, as we will elaborate later. Furthermore, we have introduced a balanced splitting technique to strictly control the size of clustering clusters. This technique enables fine-grained control over the fan-out of each buffer while ensuring a certain level of clock skew and buffer insertion, thereby effectively reducing power consumption and latency. During the experimental stage, the authors comprehensively verified the proposed IB-KSR algorithm using 10 sets of register placement data. Moreover, the authors implemented the greedy search-based register clustering (GSR) algorithm also proposed by [PERSON] (2018) and conducted a comparison between the two algorithms. The results demonstrate that in the application of ultra-large-scale clock tree synthesis, the IB-KSR algorithm exhibits significant advantages over the GSR algorithm in controlling clock skew and average delay. To further evaluate the applicability of the IB-KSR algorithm, the authors selected some benchmark test data from ISPD 2010 ([PERSON] et al., 2018) and conducted a comparison with the KSR algorithm. The authors compared exclusively with GSR and KSR because all three algorithms were developed by the same research team, sharing a common lineage and continuity. Moreover, since both GSR and KSR are clustering-based clock tree synthesis methods, the comparison with GSR more effectively demonstrates the scalability of clustering algorithms. Although the ISPD 2010 dataset is somewhat outdated, the purpose of this experiment is not to validate clock tree synthesis performance based on this dataset. Instead, it aims to compare the differences between the improved IB-KSR and KSR. Since it is difficult to apply KSR to large-scale datasets in our experimental environment, we chose the ISPD 2010 dataset. Moreover, numerous publicly available experimental results are based on this benchmark, allowing for direct comparisons. The results indicate that IB-KSR performs similarly to KSR in clock tree synthesis for medium to small-scale designs. However, IB-KSR has lower memory usage and higher computational efficiency. And as the scale increases, its unique IMST and Balanced Split mechanisms come into full play, demonstrating significant performance advantages. The results of both comparisons have verified the superiority and broad applicability of the IB-KSR algorithm in clock tree synthesis across different scales.