esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Microbiological Quality of the Experimental Meat Products  
In the conducted studies, no statistical differences between the treatments in terms of microbiological quality during storage were found (Table 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**(log CFU/g)**} & \multirow{2}{*}{**Treatment**} & \multicolumn{4}{c}{**Storage Time (Weeks)**} \\ \cline{3-5}  & & **0** & **4** & **8** \\ \hline \multirow{3}{*}{Total aerobic bacteria count *} & C & 2.65 & 2.72 & 2.34 \\  & M & 2.58 & 2.52 & 2.51 \\  & SL & 2.69 & 2.61 & 2.40 \\ \hline \multirow{3}{*}{Lactic acid bacteria *} & C & nd & nd & nd \\  & M & nd & nd & nd \\  & SL & nd & nd & nd \\ \hline \multirow{3}{*}{_Staphylococcus *_} & C & nd & nd & nd \\  & M & nd & nd & nd \\ \cline{1-1}  & SL & nd & nd & nd \\ \hline \hline \end{tabular}  
* Differences were not statistically significant (\(p>0.05\)); C—control treatment with NaNO\({}_{2}\) in an amount of 100 mg/kg; M—treatment with NaNO\({}_{2}\) in an amount of 50 mg/kg; SL—treatment with NaNO\({}_{2}\) in an amount of 50 mg/kg and L. _fremetum_ S8 at about 10\({}^{\circ}\) cfu/g and S. _carnesus_ ATCC 51,365 at about 10\({}^{\circ}\) cfu/g rd—not detected (the number of bacteria \(<\)1.20 log cfu/g).  
\end{table}
Table 4: Microbiological quality of experimental treatments during storage (means).  
Total aerobic bacteria count (ACC) was at a similar level in all of the experimental treatments and varied from 2.34 to 2.72 log cfu/g. The enumeration of lactic acid bacteria (LAB) and _Staphylococcus_ in all of the model meat products was below 1.2 log cfu/g throughout the experiment.  
The main reason of the reduction in the number of microorganisms was the heating treatment in these experimental products. Furthermore, a relatively low ACC level observed in the products may be the result of using whole ham muscles in the recipe. Whole muscles are generally of lower microbiological load compared to trimmings meat [52]. Meat batter used in the experiment had a relatively low fat content (2.0%) and high average water content (75.5%) which could cause the high effectiveness of heat destruction on the _Staphylococcus_ and _Lactobacillus_ bacteria cells.  
[PERSON] et al. [15] determined ACC, LAB, and _Staphylococcus_ bacteria <1.2 log cfu/g in a heat-treated pork product cured with NaNO\({}_{2}\) at 15 and 100 mg/kg after production and eight weeks of storage. Similar results in terms of total bacteria counts (<2 log cfu/g) in heat-treated sausages with low fat content were reported by [PERSON] [53]. [PERSON] et al. [46] determined ACC < 3 log cfu/g in Asian nitrite free hot dog sausages reformulation with cochineal, celery, and vitamin C and E after production. [PERSON] et al. [47] determined LAB on a level ranging from 7.89 to 8.20 cfu/g in minced beef roast with pork fat added and cured with a different sodium nitrite dose (50, 75, 100, 150 mg/kg) after 21 days of storage.
# Check for special characters and punctuation
## 4 Experimental Results and Analysis
### Experimental Environment and Dataset Composition  
All experiments were conducted on a high-performance workstation equipped with an AMD Ryzen 7 7840 HS processor (3.80 GHz) and 64 GB RAM. The system ran on a 64-bit Windows operating system. We implemented the system using Python 3.9 with PyTorch 2.0 framework, utilizing the OpenAI API for GPT-4-omni-integration.  
We constructed a comprehensive dataset of 8662 samples, evenly distributed between cover texts and stego texts, with the following characteristics:  
1. Cover Text Sources: - GPT-4-omni generated texts on 50 diverse topics from science, technology, culture, and society - Text lengths ranging from 13 to 125 words (mean: 68.3, std: 22.7) - Linguistic complexity varied to represent real-world use cases (average Flesch-Kincaid grade level: 11.2)2. Secret Message Types: - Random binary sequences: Generated using cryptographically secure random number generators - Structured binary data: Encrypted text messages converted to binary representation - Message lengths: Three categories (128, 256, and 512 bits) with equal representation 3. Embedding Parameters: - Standard mode: Fixed embedding capacity of 5 bits per word - Enhanced mode: Advanced embedding technique with 10 bits per word capacity - Mixed capacity mode: Variable bit allocation based on contextual factors The dataset was constructed through 100 iterations for each message length and embedding mode combination, ensuring statistical significance of the results. For steganalysis resistance evaluation, we employed a separate validation set of 2000 samples processed through four state-of-the-art detection methods. For each test case, we performed complete encode-decode cycles and measured all performance and effectiveness metrics to ensure comprehensive evaluation of the system's capabilities.
# Intelligent Recognition of Road Internal Void Using Ground-Penetrating Radar
## 3 Results
### Comparative Analysis of Void Intelligent Recognition Performance  
Using the YOLOv8 model, the performance between original images and enhanced images was compared. When constructing the dataset, the images were divided into a training set, validation set, and test set in a ratio of 6:2:2. The model performance was analyzed using precision, recall, F1, and mAP. Furthermore, to demonstrate the effectiveness of enhanced images in improving intelligent void recognition performance, statistical analysis was conducted using \(t\)-test. Specifically, the test set was divided into 5 groups, and the mean and standard deviation of corresponding metrics for each group were calculated, along with the t-value in the \(t\)-test. The model performance metrics and statistical indicators are shown in Table 2, where AV represents average value and SD represents standard deviation.  
As shown in Table 2, the YOLOv8 model trained with MHUnet-enhanced images demonstrates overall superior performance compared to the model trained with original images, indicating that enhanced images contribute to improving the performance of road internal void recognition models. In the \(t\)-test analysis, the calculated t-values for precision, recall, F1, and mAP were 10.43, 16.86, 17.24, and 13.79, respectively. Based on the degrees of freedom and significance level, referring to the t-value table, the critical value t is 2.306 under the conditions of 8 degrees of freedom and a significance level of 0.05. Since the t-values for all four metrics exceed 2.306, this indicates that the void recognition model trained with enhanced images significantly outperforms the model trained with original images.  
To further demonstrate the universality of the MHUnet model-enhanced images in improving recognition model performance, we also compared the training effects of different images on YOLOv7, YOLOv9, and Faster-rcnn model. The performance indicatorscorresponding to the three hollow intelligent recognition models are calculated and shown in Table 3.  
According to the table, compared with the original data, all four models trained on the enhanced data based on the MHUnet model have higher accuracy, recall, F1, and mAP than the models corresponding to the original data. The enhancement of image quality effectively enhances the learning and generalization abilities of intelligent recognition models for road internal voids, demonstrating higher accuracy and reliability.
# Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities
## 5 ICN-Based VANET Research Opportunities
### ICN-Based 5G-Enabled VANET  
The 5G architecture, proposed by 3 GPP, provides manageability to familiarize new control plane and user plane functions in the perspective of network slicing, which offers better elasticity to control various applications as well as devices. Thus, ICN would benefit 5G from the viewpoint of multi-access edge computing (MEC) in terms of edge computing, edge caching, and session mobility [50]. In addition, mobile nodes are positioned at the network edge that support various delay sensitive applications, e.g., virtual and augmented reality (VR/AR), and driving of autonomous vehicles. [51].  
This drift is useful in low-latency and high-bandwidth applications, e.g., VR/AR, and non-real-time applications, for instance, IoT communications and video-on-demand (VoD) [50].  
Furthermore, the caching feature of ICN assists both real-time and non-real-time applications every time there are temporal or spatial associations among data objects retrieved by edge subscribers [50]. This argument can be strengthened by the study conducted in [52], where it is argued that vehicular named networking encodes the data of geolocation into names. This is important as all request messages are sent to geolocations where contents are published. This is utterly feasible that a request message hits a vehicle with the desired content prior to reach its location.  
Moreover, the existing deployments of mobile communications hold session mobility through centralized techniques for routing, which face severe problems at the time of replication of service demands. Different from this phenomenon, name resolutions and application-restricted identifiers divide the notion deemed for ICN is exposed to grip node mobility in an effective way [53]. However, this module is quite challenging in such environments where rapid mobility is involved, for example, vehicular communications [54]. Thus, substantial determination is demanded in this area from the ICN community working on vehicular communications.  
A summary of the existing ICN-based vehicular communication proposals is presented in Table 1.
# Data Spaces in Manufacturing and Supply Chains: A Review and Insights from European Initiatives
## 2 Materials and Methods  
A multi-level research design was adopted in this study to address the three RQs identified in Section 1 related to the evolution of scientific literature and real-world initiatives on data spaces in manufacturing and supply chains. Specifically, the research approach combines bibliometric analysis, qualitative content analysis, and a document-based review of real-world projects. This triangulation allows for both a broad and deep understanding of the topic, grounded in peer-reviewed sources and institutional documentation. Despite this rigorous design, several limitations should be acknowledged. The exclusive reliance on the Scopus database may have led to the omission of relevant studies indexed elsewhere or published in languages other than English, and the April 2025 cutoff means that the very latest advances in data spaces might not be captured. Although manual screening and qualitative coding were guided by clear criteria and supplemented by co-citation checks, they remain subject to interpretive bias. The analysis of European initiatives draws solely on publicly available documentation, which may overlook proprietary or in-progress project details. Finally, the process of mapping academic publications to real-world initiatives depends on explicit references in the literature, so initiatives not directly cited or using alternative terminology could have been missed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Study** & **Technology** \\ \hline
[34] & Digital Platform \\
[35] & Digital twin \\
[36] & Industry 4.0 related \\
[37] & Electronic Data Interchange \\
[38] & Cloud \\
[39] & Blockchain \\
**This study** & **Data Spaces** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of key literature review studies on technologies enabling data sharing and integration in manufacturing and supply chains.
# Short-Term Load Forecasting Based on Spiking Neural P Systems
## 4 Conclusions  
NSNP systems are neural-like computing models, abstracted by the nonlinear spiking mechanism of neurons. NSNP systems have a distinguishing feature: a nonlinear spiking mechanism. Due to the nonlinear spiking mechanism, the NSNP system can be used to model dynamic systems. To perform power load forecasting, NSNP systems are used to develop a novel prediction model, termed as the LF-NSNP model. The LF-NSNP model is a recurrent-like model; therefore, it can effectively capture the correlation between the temporal features of the electrical load sequence. The proposed LF-NSNP model is evaluated on a benchmark load dataset and compared with several baseline prediction models. The comparison results demonstrate the advantage of the proposed LF-NSNP model for short-term load forecasting.  
Data curation, L.G.; Writing--original draft, L.L.; Project administration, J.W. and H.P. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **MAPE** \\ \hline TA & 2.10 \\ WA & 2.10 \\ OLS & 2.14 \\ LAD & 2.14 \\ PW & 2.12 \\ CLS & 2.11 \\ IRMSE & 2.10 \\ EWA & 2.18 \\ FS & 2.11 \\ ML-poly & 2.11 \\ LF-NSNP & \(4.623\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: MAPE metric value under the exploited LF-NSNP model and ten baseline models for case study B.  
Figure 5: The total load prediction results and absolute error curves of the exploited LF-NSNP mode.  
**Funding:** This work was partially supported by the National Natural Science Foundation of China (No. 62176216 and No. 62076206), Research Fund of Sichuan Science and Technology Project (No. 2022 ZYD0115), China.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are included in the article.  
**Conflicts of Interest:** The authors declare that there is no conflict of interest regarding the publication of this paper.  
**Nomenclature**  
STLF Short-term load forecasting  
SVR support vector regression  
MLP multilayer perceptron  
RBF radial basis function  
ELM extreme learning machine  
CNN convolutional neural network  
LSTM long short-term memory  
GRU gated recurrent unit  
SNP Spiking neural P systems  
NSNP Nonlinear spiking neural P  
LF-NSNP Load Forecasting Based on Nonlinear Spiking Neural P Systems  
RNN recurrent neural networks  
MIMO multiple input multiple output  
MAE mean absolute error  
MAPE mean absolute percentage error  
RMSE root mean square error  
CRM conditional residual modeling
## 5 Market Survey on Small Satellites Monopropellant Propulsion
### Commercial Off-the-Shelf Monopropellant Thrusters  
Commercial off-the-shelf (COTS) components generally receive high interest in the community of small-size spacecraft designers and developers. Recently, COTS green monopropellant thruster belonging to the High-Performance Green Propulsion (HPGP) technology by Bradford ECAPS has acquired special interest to CubeSat propulsion analysts and designers due to their extensive flight heritage and their compelling performance figures. HPGP thrusters were demonstrated and flown in various missions such as in the Mango spacecraft of the PRISMA demonstrator mission by ESA in 2010, in the LEO mission  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Propulsion System** & **MPS-135** & **BGT-X5** & **EPSS C1** & **Pinot-G** & **LFPS** \\ \hline Propellant & AF-M315E & AF-M315E & ADN-based blend & HNP225 & AF-M315E \\ Size & 4U & 1U & 1U & \(\diameter 350\)–\(135\) mm & 2.5U \\ Thrust (N) & 1 N \(\times\) 4 & & & & \\  & thrusters & 0.5 N & 0.1 N & 0.5 N \(\times\) 4 thrusters & 0.1 N \(\times\) 4 thrusters \\ Propellant Mass (kg) & \(\sim\)3.7 & \(\sim\)0.26 [12] & 0.33 & 0.4 & \(\sim\)2 (\({}^{\text{a}}\)) [58] \\ Wet Mass (kg) & 7.2 & 1.5 & 1.2 & 10 & 5.55 \\ Total Impulse (N s) & \(>\)7290 & 565 & 650 & \(\sim\)667.08 (\({}^{\text{a}}\)) [57] & \(>\)2500 \\ Feed and & & & & & \\ Pressurization & Pump & & & & \\  & & & & & \\ \hline \hline LFPS: Lunar Flashlight Propulsion System. \({}^{\text{(a)}}\) These values are first-order calculations from available data, exact values were not explicitly indicated by the source. \({}^{\text{(b)}}\) Post Launch Pressure System, a hybrid blow-down gas generator pressurization system [45]. & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: Performance data and specifications of state-of-the-art small satellites green monopropellant propulsion systems.  
of STPSaT-5 by the U.S. Government in 2018, and the SkySat LEO imaging constellation by Planet Lab in 2016 to 2020. The HPGP thrusters, Figure 8e, typically operate on the ADN-based LMP-103S green monopropellant and are available in thrust range from 0.1 up to 200 N. Of course, the thrust levels of interest to a CubeSat designer would typically be the 0.1, 0.5, and 1 N thrusters; the performance characteristics of such thrusters are shown in Table 13.  
Busek Co. also develops a family of green monopropellant thrusters, namely BGT-family, with a thrust range from 0.1 to 22 N. These thrusters operate mainly on the HAN-based AF-M315E green monopropellant but are also compatible with other blends of high-performance green monopropellants, as mentioned on the manufacturer website. The BGT-X1 and BGT-X5 are of particular interest to CubeSat designers and offer nominal thrusts of 0.1 and 0.5 N, respectively, refer to Figure 8f. Performance figures and specifications are presented in Table 14.
## 3 Mitigation Scheme for SAR
### Decomposition and Reconstruction
#### 3.4.2 Reconstruction  
Aforementioned extraction or filtering methods would lead to large signal loss to the useful echoes, especially with the presence of long-time duration and wide-band interferences, like the broadband CW RFI introduced in Section 2.3. Originated from the idea of compressed sensing, sparse recovery is a state-of-the-art semi-parametric method for interference suppression, especially in terms of reducing signal distortion. It can be considered as an optimization problem of reconstructing few coefficients with a given dictionary.  
[PERSON] et al. utilized the sparsity in range spectrum [76] and in time-frequency domain [77; 78] respectively, in which joint sparse recovery for RFI and SAR image are applied to deal with the narrow band and wide band interference. [PERSON] et al. adopted similar assumptions for sparse representation and recovery framework based on processing each range-compressed record independently [79]. [PERSON] et al. extends this idea by applying the sparse recovery on the time-frequency plane [80].  
[PERSON] et al. [81] investigated the robust principal component analysis-based signal separation in time-frequency domain, which exploits the low-rank property of RFI and the sparsity of SAR echoes. To robustly capture the spatial-temporal correlation between signals from neighboring SAR apertures to fully realize the advantages of two-dimensional cross-range processing, the joint sparsity and low-rank models are also proposed [82; 83; 84; 85; 86; 87]. However, all of these sparse and low rank-based algorithms require a fine-tuning of one or more hyperparameters, and this parameter tuning is not a simple task in practical applications due to lack of prior information on the RFI and radar signals. [PERSON] et al. [88] and [PERSON] et al. [89] provide advanced solutions by proposing hyperparameter-free methods.  
With the intrablock correlation (IBC) (i.e., the temporal amplitude correlation among the elements within each block) taken into consideration, block sparse Bayesian learning (BSBL) is more effective to deal with highly underdetermined recovery problem. The modified BSBL-based methods are proposed in [90; 91], which is verified to have superior recovery performance in various non-sparse cases.  
These recovery methods can successfully reconstruct scattered echoes as long as some low-rank structures or sparsity exists, even if RFI signals are dense or strong. Although these methods have good performance in the RFI separation, they require additional training samples to estimate the RFI structure, and much more storage and computing resources than the conventional filtering techniques, and thus are more suitable for off-line ground processing.
## 2 Materials and Methods
### Statistical Analysis  
To determine altered force control performances across different targeted force levels, we conducted paired \(t\)-tests on RMSE, SD, and ApEn. Furthermore, two-way ANOVA with repeated measures on each factor (Force Level \(\times\) Frequency Band; 2 \(\times\) 4) were used for the low-frequency oscillations in force signals. When assumptions of sphericity were violated, we report Greenhouse-Geisser's degrees of freedom adjustment. Bonferroni pairwise comparisons were used for the post hoc test. To determine the relationship between altered low-frequency oscillations in force and force control performances between two targeted force levels, we performed three multiple linear regression analyses on changes in absolute powers for each frequency band (four explanatory variables at 40% of MVC--those at 10% of MVC) and changes in force control performance (a dependent variable at 40% of MVC--those at 10% of MVC) using the stepwise method. Both explanatory and dependent variables were the changes in variables from lower to higher targeted force levels. All statistical analyses were conducted using the IBM SPSS Statistics 22 (SPSS Inc., Chicago, IL, USA) and the alpha level was set at 0.05.
## 3 Network Model  
Since existing transceivers cannot meet the ultra-high throughput requirements of SCNs, that is, the rules between MFs, transmission rate, and transmission distance applicable to transceivers in existing optical network architectures, they are not directly applicable to SCNs. Therefore, in this section, we first briefly introduce the network architecture of SCNs, then design the usage rules of MFs applicable to the SCNs, and further discuss the use of MFC for resource allocation in SCNs.
# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
## 4 Results and Discussion
### Validation Performance Measures
Observing calibration and validation results, it can be concluded that the FLORA-2D model, using variable spatio-temporal Manning coefficient values, successfully simulated the dynamics of the flood events considered, in terms of flood extent and water depth maps. In particular, the value of the Nash-Sutcliffe Efficiency coefficient, as obtained by comparing the results of variable spatio-temporal Manning coefficient simulation and SAR- and DEM-derived water depth maps, is on average equal to 0.7, which can be considered to be satisfactory for distributed models according to [60]. The channel roughness parameter \(c\) was less sensitive to variations in flood extent than water depth in both the calibration and validation period. By constraining \(c\) values to a more plausible range through calibration, it was possible to improve validation performance and further improve the real-time estimation of flood damage.