esa-sceva/satcom-chunk-collection huggingface dataset:
# FarpScun: Fully Anonymous Routing Protocol with Self-Healing Capability in Unstable Sensor Networks
## 3 Fully Anonymous Routing Protocol with Self-Healing Capability
### Fully Anonymous Routing Protocol with Self-Hailing Capability  
The anonymous identities scheme proposed in Section 3.3 is introduced into this section to implement the fully anonymous routing protocol with self-healing capability.  
When the source node \(S\) intends to communicate with the destination node \(G\), the source node \(S\) broadcasts the RREQ messages to all of its neighbors, which act as the intermediate nodes in the anonymous route. Each intermediate node broadcasts the RREQ message to its own neighbors, and so on, until the broadcasts the RREQ messages reaches the destination node. Assuming that the route \(S-A-B-G\) is the first route to the destination node, and the messages return along this route. The route establishment process is shown in Figure 2.  
During the route establishment process, each node creates an anonymous identity to hide its true identity before sending a message to the next node. After the message has been sent, the sending node updates the anonymous identity for the following interaction, in order to avoid the repeated use of the same anonymous identity.  
The broadcasting process scheduled by the neighboring nodes is as follows.  
When broadcasting the RREQ message, the broadcast node first computes its own sharing anonymous identities. Subsequently, it selects a symmetric key from the SysLocal cryptosystem and encrypts it together its address. At last, it constructs and broadcasts the RREQ message (including session ID, anonymous identity, encrypted symmetric key and address, and the public key of destination node).  
When its neighbor node receives the corresponding RREQ message, it first authenticates the validity of the message. If the message is valid, then it will perform the following steps.  
Firstly, it stores the corresponding session ID. Secondly, it selects and saves its symmetric key with the destination node locally. Thirdly, it encrypts its own address and symmetric key with the public key of the destination node. Fourthly, it constructs and broadcasts the new RREQ message to its own neighbors.  
The following nodes execute the same operations until the RREQ message reaches the destination node.  
This protocol is divided into two parts, including the establishment of the on-demand anonymous route and self-healing of the anonymous route. The anonymous identity sets up for each sensor node is used in the RREQ request and RREP reply routes, and finally the route self-healing capability is implemented in the RREP reply route.
# Implementation of a Topology Independent MAC (TiMAC) Policy on a Low-Cost IoT System
## 5 Experimental Methodology and Results  
A full graph topology is considered here for the experimental setup, consisting of five nodes. The considered network is assumed as part of a larger IoT network of maximum \(N=25\) nodes where the maximum number of neighbors is \(D=4\). Taking the above into consideration, the five nodes were set up in a full graph topology so as to simulate the maximum possible neighborhood of the assumed network. As a result, each node has exactly four neighbors, while collisions are expected to occur. The expectation regarding the performance of TiMAC is to accomplish the \"at least one transmission per node per frame\" goal, i.e., at least one transmission free of collisions. Note that transmissions may be unsuccessful for various reasons. For example, there might be interference in the 433 Mhz band, since the selected band is an ISM one and several devices placed near the system's devices may also use it during the experiment. For avoiding as many such cases as possible, special care was taken to conduct the experiments in isolated areas (e.g., the basement of the premises of the University campus).
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 5 Future Perspectives
### Direct-from-Sample Identification  
A key limitation of current MALDI-TOF MS workflows is the reliance on culturable isolates. Future research is likely to focus on overcoming this constraint through the development of protocols for direct analysis of food matrices without prior culturing. Innovations in sample preparation--such as selective protein extraction, immunoaffinity capture, and microfluidics--could allow for the selective enrichment and concentration of microbial proteins directly from complex food substrates (e.g., raw meat, leafy greens, dairy products) [18; 28; 110; 121].  
This capability would enable real-time, culture-independent microbial surveillance, significantly reducing diagnostic turnaround times and enhancing outbreak responsiveness. The integration of MALDI-TOF MS with front-end enrichment steps such as dielectrophoresis, aptamer-based capture, or nano-filtration may further improve sensitivity for low-abundance organisms [146; 147; 148; 149].
## 3 Test Results and Discussion
### Moisture Susceptibility  
Figure 5 shows the results from the moisture susceptibility tests of the PAC mixtures. It can be seen that after one freeze-thaw cycle, all the five mixtures showed good moisture resistance, with TSR values larger than 85%, and the differences in TSR among mixtures are small, implying that all PAC mixtures have excellent moisture resistance at the early stage of moisture effect. However, after two freeze-thaw cycles, the TSR value of PAC10-I4 became obviously smaller than the others, and after three freeze-thaw cycles, PAC10-I4 could not meet the minimum TSR requirement of 75%.  
Among the three novel PAC10s, the TSR value increased with the increasing content of the mineral fillers. After three freeze-thaw cycles, increasing the mineral filler content of PAC10 from  
Figure 5: TSR for moisture susceptibility of mixtures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mixture Property**} & \multicolumn{5}{c}{**Type of PAC**} & \multicolumn{2}{c}{**Technical**} \\ \cline{2-7}  & **PAC13** & **PAC10-C** & **PAC10-I4** & **PAC10-I6** & **PAC10-I8** & **Requirement [22]** \\ \hline OAC (\%) & 4.7 & 5.0 & 4.8 & 5.3 & 5.5 & — \\ Air void content (\%) & 20.5 & 19.6 & 21.9 & 20.9 & 20.2 & 18–25 \\ Marshall stability (kN) & 5.8 & 5.4 & 4.7 & 5.5 & 6.1 & Min. 3.5 \\ Marshall flow (0.1 mm) & 30.4 & 30.8 & 27.4 & 28.2 & 31.5 & 20–40 \\ Draindown ratio (\%) & 0.17 & 0.18 & 0.18 & 0.14 & 0.22 & Max. 0.3 \\ Cantabro loss 1 (\%) & 9.8 & 8.3 & 14.3 & 8.8 & 7.4 & Max. 20 \\ \hline \hline \end{tabular}  
* At Standard Cantabro test condition.  
\end{table}
Table 5: Mix design property results of PAC.  
4% to 8% increased the TSR value by more than 10%. This can be attributed to more asphalt mastic, which improved the moisture resistance of PAC10, and such effect became more significant with longer moisture conditioning. After three freeze-thaw cycles, PAC13, PAC10-C, PAC10-I6 and PAC10-I8 showed similar TSR, which all met the minimum TSR requirement.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 2 Background and Related Works
Many works related to GNSS/vision vary from the type of the measurements used (pseudorange versus carrier phase) or the type of the integration (EKF, loosely coupled, tightly coupled or ultra-tightly coupled). Some examples of these types of integration can be found in [2; 29; 42; 43]. In this work, selected line-of-sight satellite measurements are tightly integrated with the visual odometry. The satellite selection, discussed previously, helps to reduce the multipath effect, and tightly coupling the selected satellites with the vision ensures that the GNSS measurements are used even if fewer than four line-of-sight satellites are available.
# DNNs Based Computation Offloading for LEO Satellite Edge Computing
## 3 System Model  
This paper considers a LEO satellite edge computing system consisting of \(N\) users and an MEC server placed on the LEO satellite, as shown in Figure 1.  
We think that each user has \(M\) independent computing tasks, each of which can be executed locally or offloaded to the LEO satellite equipped with the MEC server for  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method** & **Advantages** & **Disadvantages** \\ \hline
[10] & OPTI toolbox & Optimal objective value & High toolbox requirements \\
[11] & Alternating direction method & Reduce the energy & Complexity is not low \\  & of multipliers & consumption & \\
[12] & Game approach & High network payoff & Unstable \\
[13] & Game-theoretic approach and & Low weighted-sum latency & Lack of energy consumption \\  & matching theory & & consideration \\
[14] & Deep imitation learning & Low task completion time & Low accuracy \\
[15] & Deep reinforcement learning & Low cost & High energy consumption \\
[16] & Alternating optimization & Low delay & Lack of energy consumption \\  & algorithm & Low delay and energy & consideration \\
[26] & Deep reinforcement learning & consumption & Time cost is not low \\
[27] & Model-free deep & Low delay and energy & Optimization effect is \\  & reinforcement learning & consumption & not obvious \\
[29] & DNN partition and genetic & Low rental cost & High delay \\  & algorithm & & \\
[30] & Iterative alternating & Low runtime & High latency \\  & optimization & Low delay-energy & Lack of consideration for \\
[31] & DDOA & weighted sum & optimal bandwidth allocation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the related works.  
Figure 1: LEO satellite edge computing system.  
computation. We denote the offloading decision as \(x_{nm}\in\{0,1\}\), when \(x_{nm}=1\) represents user \(n\) offloads computing task \(m\) to the MEC server on the LEO satellite for execution, and when \(x_{nm}=0\) represents user \(n\)'s task \(m\) chooses to be executed locally.
# Parallelizing the Computation of Grid Resistance to Measure the Strength of Skyline Tuples
## 5 Results
Figure 7b shows similar bars for the RES dataset, but, here, due to the nature of the data, the times are approximately ten times higher (0.1 s for a plain sequential execution) and all parallel strategies incur high parallel costs, as shown in the lower parts of the bars. While more cores are needed to perceive tangible improvements, all strategies attain better performance than None with 16 cores, all still having a large part of their execution time taken by the parallel phase, i.e., showing potential for further improvements in their performance with the availability of more cores (with 16 cores, Sliced already requires less than half the time taken by None to compute the result). **Final observations. ** Our experiments show that the parallelization opportunities offered by the partitioning strategies analyzed are useful for the computation of gres, provided that the application scenario is challenging enough to make the parallelization effort worthwhile, as we found to be the case with the ANT datasets and with real datasets such as RES and SEN. While there is no clear winner in all cases, Sliced provides the most stable performances across all datasets. The nature of the problem at hand, which involves many tuples that are already strong, being part of the initial skyline, makes the representative filtering optimization ineffective and does not suggest the use of an overly high number of partitions. While we conducted our analysis through the detailed counting of dominance tests, our results also indicate that a simple single-machine environment with 16 cores is sufficient to experience two-times improvements in the execution times in the most challenging datasets adopted for our experiments. The main limitations of our approach are tightly connected to the parallelizability of the problem at hand. This, in turn, requires a large enough initial skyline set so that the partitioning strategy can effectively divide the work into suitable chunks. This is only the case in uniformly distributed or smaller datasets and moderately so in the larger, real datasets that we have tested. A case in which our approach performs particularly poorly is that of datasets with correlated data: no savings are obtained in terms of dominance tests and the overall execution times are nearly one order of magnitude worse than without parallelization (although very small, in the order of 0.1 ms), which brings its overhead with no gains. We observe, however, that the cases in which the parallelization of the computation of gres does not help are also those in which speed-ups are less needed and the times are already very small. Figure 7: Execution times on ANT (**a**) and RES (**b**) as the number of cores varies.
## 4 System Design  
### Nominal Model  
One nominal model is needed in the presented hybrid control scheme, because both \(Q(s)\) and \(K(s)\) are designed based upon it. From the analysis above, the difference between the nominal model and the actual plant, that is the uncertainty of the nominal model, determines the maximum allowable bandwidth, and therefore restricts the system performance.  
However, it can get a better result of design if the nominal model approximates the actual plant as far as possible. In our system, the derived equivalent linear models \(P_{0}(s)\) with parameter variation are fifth-order models. The inverse of the fifth-order model will be extremely sensitive to high-frequency noise. Thus, a low-order model is appropriate; moreover, the low-order nominal model makes the design of the DOB simple.  
From Equation (9) and Figure 4, it is observed that system characteristics are mainly determined by the inertial frequency \(\omega_{\rm L}\) of the hydraulic actuator, the second-order resonance frequency \(\omega_{\rm v}\) of the servo valve, and the gain \(K_{\rm op}\). According to the idea of a dominant pole, if we take the middle value of \(\omega_{\rm L}\) and \(K_{\rm op}\) as nominal values and treat the second-order oscillation term of the hydraulic actuator as the perturbation, then a third-order nominal model \(P_{n}(s)\) is obtained as  
\[P_{n}(s)=\frac{424908834}{(s+38.82)(s^{2}+1759s+1579000)} \tag{24}\]  
The uncertainty \(\Delta_{\rm L}\) of the system, which is treated as a multiplicative perturbation can be calculated by  
\[\Delta_{\rm L}(s)=P(s)/P_{\rm n}(s)-1 \tag{25}\]
# SPR-Optical Fiber-Molecularly Imprinted Polymer Sensor for the Detection of Furfural in Wine +
## 2 Materials and Methods
### Standardization Curves  
The standardization curves were obtained by plotting the variation of the resonance wavelength in the normalized transmission spectra (\(\Delta\lambda\)) vs. the concentration of 2-FAL \(\Delta\lambda\) was calculated respect to the resonance wavelength of a blank solution, i.e., a solution with the same composition of the sample but not containing the analyte of interest.  
With the limited number of receptor sites in this kind of sensor, the response was linear only in very small concentration ranges. Therefore, the standardization curves were modeled by an equation deriving from the Langmuir adsorption isotherm [14; 19; 20], assuming that the signal is directly proportional to the amount of the template in the sensing layer (\(\Delta\lambda=kg\;c_{\text{Aint}}\)):  
\[\Delta\lambda=\frac{k\,g\;c_{\text{int}}K_{\text{aff}}[\text{A}]}{1+K_{\text{ aff}}[\text{A}]}=\frac{\Delta\lambda_{\text{max}}K_{\text{aff}}[\text{A}]}{1+K_{ \text{aff}}[\text{A}]} \tag{1}\]  
[A] is the concentration of the analyte in the sample solution. \(K_{\text{aff}}\) (in mg\({}^{-1}\) L) is the affinity constant of the adsorption equilibrium, \(c_{\text{int}}\) is the concentration of the specific sites of the MIP (in mg g\({}^{-1}\)), g is the polymer mass (in grams). \(\Delta\lambda_{\text{max}}=k\cdot g\cdot c_{\text{int}}\) is the maximum \(\Delta\lambda\) at high concentration of the analyte (i.e., when the analyte saturates all the specific sites).  
Equation (1) can be applied as a standardization curve if the analyte concentration at the equilibrium in the sample, [A], is equal to the total one (\(c_{\text{A}}\)), i.e., when the concentration of the analyte adsorbed is negligible.  
The parameters of Equation (1) were obtained by Solver, the Microsoft Excel add-in program.  
Once the parameters are known, the concentration of the analyte can be evaluated in the whole detection range from the measured \(\Delta\lambda\):  
\[[\text{A}]=c_{\text{A}}=\frac{\Delta\lambda}{K_{\text{aff}}\cdot(k\cdot g \cdot c_{\text{int}}-\Delta\lambda)} \tag{2}\]
## 2 Materials and Methods
### Machine Learning Models Used for SoH Prediction
In addition, ensemble decision trees, such as the ensemble boosted tree and ensemble bagged tree, have also been preferred to reduce and strengthen the variance of decision trees [22]. SVM, on the other hand, may work with a large number of independent variables and is effective in modeling complexity boundaries [23]. In this study, SVM models, such as fine Gaussian, medium Gaussian, and coarse Gaussian, were employed. Efficient linear is one of the efficient linear regression models [24]. Linear least squares (LLS) refers to the approximation of linear functions by the least squares method, while GPR is probability-based. Some of the models used in this study are rational quadratic GPR, Matern 5/2 GPR, and exponential GPR. Gaussian regressions are a collection of random variables with a Gaussian distribution. Another model that has been used to separate nonlinear classes is kernel SVM and kernel least squares regression [25]. These models may handle nonlinear structures more effectively. The ANN is a significant model that has been used to analyze complex relationships in battery data. In this study, different ANN models, such as narrow, medium, wide, double-layer, and three-layer neural networks, were used. LSTM is a method that is used in subjects such as sentiment analysis, natural language processing, and time series [26]. Using the abovementioned ML and artificial intelligence techniques, different models were developed in this study, and the existing data were analyzed in detail. All models were tested on the dataset, and the results are presented in detail.
## 3 Results and Discussions  
### Analysis of Physicochemical Properties in DX Sediments  
The results of water quality and sediment physicochemical properties of the streams at the sampling sites are shown in Tables 1 and 2. The pH of the stream at the sampling site was 7.3, showing weak alkalinity. Due to the long-term flow of the stream through the manganese mining and smelling wastewater, the concentration of Mn(II) (3.85 mg\(\cdot\)L\({}^{-1}\)) and ammonia nitrogen (14.3 mg\(\cdot\)L\({}^{-1}\)) in the water was high. The equivalent concentration of Mn (IV) in the sediment was calculated to be approximately 6.66% based on the LBB standardization method. The evidence demonstrated that the sorption capacity of manganese oxide is an important factor in the efficiency of manganese purification, and can be used to examine the potential for using this sediment to recover manganese in the construction of CW. In addition, SiO\({}_{2}\) (48.4%) were the main component of the sediment. The heavy metal cation exchange capacity (CEC) was 14.21 cmol\(\cdot\)kg\({}^{-1}\), indicating a high negative colloidal charge, which contributes to the electrostatic sorption of Mn(II). As a key substance to promote the sorption of heavy metals, iron and manganese oxides account for more than 10% of the total weight of the solid phase.  
Figure 1 reflects the textural classification and microbial community composition of the soils in the DX sediments. Soil texture was dominated by sand (49.6%) and meal (35.5%) grains. The percentage of clay particles in DX sediments is 14.9%, which is higher than the content of clay particles in many regions. As the main site of negative charge concentration in the soil, clay particles can form complex colloids with humus. The study showed that soils containing higher clay particles have a greater capacity for Mn(II) sorption.  
MOB, with a strong sorption capacity for Mn(II), are widely found in environments containing large amounts of manganese as well as an active manganese cycle. The EPS secreted by MOB can also promote the sorption of Mn(II) [31]. In DX sediments, the relative abundance of Proteobacteria and Acidic Bacteria was 43.81% and 19.10%, respectively, and were the dominant microorganisms in the soils of the sampling area, which have been shown to be MOB. The research shows that the isolated MOB is mainly concentrated in the _Firmicutes_, _Actinobacteria_, _Bacteroidetes_, _Proteobacteria_ and _Acidobacteria_[32]. In addition, the colony structure at the sediment genus level is shown in SI Table S1. Many genera with strong manganese oxidation properties were present in DX sediments, such as _Acinetobacter_, _Pseudomonas_, _Rhizobium_, _Splingomonas_, _Exigubacterium_, _Pseudoxanthomonas_ genera.
## 2 Materials and Methods  
### Moderate to High Spatial-Resolution (MHSR) Remotely Sensed Data  
In this study, the proposed method can be applied to most of the MHSR data. These data include the Thematic Mapper (TM), the Enhanced Thematic Mapper (ETM+), and the Operational Land Imager (OLI) onboard the Landsat series of satellites, the Multispectral Imager (MSI) on Sentinel-2, the Charge-Coupled Device (CCDs) on HuanJing 1 (HJ-1), the Wide Field Viewer (WFV) on Gao Fen (GF) 1 and 6, the MSI on ZiYuan 3 (ZY3), the CCD and World Field Imager (WFI) on the Chinese and Brazil Earth Resource Satellite 02B (CBERS-02B), and so on. More MHSR data can be included after adaptative modification. Only data from the Landsat series and Sentinel-2 satellites have shortwave infrared (SWIR) band and all the data have visible and near infrared (VNIR) bands. Table 1 shows the details of the above MHSR data.
## 6 Experiment and Result Analysis  
### Datasets  
The method was tested and validated on widely acknowledged benchmark datasets FaceForensics++ (FF++) [21] and CelebDF [37]. FF++ encompasses four distinct methods for generating deepfake facial data. The dataset, comprising 1000 original real video sequences and 4000 manipulated videos, is extensively utilized in training and evaluating our models. Specifically, Deepfakes employs an autoencoder-based approach to achieve precise face swapping through the training of one-to-one generation models. Face2 Face replaces original faces in videos with real ones without employing generative models to alter facial features. FaceSwap utilizes a 3D-based technique to swap faces by learning and reconstructing facial features. NeuralTextures adopts deep neural texture transfer methods to ensure the 3D consistency of generated faces by incorporating texture details from reference images.  
To cater to researchers with diverse requirements, the original data was encoded using H264 compression for both real and fake videos, with varying levels of compression applied. In our experiments, the datasets consisted of lightly compressed high-definition facial video images, specifically graded at the C23 quality level. This ensures dataset clarity and establishes an effective benchmark to assess the influence of different compression techniques on forged videos. This study selected 1000 real videos and 500 forged videos from each of the Deepfakes, FaceSwap, Face2 Face, and NeuralTextures datasets for training and testing. Examples of dataset samples are illustrated in Figure 14.  
Moreover, the CelebDF dataset exists in two versions, with this study opting for CelebDF-v2. This dataset effectively compensates for the FF++ dataset's lower-quality deepfake content by offering synthetic videos that rival the high-quality fake data prevalent on the internet. Specifically, CelebDF-v2 consists of 590 real videos and 5639 synthetic videos of generally high quality, serving as the transfer test dataset for evaluating the deepfake face detection model in this study.