esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This study has proposed a direct instantaneous frequency rate tracking approach to improve the carrier estimation performance in Mars entry, descent, and landing flight. The basic algorithm of the two-dimensional maximum energy search approach has been reviewed and reevaluated using the released Mars Science Laboratory EDL trajectory, and the discussion has been carried out on the selection of the processing parameter, which shows that the length of the coherent integration time is the most influential parameter, and the Doppler frequency rate caused by the trajectory dynamics is the main limiting factor. To extend the coherent integration time and improve the performance, the MLE based two-dimensional maximum energy joint search has been transformed to two groups of one-dimensional maximization problems, which separately estimate the instantaneous frequency rate and instantaneous frequency. As in the case of Mars EDL carrier reconstruction, in which a priori Doppler phase model with moderate accuracy is available, a sequential CPF estimator based on the sequential estimation manner is proposed to largely reduce the noise squaring loss issue, and the simulation results show that the SNR threshold of instantaneous frequency rate estimation by sequential CPF is reduced by 6 dB with respect to the standard CPF. The selection strategy of the processing parameters in the new algorithm has been analyzed in detail, and the simulation results show that the accuracy of carrier estimation has been improved by a factor of about 50 for the aerobraking flight in EDL at the same threshold SNR with the 2D search approach. Due to the high dynamics of the parachute deployment by the spacecraft, the coherent integration time has to be reduced for a better dynamic response, which leads to a higher threshold SNR than the 2D search approach, while the frequency estimation accuracy is still considerable if the lock could be maintained.  
Considering the better performance of 2D search approach on robust estimation and free from a priori Doppler phase information, it is a possible direction for combining the two approaches to reconstruct the carrier in EDL flight precisely and firmly in future study.  
[PERSON], [PERSON] and [PERSON] derived and developed the method. [PERSON] and [PERSON] designed and made the simulations. [PERSON], [PERSON] and [PERSON] analyzed the results. [PERSON] wrote the paper. This research received no external funding.  
The authors would express their grateful thanks to Jet Propulsion Laboratory and NAIF group on the open release of the reconstructed EDL trajectory of Mars Science Laboratory.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Potentially Bioactive Peptides
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Biological Activity**} & \multicolumn{6}{c}{**Aging Time [Days]**} & \multirow{2}{*}{**References**} \\ \cline{2-2} \cline{4-7}  & & **28** & **90** & **180** & **270** & **360** \\ \hline \multirow{3}{*}{Antiradical} & WSF & LOCK, BB12 * & BB12 & BAUER & BB12 & BB12 \\ \cline{2-7}  & SSF & BAUER & C, BAUER & C, BB12 & LOCK & BAUER \\ \hline \multirow{3}{*}{ACE-inhibitory activity} & WSF & N.A. & LOCK & BB12, C & C, LOCK & BAUER \\ \cline{2-7}  & SSF & N.A. & C, LOCK, BAUER & BAUER & BB12 & BAUER \\ \hline \multirow{3}{*}{DPP-IV-inhibitory activity} & WSF & BAUER & LOCK, BB12 & BB12 & LOCK, BAUER & C \\ \cline{2-7}  & SSF & C, BAUER & BB12 & BB12, LOCK & LOCK & BB12 \\ \hline \end{tabular}  
* N.A., not available;
* difference not significant (\(p>0.05\)).  
\end{table} 
Table 2: Test variant showing the strongest biological effect—influence of probiotic LAB strains on the biological activity of peptides after digestion with pepsin and pancreatin(<7 kDa). Figure 3: Distribution of the identified peptides typical for BB12 and BAUER from water-soluble fraction after 180 days. Figure 4: Distribution of the identified peptides typical for BB12 and BAUER from water-soluble fraction after 270 days. Myosin-derived peptides were predominant in both BB12 and BAUER variants after 180 days. In addition, at this time, phosphoglycerate mutase protein chains were degraded and a large number of different peptides was observed in the BAUER variant. Phosphoglycerate mutase is an enzyme involved in glycolysis, and its shorter fragments (peptides) can be found in the meat matrix. In a previous study, peptides from this enzyme remained stable during a long storage period in pork loins; however, these products were not incouled with LAB [17]. The participation of the BAUER strain observed in this study may indicate their role in shaping the pool of various peptides, first in the meat matrix and then during hydrolysis. Keratin is a structural protein of animal origin, and present as cytoskeletal and microfibrillar keratin in two types: type I (acidic; 40-55 kDa) and type II (neutral to basic; 56-70 kDa) [28]. It is commonly found in meat processing wastes, such as chicken feathers or pig hair. Keratin peptides formed after the hydrolysis of these waste products exhibit valuable biological properties, including antioxidant, ACE-inhibitory, and DPP-IV-inhibitory activity [38; 39; 40; 41]. In the present study, a significant share of keratin-derived peptides (BAUER--type I, BB12--types I and II) was observed in the hydrolysates after 270 days of aging. Recently, [PERSON] et al. [42] discovered a strong antioxidant peptide FLEQQNQVLQTK derived from keratin in liver porcine hydrolysates.
# Adaptive Damping Log-Domain Message-Passing Algorithm for FTN-OTFS in V2X Communications
## 3 Adaptive Damping Log-Domain Message-Passing Algorithm
### An Overview of the Message-Passing Algorithm
The computational complexity of the proposed ADL-MP algorithm is significantly reduced compared to that of full MAP detection, which grows exponentially with the problem size. This reduction in complexity is achieved by leveraging the sparse structure of the channel and the iterative nature of message passing. The channel matrix \(\mathbf{H}_{\text{DD}}\) is highly sparse, with only \(P\) non-zero entries per row and column. This sparsity reduces the number of computations required for each node from \(O(NM)\) to \(O(P)\), where \(P\ll NM\). For each observation node \(y[d]\), the mean \(\mu^{(i)}_{d,c}\) and variance \(\sigma^{(i)}_{d,c}\) of the interference term are computed by summing over \(P\) terms, leading to a complexity of \(O(P)\). For each variable node \(x[c]\), the log-domain message \(\ln\hat{p}^{(i)}_{c,d}\) is updated using LSE normalization, which involves summing over \(Q\) constellation points and \(P\) connections, resulting in a complexity of \(O(PQ)\). The adaptive damping factor \(\Delta^{(i)}\) is calculated using the convergence indicator \(\eta^{(i)}\). The computation of \(\eta^{(i)}\) involves summing over all variable nodes and their connected edges, resulting in a complexity of \(O(NMP)\). Considering \(NM\) variable nodes and \(NM\) observation nodes, and assuming each node has \(P\) connections, the total complexity per iteration is \(O(NMPQ)\), where \(Q\) is the size of the symbol constellation and \(P\ll NM\) due to channel sparsity. The number of iterations required for convergence depends on the channel conditions and signal-to-noise ratio (SNR). In practical scenarios, the algorithm typically converges within a small number of iterations. Thus, the overall complexity is approximately \(O(KNMPQ)\), where \(K\) is the number of iterations.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 4 Experiments and Results
### Evaluation Metrics
#### 4.3.1. Dice Similarity Coefficient (DSC) and Intersection over Union (IoU)  
The DSC and the IoU are pivotal metrics for assessing the performance of segmentation models. These metrics measure the extent of overlap between the predicted outcomes and the ground truth labels, with values ranging from 0 to 1. A value of 0 indicates no overlap, whereas a value of 1 denotes perfect alignment, with higher values indicative of superior model performance.  
The DSC is computed according to the following formula:  
\[\text{DSC}(T,P)=\frac{2|P\cap T|}{|P|+|T|}\]  
Here, \(P\) and \(T\) represent the sets of predicted and ground truth pixels, respectively, \(|P\cap T|\) is the intersection of these sets, and \(|P|+|T|\) is the sum of pixels in both sets.  
The IoU is defined by the subsequent equation:  
\[\text{IoU}(T,P)=\frac{|P\cap T|}{|P\cup T|}\]  
In this formula, \(|P\cup T|\) represents the union of the predicted and ground truth pixel sets.
# The Evaluation Method of Particle Clustering Phenomena in RAP
## 3 Methodology
### Aggregate Image Measurement System  
The aggregate image measurement system (AIMS) is an integrated system, consisting of image acquisition hardware and computers for system operation and data analysis. The image acquisition hardware uses cameras, microscopes, aggregate trays, backlight and overhead lighting systems. The computer software, including the hardware and the user interface, were designed for the purpose of data analysis. It can also combine the software, such as Excel or Matlab, to output data in the form of chart or figure. In this paper, the AIMS was used to capture the shapes of RAP materials and to analyze clustering degree with respect to angularity and sphericity [29; 30]. RAP material particles with sizes of 13.2 mm to 16 mm were subjected to the test, and a total of five kinds of mixtures were used. They include new materials, secondary crushing old materials, pavement milling materials, flat secondary crushing old materials, and flat pavement milling materials, respectively. The angularity (GA) and sphericity index (SP) were calculated.  
Angularity (GA) is defined to character the shape of the particle boundary variation, which is an efficient way to measure the change in high gradient value along the boundary of polygonal particles. The calculation of GA is shown in Equation (3).  
\[\text{GA}=\frac{1}{\frac{\pi}{3}-1}\sum_{i=1}^{n-3}|\theta_{i}-\theta_{i+3}| \tag{3}\]  
where: \(\theta\)-Azimuth at an edge point; \(n\)-The total number of points; \(i\)-The \(i\)th point on the edge.  
Sphericity (SP) is applicable for coarse aggregate size and describes the overall three-dimensional shape of particles, as is defined in Equation (4).  
\[\text{SP}=\sqrt{\frac{d_{s}d_{l}}{d_{L}^{2}}} \tag{4}\]  
Figure 2: Samples before and after the Cantabro-crushing test, (**a**) Cantabro-crushing test apparatus; (**b**) Samples before the test; (**c**) Samples after the test.  
where \(d_{s}\): Minimum particle size; \(d_{l}\): Intermediate particle size; \(d_{L}\): Intermediate particle size.
# Cooperative Reception of Multiple Satellite Downlinks
## 2 System Description and Design
### SIMO System
The following step was for two sites combining and so on until the last trial was for combining all the \(N\) receiving sites together. This helped visualize the system BER improvement when gradually increasing the number of cooperative sites. The simulation was performed on a million-bit data stream received by 14 uncorrelated sites with _SNRs_ of (0-13) dB.
###### Abstract  
Reservoir characterization on offshore fields often faces specific challenges due to limited or unevenly distributed well data. The object of this study is the North Adriatic poorly consolidated clastic reservoir characterized by high porosity. The seismic data indicate notable differences in reservoir quality spatially. The only two wells on the field drilled the best reservoir area. Seismic data, seismic reservoir characterization, and accurate integration with scarce well data were crucial. This paper demonstrates how the application of machine learning algorithms, specifically a Deep Forward Neural Network (DFNN), and the incorporation of pseudo-well data into the reservoir characterization process can improve reservoir properties prediction. The methodology involves creating different reservoir porosity and thickness scenarios using pseudo-well data, synthetic pre-stack seismic data generation, seismic inversion, and DFNN utilization to improve porosity prediction. This study also highlights the importance of lithology discrimination in the geological model to better constrain reservoir properties distribution in the entire reservoir volume. Facies probability analysis was utilized to define interdependence between litho-fluid classes established from the well data and acoustic impedance volume. Apart from the field well data, seismic inversion results, and DFNN porosity volume as main inputs, acknowledgments from the neighboring fields also had an important role.  
machine learning; reservoir characterization; porosity distribution; lithography discrimination; Adriatic offshore 2023  
3 7671
## 5 Experiments
### Experiment of the O-NeuMF-p Model  
In this part, firstly, we compare O-NeuMF-p with NeuMF-p on the ML-100K dataset and the ML-1M dataset. Then, we count the degree of optimization of NeuMF-p. Finally, we analyze the influence of the parameter of O-NeuMF-p on experimental results on both datasets.  
Table 3 shows that the performance of O-NeuMF-p is better than NeuMF-p on both datasets. Furthermore, it can be found in Table 3 that the optimization effect of NeuMF-p is more obvious on the ML-1M dataset. The O-NeuMF-p model is more focused on the learning of counterexamples than the NeuMF-p model, and is more robust. Additionally, the experimental results confirm that the idea of introducing random numbers into the label \(y_{uw}\) in \(y^{-}\) is beneficial and improves the accuracy of NeuMF-p.  
Figure 4: Performance of various parameters in CIDAE.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Datasets** & **Models** & **HR05** & **HR010** & **HR015** & **NDCG05** & **NDCG010** & **NDCG015** & **MAP05** & **MAP010** & **MAP015** \\ \hline \multirow{4}{*}{**ML-100K**} & **O-NeuMF-p** & **0.40319** & **0.341947** & **0.304335** & **0.42751** & **0.377540** & **0.342528** & **0.324252** & **0.244377** & **0.202115** \\  & NeuMF-p & 0.390153 & 0.332802 & 0.297135 & 0.412432 & 0.364942 & 0.333371 & 0.308308 & 0.233447 & 0.193468 \\  & O-NeuMF-p & 0.033284 & 0.027478 & 0.024231 & 0.036562 & 0.029586 & 0.027469 & 0.051713 & 0.046820 & 0.044698 \\  & O-NeuMF-p & 0.006162 & 0.002480 & 0.002021 & 0.006155 & 0.003796 & 0.002892 & 0.007397 & 0.004844 & 0.003379 \\ \hline \multirow{4}{*}{**ML-10M**} & **O-NeuMF-p** & **0.401836** & **0.349859** & **0.315786** & **0.420715** & **0.377941** & **0.348670** & **0.321740** & **0.250935** & **0.212394** \\  & NeuMF-p & 0.376824 & 0.332281 & 0.302629 & 0.392953 & 0.356452 & 0.331099 & 0.294546 & 0.230894 & 0.196529 \\ \cline{1-1}  & O-NeuMF-p & 0.066374 & 0.052902 & 0.043474 & 0.070650 & 0.060286 & 0.053067 & 0.092326 & 0.086797 & 0.080725 \\ \cline{1-1}  & O-NeuMF-p & 0.005920 & 0.005609 & 0.005026 & 0.005747 & 0.005484 & 0.005080 & 0.005579 & 0.004942 & 0.004452 \\ \cline{1-1}  & SD & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of O-NeuMF-p and NeuMF-p based on HR@N, NDCG@N, and MAP@N. (N = 5, 10, 15).  
We also analyze the effect of the hyper-parameter \(r\) controlling the range of random numbers on the accuracy of O-NeuMF-p. It is worth noting that when \(r=0\), the O-NeuMF-p model is transformed into the NeuMF-p model.  
Figure 4(a) shows that when \(r=0.7\), O-NeuMF-p achieves the highest accuracy on the ML-100K dataset. Figure 4(b) shows that when \(r=0.6\), O-NeuMF-p achieves the highest accuracy on the ML-1M dataset. Furthermore, it can be seen that on the ML-1M dataset, the value of \(r\) has little effect on the value of NDCG@10. It is worth emphasizing that for all values of \(r\), the value of NDCG@10 of O-NeuMF-p is higher than that of NeuMF-p on the ML-100K dataset and the ML-1M dataset. This proves that O-NeuMF-p is more accurate than NeuMF-p on both datasets.
## 3 Results and Discussion
### Resin Swelling Studies: Swelling Test  
A good solvent should adequately swell a variety commercially available resins, demonstrating broad applicability to SPPS. The choice of resin is crucial because, along with the linker, it determines the functional group of the C-terminal and the synthetic strategy [22; 23]. For this test, different resins were selected: resins based on polystyrene (PS), polyethylene glycol (PEG) resins functionalized with PS, and resins consisting 100% of PEG. Polystyrene resins contain 1-2% divinyl benzene (DVB), which forms a lattice between polystyrene monomers and provides rigidity to the resin itself and better swell in non-polar solvents. TentaGel (TG) resins consist of PEG (50-70%) bonded to a low cross-linking polystyrene base and swell in both polar and non-polar solvents. Finally, ChemMatrix, a totally PEG resin, has better swelling properties in most of the solvents used in SPPS [24], making it suitable for microwave-assisted synthesis. During the swelling test (Figure 1), resin was introduced in a syringe and swelled for 45 min under shaking with the selected solvent (DMF or DMM). Then, the solvent was removed from under vacuum, and the swelling degree was evaluated using the formula described above. These values were compared to the values obtained when using DMF. As shown in Figure 1, TentaGel resins, Trityl, and Wang ChemMatrix show a lower degree of swelling in the green solvent, while Rink amide resins and the 2-chlorotriryl resin have almost the same degree of swelling in both solvents. Finally, DMM is able to swell Wang PS resin better than DMF. Ethers are unresponsive substances, making them good solvents for reactions. Most ethers are polar, but DMM has a relatively high molecular weight, giving it both hydrophilic and lipophilic properties. This may explain why it is able to swell polystyrene-based resins.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical–Chemical Properties** & \multicolumn{2}{c}{**Solvent**} \\ \hline  & **DMF** & **DMM** \\ \cline{2-3} Boiling point & 153 \({}^{\circ}\)C & 175 \({}^{\circ}\)C \\ Density & 0.95 g/cm\({}^{3}\) & 0.90 g/cm\({}^{3}\) \\ Viscosity & 0.85 mm\({}^{2}\)/s & 1.12 mm\({}^{2}\)/s \\ Log P & –0.85 & 0.42 \\ Melting point & \(-\)31 \({}^{\circ}\)C & \(-\)80 \({}^{\circ}\)C \\ Flash point & 58 \({}^{\circ}\)C & 65 \({}^{\circ}\)C \\ Cost & 72.50 Eur/liter & 128.00 Eur/liter \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical–chemical properties and cost of DMF and DMM.  
The test was performed in triplicate, and results show the average of the three tests. The standard deviation for each measurement is reported in Table S3.
## 3 Methodology  
### Method for Identification of Critical Scenario Components  
#### 3.1.1 Overview  
Figure 1 illustrates the framework of the method for identification of critical scenario components proposed in this study. This method, which can be divided into two steps, utilizes a human driving database. In Step 1, the near-miss incident database (NMI-DB) of the Tokyo University of Agriculture and Technology (TUAT) was used to analyze the driver errors in the near-miss incident (NMI) data on a target driving scenario. Critical factors were extracted in this step using the driving reliability and error analysis method (DREAM) [39]. The details of Step 1 are described in Section 3.1.3. In Step 2, the NMI data were analyzed in-depth, and environmental components related to the critical factors extracted in Step 1 were identified. The details of Step 2 are described in Section 3.1.4.  
A human driving database and a driver error analysis method were utilized to identify critical factors for ADS, which is the originality of this study. Configurations and specifications of ADSs vary between systems. Thus, it is difficult to set critical factors from the system perspective. The various ADSs and human drivers will exist inside the same mixed traffic and share the same driving situations. ADSs and human drivers will not react identically to situations, and ADSs are considered superior to human drivers. However, ADSs and human drivers share similar limitations, such as observing the surroundings and controlling only the vehicle. Therefore, human driving data were adopted in the proposed method. Due to this setting, this proposed method cannot identify the most critical scenario components for ADS. Still, it can identify critical factors expected to be handled by ADSs that are expected to be superior to human drivers.
## 4 Deployment of CATIM in an Industrial Case Study  
In this first evaluation, we show how CATIM can be applied to a maintenance procedure for a hydraulic elevator component. Specifically, we examine how context-aware AR can assist in the inspection of a valve that manages oil pressure (Figure 3). Since this inspection is typically performed before the valve's installation, we set up the valve on a table in our laboratory to represent an inspection station.  
The operator's inspection instruction is representative of some of the most common tasks in the industrial field: part localization, manual and checking operations. We used the CAD model of the valve and the text instructions taken from the manufacturer's technical manual for the valve: \"Open shut-off valve 7 until a working pressure value is read on the pressure gauge\". The case study presented herein is limited to a single inspection instruction, thus the interaction between users and the AR application is not related to browsing different instructions, but to the request of additional visual assets respect to those proposed by CATIM. However, the system can support also lengthy procedures consisting of many inspection instructions, thus producing an interactive AR manual that can be browsed by operators.  
The ontology architecture for the case study is designed specifically for the information contained in technical manuals and it is composed of three levels: upper ontology, lower ontology, and database as described in Figure 4.  
Figure 4: Example of the ontology architecture used in the first evaluation of CATIM.  
Figure 3: Example of user interacting through (**a**) air-tap with the AR interface displayed on the HoloLens (**b**) touch on screen with the AR interface displayed on smartphone.  
The PC used as the backend in the case study run an i7-6700 HQ (2.60 GHz) with 16 GB RAM, with Windows 10 operating system. The AR visualization devices were:  
* Microsoft HoloLens (2016);
* Samsung Galaxy S7 Edge (AnTuTu v7 [25] score: 158.313);
* Sony Xperia Z3 compact (AnTuTu v7 score: 59.887);
* Google LG Nexus 5 (AnTuTu v7 score: 57.006).
## 2. Mobility Models and Related Work  
Mobility plays a significant role in the overall performance of WBAN as mobile nodes can produce different topology at different points of time, which may cause a deficiency in the overall performance of WBAN. Mobility models help us to identify the pattern that a node follows during its movement; hence, with the help of this, we can identify the upcoming location and make our WBAN more efficient with respect to delay, throughput, and packet delivery. In this section, we have discussed two different mobility models for WBAN, which will be evaluated later with different routing protocols.