esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Microbiological Quality and Safety  
Table 3 presents the results of microbiological quality and safety of dry fermented loins and sausages, produced under industrial conditions with or without the addition of probiotic starter culture (_L. rhamn_s  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Samples (n = 12)** & **Time 0** & **Time 1** & **Time 2** \\ \hline \multicolumn{3}{c}{**Number of LAB (log cfu g\({}^{-1}\))**} \\ \hline Control LOINS & 6.11\({}^{\text{AA}}\)\(\pm\) 0.53 & 6.99\({}^{\text{AA}}\)\(\pm\) 0.67 & 7.04\({}^{\text{AB}}\)\(\pm\) 0.60 \\ Probiotic LOINS & 7.12\({}^{\text{BA}}\)\(\pm\) 0.87 & 7.54\({}^{\text{BA}}\)\(\pm\) 0.63 & 7.17\({}^{\text{AA}}\)\(\pm\) 0.43 \\ \hline Control SAUSAGES & 6.89\({}^{\text{AA}}\)\(\pm\) 0.86 & 7.10\({}^{\text{AB}}\)\(\pm\) 0.95 & 7.31\({}^{\text{AB}}\)\(\pm\) 0.96 \\ Probiotic SAUSAGES & 8.08\({}^{\text{BA}}\)\(\pm\) 0.97 & 8.30\({}^{\text{BA}}\)\(\pm\) 0.87 & 8.21\({}^{\text{BA}}\)\(\pm\) 0.09 \\ \hline \multicolumn{3}{c}{**Number of Isolates Identified as L. Rhamosus (\%)**} \\ \hline Probiotic LOINS & 80 \(\pm\) 6.67 & not analyzed & 92 \(\pm\) 0.86 \\ Probiotic SAUSAGES & 84 \(\pm\) 7.69 & not analyzed & 94 \(\pm\) 5.09 \\ \hline \hline \end{tabular} Explanatory notes: Number of LAB: Data are expressed as the mean \(\pm\) SD of n = samples. Means in the same column followed by different lowercase letters represent significant differences (\(p<\) 0.05). Means in the same row followed by different uppercase letters represent significant differences (\(p<\) 0.05).  
\end{table}
Table 2: The count of lactic acid bacteria (LAB) and the number of isolates identified as _L. rhamn_s of dry fermented pork loins and sausages after production (time 0), 6 (time 1) and 12 (time 2) weeks of storage.  
Figure 1: Example comparison of the PCR product (290 bp) of the _L. rhamn_s LOCK900 strain (L) with other random isolated colonies of lactic acid bacteria (numbered 1–8) from stored dry fermented meat products: (**a**) negative control, (**b**) LOINS, (**c**) SAUSAGES; M—molecular weight DNA ladder 100–500 bp (base pairs).  
Microbiological analyses of all types of dry fermented meat products did not show any presence of pathogenic bacteria, such as _Salmonella_ spp. and _Listeria monocytogenes_, during the whole of the storage time.  
The number of _Enterobacteriaceae_ was, respectively, for probiotic sausages and loins, approximately 4.0 log cfu g\({}^{-1}\) immediately after 21 days of the ripening process. However, analyses carried out after 6 and 12 weeks of chilled storage under anaerobic conditions, shown in both cases of the study products, provided a statistically significant decrease in the total number of ENT below 1.0 log cfu g\({}^{-1}\). In the case of the control samples without the addition of a probiotic starter culture LOCK900, the number of ENT for loins was 4.25 log cfu g\({}^{-1}\) and 5.67 log cfu g\({}^{-1}\) for the sausages. Compared to products, with the addition of a probiotic strain LOCK900, in control products the number of ENT decreased significantly during 12 weeks of storage.
# Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification
## 2 Materials and Methods
### Data Collection and Preliminary Analysis
#### 2.1.2 Data Preprocessing and Selection  
FreeSurfer (Version 7.4.1), a specialized neuroimaging software, was used for skull-stripping and segmentation of the MRI brain scans into volumes of interest (VOIs) [38].  
The data were labeled and categorized into three distinct classes: CN, MCI, and AD, corresponding to the type of dementia as well as characteristic brain atrophy patterns. The labels were then used to sort the files into three directories, ensuring an organized structure for subsequent analysis.  
The segmented and labeled brain VOIs were initially stored as Neuroimaging Informatics Technology Initiative (NIfTI) files. They were all then converted into two-dimensional JPEG representations, as shown in Figure 1, using NiBabel and Nilearn libraries. From each 3D brain volume, exactly 50 contiguous middle slices in the axial orientation were extracted. This specific range was selected because central axial slices are more likely to capture brain regions where typical neurodegeneration indicative of early dementia occurs, such as ventricular enlargement, cortical thinning, and atrophy of the medial temporal lobe. This particular number of slices was chosen to balance anatomical coverage with computational feasibility and operational resources.  
The processing resulted in 4060 JPEG files per class (AD, MCI, and CN), totaling 12,180 two-dimensional brain-representative ROIs used as normalized inputs. The ROIs were  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Age** & \multicolumn{2}{c}{**Gender**} & \multicolumn{3}{c}{**Research Group**} \\ \hline  & **Female** & **Male** & **CN** & **MCI** & **AD** \\ \hline
40–49 & 1 & 0 & 1 & 0 & 0 \\
50–59 & 78 & 32 & 54 & 30 & 26 \\
60–69 & 327 & 250 & 302 & 187 & 88 \\
70–79 & 478 & 563 & 440 & 377 & 224 \\
80–89 & 154 & 237 & 116 & 150 & 125 \\ Above 89 & 13 & 12 & 6 & 7 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of the study participants with reference to their age, gender, and research group.  
Figure 1: Sample of a few chosen brain ROIs with their associated labels.  
then randomly assigned into training, validation, and testing sets--3200 images per class for training, 800 for validation, and 60 for testing.  
The brain ROIs were randomly distributed into training, validation, and testing sets. 3200 JPEG images per class (AD, MCI, CN) were allocated for training, 800 for validation, and 60 for testing.
## 3 Preliminaries
### Network Model  
In WSN scenarios in this article, different number of sensor nodes are randomly distributed to the network area in different sizes. The distance of a sensor node from the adjacent nodes can be calculated according to the received signal strength indicator (RSSI) parameter. In this study, mobile heterogeneous distributed clustered WSN was created. The WSN in our study has three levels of heterogeneous structure, including normal, advanced, and super nodes. In the network, the selected nodes are selected from advanced nodes according to the proposed bacteria foraging based CH selection algorithm. Mobile Sink (MS) node, which collects data from selected CHs, has super node feature. We assume that super nodes have no energy limitation. These different nodes have different properties, such as data processing, communication, and transmission scope. The remaining nodes in the sensor network have the normal node property. Here, it is important to note that, in the proposed algorithm, each CH is an advanced node, but not every advanced node can be assigned a CH. This event is carried out by controlling the energy. Both non-CH and advanced nodes can transfer energy to other normal nodes and provide both a special network model and energy balance. In our study, a different number of sensor nodes were used in the scenarios in order to measure the energy efficiency and network life more accurately. In addition, network size is expressed in numbers in different scales and simulations were performed. Figure 3 shows the proposed clustering formation heterogeneous network model for 200 nodes that were distributed in a 250 \(\times\) 250 m\({}^{2}\) network size. In the sensor network, CHs selected from advanced nodes are represented by square-plotted nodes, while others are normal nodes.
## 4 Surface Characterizations Based on Surface Susceptibilities  
In Section 3, it is shown that scattering coefficients of EM surfaces for arbitrary \(\theta\) and polarizations can be analytically computed through Equations (21) and (22) once surface susceptibilities are determined. This section will present how to extract surface susceptibilities of given surfaces from limited sets of unit cell simulations and show the procedure of surface characterizations based on surface susceptibilities. In order to make related formulas shown in Section 3 applicable, it is assumed that the surfaces discussed here are single-layer isotropic metafilms which have period of unit cells much smaller than wavelength and are composed of periodic isolated scatterers.
## 4 Model to Minimize Cost Dynamically
### Local Computing in the MEC Server  
A local MEC server can have several CPU cores. Thus, the CPU cores available on a local server \(S_{j}\) are given by \(PS_{j}=\{ps_{j,1},ps_{j,2},ps_{j,3},\ldots,ps_{j,n}\}\). Each core \(ps_{j,k}\) has an operating frequency (\(f_{mec,j,k}\)), an effective commutative capacitance (\(C_{mec,j,k}\)), and a supply voltage (\(V_{mec,j,k}\)).  
IoT devices and MEC servers cause mutual interference between each other (\(I_{i}\)) because they use the same wireless channel. Thus, the data transfer rate (\(r(h_{i})\)) to offload task \(i\) to the channel (\(h_{i}\)) attenuates according to [PERSON]'s formula [25]. The data transfer rate is determined in Equation (8) and the mutual interference between wireless channels bandwidth (\(B\)) is computed in Equation (9).  
\[r(h_{i})=B*\log_{2}\left(1+\frac{p_{j}*\delta(S_{i,j})}{N+I_{i}}\right) \tag{8}\]  
\[I_{i}=\sum_{n\in A\mid\{i\}:h_{n}=h_{i}}p_{j}*\delta(S_{i,j}^{\prime}) \tag{9}\]  
For Equation (8), the variable \(p_{j}\) is the transmission power of a mobile device \(j\) during offloading task \(i\) to the local server, and \(N\) is the power of the thermal noise of the wireless channel. \(g_{(S_{i,j})}\) is the channel gain between the local MEC server and the mobile device.  
In the local server, data and source code need to be sent to the application processing, and the generated results must be sent back to the origin. Thus, the time required for an IoT device to sent data (Equation (10)) and after to download the results (Equation (11)) from the local server can be computed as:\[T_{i,mec-up}(h_{i})=\frac{sc_{i}+d_{i}}{r_{i}(h_{i})} \tag{10}\]  
\[T_{i,mec-down}(h_{i})=\frac{d_{i}^{\prime}}{r_{i}(h_{i})} \tag{11}\]  
The total time required to complete the task execution in the local server considers the send (Equation (10)), the download (Equation (11)), and the task execution time in the MEC server calculated in Equation (5). The total time for a MEC server is given as in Equation (12).  
\[T_{i,mec,total}=T_{i,mec-up}(h_{i})+T_{i,mec}+\] \[T_{i,mec-down}(h_{i}) \tag{12}\]  
The energy spent for the data communications from the local MEC server is also calculated by (Equation (1)), which can be either the time to sent (_nec-up_) or download (_mec-down_) data. Furthermore, the dynamic energy consumed by the MEC server is calculated in the same fashion as that in the IoT device. Equation (13) gives the total dynamic energy consumption.  
\[E_{i,mec,total}=E_{i,mec-up}(h_{i})+E_{i,mec}+ \tag{13}\] \[E_{i,mec-down}(h_{i})\]  
Moreover, the cost computation for the local server is expressed in Equation (14).  
\[Cost_{i,mec}=u_{mecT}*T_{i,mec,total}+ \tag{14}\] \[u_{mecE}*E_{i,mec,total}\]
## 4 Result and Discussion
### Charging and Discharging of Battery  
From the VSC controller of the battery, as depicted in Figure 2b, the battery can be charged and discharged by setting the P\({}_{\text{Battery}}\) to positive or negative. The positive value of P\({}_{\text{Battery}}\) indicates discharging, whereas the negative value of P\({}_{\text{Battery}}\) indicates charging. Figures 32 and 33 illustrate the battery charging and discharging. As can be seen from Figure 32, the battery is charged at 50% nominal power of PV array (1.74 MW), which reduces 0.87 MW net power injection from 1.74 MW at the PCC. Therefore, the power injection due to 56 units of PV array at the PCC1 reduces from around 96 MW to 47 MW. Normally, the battery is charged with a small percentage of PV power over a longer duration. However, high charging operation has been carried out in Figure 32 to show the significant charging  
Figure 33: Battery discharging.  
profile change within a short period. The state of charge (SOC) of battery is increased from 73.5% to 78% within this period. Apart from power leveling due to temporary solar radiation and temperature change, the battery is discharged at night. As can be seen from Figure 33, the PV array power is zero, and the battery is discharged at 0.87 MW power to the PCC. As the PV power is zero, the net power is equal to the battery power. Fifty-six units of PV-battery combination yield around 46.7 MW power at PCC1. The state of charge is reduced from 80% to 73% within this period. Charging and discharging of the battery is performed during the constant PCC voltage. However, balanced and unbalanced fault at PCC1 changes the PCC voltage, which switches the battery's normal operation to excess energy control mode in the HVDC link.
# DAEOM: A Deep Attentional Embedding Approach for Biomedical Ontology Matching
## 3 Experiments and Discussion
### Results and Discussion  
Table 2 lists the matching results of DAEOM compared with baseline systems. We can see that our method clearly outperforms all the baselines across all the evaluation metrics. Comparing the F-measure of TF-IDF and our proposed TBERT, the latter achieves a higher performance than TF-IDF. The main reason for this is that continuous vectors representing tokens can provide more semantic information than single strings for calculating the similarity of concepts. To validate the importance of our ontology term encoder component (TBERT), we further analyzed the behavior of aligning ontologies based on the LSTM. As we can see, TBERT achieves a statistically significant higher performance than LSTM in all experiments. This behavior indicates the superiority of TBERT in injecting semantic similarity to terminological description embeddings. Moreover, the performance of DAEOM is better than TBERT in term of F-measure. We further extended the LSTM experiment by applying our proposed SGAT. This extension leads to the same effects as the ones summarized in the TBERT-DAEOM comparison. This observation demonstrates that both the network structure and terminological description contain useful information for ontology matching, and illustrates the significance of capturing the interplay between two-sides information. As our proposed model is graph-based, we also select two graph-based models GraphSAGE and TransE as the baselines. Notice that the performance of DAEOM is much more better than combining TBERT with GraphSAGE or TransE. The main reason is that the GAT can learn node embeddings that allow for assigning different importance to different nodes within a neighborhood while dealing with different sized neighborhoods. Moreover, the performance of DAEOM is better than DAEOM in terms of the F-measure. This further proves that employing structural relations are helpful to distinguish the vector representations of concepts. Table 3 lists a comparison of DAEOM with six top-performing systems based on feature engineering and representation learning, according to the results published in the Anatomy track and Large BioMed track by OAEI 2019. The preliminary result shows that DAEOM obtains the top performance in the two ontology mappings tasks (FMA-NCI (small)) and FMA-SNOMED (small)). In those two, the precision and F-measure are significantly better than all other systems. In terms of recall, DAEOM demonstrates lower performance in the ontology matching tasks. However, we would like to note that we have not used any semantic lexicons specific to the biomedical domains compared to the other systems. For instance, AML uses three sources of biomedical background knowledge to extract synonyms. Specifically, it exploits the Uber Anatomy Ontology (Uberon), the Human Disease
## 2 Related Work
### Trust Models in Vehicular Networks
#### 2.1.2 Data-Oriented Trust Models  
Data-oriented trust models come up as one of the great efforts as they evaluate the authenticity of the shared data among vehicles. [PERSON] et al. in [22] proposed the data-oriented trust models for VIoT. In these trust models, a user is more interested in the trustworthiness of the shared data than that of a an important evaluation metric. Data-oriented trust models need cooperation among neighbor vehicles to assess the authenticity of the disseminated data. [PERSON] et al. [23] proposed a voting-based system with different voting weights depending on its closeness to the place of the event. The authors in [24] proposed a deterministic approach to measuring the trust level of the received message by using the received signal strength (RSS) to calculate the distance and vehicle's geolocation, i.e., position coordinate. [PERSON] et al. [13] proposed a multi-hop trust assessment scheme using a subjective logic framework among vehicle groups. This scheme describes the multi-hop trust assessment by considering prior and generated uncertainties in the shared data among vehicles. However, a major concern in data-oriented trust models is in latency and sparsity. As various sources will generate a large amount of data, a central entity is needed to perform operations efficiently. Subsequently, it increases the communication delay.
# A Combined Method for Localizing Two Overlapping Acoustic Sources Based on Deep Learning
## 3 Sound Source Separation  
### General Principle of Sound Source Separation Based on Deep Learning Methods  
The task of the sound source separation method is to reconstruct signals from two individual acoustic sources from the mixed signal \(x(t)\) obtained using a microphone array. The proposed source separation model based on DL methods is schematically shown in Figure 3:  
Recurrent neural networks (RNNs) have demonstrated superior performance in modeling time-varying functions, predicting sequential data, and solving sound source separation problems. Bidirectional long short-term memory (BLSTM), a recurrent network designed to solve the vanishing gradient problem, has shown good performance in sound source separation, acoustic echo suppression, and quality enhancement [19].  
STFT is applied to the mixed microphone signal. The amplitudes of STFT are the input features for network BLSTM. In sound source separation tasks, the ideal binary mask (IBM) is one of the most widely used masks as a training target for DNNs. Using this mask, the signal spectrum of the two sources can be estimated. Using the phase of the microphone signal spectrum and the estimated amplitude of the signal spectrum of each source, the signals are reconstructed using the inverse short-time Fourier transform (ISTFT).
## 2 Configuration Topography
### Configuration Specific Topology  
Due to constraints in spacecraft payload space and weight, the development of multi-wing deployment mechanisms has been significantly advanced. As shown in Figure 1, the folding wings are initially positioned close to the spacecraft. Three sets of folding wings are circumferentially distributed at the top, left, and right positions of the spacecraft. After launch, these three sets of folding wings deploy into place under the actuation of the mechanism. This study focuses on the configuration topology and optimization of a multi-wing deployment mechanism designed to achieve the synchronized deployment of the three sets of folding wings, as illustrated in Figure 1, using a single-drive system.  
In the configuration synthesis and innovative design of planar mechanisms, the first step is to determine the degrees of freedom, the number of components (the links primarily constitute the main components of planar mechanisms), and the number of joints (joints serve as the movable connections between the components). This determination is contingent upon the input and output requirements of the mechanism. To achieve the synchronous deployment of the three tails, the mechanism must include three rotational joints. Additionally, folding wings require sub-second actuation, which necessitates overcoming aerodynamic loads during the deployment process. The pyrotechnic drive is a commonly used driving method that provides high driving force within a short transient time, and the pyrotechnic drive has a small volume and light weight, meeting the drive source requirements for folding wings. Since the driving source is typically pyrotechnics that generate linear motion, the mechanism should incorporate a translational joint. The transient output force of the pyrotechnic is substantial, necessitating a direct connection to the frame (the frame is the stationary component in a planar linkage mechanism, serving as the main support structure. It is used to support, fix, or connect other parts of the mechanism). Since the three wings rotate around a fixed point on the frame during deployment, the frame should incorporate more than four motion joint connections. The mechanism operates with a single degree of freedom and requires at least four motion joints. To avoid excessive complexity, the fewer the components, the simpler the mechanism while achieving the desired motion. Therefore, the number of active components is set to 7, corresponding to an 8-link and 10-joint single-degree-of-freedom mechanism, for the configuration synthesis of the multi-wing deployment mechanism.  
As illustrated in Figure 2, the topology of the seven specific eight-bar, ten-joint configurations that meet these requirements is presented.
## 3 Data Description and Evaluation Indexes
### Performance Criteria  
Three criteria are employed to assess the forecasting performances, namely: mean absolute percentage error (MAPE; %), root-mean-square error (RMSE; dimensionless value), and threshold statistics (TS; %). The definitions of three criteria are listed as follows:  
\[\begin{array}{l}\mathit{MAPE}=\frac{100}{B}\sum\limits_{i=1}^{B}\left|\frac {\mathit{obj}-\mathit{pr}_{i}}{\mathit{obj}_{i}}\right|,\ \mathit{RMSE}=\sqrt{\sum_{i=1}^{B}\left(\mathit{obj}_{i}-\mathit{pr}_{i} \right)^{2}/B},\\ \mathit{TS}_{a}=\frac{n_{a}}{B}\times 100,\end{array} \tag{4}\]  
where \(B\) is the length of the prediction; \(\mathit{obj}_{i}\) and \(\mathit{pr}_{i}\) represent the \(i\)-th observation and prediction, respectively; and \(n_{a}\) is the number of data predicted having relative error in forecasting less than \(a\) %. In this paper, \(\mathit{TS}_{a}\) is calculated for three levels of 1%, 5%, and 10%.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 4 Hyperbranched Polymer Toughening  
Hyperbranched polymers (HBPs) are a class of highly branched three-dimensional macromolecules characterized by numerous branching points, which prevent extensive chain entanglement. They feature a high density of terminal functional groups, leading to increased reactivity, reduced viscosity, and higher solubility. These properties facilitate modifications and functionalization, making HBPs suitable for synthesizing a variety of functional materials [76; 77]. As the demand for improved performance in polymer composites grows, researchers have begun to explore the incorporation of hyperbranched polymers into epoxy resins and other resin systems to enhance the materials' toughness, strength, and impact resistance.