esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Mining: A Novel Training Strategy for Convolutional Neural Network
## 3 Materials and Methods
### Feature Mining
#### 3.2.1 Feature Segmentation  
In order to make the network learn different local features, we first divided the high-dimensional feature into two complementary parts by using a binary mask. In the training phase, the training sample \((x,y)\) would be processed by the network, and we could obtain the feature \(X\) of this sample. After obtaining the complete feature \(X_{0}\) before the last pooling layer, we generate a binary mask \(M\) with a bounding box with coordinates \(B=(r_{x},r_{y},r_{w},r_{h})\) indicating the cropping regions on the binary mask. As CutMix [37], the box coordinates are uniformly sampled according to:  
\[r_{x} \sim\text{Unif}(0,W),r_{w}=W\sqrt{1-\lambda} \tag{1}\] \[r_{y} \sim\text{Unif}(0,H),r_{h}=H\sqrt{1-\lambda}\]  
making the cropped area ratio \(\frac{r_{w}r_{h}}{WH}=1-\lambda\). \(\lambda\) is sampled from the uniform distribution \((0,1)\). The elements in \(B\) are set to one, and other elements in \(M\) are set to zero. We define the segmentation operation as:  
\[X_{1} =X_{0}\odot M \tag{2}\] \[X_{2} =X_{0}\odot(1-M)\]  
where \(X_{1}\) and \(X_{2}\) are complementary parts of \(X_{0}\) and contain different local semantics of \(X_{0}\), which means \(X_{1}\) only contains the feature information within \(B\) while \(X_{2}\) only contains the feature information outside \(B\). We used the block to segment the feature based on the characteristics of the CNN: adjacent neurons have similar semantics, and the block-form segmentation can ensure that the two parts of the feature have great differences, which could make the network efficiently obtain different local features. Note that this operation can also be applied to any layer to obtain different representative features and is not limited by the network structure. For a detailed discussion of the effect of the segmentation method, please refer to Section 4.5. Besides, many methods preprocess the data to improve the Feature Segmentation. However, our approach only performs segmentation on the feature space and is based on raw data.  
Figure 2: This figure shows the details of a CNN trained with the proposed Feature Mining. There are two stages in Feature Mining: Feature Segmentation and Feature Reusing. Feature Segmentation means that the feature is divided into two complementary parts by two binary masks. Feature Reusing means that these two parts of the feature would respectively pass through a global average pooling layer and a fully connected layer to generate two independent outputs, and the three cross-entropies including the original feature will be added to form the final loss function.
## 3 Literature Review
### Twitter Data Analytics in Healthcare (Arabic)  
We have found only three works on Twitter data analytics for healthcare in the Arabic language. [PERSON] et al. [15] introduce a new Arabic annotated dataset about health services which they state is a necessary component in sentiment analysis studies. The dataset consists of about 2026 tweets in the Arabic language. The tweets were collected over a six-month period using the four most popular hashtags about health services. Pre-processing including the normalizing steps was applied. The clean tweets were annotated by three annotators to be either positive or negative. The classification of tweets was limited to two classes only, due to the difficulty of rating the opinion in the Arabic language compared to English. Their experiments were performed by various machine learning algorithms and deep neural networks using different settings. They reported to have obtained a best classifier results using SVM with Linear Support Vector Classification and Stochastic Gradient Descent. [PERSON] and [PERSON] [16] detect influenza in the UAE from Arabic tweets. They classified tweets and used them to predict the number of future hospital visits using a linear regression model. Their work focused on analyzing tweets in Arabic MSA and in the UAE dialect. The authors reported correlations between their reported results and those obtained from the UAE Ministry of Health. This work did not use any big data technologies. [PERSON] and [PERSON] [17] used tweets in Arabic to track diseases in the Gulf Cooperation Council (GCC) countries. This work used a small number of tweets and did not use any AI and big data technologies.
## 4 Discussion
[38], the exposure levels were evaluated in a realistic smart mobility communication scenario operated at 5.9 GHz, where four V2V antennas were modeled as omnidirectional monopoles and placed on a realistic 3D model of a city-car to estimate the SAR levels of a human adult male phantom inside the car. Also in this study, the maximum value of SAR (equal to 1.58 W/kg for the head/tors area) was higher than that found in the present work. However, it is important to notice that, in [38],the input power of each of the four V2X antennas was equal to 30 W (for an input power of 1 W, as in this work, the maximum SAR value became only then equal to 0.05 W/kg), and the distance between the antennas and the phantom was shorter than that in our scenario. Furthermore, interestingly, in the work of [PERSON] et al. [38], the highest exposure levels were also individuated in the skin tissue and localized only in the body regions closer to the antennas, as in this work. Instead, we found no literature studies that dealt directly with the assessment of the exposure levels of a person in close proximity to the car and not inside. Indeed, to our knowledge, this is the first study about this topic.
## 4 Materials and Methods
### Network Pharmacology Analysis
#### 4.3.3 Construction of Protein-Protein Interaction (PPI) Network  
Initially, shared targets between spilanthol and targets related to NDDs were identified and depicted using Venny 2.1.0, an online tool used to generate Venn diagrams ([[https://bioinfogp.cnb.csic.es/tools/venny/](https://bioinfogp.cnb.csic.es/tools/venny/)]([https://bioinfogp.cnb.csic.es/tools/venny/](https://bioinfogp.cnb.csic.es/tools/venny/)), accessed on 19 October 2023). By using common targets, an interactive network integrating therapeutic targets of spilanthol and NDD-related targets was established, and the network was visualized using Cytoscape (version 3.9.1). The top 10 genes exhibiting the most significant degree of interaction were identified utilizing Cytoscape's cytoHubba plugin v.0.1.
## 4 Classification Based on Taxonomy and Design Drivers
### Multi-Replication
The schemes, specifically, consider the case where nodes can determine the current set of \(N_{r}\) replicas associated with a given application by generating \(N_{r}\) random spatial locations with a hash function \(hash(app\oplus epoch\oplus i),\forall_{r}\in[0,N_{r}-1]\), where \(app\) is the application's name and \(epoch\) is a shared time identifier employed to change replicas over time. The paper demonstrates that by placing a replica node randomly in the network it is possible to outperform ToW, QAR and GHT. Also by changing replication nodes over time it is possible to equalize the energy burdens across the network and hence a 60% improvement in network lifetime may be possible. However, the wireless network in the model is assumed to be static and the application is considered to be spatially homogenous.
# Research on the Influence of Anxiety Psychology on Unsafe Behavior Among Construction Workers
## 4 Model Validation and Result Analysis
### Regression Analysis
#### 4.4.1 Main Effect Testing  
Model 1 in Table 7 represents the regression analysis of unsafe behaviors controlled for demographic variables (gender, career age, education level, work experience, and anxiety level). Model 2 introduces anxiety psychology as an independent variable while controlling for demographic factors to assess its direct effect on unsafe behaviors.  
Model 1 demonstrated that career age (\(\beta=0.073\), \(p<0.01\)), education background (\(\beta=-0.136\), \(p<0.05\)), and anxiety level (\(\beta=0.173\), \(p<0.001\)) significantly predicted unsafe behaviors, while other demographic variables showed no statistical significance.  
Model 2 revealed that anxiety psychology significantly predicted unsafe behaviors (\(\beta=0.679\), \(p<0.001\)) after controlling for demographics. Career age exhibited a significant positive impact (\(\beta=0.077\), \(p<0.01\)), whereas education level showed a negative association (\(\beta=-0.115\), \(p<0.05\)). The overall regression model achieved statistical significance (R\({}^{2}=0.601\), adjusted R\({}^{2}=0.586\), F = 82.63, \(p<0.001\)), confirming the significant positive effect of anxiety psychology on unsafe behaviors. Thus, Hypothesis H1 was validated.
# A Framework for Frugal Supervised Learning with Incremental Neural Networks
## 2 Materials and Methods
### Learning and Control
#### 2.3.2 Control  
The influence threshold \(s_{inf}\) of the classifier is the maximal allowed distance \(\delta\) between a sample \(\mathbf{x}\) and its best prototype \(P_{best}\), so that \(\mathbf{x}\) is said to be under the influence of \(P_{best}\). If a sample does not pass this requirement during the learning phase, then a new prototype is created.  
\[\delta(P_{best},\ \mathbf{x})\leq s_{inf} \tag{2}\]  
The confusion threshold \(s_{conf}\) helps to discriminate ties at the frontier between distinct classes. It represents a distance between two other distances; the first one being the distance between an observation \(\mathbf{x}\) and its best prototype \(P_{best}\), and the second being the distance between the very same observation \(\mathbf{x}\) and its second-best prototype with a different class \(P_{second}\). If this condition is not satisfied, a new prototype is created.  
\[\delta(P_{best},\ \mathbf{x})-\delta(P_{second},\mathbf{x})>s_{conf} \tag{3}\]  
The optimal values for the influence and confusion thresholds strongly rely on the data. Thus, data normalization and variance computation can facilitate optimization.  
The update coefficient \(\alpha\in[0,1]\) is used when a sample \(\mathbf{x}\) passes both requirements of Equations (2) and (3), i.e., when \(\mathbf{x}\) is under the influence of its best prototype and when there is no risk of confusion. In this case, the weights of \(P_{best}\) are updated to bring it closer to the passing sample. Let \(\mathbf{w}\) be the weight of \(P_{best}\). Then the update can be expressed as:  
\[\forall i\in\{1,\dots,m\},\ w_{i}\gets w_{i}+\alpha*(x_{i}-w_{i}) \tag{4}\]  
A large value for \(\alpha\) implies that the prototypes will be strongly brought closer to passing samples, thus increasing the overall prototype cardinality. In contrast, a low value limits the prototypes' moving, thus limiting their cardinality.  
Computing the similarity between the prototypes and samples is performed with a similarity measure or with a distance, commonly chosen with respect to the problem to solve. This possibility makes INNs flexible and adaptable. Among the common measures, we have the Minkowski family measures (see Equation (5)), where \(p=2\) is like computing an Euclidean distance.  
\[d_{mink}(x,y)=\Bigg{(}\sum_{i=1}^{n}|x_{i}-y_{i}|^{p}\Bigg{)}^{1/p} \tag{5}\]  
Prototype Ratio  
During the learning phase, an interesting metric to monitor is the ratio of prototypes \(\rho\), which is the number of stored prototypes divided by the size of the learning set. Low \(\rho\) values are preferable, as they show that the classifier could learn effectively from the data, implying that the hyperparameters are correct. In contrast, high values are a sign of overfitting. Thus, learning can be stopped whenever \(\rho\) is above a defined threshold.
## 3 Results and Discussion
### Organic Acids  
Organic acids are located in the fleshy parts of fruits and can affect sensory properties and consumer acceptability. A study conducted on juices of six cultivars of sea buckthorn (_Elaagens rhamnides_ L.), a berry tree of the Elaeagnaceae family, found that titratable acidity had a positive correlation with the intensity of sourness and astringency [35]. The quantity of organic acids can vary depending on many factors; for example, in a study on two blueberry species native to Turkey (_Vaccinium arctostaphylos_ and _V. myrtillus_), the amount of malic, citric, and quinic acids changed significantly during different ripening stages [36]. Other factors include cultivar choice, soil, and climate conditions. Organic acids were the most represented secondary metabolites (1890 \(\pm\) 182.02 mg/100 g FW) in _E. umbellata_ berries. The compounds detected were citric, malic, oxalix, quinic, succinic, and tartaric acids (Table 2). Succinic was the primary organic acid (490.78 mg/100 g FW) accounting for about 25% of the total acid concentration, followed by quinic (468.83 mg/100 g FW) and citric (370.15 mg/100 g FW). Results are in accordance with previous studies on the autumn olive, where the main organic acids detected were malic, quinic, and citric acids, although in smaller quantities [5; 6]. Among the acids, citric was the one with the higher correlation to titratable acidity (r = 0.932).
## 1 Introduction
### Optical Proxies of Properties of Sediment Particles  
#### 1.1.1 Volume and Mass Concentration  
Most of the variation in optical signals measured in the field is due to changes in suspended particle mass concentrations (SPM; e.g., [4]). Optical estimates of SPM typically are made with measurements of attenuation or scattering at long visible wavelengths (e.g., 660 nm) or at infrared wavelengths (e.g., 850 nm), minimizing the impacts from varying dissolved materials and particulate absorption [5]. Transmitted light, which is reduced by scattering and absorption by particles, is measured in the near forward direction by so-called transmissometers, and scattered light can be measured at an angle near 90\({}^{\circ}\) by nephelometers, or at an angle greater than 90\({}^{\circ}\) by optical backscatter sensors (OBS). A multi-site comparison of the application of backscattering, side-scattering, and attenuation as proxies for SPM demonstrated their ability to predict SPM within 36%, 51%, and 54% respectively, for 95% of all cases [4]. The differences likely are due to variable sensitivity of each property to particle size, packing, and composition [1; 6; 7; 8]. For example, the acceptance angle of transmissometers acts to filter out responses from larger particles [9]. SPM has also been estimated from space-based measurements of radiance (e.g., [10]). Remotely-sensed reflectance is most sensitive to the particulate backscattering coefficient in red and NIR wavelengths [11].
## 5 Conclusions  
This was the first study to evaluate the surface-active agent HTAB, which has vertical self-alignment ability. We evaluated how this ability was affected by the cell gap and its phase-change characteristics to investigate its applicability to RF applications that use LCs. The cell gap is much smaller in RF applications than in displays, but no previous study of [HTAB] has considered whether successful homeotropic alignment is affected by the cell gap. We analyzed the non-uniformity caused by insufficient [HTAB], and the effectiveness of HTAB as a surface-active agent with 5 CB in various cell gap conditions. The results indicated a minimum [HTAB] for fully-vertically aligned LC molecules up to 20 \(\upmu\)m. To achieve fully-aligned LC molecules in this range of the cell gap, [HTAB] increases as the cell gap increases. However, for the cell gap \(\geq\)50 \(\upmu\)m, we identified a limitation in achieving homeotropic alignment using HTAB because of its weak anchoring nature. We also investigated the effect of [HTAB] variation on the phase-change nature of LC mixtures (5 CB and 8 CB) and concluded that the phase-change temperature of the solutions is stable for all [HTAB] tested. We believe our work will be useful in the development of new design processes for RF devices that use LCs.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122412582/s1](https://www.mdpi.com/article/10.3390/app122412582/s1)]([https://www.mdpi.com/article/10.3390/app122412582/s1](https://www.mdpi.com/article/10.3390/app122412582/s1)), Figure S1: The POM images about the no-alignment state caused by a thick cell gap.; Figure S2: The differential scanning calorimetry (DSC) heat flow results about 8 CB+HTAB solutions.  
Conceptualization and formal analysis, J.-S.M.; experiment and formal analysis, J.-Y.C., H.-J.S. and J.-H.L.; supervision, W.-S.K. and S.-W.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-TE2013.  
Not applicable.  
All data that support the findings of this study are included within the article.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 2 Materials and Methods
### Conditioning Factors
#### 2.3.3 Distance to Faults  
Faults cause fragmentation of the surrounding rocks and provide a source of material and power for the generation of landslides [53]. According to the distance to the faults, the work was divided into eight grades at the intervals of 300 m: 0-300, 300-600, 600-900, 900-1200, 1200-1500, 1500-1800, 1800-2100, >2100 m (Figure 3e).
## 3 Research on BPL Decoding Algorithm for Polar Codes Based on Information Geometry  
In this section, we first establish an information geometric framework for the BPL (Belief Propagation List) decoding algorithm of polar codes. Secondly, the concept of the \"soft information centroid\" is introduced, and a path selection criterion based on the soft information centroid is proposed. Finally, a weight selection method for this path selection criterion based on geometric distance is presented.
# SALMA: A Novel Middlebox Infrastructure System Based on Integrated Subnets
## 6 Implementation
### Load-Balancing Challenge  
The motivations of load balancing are not bonded by spreading the traffic load among available resources. In addition, the value of middleboxes' resources and denial of service concerns include unacceptable delays that could reach milliseconds for multimedia services. In this work, we used an optimal flow assignment method to spread the load among the available subnets of the middleboxes. When the middlebox manager receives a flow-setup request of in-scope flow, it selects the middleboxes' subnet based on the optimization model that is explained in Section 3, and builds an end-to-end tunnel between the edge switch and the selected middleboxes' subnet.
## 3 Results
### Cochlear Implant Users
#### 3.2.2. Statistical Analysis  
The ANOVA indicated a significant effect of SNR [F (1,301) = 146; \(p\) < 10\({}^{-4}\)] but not for the coding scheme [F (1,43) = 0.66; \(p\) = 0.42]. A significant interaction was seen between them [F (1,301) = 2.23; \(p\) = 0.032], which may need further investigation.  
Figure 4: Syllable recognition function of the signal-to-noise ratio by CI users. Bars indicate the standard deviation. The asterisks indicate the significant differences (5% threshold).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Normal-Hearing Subjects**} & \multicolumn{2}{c}{**Fixed-Channel**} & \multicolumn{2}{c}{**Channel-Picking**} & \multirow{2}{*}{_p_ **(Wilcoxon)**} & \multicolumn{2}{c}{**Effect Size**} \\  & & **(n = 20)** & & & \\ \hline \multirow{2}{*}{\(x_{50\%}\)} & m & -0.28 & 0.39 & \multirow{2}{*}{0.038} & \multirow{2}{*}{0.85 (strong)} \\  & \(\sigma\) & 0.85 & 0.73 & & \\ \hline \multirow{2}{*}{\(\Delta_{25\text{-}75\%}\)} & m & 2.19 & 2.54 & \multirow{2}{*}{0.287} & \multirow{2}{*}{0.27 (small)} \\  & \(\sigma\) & 1.38 & 1.23 & & \\ \hline \multirow{2}{*}{\(y_{\text{max}}\)} & m & 98.5 & 99.25 & \multirow{2}{*}{0.374} & \multirow{2}{*}{0.31 (small)} \\  & \(\sigma\) & 2.86 & 1.83 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the sigmoid parameters, for normal-hearing listeners.  
The recognition percentages are indicated in Figure 4 and Table 4. We can see that the plateau was not reached for high SNRs and an inversion of the performances may be noticed between CP and FC at +15 dB.
# LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements
## 4 LPWAN Systems and MG Control
### Widespread DGs, LPWANs or Cellular Networks?  
In the case of widely spread devices, the first solution is to use cellular networks. The idea of improving the efficiency and robustness of data transmissions through the existing cellular links started with the introduction of general packet radio service (GPRS), which was an extensive upgrade for the global system of mobile communication (GSM) [68]. GPRS idea is based on packetizing the data for transmission. Although GPRS can be used for IoT applications, there are more recent lightweight and low-power M2M protocols designed specifically for M2M over cellular networks. For example, narrowband internet of things (NB-IoT) is a very popular protocol whose operation is based on existing LTE cellular networks [69]. The performance of cellular-based long-range and low-power protocols have been compared to LPWANs before, and the general findings state that cellular protocols offer a better quality of service and resiliency while their power consumption and operational expenses are higher [70; 71]. Apart from the additional expenses and complications regarding the usage of licensed frequencies by cellular-based protocols, there  
Figure 4: MG electrical and cyber layers.  
is another important consideration: network availability and signal quality. In remote and rural areas, depending on the region and the operators, weak network coverage is a popular problem for cellular networks which may cause cellular-based IoT protocols to down perform. This factor helps to justify the use of LPWANs, which operate in unlicensed sub GHz frequencies.  
LoRaWAN and NB-IoT are implemented for smart system communication infrastructures when long-range capabilities are required. As a matter of fact, they are considered rivals since both provide essential features. In addition, both LoRaWAN and NB-IoT are leading technologies in their own categories. However, due to their structural differences, their benefits and drawback are almost in contradiction with each other. A comparison between LoRaWAN and NB-IoT characteristics is illustrated in a Venn chart, Figure 5 [72; 73; 74]. As previously explained, for this application the utilization of LoRaWAN is more favorable than NB-IoT due to its private network creation feature and unlicensed allocated spectrum.