esa-sceva/satcom-chunk-collection huggingface dataset:
# Critical Review of Techniques for Food Emulsion Characterization
## 3 Techniques for Emulsion Characterization
### Determination of Electrical Conductivity  
Electrical conductivity plays a crucial role in characterizing emulsions, providing essential information about their stability, composition, and flow properties. The measurement of electrical conductivity serves as a valuable tool for researchers to gain insights into the nature of emulsions and efficiently control their stability over time [154]. Typically, higher concentrations and increased charge density of surfactants lead to higher electrical conductivity in the emulsion.  
Several methods have been reported to measure the electrical conductivity of emulsions [71; 155]. Electrical conductivity can be measured with a conductivity meter or probe, which is inserted into the emulsion sample to determine its ability to conduct an electric current. Conductivity measurements can be used to investigate the interactions between surfactants, predict emulsion long-term stability, and comprehend the mechanism of emulsion separation [155]. Numerous studies show that emulsions exhibiting elevated electrical conductivity display enhanced stability and minimized droplet coalescence [119]. The heightened electrical conductivity contributes to the development of electric double layers surrounding the droplets, acting as a barrier against droplet aggregation and flocculation. These double layers generate repulsive forces between the droplets, effectively impeding coalescence. Consequently, augmenting the concentration of surfactants with higher charges serves to bolster emulsion stability. Additionally, the introduction of conductive materials, such as carbon nanotubes, to emulsions can augment both their electrical conductivity and stability [156]. Tracing changes in the conductivity of emulsions containing different types and concentrations of emulsifiers, salts, or other conductive materials allows rapid prediction of emulsion stability.
## 2 Numerical Calculation Method
### Grid Scheme  
Hybrid grids are adopted to satisfy both accuracy and efficiency of the numerical simulation. For the moving domain, unstructured girds are used due to the complex shape of the train. The deformation domains before and after the train, which have regular shapes, can be meshed by high-quality structured grids as shown in Figure 4a. The adjacent domains are connected by setting node coincidence on the overlap boundaries. Although there are hood structures and other structures in the fixed domain, the structured grids are still feasible to the fixed domain, as shown in Figure 4b.
# Statistical Model for Mobile User Positioning Based on Social Information
## 3 Methodology
The flow diagram of the proposed algorithm is shown in Figure 1.  
The model receives 4 inputs: (a) the information recorded in the radio connection traces, (b) data of land uses in the scenario, (c) information on the location and orientation of the cells in the scenario and (d) posts obtained from social networks that have associated geopositioning metadata. These data sources have been selected because of the important information they provide. On the one hand, the location and orientation of the cells in the scenario allows to delimit the area in which the user can be found, while, on the other hand, both the connection traces and the types of land use provide information about the user and its context. Finally, the geopositioned publications from social networks are used as an approximation of the spatial distribution of the population. Due to the increasing interest in the exploitation of huge amounts of data, privacy concerns have become highly significant. Data processing must ensure user privacy during the whole process of data management. This is usually achieved through anonymizing methods run at the very first stage by social network owners. The anonymizing process substitutes end-user data with some unique user ID. Client and user ID association is only available for social network  
Figure 1: Flow diagram of the spatial distribution model. managers, but not for positioning engineers, including all researchers involved in this work. A map with the statistical distribution of users in the scenario is obtained with these inputs, where each tile corresponds to an aggregation of the percentages of connections occurred in the rings that cover it. In this work, the positioning process is based on the spatial context of the users in the scenario (i.e., indoor/outdoor), instead of the traditional measurement-based methods, which are susceptible to radio propagation conditions. Three main steps can be established. First, a delimitation of the area where the connection occurs is obtained from the network layout and the cell ID and TA statistics. Secondly, this area is further reduced, matching the user indoor context with the indoor/outdoor locations in the topology of the scenario. The user indoor context is obtained from the KPIs registered on radio traces. At the same time, the indoor/outdoor locations are obtained from the data of land uses. Finally, network population distribution, estimated from the social media posts, is applied to weight which parts of the limited area are more likely to embrace the studied connection.
# An Improved CNN Model for Within-Project Software Defect Prediction
## 3 Approach
### Predicting Software Defects  
We employed a logistic regression model as our classifier in compliance with previous work [24] because it was easy to implement in Keras and we focused on the overall deep learning models rather than the last layer of the CNN model as classifiers. After preprocessing of the labeled source files, we split the dataset into a training set and a test set following a split ratio of 80:20. We performed a stratified split to ensure that the ratio remained unchanged in both the training test and the test set. After we fed the training set to our improved CNN model, all parameters, including weights and biases, were fixed. Then, for each file in the test set, we ran the trained CNN model to obtain our prediction results. The results were in the form of a decimal number between zero and one, based on which we predicted a source file as buggy or clean. If the result was above 0.5, the prediction was regarded as buggy; otherwise it was regarded as clean.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Li’s CNN** & **Improved CNN** \\ \hline Embedding layer & Yes & Yes \\ \hline \#Convolutional Layers & 1 & **3** \\ \hline \#Pooling layers & 1 & **3** \\ \hline Activation function & ReLU + sigmoid (last dense layer) & ReLU + sigmoid (last dense layer) \\ \hline \multirow{2}{*}{Parameter initialization} & \multirow{2}{*}{None} & **He\_normal (embedding layer, convolutional layer, dense layers)** \\  & & **Glorot\_normal (last dense layer)** \\ \hline Dropouts & None & **Between dense layers** \\ \hline \multirow{2}{*}{Regularization} & \multirow{2}{*}{None} & **L2 Regularization (embedding layer, dense layers)** \\ \cline{3-3}  & & **L2 Regularization (embedding layer, dense layers)** \\ \hline \multirow{2}{*}{Training and optimizer} & Mini-batch SGD + Adam (loss function not given) & Mini-batch SGD + Adam + binary cross-entropy \\ \hline \hline \end{tabular}
\end{table}
Table 1: Architectural comparison between [PERSON]’s CNN model and our improved CNN model. Differences are highlighted in bold.
# Hybrid Beamforming in Massive MIMO for Next-Generation Communication Technology
## 6 Results  
In this section, the numerical simulation is used to show the outcomes of the proposed hybrid beamforming algorithm in conjunction with digital linear precoding methods like matched, filter, and regularized zero-forcing in the MIMO system using MATLAB software, taking into consideration the system with 28 GHz frequency, a channel sampling rate of 100 Msps, and the modulation technique used is 16 QAM. A matching number of antennas at the sender and receiver are taken into consideration. Figure 7 proves the concept of better SNR using beamforming using different numbers of antennas in the hybrid regularized zero-forcing precoding method in the MIMO system.  
The maximum possible rate for each of the four receiver types, i.e., matched filter, zero-forcing, regularized zero-forcing, and hybrid regularized zero-forcing, as well as each of the channel realizations, are initialized as variables. Each channel realization results in the following.  
* Production of a channel realization, which is an H-matrix with dimensions \(\xi\)t \(\times\)\(\xi\)r.
* Calculation of each stream's output SNR to determine the maximum possible rate for the matching filter receiver.
* Compute the maximal achievable rate for the zero-forcing receiver using the matrix inverse of H.
* Determine the maximum feasible rate for the regularized zero-forcing receiver using the matrix inverse of (H
* H + LI) and a regularization parameter L, where I is the identity matrix.
* Compute the maximal achievable rate for the hybrid regularized zero-forcing receiver using two regularization parameters L\({}_{\text{h}}\) and L\({}_{\text{d}}\), and the matrix inverse of (H
* H + L\({}_{\text{d}}\)I + L\({}_{\text{h}}\)HH*).
* For a specific SNR value, average the highest rates that can be achieved across all channel realizations.
* Plot the maximum attainable rate against the average SNR (in dB) for each type of receiver, as shown in the below figures for different values of several antennas.  
Meanwhile, in Figures 8-10, it is quite evident that the proposed technique establishes better performance as compared to other competitor algorithms at higher SNR values (SNR > 4).  
Figures 8-10 show the results for 4 \(\times\) 4, 16 \(\times\) 16, and 64 \(\times\) 64 antenna configurations. Also, the regularization parameter for channel inversion is taken as 0.05, and the regularization parameter for data covariance is 0.1 for the proposed technique. Also, it can be seen that at lower SNR values (approx. SNR < 4), the MRT technique dominated the performance curves, but as the SNR increases, its performance goes beyond the ZF and RZF as well. On comparing the results, it is observed that HRZF shows continuous growth, while other techniques show decreasing performance.  
Figure 7: Performance with and without hybrid beamforming with different numbers of antennae.
## 3 Data Processing
### Land Masking  
Coastline detection algorithm can operate with two modes, namely:  
* **Shape**. This mode uses external coastline shape files that limit land extension via collections of latitude and longitude pairs. The higher the number of points, the higher the accuracy on isolating the sea from the land. Right now, the system comprises a World file with medium resolution and a Spanish file with very fine resolution. As such, this mode is simple and efficient and normally needs coordinate system transformations to adopt the reference used in SAR images. However, it has an important limitation that results from the fact that performance depends excessively on the available resolution. Certainly, the distance between two pairs shall be as fine as possible in order to avoid false alarms (see Figure 3). This especially applies for small islands and irregular shores, which, in general, are not precisely mapped. The experience acquired with SIMONS has shown that resolutions worse than 25 m are not desirable.
* **Image**. This mode adopts image processing techniques to isolate the coastline with no additional (external) information. In the current version, land is isolated from the sea by evaluating RCS gradients. The procedure can be summarized as follows:
* Moving windows of specific size are used to locally evaluate RCS histograms.
* The information inferred from all the sub-images is analyzed to evaluate whether there are two histogram clusters with differentiated mean RCS values (land and sea) or not.
* If the sea is present in the scenario, then a mask is generated to filter out sea areas, i.e. those pixels that fall into the histogram cluster with the lowest mean RCS.
* Mask perimeter is isolated with an algorithm based on Geodesic Active Contours [4].
* Finally, coastline perimeter is refined by applying some morphological constraints. There are two main rules, which permit the suppression of sea areas within closed land surfaces and of those objects with a perimeter to area ratio higher than a fixed threshold.
For images with standard quality, the algorithm becomes very accurate. But for images with noisy reflectivity information or inaccurate calibration, some limitations are observed. To solve this issue, an improved version based on wavelets is being defined. Until it becomes operational, inaccuracies are solved by supporting image processing with shape files.  
Figure 3 (left) shows an example of coastline isolation (blue line) with the algorithm developed for SIDECAR. The image corresponds to an ENVISAT set of the bay of Gibraltar imaged in Image Precision Mode at 27-12-2007. As observed, coastline is perfectly isolated, even in difficult harbor areas. Figure 3 (right) shows the differences between shape files and image processing. There, a coastline detection algorithm (red line) avoids the wrongly filtered areas (where sea is assumed to be land) and false positives (where land is assumed to be sea) that a low resolution shape file (yellow line) can cause. This indicates how careful the usage of shape files shall be.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 2 Materials and Methods
### Accuracy Assessment  
We used confusion matrices to validate classified maps for all the study years. The matrices were compared to the classified pixels of the test samples. The confusion matrices consist of the overall accuracy, kappa index, producer's, and user's accuracies. The overall accuracy is the proportion of samples that are correctly classified. In contrast, the producer's accuracy is the LULC map's accuracy from the view of the maker or the map producer [33]. The user's accuracy is defined as the viewpoint of the map user, and the kappa index assesses the agreement between the test data and the classifier [39]. The kappa index can be calculated using the following equation:  
\[K=\frac{\big{(}f_{o}-f_{E}\big{)}}{\big{(}N-f_{E}\big{)}} \tag{4}\]  
where \(f_{o}\) denotes the number of observed agreements, \(f_{E}\) stands for the number of agreements that are by chance, and the total number of observations is represented by \(N\)[40]. Ifthe observations are in total agreement, then \(K\) is 1, and if there is no agreement among the observations other than the one expected by chance (denoted by \(f_{E}\)), then \(K\leq 0\)[41].
## 3 Simulation Results
### Cost Analysis  
Figures 1-3 show the main converters in the paper. The next table summarizes the main components of each converter. As seen in Table 4, the proposed converter has only one switch compared with the other two converters. However, the proposed converter has a higher number of components (diodes, inductors, coupling capacitors, and low-pass filters). Therefore, it has a higher cost compared with other individual converters. The cost of components differs based on the selected components (electrical and thermal characteristics), the power range, the manufacturer, and the supplier. Check [39,40].  
The proposed converter distinguishes itself from conventional designs, such as the Zeta and Mahafzah converters, by incorporating a higher count of passive components.  
These additional passive components play a crucial role in facilitating the smooth transfer of energy to the load side. Unlike its counterparts, the proposed converter employs this surplus of passive elements specifically to enhance the efficiency and effectiveness of energy transmission. It's noteworthy that, despite the increased number of passive components, the proposed converter maintains parity with the Zeta and Mahafzah converters in terms of the passive components involved in energy transfer during each mode of operation. This ensures that, while the converter benefits from enhanced functionality, it does not sacrifice the fundamental principles of energy conversion efficiency.
## 3 Limit States and Input of Ground Motions  
### Limit States and Response Measure  
An interstory drift ratio is considered as a global failure criterion, and thus the interstory drift ratio of each structure from a nonlinear pushover analysis with the loading profile of the first mode shape was estimated. Three limit states associated with the desired structural performance levels were defined: serviceability, damage control, and collapse prevention [20]. The serviceability level is defined when longitudinal reinforcement reaches yielding and the damage control level is defined when concrete strain reaches the maximum confined stress. The collapse prevention level is defined when concrete strain reaches the ultimate confined strain that is suggested by the Eurocode 8 [21]. More details can be found elsewhere.  
Figure 5 shows the estimated interstory drift ratios corresponding to the limit states of each analytical model. It was observed that drift ratios corresponding to the damage control and collapse prevention levels increased significantly with an increase in concrete strength. For example, for the collapse prevention level, the drift ratios for the models with concrete strengths of 18 MPa and 30 MPa were 1.31% and 1.95%, respectively. Thus, the drift ratio increased by 48.9%. In addition, the notable increase in the drift ratio associated with the collapse prevention level can be found as the volumetric ratio of transverse reinforcement increases. The TR0.23 and TR0.54 models reach drift ratios of 1.26% and 2.14%, respectively (an increase of 69.8%). However, a minor variation in the drift ratio corresponding to the serviceability level was observed for all cases. Compared to the variation of the concrete compressive strength and volumetric ratio of transverse reinforcement, the effect of the yield strength of reinforcement and longitudinal reinforcement ratio on the drift limit was minimal.  
Figure 4: Material constitutive laws in OpenSees [18]: (**a**) _Steel01_; (**b**) _Concrete02_.
# C-V2X Centralized Resource Allocation with Spectrum Re-Partitioning in Highway Scenario
## 3 Bandwidth Re-Partitioning Techniques among RSUs
### Partial Frequency Reuse Technique (PFR)  
In the PFR technique, a given band is partitioned between two adjacent RSUs (see Figure 3b) with each part denoted as \(RSU_{band}\). Further, the \(RSU_{band}\) is re-partitioned into two adjacent bands, named the Center (inner) Frequency Band (CFB) with the maximum distance from RSU to vehicles equal to \(D_{in}\) to serve Center Users (CUs), and the Edge (outer) Frequency Band (EFB) with the maximum distance from RSU to vehicles equal to \(D_{Total}\) to serve Edge Users (EUs) located in the distance between \(D_{in}\) and \(D_{Total}\). Let us call the RSUs that use the same \(RSU_{band}\) twin-RSUs. The distribution and usage of these bands can be illustrated by the example in Figure 3. The CFB of an RSU is the EFB for the nearest twin-RSUs using the same \(RSU_{band}\) and vice versa. The next resource location will be a free resource in the current and nearest twin-RSUs. For example, if we assume vehicle A (in Figure 3a) to be a central user in RSU X that needs a new resource to allocate, the RSU denoted by X will compare the resource grid of its CUs with the resource grid of edge users in the RSU denoted by Z (a twin-RSU with X) to identify unused resources in both RSUs in order to avoid high interference. Moreover, if the unused resources in both RSUs cannot be found, then the next allocated resource will be a free one in the CU resource grid of RSU X.  
In this technique, the \(h_{reuse}\) is undoubtedly increased. In the current case, the \(h_{s\_band}\) is calculated as follows:  
\[h_{s\_band}=RSU_{diameter}+D_{Total}-D_{in} \tag{22}\]  
As a result of increasing the distance between zones using the same resources, the signal-to-interference plus noise ratio is increased compared with the FFR technique.  
As mentioned before, the radio resource partitioning in RSU has been divided into two sub-bands denoted as \(RSU_{band}\) as follows:  
\[RSU_{band}=\frac{B_{Total}}{2} \tag{23}\]  
where \(B_{Total}\) is the given system bandwidth.  
Let us assume that resource partitioning between CUs and EUs in an RSU is proportional to the ratio of the inner radius \(D_{in}\) and the RSU radius \(D_{Total}\). Thus, the \(RSU_{band}\) is divided into two sub-bands: CFB to be allocated to CUs, and the band adjacent to EFB reserved for EUs. The sub-bands received due to bandwidth re-partitioning are given by the formulas:  
\[CFB=RSU_{band}\frac{D_{in}}{D_{Total}} \tag{24}\]  
\[EFB=RSU_{band}\frac{D_{Total}-D_{in}}{D_{Total}} \tag{25}\]The main achievement of this method is that the \(h_{\textit{reuse}}\) is increased compared with the FFR technique. Consequently, the probability of existence of BCZ is decreased as compared with the FFR technique.
## 3 Analysis of Students' Consumption Behavior
### Clustering Process
#### 3.4.2 Determining Clustering Center
The fractional-order term is introduced into the standard firefly position update formulation by utilizing the last memory of each firefly during the search process of the firefly algorithm, which effectively improves the issue of getting trapped in local optima. In this paper, the firefly algorithm is defined through the Gamma function. This function converts the original integers into fractions, and the gamma function is employed to extend the order from the integer order to the fractional order. Equation (9) can be rewritten as Equation (11):  
\[X_{\text{i}}(t+1)-X_{\text{i}}(t)=\beta_{r_{ij}}\big{(}X_{j}-X_{\text{i}}\big{)} +\alpha(rand-0.5) \tag{11}\]Using only the first \(r=4\) terms of the differential equation given in Equation (11), the position update equation is obtained as Equation (12):  
\[ \begin{array}{l}X(t+1)=vX(t)+\frac{1}{2}v(1-v)x(t-1)-\frac{1}{6}v(1-v)(2-v)x(t-2 )+\\ \frac{1}{24}v(1-v)(2-v)(3-v)x(t-3)+\beta_{r_{ij}}\big{(}X_{j}-X_{i}\big{)}+ \alpha(rand-0.5)\end{array}  \tag{12}\]  
The main procedure of the fractional-order firefly optimization clustering algorithm k-means algorithm (FFA-k-means) is shown in Algorithm 2. The specific steps of the algorithm implementation are as follows:  
Step 1. Utilize the Elbow Method and the Calinski-Harabasz index to determine the optimal number of clusters, \(K\). Step 2. Implement the fractional-order firefly algorithm (FFA). Step 3. Use the cluster centers obtained from the FFA as the initial centroids for the k-means algorithm, serving as the starting points for the clustering process. Step 4. Compute the Euclidean distance between the remaining sample points and the cluster centers. Step 5. Assign each sample to the cluster that is most similar to it based on the calculated Euclidean distances. Step 6. Determine the average value for each cluster to establish the new center of that cluster. Step 7. Repeat Steps 4, 5, and 6 until it stops when the cluster centers no longer change.