esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 3 Methodology
### Input Layer  
The MTL model's input layer was responsible for preparing the raw data for further processing. After constructing the MultiProSE dataset to make it suitable for use in the proposed MTL, as we explained in our previous study [22], the preprocessing of the input text (\(x_{i}\)) was applied. Generally, the data preprocessing process was divided into two main phases, as Figure 2 shows. The first phase involved handling the raw news text, whereas the second phase focused on formatting the data to meet the input requirements of transformer-based models. The first stage included character normalization, diacritical mark elimination, stop word elimination, and the exclusion of non-Arabic text. Subsequently, the input text was tokenized using the WordPiece tokenizer [47], which was compatible with the BERT backbone model. Tokenization enabled the creation of word vectors and effectively addressed the challenge of out-of-vocabulary words by breaking them down into root words and sub-words.  
Once the data preprocessing and tokenization were completed, the multi-task dataset was created by merging samples from three task-specific datasets (i.e., propaganda, sentiment, and emotion). Each sample of the multi-task dataset consisted of the news text, a label, the task type, and the task ID. All three tasks were assigned the \"seq_classification\" task type because they involved sentence classification. Furthermore, a unique task ID was added to each sample as a new token, labeled \"task_ids,\" which assisted the model in properly handling the samples for each task. Next, the tokenized batch was fed into the shared layer, as the next subsection explains.
## 2 System Modeling
### Perturbation and Observation (P&O)-Based MPPT  
In P&O technique [12] the tracker operates by periodically increasing or decreasing the voltage. The controller changes the reference voltage for the DC link voltage controller by tracking and comparing the present and one-step-behind power and voltage values. The increasing or decreasing of the reference value is applied with a fixed small value. The flowchart of a perturbation and observation-based algorithm is given below in Figure 3.  
Figure 3: Perturbation and observation-based maximum power point tracking (MPPT).  
Figure 2: Equivalent circuit of PV cell; I–V and P–V characteristics curves.
## 3 Results and Discussion  
Following that, a GMDH model will be introduced for 30-, 60-, and 90-degree diversion angles and 1.4, 1.2, 1.0, 0.8, and 0.6 width ratios to forecast the mean velocity of the stream. It is essential to model and verify a numerical model to train the model under conditions where there are no experimental results; therefore, [PERSON] et al.'s [25] experimental model was simulated through using the ANSYS-CFX software. This model is then verified using the existing experimental data. After making sure that the CFX has simulated the model accurately, different models were run by using CFX and used to train and verify GMDH to forecast the mean velocity of the stream. Following that and after making sure that GMDH performs well, the mean velocity is predicted for the width ratios and angles on which there is no experimental model. For the model is based on the criteria of the coefficient of determination (\(R^{2}\)), root mean square error (\(RMSE\)), mean absolute percentage error (\(MAPE\)) and \(SI\) as defined in the following forms:  
\[R^{2}=\left[\sum_{i=1}^{n}(x_{i}\text{n}-\ \overline{x})(y_{i}\text{n}- \overline{y})/\sqrt{\sum_{i=1}^{n}(x_{i}\text{n}-\overline{x})^{2}\sum_{i=1} ^{n}(y_{i}\text{n}-\overline{y})^{2}}\right]^{2} \tag{13}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_{i}\text{n}-y_{i})^{2}} \tag{14}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|x_{i}\text{n}-y_{i}\right|}{x_{i}} \tag{15}\]  
\[SI=\frac{RMSE}{\overline{x}} \tag{16}\]  
where \(y_{i}\) and \(x_{i}\) are the calculated and real mean velocity values, respectively, and \(\overline{y}\) and \(\overline{x}\) are the mean calculated and real mean velocity values, respectively.
# Comparison of Manual versus Semi-Automatic Segmentations of the Stenotic Carotid Artery Bifurcation
## 3 Results  
### Segmentation Comparisons  
The equivalent diameter curves of both segmentations are plotted along the centerline and their differences can be visualized between the semi-automatic and manual segmentations. These difference curves are depicted for each case in Figure 3. An average diameter difference normalized by the voxel length (ADDV) from the aforementioned diameter difference curve is computed for all patients. Group averages and standard deviations were also computed to compare the outcome between the groups. Furthermore, the value of the minimum diameter (D\({}_{\text{min}}\)) was extracted for all the patients and averaged for each group.  
The summary of the evaluation can be seen in Figure 3 for all the cases. In the left panel of Figure 4 only the diameter difference curves are shown, normalized by the voxel dimension (DDV curves). These diagrams collectively show the cases in each group for visual interpretation. By visual inspection an excessive over-segmentation can be seen in the first group by the manual segmentation. In Group II difference curves tend to move closer to the 0 value with some cases of under-segmentation. Visually, the cases of Group III. are similar to those in Group II. with less under-segmentation. In the right panel of Figure 4 a Bland-Altman diagram can be seen to analyze the average diameters obtained from both methods that comprises all three groups. The quantitative results confirm the visual interpretations closely. Furthermore, it shows that the cases in Group II. are less scattered around the overall average than in Group III.  
Group averages were computed with weighted averaging by the centerline length. As it can be seen from the quantitative results of the ADDV measurements in Table 2, the largest mean value of ADDV and corresponding standard deviation (std) was in Group I. with 1.16 and 1.33 respectively. The mean values in Group II. and III. are 0.65 and 0.75, while the deviations are 1.09 and 0.91, respectively. The average for the present whole study population was 1.17 with a standard deviation of 1.12.  
Figure 3: All evaluated cases as described in Figure 2.  
automatic segmentation in the collective group. Additionally, significant (_p_ < 0.01) although weak correlation (r = 0.138) was found in the first individual group. Non-significant and almost zero correlation was obtained in the separated segmentation group.
## 2 Background  
### Underground Geological Conditions  
The Jintong Mine is situated in Ordos, Inner Mongolia Autonomous Region, China, as illustrated in Figure 1a. The 51,102 working face is positioned in the northern wing area of the Jintong Coal Mine, with an 8 m coal pillar left between the two working faces. The primary mining coal seam, designated as 5-1, is buried at depths ranging from 42 m to 74 m, with an average depth of 58 m. The 51,102 working face has a knife handle shape, approximately 737 m from the opening, and its width decreases from 240 m to 149 m. The underground spatial relationships are depicted in Figure 1b. According to data from J2-4 drilling (Figure 1c), the direct roof of the 51,102 return airway consists of siltone that is grey to dark grey, mud-based, occurring in clumps, and containing fine mica flakes and fossilized plant fragments, characterized by horizontal laminations and a limited number of developed fissures. The basic roof is composed of coarse-grained sandstone, light grey in color, with a fine-grained sandy structure primarily made up of quartz and muddy cementation, locally exhibiting horizontal and interlacing laminations, and is relatively soft, underlain by a thin layer of sandy mudstone. The direct floor comprises sandy mudstone, grey in color, with a sandy mud structure that contains numerous plant fossil fragments and coal packages, along with a small number of cracks; the core is soft and prone to weathering. The basic floor is formed from fine-grained sandstone, grey in color, primarily consisting of feldspar and quartz. The main aquifers associated with the 51,102 working face include the sandstone fissure aquifer of the Yan'an Formation located in the roof of the working face and the surface backfill management layer. It is anticipated that the maximum water influx in the 51,102 working face will reach 8 m\({}^{3}\)/h, with a normal water influx of 4 m\({}^{3}\)/h. Observations indicate that the degree of weakening of the roof strength due to drenching is significant, with the roof rock layer in the exposed section being easily broken by hand. The exposed sections of the anchor rods and anchors are continuously dripping, and the rods exhibit significant corrosion. This corrosion increases the likelihood of fractures in the anchor rods and anchors due to mining influences, thereby elevating the safety risks associated with roadway support. Detailed observations are illustrated in Figure 1d.
## 5 Data Analysis and Results
### Analysis of Neural Network for Cloud Computing Adoption
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Original** & **Sample** & **Standard Deviation** & **\(t\) Value** & **\(p\) Values** & **Result** \\  & **Sample (O)** & **Mean (M)** & **(STDEV)** & **(Q/STDEV)** & **\(p\) Values** & **Result** \\ \hline Compatibility \(>\) CC adoption & 0.249 & 0.243 & 0.058 & 4.311 & 0 *** & Supported \\ Competitive pressure \(>\) CC adoption & 0.381 & 0.375 & 0.069 & 5.516 & 0 *** & Supported \\ Complexity \(>\) CC adoption & -0.153 & -0.15 & 0.053 & 2.887 & 0.004 *** & Supported \\ Cost saving \(>\) CC adoption & 0.102 & 0.102 & 0.045 & 2.266 & 0.023 ** & Supported \\ Vendor support \(>\) CC adoption & 0.112 & 0.117 & 0.077 & 1.447 & 0.148 & NS \\ technology readiness \(>\) CC adoption & 0.285 & 0.277 & 0.058 & 4.888 & 0 *** & Supported \\ Top manager’s support \(>\) CC adoption & 0.129 & 0.13 & 0.065 & 1.978 & 0.048 ** & Supported \\ Security-\(>\) CC adoption & -0.173 & -0.167 & 0.078 & 2.226 & 0.026 ** & Supported \\ \hline \hline \end{tabular} Note: ** \(<\) 0.05, *** \(<\) 0.01.  
\end{table} 
Table 7: Summary of hypothesis tests. a high predicting accuracy, based on minor RMSE scores and it shows that the model is reliable in depicting the relationship between predictors and output. Therefore, all the factors are suitable for forecasting CC adoption as a dependent variable. The normalized importance is the ratio of the relative importance of each factor with its maximum relative significance, and it is stated in percentage form. Based on the PLS-SEM examination, just significant linear factors have been employed in the input parts of the ANN model. In this regard, just linear relationships have been checked. The normalized importance was calculated in the sensitivity analysis according to relative factor importance weights (see Table 9). The variable \"technology readiness\" resulted as the most significant factor in predicting the CC adoption, and \"security\" is the second important factor. Figure 3: The anticipated ANN architect for 10 neurons. Note: CC adoption (CCA); Compatibility (CT); Competitive pressure (CP); Complexity (CX); Cost saving (CS); Technology readiness (TR); Top manager’s support (TMS); Security (SC). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Configures** & **Testing** & **Training** \\ \hline
**ANN1** & 0.1174 & 0.0994 \\
**ANN2** & 0.1079 & 0.1004 \\
**ANN 3** & 0.1132 & 0.1013 \\
**ANN 4** & 0.1105 & 0.1015 \\
**ANN 5** & 0.1137 & 0.105 \\
**ANN 6** & 0.1054 & 0.1015 \\
**ANN 7** & 0.1069 & 0.1029 \\
**ANN 8** & 0.1124 & 0.1045 \\
**ANN 9** & 0.1066 & 0.1053 \\
**ANN 10** & 0.1141 & 0.0973 \\
**Average** & 0.1101 & 0.1022 \\
**Standard deviation** & 0.0034 & 0.0026 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: RMSE values of artificial neural networks.
# Challenges of Hydrodynamic Cavitation of Organic Wastes
## 2 Materials and Methods  
### Characteristic of Substrates  
Three substrates were used in the present study: MW, MLL and LB represented by brewery spent grain.  
The first component was sourced from the Hajdow municipal wastewater treatment plant with an average flow of 120,000 m\({}^{3}\)/d (Lublin, Poland) involving mechanical and biological steps. This sample of 35 L was taken after mechanical treatment from the effluent of the primary settling tank.  
The MLL sample originated from the municipal solid waste landfill located in Rokitno (Lublin, Poland). This facility has been operated for over 20 years and is used for disposal of non-hazardous municipal solid waste from residential urban areas. The total area of this landfill is over 38 ha. A 35 L sample was taken from a leachate storage tank.  
In the present study, brewery spent grain was adopted as LB. It was obtained from a craft brewery situated in Lublin (Poland) using barley for beer production. It was used in fresh form and was only milled to obtain a particle size of 2 mm.  
After collection, all the samples used in the present study were immediately transported to the laboratory. For each HC, new portions of substrates were taken; their composition was estimated after each subsample collection. The detailed characteristic of all the substrates is presented in Table 1, and the composition of LB is presented in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Unit** & \begin{tabular}{c} **Lignocellulosic** \\ **Waste (LB)** \\ \end{tabular} & \
\begin{tabular}{c} **Municipal Wastewater** \\ **(MW)** \\ \end{tabular} &
\begin{tabular}{c} **Mature Landfill** \\ **Leachate (MLL)** \\ \end{tabular} \\ \hline Chemical oxygen demand (COD) & mg/L & 323.3 \(\pm\) 8.1 \({}^{*}\) & 533.5 \(\pm\) 52 & 6095 \(\pm\) 62.9 \\ Soluble chemical oxygen demand (SCOD) & mg/L & & 471 \(\pm\) 40 & 4736 \(\pm\) 48.9 \\ Biological oxygen demand (BOD\({}_{5}\)) & mg/L & & 256.9 \(\pm\) 12 & 263 \(\pm\) 19.8 \\ Total organic carbon (TOC) & g/kg & 46.2 \(\pm\) 1.1 & 1792 & \\ Biodegradability Index (BI) & - & 0.4760 \(\pm\) 0.03 & 0.0435 \(\pm\) 0.01 \\ Total solids (TS) & g/kg & 235.1 \(\pm\) 5.4 & 0.8 \(\pm\) 0.02 & 14.5 \(\pm\) 0.04 \\ Volatile solids (VS) & g/kg & 228.6 \(\pm\) 4.3 & 0.62 \(\pm\) 0.01 & 3.25 \(\pm\) 0.7 \\ Alkalinity (ALK) & mg/L & & 526 \(\pm\) 47 & 14573 \(\pm\) 329 \\ pH & - & & 7.42 \(\pm\) 0.07 & 8.09 \(\pm\) 0.05 \\ Volatile fatty acids (VFA) & mg/L & & 158.9 \(\pm\) 27 & 1171 \(\pm\) 49 \\ Total nitrogen (TN) & mg/L & & 79 \(\pm\) 5 & 3552 \(\pm\) 167 \\ Total phosphorus (TP) & mg/L & & 21.4 \(\pm\) 2.1 & 20.8 \(\pm\) 2.3 \\ Ammonium nitrogen (N-NH\({}_{4}\)\({}^{*}\)) & mg/L & & 58 \(\pm\) 2.2 & 1888 \(\pm\) 150 \\ Ortho-phosphate phosphorus (P-PO\({}_{4}\)\({}^{3-}\)) & mg/L & & 17.1 \(\pm\) 1.2 & 22.5 \(\pm\) 1.5 \\ Phenols & g/kg TS & 0.24 \(\pm\) 0.01 & & \\ \hline \end{tabular} \({}^{*}\) g/kg.  
\end{table}
Table 1: Characteristics of substrates (average values and standard deviation).
### Design of Forging Gear Blank for Spiral Bevel Gear  
Forging-finishing is a common metal forging process which is often used to manufacture hubs, gears and other similar parts. According to the requirements of the geometric parameters of the forging target spiral bevel gear, it is necessary to preprocess the gear blank. In the forging process, considering that the forging process of the spiral bevel gear belongs to unsteady large deformation, it is necessary to use a two-step forming process to forge the bar-shaped material into a blank with a gear ring. In this paper, the warm forging and finishing of the spiral bevel gear adopts the semi-closed forging process with one fire and two forgings, as shown in Figure 2. The forming process of the spiral bevel gear includes upsetting, punching, rolling preforming and final forging.  
In terms of material selection, spiral bevel gears are usually made of high-strength alloy steel or carbon steel, and AISI-1045 is selected as the material in this experiment. The material has good cold and hot workability and is suitable for forging-forming. Its comprehensive mechanical properties are excellent, and it is widely used in the manufacturing of gear, bearings and other parts. According to the design principle that the diameter of the blank is equal to the diameter of the middle point of the tooth profile of the spiral bevel gear and the condition that the volume is unchanged before and after forging, the bar material of \(\Phi\) 110 \(\times\) 25 mm is selected as the specification of the gear blank.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Gear** \\ \hline Tooth number & 44 \\ Module (mm) & 3 \\ Average pressure angle (\({}^{\circ}\)) & 20\({}^{\circ}\) \\ Midpoint helix angle (\({}^{\circ}\)) & 35\({}^{\circ}\) \\ Hand of helix & LH \\ Diameter of indexing circle (mm) & 132 \\ Tooth top height (mm) & 1.516 \\ Tooth root height (mm) & 4.148 \\ Matching gear teeth number & 15 \\ Top cone angle (\({}^{\circ}\)) & 73\({}^{\circ}\)39/3\({}^{\prime\prime}\) \\ Partial cone angle (\({}^{\circ}\)) & 71\({}^{\circ}\)10/31\({}^{\prime\prime}\) \\ Root cone angle (\({}^{\circ}\)) & 67\({}^{\circ}\)0/14\({}^{\prime\prime}\) \\ External taper distance (mm) & 69.73 \\ Mounting distance (mm) & 27 \(\pm\) 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical parameters.  
Figure 1: Three-dimensional modeling of spiral bevel gear.  
Figure 2: Forging process for spiral bevel gear blank.  
In the design process of the gear blank, it is necessary to consider the various geometric parameters of the gear blank, including the pitch circle diameter, outer cone distance, etc. The setting of these parameters needs to be combined with factors such as the gear ratio, torque and strength [1]. At the same time, it should be noted that the inclination angle of the tooth root is very important for the manufacturing and strength of the gear blank, and it needs to be designed reasonably. The gear blank designed by CAD is shown in Figure 3, and the three-dimensional model of it is established by NX software, as shown in Figure 4.
## 5 Result of Experiment
### Data Model Development
#### 5.2.3 Consistency Design  
The consistency step ensures consistency of the dataset across all subsystems and allows for data fragmentation. As polyglot supports NoSQL data models, the eventual consistency provided by BASE properties has to be taken into account during the data model creation step. Polyglot persistence does not have to be consistent across the entire database, but some data groups must be consistent to be valid. These groups are called consistency units and play a key role in allowing data fragmentation and horizontal scalability. The consistency unit has to ensure a guarantee that, provided no new updates to an entity are made, all reads of the entity will eventually return the last updated value. An example of one consistency unit in the SSI application is shown in Figure 15. Customer, agreement, and receive instruction comprise a consistency unit, and in the case a query returns the response with different versions of items, we have an inconsistency that may cause a system failure.  
Figure 14: The segmentation units are identified in the simplified conceptual database schema.  
Figure 15: The consistency unit identified in the simplified conceptual database schema.
## 3 System Model
### Downlink PD SISO-NOMA Communication Scenario  
Figure 2 represents the downlink power-domain communication scenario in TI. Let \(m_{1}\), \(m_{2}\),..., \(m_{N}\) denote users' messages to be transmitted from the BS, with transmitted power as \(P_{T}\). Let the PA coefficients be \(\alpha_{1}\), \(\alpha_{2}\),..., \(\alpha_{N}\), with the corresponding channel coefficients as \(h_{1}\), \(h_{2}\),..., \(h_{N}\).  
Let us consider \(U_{1}\) to be the farthest user, followed by \(U_{N}\) to be the nearest user to the BS.  
As superposition coding is performed at the BS, the transmitted signal (\(t_{s}\)) from the BS is given by  
\[t_{s}=\sqrt{P_{T}}(\sqrt{\alpha_{1}}m_{1}+\sqrt{\alpha_{2}}m_{2}+\ldots+\sqrt{ \alpha_{N}}m_{N}) \tag{1}\]  
In short, \(t_{s}\) at \(i^{th}\) user can be written as  
\[t_{s}=\sum_{i=1}^{N}\sqrt{P_{T}}\alpha_{i}\times m_{i}\]  
\[t_{s}=\sqrt{P_{T}}\sum_{i=1}^{N}\alpha_{i}\times m_{i} \tag{2}\]  
Therefore, the received signal (\(r_{s}\)) at the \(i^{th}\) user is given by  
\[r_{s}=h_{i}t_{s}+n_{i} \tag{3}\]  
Figure 2: Downlink power-domain communication scenario in TI.  
Comparing Equations (2) and (3), \(r_{s}\) can be written as  
\[r_{s}=h_{i}\bigg{[}\sqrt{P_{T}}\sum_{i=1}^{N}\alpha_{i}\times m_{i}\bigg{]}+n_{i}\]  
\[r_{s}=\sqrt{P_{T}}h_{i}[\sqrt{\alpha_{1}}m_{1}+\sqrt{\alpha_{2}}m_{2}+\ldots+ \sqrt{\alpha_{N}}m_{N}]+n_{i} \tag{4}\]  
where \(n_{i}\) is Additive White Gaussian Noise (AWGN), with mean 0 and variance \(\sigma^{2}\).  
By considering Equation (4), the \(r_{s}\) for User 1 (\(U_{1}\) = farthest user), where \(i=1\), will be  
\[r_{s}^{U_{1}}=\sqrt{P_{T}}h_{1}\bigg{[}\underbrace{\sqrt{\alpha_{1}}m_{1}}_{ Desired\ Signal}+\underbrace{\sqrt{\alpha_{2}}m_{2}+\ldots+\sqrt{\alpha_{N}}m_{N}}_{ Interfence\ Signal}\bigg{]}+n_{1} \tag{5}\]  
As User 1 is allocated with the highest power coefficients (\(\alpha_{1}\)), the \(r_{s}\) component of User 1 is the desired and dominant signal, and other components are considered undesired (interference).  
Along the same lines, \(r_{s}\) for User 2 (\(U_{2}\) = relatively near to BS), where \(i=2\), will be  
\[r_{s}^{U_{2}}=\sqrt{P_{T}}h_{2}\bigg{[}\underbrace{\sqrt{\alpha_{1}}m_{1}}_{ Undesired\ Signal}+\underbrace{\sqrt{\alpha_{2}}m_{2}}_{Desired\ Signal}+\underbrace{\ldots+\sqrt{\alpha_{N}}m_{N}}_{ Interference\ Signal}\bigg{]}+n_{2} \tag{6}\]  
As User 2 is allocated with a relatively lower power coefficient (\(\alpha_{2}<\alpha_{1}\)), the \(r_{s}\) component of User 2 is considered as desired but not dominant. The dominant \(r_{s}\) signal is still the User 1 signal component, which is considered undesired (interference), along with other remaining user signal components. Hence, SIC is performed on the \(r_{s}\) to decode/retrieve the user's original message/signal to remove undesired (interference) signal components.
# A Low-Profile Dual-Polarized Magneto-Electric Dipole Antenna for 5G Applications
## 4 Conclusions  
A low-profile dual-polarization MED antenna was proposed for 5G applications. With meandering slots on the vertical magnetic dipole, a low-profile MED was realized. Moreover, the magnetic dipole based on a three-sided structure was designed and processed independently, which made the antenna structure simpler and more stable. The proposed antenna achieved the following performances: (1) the common frequency bandwidth with S11 \(\leq-\)10 dB, S12 \(\leq-\)20 dB was 36.8% from 3.1 GHz to 4.5 GHz; (2) the peak gain reached 10.2 dBi, and the average gain exceeded 8.5 dBi; (3) a 3 dB beamwidth with more than 45 degrees was realized in both E-plane and H-plane; (4) the cross-polarization levels were lower than \(-\)22 dB, covering the above frequency band. In other words, the proposed antenna has significant application value.  
Conceptualization, Y.Z. and Q.T.; methodology, Y.Z.; software, Y.Z. and Q.T.; validation, K.F., Q.T. and Y.Z.; formal analysis, Q.T.; investigation, Y.Z.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, K.F. and Q.T.; visualization, Q.T.; supervision, K.F.; project administration, K.F.; funding acquisition, K.F. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Open Fund of the State Key Lab of Millimeter Waves of China under Grant K202329.  
Not applicable.  
The authors declare no conflict of interest.