esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents the enhancements performed on the adaptive linear neuron (ADALINE) technique so that it can be applied for active power filtering purposes in a three-phase four-wire system. In the context of active power filtering, the ADALINE technique which was initially developed for a single-phase two-wire system has been further expanded to suit three-phase three-wire system. For both systems, ADALINE techniques have been reported to be effective even when the grid voltage is distorted and/or unbalanced. However, further works that study the possibility to apply ADALINE technique in a three-phase four-wire system which invariably carries unbalanced loads, are rather limited. Hence, in this work, a control algorithm (named as enhanced-ADALINE) which combines the strength of highly selective filter (HSF), ADALINE concept and averaging function is proposed, to manage harmonics mitigation by shunt active power filter (SAPF) under non-ideal grid and unbalanced load scenarios. MATLAB-Simulink software is utilized to conduct an exhaustive simulation study which includes circuit connection of SAPF in a three-phase four-wire system, design of control algorithms, and performance assessments. Benchmarking with the existing algorithm is performed to examine the benefits of using the proposed algorithm. From the analysis, simulation findings are presented and thoroughly discussed to verify design concept, capability, and relevance of the proposed algorithm.  
active filtering; artificial neural network; control algorithm improvement; harmonics solution; intelligent technique; simulink/MATLAB; unbalanced load scenarios +
Footnote †: journal: Applied Soft. 2019, 9, 5304; doi:10.3390/appr9245304
# AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus
## 2 Related Work
### Sentiment Annotation
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study Dataset** & **Year Size** & **Domain** & **Annotation Method** & **Type** \\ \hline \hline \multirow{2}{*}{[10]} & 2013 & \multirow{2}{*}{Book reviews} & \multirow{2}{*}{Automatic} & \multirow{2}{*}{DA} \\ Labr & 63,257 & & & \\ \hline \hline \multirow{2}{*}{[11]} & 2015 & Movies, hotels, restaurants, and products reviews & \multirow{2}{*}{Automatic} & \multirow{2}{*}{MSA/DA} \\ \cline{1-1}  & 2016 & & & \\ \hline \hline \multirow{2}{*}{[12]} & 2016 & \multirow{2}{*}{Book reviews} & \multirow{2}{*}{Automatic} & \multirow{2}{*}{MSA} \\ BRAD 1.0 & 510,600 & & & \\ \hline \hline \multirow{2}{*}{[13]} & 2018 & \multirow{2}{*}{Book reviews} & \multirow{2}{*}{Automatic} & \multirow{2}{*}{MSA/DA} \\ BRAD 2.0 & 692,586 & & & \\ \hline \hline \multirow{2}{*}{[14]} & 2017 & \multirow{2}{*}{Reviews} & \multirow{2}{*}{Automatic} & \multirow{2}{*}{MSA/DA} \\ HARD & 490,587 & & & \\ \hline \hline \multirow{2}{*}{[15]} & 2018 & \multirow{2}{*}{Tweets} & \multirow{2}{*}{Automatic} & \multirow{2}{*}{DA} \\ SentiALG & 8000 & & & \\ \hline \hline \multirow{2}{*}{[16]} & 2018 & \multirow{2}{*}{Tweets} & \multirow{2}{*}{Automatic} & \multirow{2}{*}{DA} \\ SSA-SDA & 5456 & & & \\ \hline \hline \multirow{2}{*}{[17]} & 2018 & \multirow{2}{*}{Tweets} & \multirow{2}{*}{Automatic} & \multirow{2}{*}{MSA/DA} \\ TEAD & 6M & & & \\ \hline \hline \multirow{2}{*}{[21]} & 2017 & \multirow{2}{*}{Tweets} & \multirow{2}{*}{Semi-automatic} & \multirow{2}{*}{MSA/DA} \\ AraSenTi-Tweet & 17,573 & & & \\ \hline \hline \multirow{2}{*}{[22]} & 2018 & \multirow{2}{*}{Tweets} & \multirow{2}{*}{Semi-automatic} & \multirow{2}{*}{MSA/DA} \\  & 4000 & & & \\ \hline \hline \multirow{2}{*}{[4]} & 2015 & \multirow{2}{*}{Tweets} & \multirow{2}{*}{Manual} & \multirow{2}{*}{MSA/DA} \\ ASTD & 10,000 & & & \\ \hline \hline \multirow{2}{*}{[26]} & 2019 & \multirow{2}{*}{Tweets} & \multirow{2}{*}{Manual} & \multirow{2}{*}{DA} \\ SDCT & 32,063 & & & \\ \hline \hline \multirow{2}{*}{[27]} & 2019 & \multirow{2}{*}{Tweets} & \multirow{2}{*}{Manual} & \multirow{2}{*}{DA} \\ ArSentD-LEV & 4000 & & & \\ \hline \hline \multirow{2}{*}{[29]} & 2019 & \multirow{2}{*}{Comments} & \multirow{2}{*}{Manual} & \multirow{2}{*}{DA} \\ SANA & 513 & & & \\ \hline \hline \multirow{2}{*}{[30]} & 2018 & \multirow{2}{*}{Tweets} & \multirow{2}{*}{Manual} & \multirow{2}{*}{DA} \\  & 5400 & & & \\ \hline \hline \multirow{2}{*}{[31]} & 2019 & \multirow{2}{*}{Tweets} & \multirow{2}{*}{Manual} & \multirow{2}{*}{DA} \\  & 1000 & & & \\ \hline \hline \multirow{2}{*}{[32]} & 2018 & \multirow{2}{*}{Reviews} & \multirow{2}{*}{Manual} & \multirow{2}{*}{MSA/DA} \\ MASC & 8860 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing Arabic sentiment annotated corpora (MSA: Modern Standard Arabic and DA: Dialelectical Arabic.
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 4 ML-Based Designs
### Choice of the Type of UAV  
The main energy expenditures of UAVs with communication capabilities are due to the aircraft operations (i.e., hovering and flying) and radio transceivers. Different UAVs features such as speed, wings type, motion features, and size may affect the energy consumption of the radio interface [53; 63; 66; 67; 69]. Therefore, the choice of the type of UAVs (i.e., hovering time, speed range, storage capacity, maximum download/upload speed) is important to support users with different communication requirements. This task can be accomplished via ML techniques.  
As a first step, the area to be covered is divided in sub-areas, each one has to be served by a specific UAV. By using available traffic patterns, each area can be classified on the basis of the number of users and their communication requirements by means of SL approaches. The obtained classification is then used to choose the most appropriate type of UAV. For instance, if we have \(N\) different UAVs with different features, an appropriate match for a subarea can be chosen among these UAVs. For a given time slot, when the feature of both area traffic and UAV is changed, the class of that area is changed to a new one and a new UAV is employed to cover the considered area. This new UAV can again be chosen from \(N\) different type of available UAVs (classification task), by choosing the one ensuring the best functionality in terms of hovering time, buffer size, capability to have a certain speed and different capability on their maximum altitude. Based on the data set that can be achieved in an offline manner, the aforementioned features can be chosen to be injected to an ML box. A schematic view of this process is given in Figure 7.  
In a more complex multi-UAV scenario, if \(N\) different types of UAVs each with different type of communication interface are available and \(M<N\) UAVs are needed to support users with different communication requirements, different types of UAVs should be chosen in a way that their features match as much as possible with users' requirements. Each class can be a combination of different type of UAVs, whose choice is an open problem that deserves further investigations.
# A Survey on Bias in Deep NLP
## 4 Discussion
### Association Tests  
There are several approaches to bias measurement and mitigation. [PERSON] et al. [26] laid the foundations for much of the work that was to follow. The main contribution was on showing that embeddings captured the correlation between terms so that they could correctly resolve analogies such as man:king \(\rightarrow\) woman:queen, but also some similar analogies were biased. For example, it associates man:doctor and woman:nurse while the association woman:doctor would be more adequate. Using this same mechanism, it was obtained a set of terms that were stereotyped to each gender, to prove that this was not an isolated case. To remove that bias, they proposed to find the gender vector subspace direction and adjust the vector to make the occupational terms gender-neutral. [PERSON] et al. [10] took the idea of measuring bias of using the Implicit Association Test and proposed the Word Embedding Association Test (WEAT). WEAT measures the similarity of words by using the cosine between the pair of vectors of those words. It was applied to GloVe (Global Vectors for word representation) [27] and also to Word2 Vec (word vectors) [28] with very similar findings. Other extension to WEAT was proposed by [PERSON] and [PERSON] [29], [PERSON] et al. [30] with the name XWEAT, a cross lingual extension for WEAT. XWEAT was later extend to Arab [PERSON] et al. [31]. WEAT can also be applied to other models. [PERSON] and [PERSON] [32] applied it to the gender neutral version of GloVe called GN-GloVe from [PERSON] et al. [33]. [PERSON] et al. [34] use it with a skip-gram network in the context of Question Answering and Decision Making and [35] creates an algorithm to discover offensive association related with gender, race and other attributes, generating WEAT tests for them. They called this technique Unsupervised Bias Enumeration (UBE). UBE is applied to Word2 Vec, fastText and GloVe. [PERSON] and [PERSON] [36] propose two complementary tests that measure the bias removal effect (ECT, EQT).
## 2 System Model and Problem Analysis  
As mentioned above, the broadband RF front-end with sufficient linearity is an important guarantee of a reliable spectrum sensing. In this section, first, a classical broadband RF front-end of a sensor is chosen as an example and then a simplified model is built to discuss its joint effect of group delay distortion and nonlinear distortion. Finally, an identification criterion for nonlinearity mitigation is suggested.
## 5 Challenges
### Interoperability  
Healthcare ecosystems struggle with interoperability between devices, systems, and software platforms. Legacy systems, incompatible with newer technologies, make integration difficult. Proprietary communication protocols complicate efforts to enable devices and platforms to work together. This results in data fragmentation [221], which reduces the effectiveness of patient care. Inconsistent adoption of standardization efforts such as HL7 and FHIR [222] leaves gaps in system compatibility, which pose a significant barrier to adopting smart healthcare technologies. There are four basic levels of interoperability [47; 223; 224], as indicated in Table 12.  
However, existing healthcare systems were not designed for Level 3 IoMT devices. Integrating DL-powered IoMT solutions into legacy infrastructure requires substantial investment, staff retraining, and overcoming technical barriers [223]. In the IoMT, there are concerns about bias in DL models, especially when training data are not diverse. Ethical dilemmas arise when using AI to make critical healthcare decisions. Legal frameworks lag behind technological advancements, leading to ambiguity in accountability and liability. Integrating DL-powered IoMT solutions into existing healthcare infrastructures requires substantial financial investment to upgrade legacy systems, retrain staff, and address technical limitations, and to ensure seamless data exchange between devices, hospital information systems, and AI models. Key standards concentrate on three areas that are widely adopted in the IoMT area. These include (i) Fast Healthcare Interoperability Resources (FHIRs) [225]: A modern, API-based standard for enabling the sharing of electronic health records (EHRs) between systems. (ii)Health Level 7 (HL7) [226]: A widely used framework for exchanging, integrating, and retrieving electronic health information. (iii) Digital Imaging and Communications in Medicine (DICOM) [227]: The standard for medical imaging data interoperability, including X-rays, MRIs, and CT scans. (iv)Integrating the Healthcare Enter
## 3 Number of Dominant Rays: Simulation Results
### Directive Link
As expected, no dispersive effect was noted during the first 30.47 m, where no multi-path was revealed. Incremented phase shifts appeared afterwards, due to the multi-path effects, first from the vertical (ceiling and floor) reflections. More reflections are realized as one advances along the tunnel, increasing the group delay correspondingly, as the channel becomes more and more frequency-selective. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Definition** & **Parameter** & **Value** \\ \hline Tunnel Width & \(2a\) & 6.6 m \\ Tunnel Height & \(2b\) & 4 m \\ Cross-Section Shape & - & Rectangular \\ Polarization & - & vertical \\ Frequency & f & 28 GHz \\ Effective Equivalence Vertical Beamwidth & \(\theta_{V}\) & \(15^{\circ}\) \\ Effective Equivalence Horizontal Beamwidth & \(\theta_{H}\) & \(10^{\circ}\) \\ Transmitting antenna gain & \(G_{t}\) & 24 dBi \\ Receiving antenna gain & \(G_{r}\) & 24 dBi \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters of the wireless link operating inside Lincoln Tunnel. Figure 8: _Cont._  
Using criterion Equation (15), it was found that for the first 140m of Lincoln Tunnel, the required number of rays in the horizontal and vertical dimensions was \(N=1\) and \(M=4\), respectively (see Figure 8a). Examination of the convergence as a function of the number of reflected rays \(M\) and \(N\) via the normalized RMS error is shown in Figure 9. Inspection of the resulted modification error above four reflections revealed insignificant modifications.
## 5 Applications of DRA
### RF Energy Harvesting
This feeding mechanism effectively couples the energy for a broad range of frequencies between the feed line and the radiating elements. In addition, a metallic reflector is placed below the antenna for gain enhancement. Circularly polarized DRAs for RF energy harvesting are proposed in [68; 87; 88]. Different shaped geometry has been considered for bandwidth and gain improvement [87; 88]. For instance, in [87], a one-fourth of the cylindrical DRA is proposed for bandwidth and gain improvement. A dumbbell-shaped slot is etched on the ground plane and placed on the other side of the substrate to achieve the resonance within the desired operating frequencies. In order to obtain circular polarization, two metallic strips V-shaped are etched on the upper edge of the DR. The proposed antenna can capture electromagnetic signals from all directions because it radiates in an omnidirectional direction pattern. Therefore, this makes it suitable for wireless energy harvesting applications in a smart city. In [68], the proposed antenna consists of a 90\({}^{\circ}\) twisted quarter sectored CDRA, a metallic plate, and a shunt-diode rectifier circuit. This antenna offers a high gain of 7.02 dBc. However, it suffers from a narrow bandwidth between 5.59 to 5.88 GHz. In addition, this antenna is complicated because the authors need to design and optimize the shunt-diode rectifier for rectification purposes. Table 7 summarized a comparison between various DRAs for RF energy harvesting. It can be concluded that by introducing a thin air gap between the DR and the ground plane, a reasonable bandwidth is achieved, and the gain is improved. Moreover, a thin air gap is easier and more practical to apply compared to a wide air gap. Figure 20: High gain DRA: (**a**) bottom view; (**b**) fabricated prototype [86].
# Last-Mile Optimization Using Neural Networks
## 2 Related Work
### Network Traffic Classification  
[PERSON] et al. (2020) ([PERSON] et al., 2020) crafted an application aimed at categorizing network traffic through a neural network approach, responding to the surge in Distributed Denial of Service attacks on internet-connected devices. The application incorporated two distinct neural network topologies, each developed using Stochastic Gradient Descent (SGD) and Adam optimizers. The outcomes demonstrated a remarkable classification accuracy of 99%.  
The adoption of Virtual Private Networks (VPNs) has witnessed a significant upswing in popularity. Presently, the market offers a myriad of VPN services, surpassing previous availability, and a substantial portion of these services boast user-friendly setups. Unfortunately, this ease of use has created a need for businesses to identify whether incoming network connections are from original IP addresses or if they are being routed through a VPN. An MLP was conducted to learn the typical patterns of VPN and non-VPN traffic, ultimately creating a model capable of distinguishing between the two in real time. The result was an accuracy rating of 91.6996% ([PERSON] et al., 2017).  
[PERSON] et al. (2017) ([PERSON] et al., 2017) illustrated a DL approach for a network intrusion detection system. The proposed classifier outperformed other ML models in the literature with 98.99% accuracy.  
[PERSON] et al. (2021) ([PERSON] et al., 2021) developed two approaches to network traffic classification using ML methods: one with feature selection and one without. The results indicate that classification without features has an average accuracy of 94.14%, while the method with feature selection has an accuracy of 95.61%.  
[PERSON] et al. (2017) ([PERSON] et al., 2017) highlighted that accurate traffic classification using Software-Defined Networking (SDN) is crucial for various other network operations, including security monitoring, accounting, Quality of Service (QoS), and long-term provisioning forecasts. This study employed four variations of Neural Network estimators to classify traffic. The findings revealed that the Naive Bayes (NB) model attained the highest accuracy, reaching 97.6%.  
In the face of an increasing number of potential network attacks, traditional Intrusion Detection Systems face challenges in effectively handling these threats. Thus, [PERSON] et al. (2021) ([PERSON] et al., 2021) introduced several ML methods, including KNN, SVM, DT, NB, RF, and the Artificial Neural Network (ANN). The performance of these ML algorithms was evaluated on the Bot-IoT dataset for both binary and multiclass classification. Notably, RF demonstrated superior performance in binary classification across all attack types. However, for multiclass classification, KNN outperformed other ML algorithms, achieving an accuracy of 99%, which is 4% higher than that of RF.
## 3 Results and Discussion
### Effect of Aspect Ratio h/r  
For soil-structure interacting systems, the aspect ratio, which is defined as the ratio of the height of the system (h) to the radius of the foundation (r), has been considered as the index for the slenderness ratio in many previous studies. Based on the review of commonly used values of the aspect ratio, the (h/r) values are taken as 1, 3, and 5 in this study. The effects of the aspect ratio of flexible systems on the inelastic displacement ratio is shown in Figure 13 for \(\beta=0.25\) and \(\beta=0.5\). It can be seen from the figure that increasing the aspect ratio can generally decrease the C\({}_{\mu}\) values, especially for short period ranges. The main reason for that is the peak inelastic displacement increases at a slower rate than the peak  
Figure 11: Variations in residual displacements according to energy dissipation ratio; **(a)** fixed-base and **(b)** interacting cases.  
Figure 12: Variations in residual displacement ratios according to energy dissipation ratio; **(a)** fixed-base and **(b)** interacting cases.  
elastic displacement for shorter periods. But from a certain period, say 0.75 s., the variation in the inelastic displacement ratio with the aspect ratio on is negligible.  
Figure 14 presents the effects of the aspect ratio on residual displacement demands for \(\beta=0.25\) and \(\beta=0.5\). It can be seen from the figure that the aspect ratio has a negligible effect on residual displacement demands. A very slight difference is visible for short period ranges; thus, neglecting the effect of slenderness for residual displacements does not cause a major error. This condition is also valid for residual displacement ratios.  
Figure 14: Variations in residual displacement demands according to aspect ratio.  
Figure 13: Variation in inelastic displacement ratios according to aspect ratio.
# Selected Aspects of Designing Modular PEMFC Stacks as Power Sources for Unmanned Aerial Vehicles
###### Abstract  
Two types of air-cooled modular polymer membrane fuel cells (PEMFC) stacks with full equipment were constructed and investigated as components of hybrid power sources. The first, a 2-kW PEMFC stack, was assembled from two 1-kW PEMFC modules electrically connected in parallel and compared with a commercial PEMFC stack built from one 2-kW PEMFC module. The second, a 500-W PEMFC stack, was assembled with three modules connected in parallel. It was found that the two-module PEMFC stack was capable of operation with nominal power of 2 kW. Analysis of the distribution of the air cooling system in both modules was also conducted. The two-module PEMFC stack reduced hydrogen consumption compared to the reference 2-kW PEMFC stack consisting of only one module. The elaborated two-module PEMFC stack was successfully tested in a propulsion system designed to supply an electrical engine with a propeller. The electrical performance of the three-module PEMFC stack was tested separately as well as in a hybrid system in connection with a 5 s Li-Pol battery. It was found that the elaborated PEMFC stack was capable of operation with nominal power of 500 W and variable rapid dynamic electrical loads. It was also successfully tested as a power source to supply servomechanisms and other auxiliary devices.  
PEMFC fuel cell stack; hydrogen; unmanned aerial vehicles; modular power source +
Footnote †: journal: Applied Sciences
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 2 Background  
In this section, we introduce the background and basic knowledge about the DIFT framework regarding tag setup, propagation and checking.
## 3 Results
### Result Comparison of Different Methods  
The parameters were sorted according to the parameter sensitivity index calculated with each method, and the top 10 parameters were then selected from each method, as shown in Table 3. The sensitivity parameters calculated with each analytical method show significant differences. This also indicates that the sensitivity analysis of hydrological model parameters using a single sensitivity method has certain limitations. However, some parameters were highly sensitive in different methods, such as SMTMP, SOL_AWC, GWQMN, and RCHRG_DP, which also indicates that they may be very sensitive parameters and greatly influence the results of the SWAT model.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ranking** & **Morris** & **Sobol’** & **FAST** & **EFAST** \\ \hline
1 & SOL\_Z & SMTMP & SMTMP & SMTMP \\
2 & SMTMP & SOL\_AWC & SFTMP & RCHRG\_DP \\
3 & ALPHA\_BNK & SOL\_Z & SOL\_Z & SFTMP \\
4 & SFTMP & SOL\_K & HRU\_SLP & SOL\_BD \\
5 & HRU\_SLP & GWQMN & SOL\_K & SOL\_AWC \\
6 & ALPHA\_BF & RCHRG\_DP & ESCO & SOL\_K \\
7 & SOL\_AWC & GW\_REVAP & ALPHA\_BNK & HRU\_SLP \\
8 & SOL\_BD & SMTMX & RCHRG\_DP & GWQMN \\
9 & GWQMN & SMFMN & SOL\_AWC & ESCO \\
10 & RCHRG\_DP & CH\_K2 & GWQMN & GW\_REVAP \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of sensitive parameters calculated with different methods.  
Figure 12: Total sensitivity (\(S_{T}\)) of each parameter of EFAST.