esa-sceva/satcom-chunk-collection huggingface dataset:
# AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus
## 5 Discussion and Future Work  
The major finding in our research is a large-scale Arabic sentiment corpus named \"AraSenCorpus\". The introduced techniques in building our corpus help in reducing human effort and automate the annotation process. The corpus contains 4.5 million tweets and achieves potential results when compared with state-of-the-art system using the same benchmark datasets.  
The sentiment classification on two-way classification using our corpus improves the results by 7% and 5% using the SemEval 2017 and ASTD benchmark datasets, respectively. The three-way classification using our corpus also improves the classification results by 6% and 4% using both benchmark datasets, respectively. The tweets in our corpus are mostly subjective; as our corpus was collected using terms/phrases from Arabic sentiment lexicons. From the results and analysis, we observe that the two studies that we compared our results with used a mixed of subjective and objective tweets. Along with this reason,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **[PERSON], [PERSON] et al., 2019** [41] & **Our Models** \\ \hline SemEval 2017 & 63.38 & **69.4** \\ ASTD & 64.10 & **68.1** \\ \hline \hline \end{tabular} The bold values represent the best obtained results.  
\end{table}
Table 10: Three-way sentiment classification comparison of existing methods with our models (F1-Score).  
Figure 6: Three-way classification results (F1-Score): the graph shows the improvement of results after each expansion process.  
our collected tweets spanned 14 years to ensure the coverage of different topics, while in the two studies used in the comparison, the tweets spanned 2 months and 3 years, respectively.  
This semi-supervised learning approach proves that the expansion of a few samples that have been carefully annotated will lead to better results, as shown in the presented corpus. As the expanded corpus has millions of tweets, we used LSTM deep learning classifier which gives better results with sufficient data, as in our corpus.  
AraSenCorpus is limited by the weaknesses of self-learning, such as skewed class distributions and error propagation. To overcome this problem, we selected tweets from the unlabeled dataset above or greater than a certain threshold, to be added to the training set. Other alternative semi-supervised approaches, such as co-training, can be used.  
Improvements to AraSenCorpus could include improvement of the guidelines of the manual annotation, which could improve the classification results. Further preprocessing steps will be added to improve the overall performance of the sentiment classification.
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 5 Results and Discussion
### Variable Reduction Using Mahalanobis-Taguchi System  
The variables of all 20 datasets were optimized using MTS procedures. Table 7 shows the optimized variables of respective datasets obtained (After Optimize) against  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S.No** & **Variables** & **Notation** & **Notation for Analysis** \\ \hline
12 & Total cholesterol & TCH & X\({}_{12}\) \\ \hline
13 & Triglyceride & TG & X\({}_{13}\) \\ \hline
14 & Phospholipid & PL & X\({}_{14}\) \\ \hline
15 & Creatinine & Cr & X\({}_{15}\) \\ \hline
16 & Blood urea nitrogen & BUN & X\({}_{16}\) \\ \hline
17 & Uric acid & UA & X\({}_{17}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 7: Example of variation and abundance instances of character “5”.  
their original variables set (Before Optimize). Note that the reduced number of variables (optimized) are the significant variables suggested by MTS for future prediction and classification purposes. Figures 8-10 illustrate the optimization results based on SNR Plots and SNR Gain Charts. Due to page limitation, three datasets, namely Medical Diagnosis of Liver Disease, Wdbc and the Spambase, were displayed since these datasets showed a higher number of variable reductions as compared to the rest. The SNR Plots show the average values of SNRs based on the level of OA. The SNR Gain Charts illustrate the SNR gain between the level averages that correspond to each variable in the dataset. The positive SNR gains denote useful variables for future purposes while negative SNR gains were considered insignificant variables and thus were discarded.  
Table 7 shows that more than half (>50%) of the original number of variables were removed for Wdbc, Spambase and Medical Diagnosis of liver disease datasets, while almost half (>40%) of the original variables were removed from the Appendicitis and the Coil2000 datasets. These results could significantly reduce the classification effort with a much smaller number of variables to process in those particular datasets. Unlike the rest of the datasets, the Banana, Haberman-2, Monk2, Ring and Taguchi Character Recognition datasets, however, produced no reduction in the number of variables when they were optimized using the MTS. This indicates that all original variables for these particular five datasets are found to be significant and will be fully used for future classification purposes.  
Figure 10: (**a**) SNR Plot and (**b**) SNR Gain Chart for Spambase dataset.  
Figure 9: (**a**) SNR Plot and (**b**) SNR Gain Chart for Wdbc dataset.
# High Resolution Brillouin Sensing of Micro-Scale Structures
## 5 Sensing of Micro-Scale Structures  
Brillouin scattering in micro-scale structures including PCFs, micro-fibres, and on-chip waveguides has been studied to detect different geometrical parameters through either transverse or longitudinal opto-acoustic interactions. In this section, we explain how Brillouin scattering of micro-scale waveguides is affected by their geometries. Then, we discuss the detection of transverse and longitudinal variations along the waveguide based on forward and distributed backward scattering.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spatial Resolution** & **Technique** & **Ref.** & **Platform** \\ \hline
40 cm & FM & [89] & optical fibre \\
10 cm & FM & [98] & optical fibre \\
5 cm & Random laser & [90] & optical fibre \\
3 cm & Random-phase code & [50] & microwire \\
1 cm & FM & [99] & optical fibre \\
1 cm & Random-phase code & [44; 100] & optical fibre, microwire \\
6 mm & FM & [46] & silica planar waveguide \\
4 mm & ASE & [47] & optical fibre \\
2 mm & FM & [101] & optical fibre \\
1.6 mm & FM & [45] & optical fibre \\
800 μm & ASE & [48] & on-chip waveguide \\
200 μm * & ASE & [97] & on-chip waveguide \\ \hline \hline \end{tabular}  
* This value reflects the experimental detection capability and not the spatial resolution.  
\end{table}
Table 1: Spatial resolution achievements in BOCDA techniques with a focus on mm- and sub-mm-scale resolutions.
## 3 System Model and Problem Modeling
### System Model
Therefore, there is no need to adjust the flight height of the UAV frequently, and the severe ground attenuation of the UAV during sensing and transmission can be ignored [28]. Meanwhile, considering that the Doppler effect generated by the UAV motion  
Figure 2: Communication mode of components participating in Figure 1.  
is compensated [29], the channel gain from the PD to the UAV is consistent with the model of free-space path loss [30], which can be expressed as  
\[h_{i,u}(t)=\frac{h_{0}}{\left[d_{i,u}(t)\right]^{2}}, \tag{2}\]  
where \(h_{0}\) denotes the channel gain per unit distance. Furthermore, since the distance between the UAV and the LEO satellite is far away, the horizontal distance can be negligible; then, the channel gain between the UAV and the LEO satellite still conforms to the free-space path loss model and can be represented as  
\[h_{u}=\frac{h_{0}}{\left[H_{g}-H_{u}\right]^{2}}, \tag{3}\]  
where \(H_{g}\) denotes the altitude of the LEO satellite from the ground. Let \(R_{i,u}(t)\) represent the data rate of the current time slot PD uploaded to the LEO satellite, which can be expressed as  
\[R_{i,u}(t)=W\cdot\log_{2}\left(1+\frac{\gamma_{i,u}(t)\cdot\gamma_{u}}{1+\gamma _{i,u}(t)+\gamma_{u}}\right), \tag{4}\]  
where \(W\) is the channel bandwidth pre-allocated for each PD before the data transmission mode is selected. For ease of expression, the signal-to-noise ratio (SNR) from the PD to the UAV is denoted by \(\gamma_{i,u}(t)\), and \(\gamma_{i,u}(t)=P_{i}(t)\cdot h_{i,u}(t)/\sigma^{2}\). The transmission power of the PD is denoted by \(P_{i}(t)\), which selects different values in different time slots, and the parameter \(\sigma^{2}\) is the variance of additive white Gaussian noise (AWGN). Similarly, the SNR of the data from the UAV to the LEO satellite is denoted by \(\gamma_{u}\), and \(\gamma_{u}=P_{u}\cdot h_{u}/\sigma^{2}\), where \(P_{u}\) denotes the transmit power of the UAV. When the PD chooses to forward data directly to the ground 5G base station, let the path loss between the PD \(i\) and the ground 5G base station as \(l_{i,b}=l_{0}\cdot\left(d_{i,b}\right)^{-l}\), where \(l_{0}\) denotes the channel gain per unit distance and \(l\) means the path loss index [31]. Furthermore, the corresponding channel gain is expressed as \(h_{i,b}=10^{-\frac{l_{i,b}}{10}}\). Then, the data rate of the current time slot PD \(i\) to directly forward data to the ground 5G base station is  
\[R_{i,b}(t)=W\cdot\log_{2}\left(1+\frac{P_{i}(t)\cdot h_{i,b}}{\sigma^{2}} \right). \tag{5}\]  
In summary, the actual data transmission rate of PD \(i\) at time slot \(t\) can be obtained as follows  
Figure 1:0  
The actual energy consumption of PD \(i\) at time slot \(t\) is given by  
Figure 1:1  
Therefore, the total long-term system energy efficiency is defined as  
Figure 1:2
# Secure Aviation Control through a Streamlined ADS-B Perception System
## 2 Background Review
### Machine Learning Classifiers  
The machine learning classifiers used in this study to create a machine learning model are described below, along with their definitions.  
* **Random forest**: [PERSON] originally invented it in 1995, and then [PERSON] and [PERSON] improved it in 2006. Also known as random decision forests, this ensemble-supervised learning technique builds several decision trees during the training phase and is typically used for classification and regression [5].  
* **AdaBoost**: this 1995 model, created by [PERSON] and [PERSON], can be used with various machine learning methods to improve performance. An ensemble-supervised learning approach uses iteration to strengthen weak classifiers by learning from their flaws [6].
* **Naive Bayes (NB)**: the Bayes theorem is the foundation of a supervised machine-learning method used for classification. Under the naive assumption that the pairs of characteristics are independent, this algorithm is regarded as fast and a good classifier. Still, it could **perform better when estimating or predicting**[7].
* **Neural** networks (NN): a machine learning subset that uses supervised learning. It consists of many algorithms that attempt to imitate the workings of the human brain to uncover links in datasets [8].
* **Support machine vector (SVM)**: [PERSON] et al. discovered it in 1993. It is regarded as one of the most reliable prediction algorithms and is another supervised machine learning technique. It is a non-probabilistic classifier because its foundation divides the samples into two groups [9].
* **K-nearest neighbors (kNN)**: [PERSON] and [PERSON] created the initial version in 1951. Another supervised machine learning model is kNN, or closest neighbor, which assigns various weights to neighbor contributions based on the assumption that objects are similar when they are close together or apart [10].
## 1 Introduction
### Background
Probabilistic logic works only with numerical values, while fuzzy logic is prone to error and offers no validation [14]. On the other hand, each technique shows good results in tackling specific problems, e.g., supervised learning for activity recognition [15]. Thus, hybrid solutions have emerged to formulate complementary techniques in order to address each other's weaknesses [16]. The IoT paradigm envisions a wide range of deployed sensors, generating large volumes of data, which CA applications need to collect and model. The abundance of sensors and smart devices also introduced issues concerning the heterogeneity of sensor protocols, which traditional applications cannot tackle efficiently. Thus, _middleware_ systems were proposed and integrated in IoT ecosystems in order to address diverse data sources and large data streams [6]. In the IoT context, a middleware system executes all stages of the context life cycle (acquiring, modeling, reasoning and dissemination). It abstracts communication details of smart devices and allows the connectivity of heterogeneous devices, while providing interoperability and diversity for applications and services [17]. _Cultural heritage_ (CH) constitutes a worldwide resource, which defines artistically and economically many cities and regions [18; 19]. Nowadays, cultural institutions seek new ways to attract their visitors, e.g., by providing digital guides, installed screens and other services, offering a new type of interactive and personalized user experience [20; 21]. Furthermore, the integration of ubiquitous computing in cultural spaces paves the way for smart CH institutions, where visitors, equipped with their mobile devices, and artifacts, equipped with appropriate devices, interact with each other. New, personalized services are available, while the delivered content and recommendations are tailored to user characteristics and preferences, and the environment is adapted accordingly [22; 23]. The latest advances in technologies that support the IoT paradigm, such as the emergence of low-cost sensors, have motivated academic research toward IoT solutions for cultural spaces [24]. A typical research project includes the localization of visitors and the delivery of content based on user proximity with points of interest [25], often integrating a recommendation system [26]. Such solutions universally exploit CA techniques, ranging from location-based processing to social context and environmental parameters [22]. Context in a cultural space is multidimensional and may include the locations and profiles of users and artifacts, environmental data, behavioral patterns of users or other external data [27]. Finally, CA can also enhance conservation procedures, such as automatic monitoring of a cultural space for detecting critical conditions [28].
## 3 Materials and Methods
### Data Preprocessing
#### 3.3.1 Handling Missing Data  
Quebec's cancer incidence data was unavailable after 2017 due to incomplete submissions to the Canadian Cancer Registry. To address this gap, Quebec's missing incidence data from 2018 to 2022 were imputed using a multiple imputation strategy based on available historical trends and region-specific cancer patterns. Other provinces with minor missing values, less than 3% of the data, were handled using multiple imputations again.
## 4 Simulation Results and Discussion
### Soundproof Panel Design with HR  
To attain noise attenuation at 500 Hz and 1000 Hz, it is necessary to construct two corresponding HRs arranged together. As shown in Figure 10a, HRs for 500 Hz and 1000 Hz are disposed serially. Figure 10b shows the effect of neck distance (ranging from 5 mm to 20 mm) between the two HRs on the transmission loss, which confirms the proceeding results for serial arrangement. Note that the peak matched well with two target resonance frequencies. However, to reduce traffic noise effectively, the noise attenuation performance between the two target frequencies needs to be improved.  
Considering proceeding analysis results, the parallel arrangement, which showed an increase in the transmission loss compared to a single HR, as shown in Figure 11a, is adopted. As shown in Figure 11b, the parallel arrangement HRs along the duct can improve the capacity of transmission between the target frequencies. One notable observation is that the peak value at the frequency of 500 Hz becomes greater than that of 1000 Hz, unlike in Figure 10b without parallel arrangement. Additionally, Figure 11b shows that as the offset distance increased between two opposite resonator arrangements, the peak values at the target frequencies also increased, but values between the target frequencies decreased.  
Figure 11: A soundproof panel model with two HRs: (**a**) a three-dimensional model of parallel arrangement; (**b**) predicted transmission loss.  
Figure 10: A soundproof panel model with two HRs: (**a**) a three-dimensional model of serial HR arrangement; (**b**) predicted transmission loss for various neck distances.
# Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs
## 3 Mobility Models
The mobile sink will, however, disregard the sequence in which the cluster heads are visited and move close to the heavily loaded cluster head if a traffic burst is detected. An ant colony optimization-based end-to-end data-gathering technique was also presented in [29]. The proposed method is based on building a data forwarding tree and arbitrarily choosing data gathering points. As a result, the mobile sink's route is estimated and described. A sink mobility model using a genetic algorithms model is presented in [30]. The mobility model uses genetic algorithms to determine the direction the mobile sink should take when moving. After that, using single-hop routing, the mobile sink will travel to the nodes that are a part of the computed path to gather data from them via single-hop routing. On the other hand, multi-hop routing is used by nodes, not members of the mobile sink movement path, to forward packets to the mobile sink. Additionally, the portable sink's movement is split into pause and movement periods to facilitate data communication and reduce changes in routing paths. To elaborate, the mobile sink migrates to new locations that are calculated using the proposed algorithm during the movement phase instead of the pause period, during which it remains in the current location. Furthermore, a model based on creating mobile pathways for the mobile sink to decrease latency and energy usage was proposed in [31]. Choosing a meeting location, designing a trajectory, collecting data, and transmitting data are the four stages of the algorithm's operation. In [32], a mobile sink-based geographic routing scheme was developed. This study used two mobile sinks to gather information from sensor nodes in cells or geographic regions. As a result, each cell's sensor nodes collect data and send it to the mobile sink. It is important to note that single-hop and multi-hop routing can connect sensor nodes to the mobile sink. Consequently, several mobility models are presented in the literature. Some of these models are based on random mobility, while others try to use the current location of the mobile sink to calculate a new location. The work proposed in this paper approaches the problem of mobile sink path planning from a different perspective. The proposed approach aims to benefit from the network topology and the node neighborhood to create a logical topology based on bipartite graphs. After that, we use bipartite graph properties to derive a sink mobility model.
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 5 Discussion
### Directional Bias in MLS Devices  
The results indicate that each MLS device exhibits a unique error distribution pattern specific to the device. Previous studies have primarily focused on the absolute magnitude of errors in MLS point clouds, with limited attention to their directional distribution. In contrast, this study confirms that MLS point cloud errors may deviate either inward or outward relative to the actual surface. The analysis results are summarized as follows:  
* MLS-01 point clouds exhibit an outward bias in error relative to the Column surface, with a representative mean error of +4.3 mm, standard deviation of 7.6 mm.
* MLS-02 point clouds exhibit an outward bias in error relative to the Column surface, with a representative mean error of +9.2 mm, standard deviation of 10.8 mm.
* MLS-03 point clouds exhibit an inward bias in error relative to the Column surface, with a representation mean error of \(-2.0\) mm, standard deviation of 9.3 mm.  
A notable finding is that MLS-02 and MLS-03, despite being the same model, exhibit opposite error distribution patterns. This suggests that such errors cannot be fully characterized based solely on manufacturer-provided specifications (e.g., Table 1). From this perspective, identifying and understanding the error distribution patterns of MLS point clouds is not only relevant to Scan-to-BIM applications, but also critical in fields that require high-precision 3D modeling, such as quality inspection, and digital twin development.
# Biomethane Potential Test: Influence of Inoculum and the Digestion System
## 1 Introduction
[17] reported a significant difference in methane yields by using inocula from two different full-scale biogas plants. Another impact was reported by [PERSON] and [PERSON] [18], where a well-adapted inoculum from digested turkey manure produced a higher methane yield when using turkey manure as a substrate. [PERSON] et al. [12] measured the methane yield of four substrates using four inocula from different origins. The effects of inocula on the results depended strongly on the substrates. [PERSON] et al. [19] reported similar results. A comparison of three inocula from different origins revealed repeatedly no significant effects in methane yield for three substrates. Only for cellulose--a common substrate standard--significant effects were observed, leading to the conclusion that the methane production rate was affected by the used inoculum. Beside the source of the inoculum, there are more possibilities influencing the methane yield, such as the incubation time and the trace elements (TE). VDI 4630 [5] prescribes the incubation of the inoculum to limit the methane production of the blanks. Other studies report no effect of incubation on their results [12; 14]. [PERSON] et al. [3] recommend adding TE and vitamins. Further effects on BMP were investigated by [PERSON] et al. [20]. They examined the influence of experimental conditions and their correction to standard conditions. The biggest effect was found for samples with low methane yield, because of the high impact of the size of headspace, which determines the volume relation between produced biogas and flushing gas. The use of standard temperature showed a deviation of about 10% compared to results without correction. Also, the ambient pressure shows an impact on the biogas yield potential. It is shown that the ambient pressure can differ between 63.1 kPa (La Paz) and 103.6 kPa (sea level) [20; 21]. The aim of this study was to investigate the effects of different inocula and digestion systems on the specific methane yield and methane percentage of different substrates using VDI 4630 as a protocol. Unlike other publications, all investigated inocula were cultivated for several years and have been used in a national inter-laboratory test in Germany. Five long-term cultivated inocula and five substrates were tested using equipment and procedure of the standardized Hohenheim biogas yield test (D-HBT1) in comparison to four other digestion systems. To the best of our knowledge, such an extensive comparison of inocula and digestion systems has not been performed before.