esa-sceva/satcom-chunk-collection huggingface dataset:
# How Successful Is Transfer Learning for Detecting Anorexia on Social Media?
## 4 Methodology
### Transformer-Based Models  
Transfer learning refers to the ML paradigm in which an algorithm extracts knowledge from one or more scenarios to help to learn performance in a specific scenario. Transfer learning has emerged as a highly popular technique in developing deep learning models [38]. With this technique the neural network is trained in two stages: (1) pre-training, where the network is generally trained on a large dataset representing a wide diversity of labels or categories; and (2) fine-tuning, where the pre-trained network is further trained on the specific task, which may have fewer labeled examples than the pre-training dataset. The first step helps the network learn general features that can be reused for the target task [39].  
Transformer-based models are based on transformers architecture, they introduce an attention mechanism that processes the entire text input simultaneously in order to learn contextual relations between words or sub-words [40]. The Transformer includes two parts: an encoder that reads the text input and generates a vector for each word, and a decoder that produces the translated text from that representation.  
In this study, we address the detection of anorexia in Spanish using different Transformer-based models. To this end, we use models that contain or are trained on that language such as BETO and other multilingual models such as BERT [31] and XLM [41]. These models are to be summarized in the following sections.  
The BERT model uses a hidden size of 768, 12 Transformer blocks, and 12 self-attention heads. For the optimizer, we leverage the _adam_ optimizer which performs well for NLP data and for BERT models in particular. The BERT architecture followed is shown in Figure 4 with an example extracted from the SAD corpus. On the contrary, XLM uses a hidden size of 1280, 16 Transformer blocks, and 16 self-attention heads. For the purposes of fine-tuning, the authors recommend choosing from the following values: batch size, learning rate, max sequence, and several epochs.  
Figure 4: The architecture of BERT.
## 2 Literature Review
### Support Vector Machines  
SVM is a statistical learning algorithm originally designed for classifying data [23]. It can also be used for regression tasks such as predicting solar radiation [24]. A kernel function transforms a nonlinear input space into a higher-dimensional space [25]. It allows efficient computation of the scalar products of multiple vectors in this higher-dimensional space. Common kernel functions include the polynomial, radial basis (RBF), and sigmoid functions [21]. In the higher-dimensional space, the optimal hyperplane, which separates the margins of errors in regression and classes in classification, can be identified.  
The use of SVMs in renewable forecasting has increased drastically in recent years [21]. The SVM is an established method, used across the renewable energy sector, especially for solar forecasting, because of its accurate prediction ability for nonlinear data. Further advantages include its fast computational speed, as no iterative tuning is required, and its capability to produce accurate predictions with a small volume of data [26]. SVMs solve a convex programming problem resulting in the global optimum, avoiding being trapped in local optima (local optimum is either the highest or lowest point, compared with nearby data points. The global optimum is the highest or lowest point in the whole function or dataset. Further reading on convex optimisation problems can be found in [27]).  
[PERSON] and [PERSON] proposed a least-square SVM to forecast global horizontal irradiance for 1-, 2- and 3-hour ahead [28]. Their model significantly outperformed an autoregressive (AR) model, as well as a radial basis function neural network. However, their evaluation was performed for a short period (10 days) without cross-validating the model performance.  
[PERSON] et al. developed an SVM model in hybrid with a genetic algorithm to forecast the power output of residential PV systems [29]. The model demonstrated good adaptability to different locations, weather patterns, and climatic conditions. As, PV power output depends on the system parameters and technologies, prediction of the power source (irradiance) is more useful in the long term. An SVM with radial basis function to global solar irradiance in a single location (Tehran) was used by [PERSON] et al. [25]. The radial basis function was chosen because it outperformed the polynomial as a kernel function. Furthermore, it outperforms an ANN in terms of root-mean-squared error (RMSE) while being computationally more efficient.
# Data Cleaning Model of Mine Wind Speed Sensor Based on LOF-GMM and SGAIN
###### Abstract  
To improve the quality of mine ventilation wind speed sensor data, a data cleaning model for mine ventilation wind speed sensors based on LOF-GMM and SGAIN is proposed. First, the LOF-GMM algorithm was used to identify wind speed sensor data, cluster the data, and determine the threshold of the local outlier factor, enabling automatic identification of abnormal data and recognition of ventilation fault state information. Abnormal data were then removed to create blank missing points. Finally, wind speed data from the normal operating state of the ventilation system were used to train the SGAIN model to obtain its optimal parameters. The trained SGAIN model was then used to fill in the blank points. The results show that the proposed method can effectively detect abnormal wind speed sensor data and identify ventilation system fault information. In terms of imputation performance, this model outperformed other data imputation models such as GAIN, RF, and DAE. Although the imputation speed was slightly lower than that of the RF and DAE models, considering the high accuracy requirements of mine wind speed data, SGAIN is more suitable for use in the field of mine ventilation.  
wind speed sensor; data cleaning; anomaly data identification; missing data filling; LOF-GMM; SGAIN
## 4 Discussion
Integrating advanced machine learning techniques to predict and compensate for environmental disturbances like vibrations and occlusions could further enhance the system robustness and success rate. With continued optimization, the system could achieve even greater sorting efficiency and reliability in real-world applications.
_Appl. Sci._**2015**, \(5\), 575-586; doi:10.3390/app5030575  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Design of a Stability Augmentation System for an Unmanned Helicopter Based on Adaptive Control Techniques  
**[PERSON] * and [PERSON]**  
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 YuDao St., Nanjing 210016, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-25-8489-2305.  
Academic Editor: [PERSON]  
_Received: 26 July 2015 / Accepted: 8 September 2015 / Published: 11 September 2015_  
**Abstract:** The task of control of unmanned helicopters is rather complicated in the presence of parametric uncertainties and measurement noises. This paper presents an adaptive model feedback control algorithm for an unmanned helicopter stability augmentation system. The proposed algorithm can achieve a guaranteed model reference tracking performance and speed up the convergence rates of adjustable parameters, even when the plant parameters vary rapidly. Moreover, the model feedback strategy in the algorithm further contributes to the improvement in the control quality of the stability augmentation system in the case of low signal to noise ratios, mainly because the model feedback path is noise free. The effectiveness and superiority of the proposed algorithm are demonstrated through a series of tests.  
**Keywords:** adaptive control; model feedback; stability augmentation; measurement noise; unmanned helicopter
## 3 Results
### Cultural Differences by Demographic Variables
#### 3.2.2 Differences by Work Location (Facility vs. Headquarters)  
Considering NPP and NPC together, facility personnel score higher than headquarters personnel in the \"Constructive\" cluster (facility M = 3.52; headquarters M = 3.34; \(\delta\) = 0.26) (Table 7; Figure 6). Within the \"Defensive\" clusters, there are no relevant work location-based differences, except in the \"Oppositional\" style (facility M = 2.79; headquarters M = 2.67; \(\delta\) = 0.26) (Table 7; Figure 7). If only NPP are considered, there are no differences by work location in the \"Constructive\" organizational culture, but facility personnel score higher in\" Defensive\" cultures (Table 7). On the contrary, in NPC there are differences in work location in the \"Constructive\" organizational culture (Table 7). Personnel working onsite perceive a more \"Constructive\" culture (facility M = 3.28; headquarters M = 3.03; \(\delta\) = 0.36). There are also differences in the \"Aggressive/Defensive\", \"Oppositional\" (facility M = 2.74; headquarters M = 2.56; \(\delta\) = 0.40) and \"Perfectionistic\" styles (facility M = 3.13; headquarters M = 3.01; \(\delta\) = 0.21), with facility personnel scoring higher. There are no work location-based differences in the \"Passive/Defensive\" styles. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**(a) NPP**} \\ \hline
**OCI Cultures** & & NPP1 & NPP2 & & NPP3 & NPP4 & \\ \hline  & M & SD & M & SD & M & SD & M & SD \\ \hline Constructive & 3.47 & 0.57 & 3.61 & 0.65 & 3.57 & 0.68 & 3.52 & 0.72 \\ Passive/Defensive & 2.84 & 0.51 & 2.95 & 0.48 & 2.83 & 0.51 & 2.95 & 0.49 \\ Aggressive/Defensive & 2.52 & 0.44 & 2.72 & 0.49 & 2.59 & 0.50 & 2.71 & 0.51 \\ \hline  & & & \multicolumn{6}{c}{Cohen’s \(\delta\)} \\ \hline  & NPP1/NPP2 & NPP1/NPP3 & NPP1/NPP4 & NPP2/NPP3 & NPP2/NPP4 & NPP3/NPP4 \\ \hline Constructive & \(-\)0.24 & \(-\)0.15 & \(-\)0.08 & 0.07 & 0.13 & 0.07 \\ Passive/Defensive & \(-\)0.22 & 0.02 & \(-\)0.22 & 0.24 & 0.01 & 0.23 \\ Aggressive/Defensive & \(-\)0.42 & \(-\)0.14 & \(-\)0.37 & 0.26 & 0.03 & 0.23 \\ \hline  & & & \multicolumn{6}{c}{**(b) (NPC)**} \\ \hline
**OCI Cultures** & & NPC1 & & NPC2 & & NPC3 & \\ \hline  & M & SD & M & SD & M & SD \\ \hline Constructive & 3.39 & 0.69 & 3.01 & 0.70 & 3.11 & 0.68 \\ Passive/Defensive & 2.94 & 0.46 & 3.13 & 0.44 & 3.05 & 0.44 \\ Aggressive/Defensive & 2.64 & 0.46 & 2.73 & 0.44 & 2.83 & 0.51 \\ \hline  & & \multicolumn{6}{c}{Cohen’s \(\delta\)} \\ \hline  & NPC1/NPC2 & & NPC1/NPC3 & & NPC2/NPC3 & \\ \hline Constructive & \(-\)0.53 & \(-\)0.40 & & \(-\)0.16 & \\ Passive/Defensive & 0.41 & 0.23 & 0.18 & \\ Aggressive/Defensive & 0.20 & & 0.39 & -0.20 & \\ \hline \end{tabular} *Note: Mean = M; Standard Deviation = SD.  
\end{table} 
Table 6: OCI organizational cultures averages and effect sizes for (a) nuclear power plants (NPP) and for (b) nuclear public companies (NPC).
## 2 Methods
### Hydro-optical model sensitivity test  
When applying the LM-based algorithm to remote sensing reflectance spectrum, the hydro-optical model is assumed to be constant for all the given spectra. However, for realistic interpretation of ocean colour data the natural temporal and spatial IOP variability needs to be taken into account [24]. In order to quantify the influence of IOP variability on the inverse algorithm retrieval accuracy a sensitivity test is performed by one of us ([PERSON]) in three steps. Firstly, the equations (1-3) were applied to simulate a reference spectrum \(T(\lambda)\) using an arbitrary vector of the CPA concentrations and a reference hydro-optical model obtained from ship borne data. Secondly, in order to test the \(T(\lambda)\) response to IOP variability the same combinations of CPAs are applied as input to the forward model, but the IOP's spectral values are shifted from their respective values in the reference hydro-optical model. Wavelength independent shifts of \(\pm\)50% are applied to specific absorption and backscattering coefficients for each of the CPA. Finally, the LM-based algorithm and the reference hydro-optical model were applied for the retrieval of CPA from the spectra \(T(\lambda)\) generated with the shifted model. Any observed difference between the inversely retrieved CPA and initial CPA concentrations is entirely caused by the corresponding change in IOP values and is, therefore, a measure of the inverse algorithm's sensitivity to the IOP variability.  
The sensitivity test showed that in waters, where _chl_ is the dominating substance, the accuracy of _chl_ retrievals remains acceptable (i.e. within ranges specified in Table 1) with _a*_ and _b__b*_ of _tsm_ or _doc_ shifted. In case 2 waters dominated by sediments or dissolved organics the difference between initial and retrieved _chl_ concentrations is unacceptably high with IOPs of _sm_ or _doc_ shifted. For _sm_ retrievals the situation is different, because this is the dominant backscattering component of natural waters. Accordingly, the _sm_ retrieval error is acceptably low in all options except with _b__b__*__sm_ shifted. The retrieval error of _doc_ is low in all cases.  
The sensitivity test reveals that even under conditions of a 50% change of IOP the results may be retrieved with acceptable accuracy. Therefore, the hydro-optical models developed on _in situ_ data  
Figure 3: Comparison of _chl_ (**A**, \(\mu\)g/L) and _sm_ (**B**, mg/L) concentrations measured _in situ_ and retrieved from shipborne optical measurements in Lake Erie.  
collected in one water body can be applied to another one. This approach is justified when both water bodies largely share one and the same catchment or in the case of geographical proximity of both water bodies when the mineralogical characteristics of _sm_ and geochemical composition of _doc_ can be expected to be similar. The same refers to chlorophyll: if the phytoplankton community composition is similar due to similar hydrological regime and trophic status then the same model can be applied for both water-bodies.
## 5 Experimental Evaluation
### Simulation Results
#### 5.2.4 The Impact of LSTM Network Structure on The Performance of FTN Signal Detection  
From Figure 12, we can see the influence of the number of LSTMayers on FTN detection in multipath channels. Among them, the red curve represents a Single-Layer-LSTM network, the black curve represents a Double-Layer-LSTM network, and the green curve represents a Triple-Layer-LSTM network. It can be seen that for all simulated compression factors, there are cases where the performance of the Double-Layer-LSTM is better than the Single-Layer-LSTM and the Triple-Layer-LSTM. The main reason is that nonlinear interference consists of two parts. Fewerayers are difficult to extract enough features, and too manyayers can easilyead to overfitting of the trained network. In addition, an interesting phenomenon was observed in the simulation, that is, as the compression factor \(\tau\) decreases, the ISI introduced by FTN becomes more and more serious, and the performance of the Triple-Layer-LSTM is getting closer and closer to the Double-Layer-LSTM. This shows that it may be more effective to use a more complex network in the case of a higher compression rate.  
In addition to comparing the influence of the number of LSTMayers, the performance of traditional RNN and LSTM structures is also compared. As shown in Figure 13, the red curve represents the result using the RNN structure, and the black curve represents the result using the LSTM structure. It can be seen that under all simulated compression factors, the performance of using LSTM is better than that of RNN, and the gain range is between 0.35 dB to 1.4 dB. Compared with LSTM using the gate structure to control the weight change in the network, the weight reusability during RNN training is too high, and some slight changes will have a huge impact on the entire network so that it is difficult to obtain appropriate network parameters.  
As shown in Figure 14, the influence of the Uni-LSTM and the Bi-LSTM network structure on FTN signal detection is compared. It can be seen that the overall Bi-LSTM structure represented by the blackine is about 1.8 dB better than the Uni-LSTM represented by the redine. This is because the interference of the FTN signaling comes from the previous traversal and the subsequent traversal, and the unidirectional network cannot extract the ISI from the backward propagation, which causes aarge performanceoss.  
Figure 11: Perform training in different channel environments under the condition of matching signal-to-noise ratio, test with PB-3 km/h, performance under the condition of compression factor \(\tau=0.7\).  
Figure 12: Under the TU channel moving speed of 60 km/h, compare the impact of the number of LSTM networkayers on performance.  
Figure 13: TU channel moving speed is 60 km/h, compare the performance of traditional RNN and LSTM unit.
# A New Computational Method for Arabic Calligraphy Style Representation and Classification
## 2 Characteristics of Arabic Handwritten Calligraphy and Its Complexity
#### Arabic Calligraphy Styles  
AC consists of two main styles (Kufi and Naskh), each style has several variants, as well as region-specific styles. In the history of Arabic calligraphy, there were six main styles namely Kufi, Diwani, Farisi, Naskh, Rekaa, and Thuluth each of which has its characteristics and writing rules. Over time, over 100 different styles appeared and identified driven from the former main styles. Nine distinctive categories of cursive styles evolved, becoming ever more refined and elegant, some of which are still used and some others that have disappeared. The commonly used styles nowadays are the six basic styles (namely: Kufi, Diwani, Farisi, Naskh, Rekaa, and Thuluth) and three other styles that are driven from them which are Maghribi, Mohakik, and Square-Kufi. Unlike other related works that dealt with a few of these styles, in this work, we consider the full list of the nine styles. Table 1 resumes the different characteristics and the cues that experts use in identifying each one of these styles. It also provides a representative sample from each style to show how it differs from others.  
\begin{table}
\begin{tabular}{c c c c} \hline
\begin{tabular}{c} - text orientation \\ - written on the baseline \\ - kufi \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: The nine most common AC styles with their characteristics and cues that are used by experts to identify them.  
Due to the complexity of AC and the shared characteristics among its styles, none of the former related works has considered the full list of nine styles. However, there were some serious efforts to come up with some specific techniques for recognizing AC styles. In the following section, we list and discuss works that aimed at recognizing Arabic calligraphy styles using computer vision techniques.
## 3 Results and Discussion
### DOC and Ni Concentrations and Mass Fractions in DOM and AEOM
The mass fractions, in each fractioned DOM/AEOM, were calculated by Equation (2). Figure 1a,b shows DOC and Ni mass percentages of size-fractionated DOM/AEOM. In most of the separation studies, molecular weight 1 kDa was a common size to distinguish high and low molecular weight DOM/AEOM [21; 22; 38; 39; 41]. In the DOM solution, the Ni mass percentage (27.7 \(\pm\) 6.4%) of high molecular weight DOM (HMW, \(>\)1 kDa) was significantly lower than the percentage (72.3 \(\pm\) 6.4%) of low molecular weight DOM (LMW, \(<\) 1 kDa, \(p\) = 0.001). The HMW DOM organic carbon (OC) mass percentage (45.5 \(\pm\) 6.7%) was insignificantly different to the LMW DOM OC percentage (54.5 \(\pm\) 6.7%) (\(p\) = 0.18). In the AEOM solutions, the mass distribution of OC and Ni had similar patterns. The HMW AEOM mass percentages (85.3 \(\pm\) 1.0% for OC and 93.5 \(\pm\) 0.4% for Ni) were significantly higher than the LMW AEOM (\(p\) \(<\) 0.001). The results suggested that Ni preferred binding with LMW DOM, but Ni preferred binding to HMW AEOM. Moreover, AEOM had a high fraction of organic carbon. The mass fractions of fractionated OC and heavy metal in DOM were varied, which depended on the DOM sources and type of heavy metals. In addition, when metal and OC are extracted from soil and sediment, the fractions may depend on the matrix, type of metal, extraction solvent, extraction method and solid/liquid ratio, and the separation method and conditions [41; 42]. For example, in one study, [PERSON] et al. [43] size-fractionated soil solution, lake water, and river water, where the HMW Ni percentages were 83%, 55%, and 43%, respectively. In other studies, the HMW OC percentage was 44.1% for soil AEOM solution [22] and 81% for sediment AEOM solution [39]. In a municipal wastewater treatment plant study, [PERSON] et al. [21] reported the HMW fractions were 67-75% for Ni and 58% for OC. Figure 1: _Cont_.
# Analytical Modeling for Underground Risk Assessment in Smart Cities
## 4 Approximation Schemes for Underground Risk Index
### Hybrid Approach Based Approximation  
Hybrid approach is a combination of both linear approximation and hierarchical FIS system to capture teh strength of both and overcome their limitations. Here, final risk index is expressed as a point on 2D plot, as shown in Figure 4.  
Figure 4: Risk index mapping using hybrid approach.  
To map any risk index on this plot, we need its two coordinates, i.e., x and y. As shown in Figure 4, x-coordinate is determined by hierarchical FIS, whereas y-coordinate is determined by linear approximation. Thus, for risk information of a given location, we first apply hierarchical FIS approximation method to get x-coordinate of the risk point. Then, linear approximation model is applied on the same data values to obtain y-coordinate of the risk point. Afterwards, we can easily map the given risk information on the plot to determine its severity. Color coding used in Figure 2 also helps in improved visual representation of risk index information over map using heat mapping.  
The bottom left corner is the safest situation and top right corner is the riskiest situation. Gradual increase along y-axis values can be observed while moving from left to right along x-axis. Hierarchical FIS tends to find final risk index by giving more preference to maximum risk index. If maximum risk index among all factors is low on x-axis, then linear approximated risk index cannot be more than low along y-axis. This is why, when maximum risk index along x-axis increases, possible average risk index also grows along y-axis.  
For numerical computation and comparison, we can quantify the risk point on hybrid map into a single risk index value, as shown in Figure 5. Final risk values on hybrid map ranges from \([0,15)\) where 0 signifies most safe situation and 14.99 implies the extremely risky condition.  
Every box is labeled with a numeric value inside showing the range of risk indexes for the corresponding point of mapping, e.g., the box for (medium, low) labeled as 4 covers the range of risk indexes from \([4,5)\), i.e., 4.0 is the bottom line and 4.99 for the top line.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 1 Introduction
Ensuring similar interference levels as the macro cell deployment at \(P_{\text{max}}\) = 23 dBm, with reduced \(P_{\text{max}}\) limit in each small cell deployment scenario, while achieving similar received SIR performance as in the macro cell deployment. The rest of the paper is organized in the following way: Section 2 presents the overview of the small cell base station deployment topology and related work. Section 3 presents the network model under consideration. Performance analysis for the proposed model is depicted in Section 4. Finally, the conclusion of this work is presented.