esa-sceva/satcom-chunk-collection huggingface dataset:
# Threatening URDU Language Detection from Tweets Using Machine Learning
## 4 Experiments and Results
### Results Using BoW Features  
Several experiments are performed to detect threatening and non-threatening tweets. This study used word n-grams and character n-grams for features ranging from 1 to 4 n  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Type of Layer** & **Weights** \\ \hline Embedding = 10,000 & 100 * (99 + 1) = 10,000 \\ \hline Dropout = 0.5 & No weights \\ \hline Dense = 10 with relu & (100 + 1) * 10 = 1010 \\ \hline LSTM = 100 & (443 + 1) * 100 = 44,400 \\ \hline Dense = 1 with sigmoid (output layer) & (100 + 1) * 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Layered-wise details for LSTM.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Type of Layer** & **Weights** \\ \hline Embedding = 10,000 & 100 * (99 + 1) = 10,000 \\ \hline Dropout = 0.5 & nothing \\ \hline Dense = 10 with relu & (100 + 1) * 10 = 1010 \\ \hline Bidirectional = 64 & (401 + 1) * 64 = 25,728 \\ \hline Dense = 1 with sigmoid (output layer) & (10 + 1) * 1 = 11 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Layered-wise details for GRU.  
grams. For features, verbs from cleaned text and preprocessed text are employed. For word level, BoW technique results are presented with stacked features (cleaned text + verbs).  
Table 8 shows the results using uni-gram features. The best accuracy of 73.10% is achieved by stacking. Similarly, the best values for precision, recall, and F1 scores are also from the stacking model. For bi-gram, BNB outperforms other classifiers regarding accuracy, recall, and F1 with 73.64%, 75.97%, and 73.59%, respectively. The highest precision of 70.47% using bi-gram is obtained by the stacking model. The highest F1 score for uni-gram and bi-gram are 72.73 and 73.59, respectively.  
Table 9 presents the results of all machine learning models using tri-gram to tetra-gram features. It is observed that increasing the features leads to better performance. For tri-grams, the best results are achieved with LR which has 73.08% each for accuracy and F1 score. For tetra-gram, SVC achieves the highest accuracy of 73.36% and an F1 score of 73.31%. Figure 3 shows the AUC of machine learning models using BoW features. ROC-ACU curve shows that the stacking model using BoW with word level produces better results.  
Figure 5 shows the confusion matrix generated by a stacked-based approach using BoW and TF-IDF features. The stacking model correctly predicts 376 threatening tweets and 406 non-threatening tweets. Incorrect predictions are 288 for the stacking model using BoW features. Figure 4(b) shows the correct and incorrect predictions when TF-IDF features are used with the stacking approach. It correctly predicts 378 threatening tweets and 405 non-threatening tweets.
## 5 Conclusions  
The mmWaves that characterize 5G networks pose some unique challenges in modeling, even using the most effective machine learning algorithms that have been employed so far in the literature. This work attempted to better model the SNR values from a simulated mmWave propagation using LTC and achieved outstanding results that are thirteen times better than the results from the best framework used in the literature, which is LSTM. Using LSTM resulted in a test Root Mean Squared Error (RMSE) of 3.44 dB, and LTC, 0.25 dB. The intricacy of the prediction task that makes LTC more suitable is confirmed by the interpretation using Symbolic Regression, which resulted in a complex arithmetic expression to model the fluctuations in the SNR values. A future direction is to use other recent and interpretable deep learning frameworks, such as Kolmogorov-Arnold Networks.  
Conceptualization, V.S.P.; methodology, V.S.P.; software, M.P.; validation, M.P.; formal analysis, V.S.P.; investigation, V.S.P. and M.P.; resources, M.P. and V.S.P.; data curation, M.P.; writing--original draft preparation, M.P. and V.S.P.; writing--review and editing, V.S.P.; visualization, M.P.; supervision, V.S.P.; project administration, V.S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluation Metric** & **Value** \\ \hline Validation RMSE & 0.41 \\ Test RMSE & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: LTC Results at 28 GHz.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
Conflicts of Interest: Author [PERSON] was employed by the company OSI Engineering Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 6 Comparisons Between Thresholds and Fuzzy Logic Models  
To test different autonomous management strategies for solving the problem of AIVs recharging batteries, we defined an initial circulation environment (Figure 6). We proposed different scenarios and compared them with the following four parameters:  
* _nbMissions_: number of missions carried out.
* _timeMission_: the average time taken to complete a mission in seconds.
* _nbRcharges_: the number of recharges performed.
* _wtRcharging_.: waiting times for recharging in seconds  
We also varied the charge threshold at which an AIV must recharge its battery. We then introduced a fuzzy inference system to determine the recharge time. We also varied the values of the fuzzy model (fuzzy linguistic values).  
In this section, we delve into a comparative analysis between different thresholds and fuzzy logic models. We propose three different scenarios:  
* Scenario 1 (_sc1_): all AIVs have a uniform recharge threshold of 30%.
* Scenario 2 (_sc2_): each AIV has a different recharge threshold, maintaining the same context as _sc1_.
* Scenario 3 (_sc3_): AIVs use a fuzzy logic model for recharge.  
We simulated these three scenarios for 1000 bags (a discussion regarding the scenario results is provided in the following three sections). The temporal results are shown in Table 2. We aim to discern the optimal threshold configurations that maximize mission throughput, minimize recharging frequency, and optimize resource utilization, thereby improving the overall efficiency of autonomous management strategies for recharging the AIV battery.
## 3 Proposed Real-Time Data Fusion Method Based on a New Distance Metric
### Data Fusion for Object Information  
#### 3.1.1 Dempster-Shafer Theory of Evidence  
In general, with more data available, higher credibility in the prediction can be achieved. At the same time, under the autonomous vehicle environment, the decision should be made in real-time. In a multisensor environment, there exists uncertainty due to limited accuracy of sensor measurements or complicated sensing environment [15]. To address these issues, we adopt Dempster-Shafer theory of evidence, in short DS [17], as a tool for combining gathered data in order to improve detection accuracy by reinforcing the output based on the sensed data from peer vehicles. Misdetection of objects due to erroneous sensing can be avoided by fusing the data obtained from peers. Moreover, DS is effective in managing uncertainty in multisensor environments [15]. We begin by summarizing the mathematical framework of DS [16; 19; 20; 40; 41].  
In DS, the _frame of discernment_[17] is the set of all possible mutually exclusive and complete states of system denoted by \(\Theta\). A _Basic Probability Assignment_ (BPA) function \(m(\cdot)\) is a mass function which assigns confidence in each subset of \(\Theta\), i.e., \(m:2^{\Theta}\rightarrow[0,1]\). The BPA function satisfies the following conditions,  
\[m(\varnothing)=0,\quad m(X)\geq 0,\ \forall X\subseteq\Theta, \tag{1}\] \[\sum_{X\subseteq\Theta}m(X)=1. \tag{2}\]  
In this paper, we will use term of _confidences_ to indicate BPAs. The elements of the power set \(2^{\Theta}\) having a non-zero mass are called _focal elements_.  
We combine multiple confidences into a single output using the DS _rule of combination_. Let us consider two confidences, \(m_{1}(X)\) and \(m_{2}(X)\), which believe \(X\) is true. The DS fuses these confidences [17], denoted by \(m_{1}(X)\oplus m_{2}(X)\), as follows,  
\[m_{1}(X)\oplus m_{2}(X)=\frac{\sum_{Y\cap Z=X}m_{1}(Y)m_{2}(Z)}{1-\sum_{Y\cap Z =\varnothing}m_{1}(Y)m_{2}(Z)}. \tag{3}\]  
We determine the existence and class of an object by two consecutive stages of DS. Prior to classifying objects, we identify whether a detected object indeed exists or not, i.e., an inaccurate sensor can report that an object is detected even if there is nothing, and vice versa. Thus, we first identify the existence, and, if the existence of an object positively identified, second determine the class of the object. The process of the object information data fusion is depicted in Figure 1.
# Early Neighbor Rejection-Aided Tabu Search Detection for Large MIMO Systems
## 2 Background
### QR-Ts  
The main complexity load of the conventional TS algorithm comes from computing the metrics of the neighbors to find \(\mathbf{x}^{(b)}\) in each iteration. The ML metric in (9) can be rewritten as  
\[\phi(\mathbf{x})=\sum_{i=1}^{N}|u_{i}+h_{i,d}\delta_{d}|^{2}. \tag{10}\]  
In (10), it is prohibitive to compute \(N\) terms, i.e., \(|u_{i}+h_{i,d}\delta_{d}|^{2}\), \(i=1,\ldots,N\), in each iteration when N is large.  
By applying QR decomposition to the channel matrix \(\mathbf{H}\), where \(\mathbf{Q}\) and \(\mathbf{R}\) are unitary and upper triangular matrices, respectively, computational complexity can be reduced. Specifically, the ML metric of \(\phi(\mathbf{x})\) can be expressed as  
\[\phi(\mathbf{x}) =\|\mathbf{y}-\mathbf{H}\mathbf{x}\|^{2}=\left\|\mathbf{Q}^{T} \mathbf{y}-\mathbf{R}\mathbf{x}\right\|^{2} \tag{11}\] \[=\left\|\mathbf{Q}^{T}\mathbf{y}-\mathbf{R}(\mathbf{x}^{(c)}- \Delta\mathbf{x})\right\|^{2}.\]  
By defining \(\mathbf{z}=\mathbf{Q}^{T}\mathbf{y}-\mathbf{R}\mathbf{x}^{(c)}\), ML metric (8) can be rewritten, as in [12],  
\[\phi(\mathbf{x}) = \|\mathbf{z}+\mathbf{R}\Delta\mathbf{x}\|^{2}=\|\mathbf{z}+ \mathbf{r}_{d}\delta_{d}\|^{2} \tag{12}\] \[= \underbrace{\sum_{i=1}^{d}|z_{i}+r_{i,d}\delta_{d}|^{2}}_{\triangleq \phi_{A}}+\underbrace{\sum_{i=d+1}^{N}|z_{i}|^{2}}_{\triangleq\phi_{B}}, \tag{13}\]where \(\mathbf{r}_{d}\) is the \(d\)th column of \(\mathbf{R}\). By accounting for the fact that in each iteration of (11), \(\mathbf{x}^{(c)}\) is the same for all neighboring vectors, \(\phi_{B}\), in (13) needs to be computed only once and can be reused for all neighbors, which reduces the computational complexity of the search process [11]. However, the number of neighbors increases with \(N\). Thus, the number of iterations to achieve near-optimal performance in TS also increases. This implies that the QR-TS scheme may be still computationally expensive in large MIMO systems, which motivates us to find a technique for the early rejection of unpromising neighbors from examination. In the next section, we propose early neighbor rejection (ENR) for this purpose. A layer ordering scheme is also applied to further reduce the complexity of the TS algorithm.
# Spatiotemporal Dynamics of Net Primary Productivity in China's Urban Lands during 1982-2015
## 3 Results
### Spatiotemporal Distribution of NPP in Three Urban Agglomerations in China  
In this study, the difference between NPP in 2015 and 1982 was used to explore NPP changes within urban lands in 2015. As shown in Figure 7, the regions with a significant decrease in NPP were mainly distributed in the central cities of urban agglomerations, such as Beijing in the BTHUA (Figure 7a), Shanghai in the YRDUA (Figure 7b), and Guangzhou and Foshan in the PRDUA (Figure 7c). In the areas surrounding the central cities of urban agglomerations, the NPP reduction decreased, and many regions even showed an increasing trend.  
The NPP trends between 1982 and 2015 in the BTHUA, the YRDUA, and the PRDUA within urban lands in 2015 are shown in Figure 8. The NPP values of the three urban agglomerations present a decreasing trend. Specifically, the NPP in the BTHUA has a slightly decreasing trend, with a rate of change of \(-1.67\) g C/m\({}^{2}\)/yr. The NPP values in the YRDUA and PRDUA both had significantly decreasing trends, with rates of \(-2.45\) g C/m\({}^{2}\)/yr and \(-4.18\) g C/m\({}^{2}\)/yr, respectively.  
The buffer analysis results indicated the NPP trends in different buffer zones of three urban agglomerations based on urban lands in 2015 (Figure 9). As shown in Figure 9a, the NPP variations in buffer zones of urban lands in the BTHUA indicated that the NPP increased in different buffer zones. With the expansion of the buffer zone in the BTHUA, the NPP trend changed from a nonsignificant (\(p>0.1\)) increase to a significant (\(p<0.001\)  
Figure 8: NPP trends between 1982 and 2015 in the BTHUA, YRDUA, and PRDUA within urban lands in 2015. The mark (***) indicates relative levels of significance with \(p<0.001\).  
Figure 7: NPP difference between 1982 and 2015 in **(a)** the BTHUA, **(b)** the YRDUA, and **(c)** the PRDUA within urban lands in 2015.  
increase. The rate of change increased from 0.6734 g C/m\({}^{2}\)/yr to 2.8977 g C/m\({}^{2}\)/yr. For the YRDUA (Figure 9b), the NPP significantly decreased (\(p<0.001\)) in the 5 km buffer zone, with a change rate of \(-1.7282\) g C/m\({}^{2}\)/yr. As the buffer zone expanded to 15 km, the NPP decreased, but not significantly (\(p<0.01\)). The change rates for the 10 km and 15 km buffer zones were \(-1.0591\) g C/m\({}^{2}\)/yr and \(-0.4442\) g C/m\({}^{2}\)/yr, respectively. However, when the buffer zone expanded to 20 km, the NPP showed a slightly increasing trend (\(p<0.1\)), where the change rate was 0.042 g C/m\({}^{2}\)/yr. Unlike the BTHUA and the YRDUA, the NPP in the PRDUA buffer zones showed a significantly decreasing trend (\(p<0.001\)), with a decreasing rate from \(-3.1007\) g C/m\({}^{2}\)/yr to \(-1.7506\) g C/m\({}^{2}\)/yr.
## 2 Theory and Experiments
### Experimental Methods  
#### 2.4.1 Phantom Preparation  
A tissue phantom was prepared based on the recipe of polyacrylamide electrophoresis gel [47], in which micron-sized polystyrene microspheres were added to adjust its attenuation and phase velocity dispersion. The formula of phantom contained 100 mL degassed water, 10 g acrylamide (A9099, Sigma-Aldrich, St Louis, MO, USA), 0.05 g ammonium persulfate (A9164, Sigma-Aldrich), 0.3 g methylene double acrylamide (146072, Sigma-Aldrich), 0.2 mL TEMED (411019, Sigma-Aldrich), and 4 mL 10-micron microsphere solution (P107798, Aladdin, Shanghai, China, original concentration 5% \(w/v\)).
# Urban Road Collapse Risk Assessment Study Based on InSAR Spatiotemporal Data +
## 5 Conclusions  
This thesis proposes an LSTM-based urban road collapse risk assessment algorithm to conduct risk assessment research and analysis on road collapse accidents that occurred in Guangzhou from 2014 to 2021. By processing InSAR data with high spatial and temporal resolution, an urban road collapse risk assessment dataset is established to support the construction of deep learning models. The LSTM network is employed for model construction, training, and validation, while algorithms such as GRU, SVM, KNN, and RF are utilized to model and comparatively analyze the same collapse accident dataset. Experimental results demonstrate the superior performance of the LSTM model, exhibiting high accuracy in predicting and classifying road collapse risks, with an accuracy of 76.4% and an AUC value of 0.766, indicating its excellent feature extraction and representation capabilities. Furthermore, the risk assessment map generated based on the model training results reveals significant regional differences, providing auxiliary decision support for road maintenance.  
Conceptualization, F.W., W.B. and Y.W.; methodology, F.W. and J.J.; formal analysis, Z.W.; investigation, Q.H. and L.C.; data curation, H.L.; writing--original draft preparation, J.J.; writing--review and editing, F.W. and Y.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangdong Provincial Key Laboratory of New Technology in Urban Rail Transit Engineering Construction (2017B030302009) and Guangzhou Academician Expert Workstation (2021 CXZX030).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Guangzhou Metro Design Research Institute Co., Ltd. Author [PERSON] was employed by the company Foshan Urban Safety Research Center. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Moving Deep Learning to the Edge
## 2 Survey Methodology  
In this review, the solutions proposed to deploy deep learning on the edge are identified and analyzed. Two questions are addressed: (1) Why deep learning should be executed on the edge and what applications can benefit from this? (2) How can this be achieved efficiently and effectively?  
The survey described in this paper was elaborated in two steps. The first step was the collection of the bibliography associated with the topic. The second step consisted of the analysis and the review of this bibliography. The search for the bibliography about the design and deployment of deep learning on the edge was done using one main keyword and a list of secondary keywords. The search was carried out using several scientific databases like IEEE Xplore, ScienceDirect, Directory of Open Access Journals (DOAJ), and Scopus and web scientific indexing services, including Google Scholar and Web of Science.  
The search focused on conference papers and journal articles published after 2015 since deep learning on the edge is a recent topic of research. However, some important works on deep learning are older than 2015. These works highly referenced in recent papers as fundamental for the development of deep learning were also included. Some technical reports of known global vendors were also included since these provide important data that justify some trends in the area of deep learning.  
The main keyword used in the search was deep learning. This was followed by one or two keywords from a group of keywords associated with the organization of the paper: models, algorithms, application, mobile, edge, inference, training, hardware, device, processor, optimization, and architecture. Among all papers retrieved by the search process, the review considered only papers whose main topic was deep learning. These included papers that introduced advances in the design and development of deep learning models, algorithms, and architectures with emphasis on edge computing.  
Even reducing the focus of deep learning to the edge, the review still has a broad spectrum of themes, and so, the volume of bibliographical references is enormous. To reduce this set to a manageable number, we considered the most cited and recent references whenever there were several references to the same sub-area. This permitted maintaining the flow of reading and considered many works referenced indirectly from the main references.  
The articles collected were then analyzed to determine the evolution trend in the deployment of deep learning on the edge considering the following research directions  
* Evolution of deep learning models from accuracy oriented to edge oriented;
* Application areas of deep learning when executed on the edge;
* Optimization of deep learning models to improve the inference and training;
* Computing devices to execute deep learning models on the edge;
* Edge computing solutions to run deep learning models;
* Methods to train deep learning models on the edge.  
The evolution and trends of each of these research directions are reviewed and analyzed in the following sections.
# Intelligent Dynamic Backlash Agent: A Trading Strategy Based on the Directional Change Framework
## 2 Directional Changes: An Introduction
### Tsfdc  
In 2018, [PERSON] et al. (2018) presented a DC-based trading strategy named TSFDC. They presented two versions of TSFDC, namely TSFDC-down and TSFDC-up. However, they reported that there is no difference between the performances of both versions. Therefore, in this section, we describe only TSFDC-down (henceforth, TSFDC). TSFDC relies on the forecasting model previously developed by [PERSON] et al. (2018) which aims to answer the question of whether the current DC trend will continue for a specific percentage, denoted as _BT Theta_, before the trend reverses. To formalize this objective, the authors tracked price changes with two thresholds simultaneously: _BT Theta_ and _ST Theta_ (with _BT Theta_ > _STTheta_; as in Figure 4) [6]. For example, in Figure 4, the first DC event observed under the threshold of 0.1% is [AA\({}^{0.1}\)]. Point A\({}^{0.1}\) is the DCC point of the DC event [AA\({}^{0.1}\)]. The objective, in this example, is to predict at A\({}^{0.1}\) whether the trend of the DC event [AA\({}^{0.1}\)] will continue so that its total magnitude will be at least equal to _BTTheta_.  
TSFDC decides when to trigger a new trade based on the made forecast. TSFDC is a contrarian strategy (e.g., it triggers buy order when the market exhibits a downtrend). Consider, for example, the downward DC event [AA\({}^{0.1}\)]. The forecasting model make prediction at point A\({}^{0.1}\). If it predicts that the trend will reverse before reaching the threshold _BTTheta_ than TSFDC generates buy order immediately at point A\({}^{0.1}\). Otherwise, TSFDC will wait until a DC event of threshold of _BTTheta_ is confirmed (i.e., at point A\({}^{0.2}\) in this example) and then generates a buy order.  
The performance of TSFDC was examined using eight currency pairs and a rolling window approach. Each rolling window was composed of a training (24 months in length) and an applied period (one month in length). For each currency pair, the authors considered seven rolling windows; thus, the overall applied period's length is seven months. The results reported in [13] suggested that TSFDC can be amazingly profitable with a rate of return of more than 500%, and Sharpe ratio of up to 4.2. However, the authors did not consider the bid and ask prices nor the transaction costs.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 4 Fair Editions of Logistic Regression
### Group-Level Logistic Regression  
Unlike existing fairness-aware models that explicitly impose fairness constraints, this variant of Logistic Regression [20] seeks to mitigate bias inherently by structuring model training around equal representation. The approach partitions the training data based on the sensitive attribute, after which the coefficients are updated using the median derivative value across groups. This ensures that underrepresented groups have an equal influence in defining model parameters. The model was evaluated on two real-world datasets: the Adult dataset and the Open University Learning Analytics dataset. While this method does not surpass fairness-optimized approaches such as Fairlearn, it consistently enhances fairness compared to standard Logistic Regression, particularly in binary-sensitive attribute settings.