esa-sceva/satcom-chunk-collection huggingface dataset:
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 7 Conclusions  
In this article, we proposed a spoken LID system using a CRNN architecture that works on combined GTCC and MFCC features of speech signals. At the feature extraction stage, the GTCC and MFCC features were compared, as well as a combination of both. The average precision of GTCC for spoken LID in Arabic was 94.88%, Chinese 94.20%, English 91.30%, French 90.58%, German 74.84%, Russian 92.98%, and Spanish 89.86%. In Experiment 1, by contrast, the average precision of MFCC for spoken LID in Arabic was 94.82%, Chinese 94.88%, English 89.76%, French 92.14%, German 76.78%, Russian 95.32%, and Spanish 91.72%. The results indicate that GTCC outperformed MFCC in Arabic and English spoken LID. We performed extensive experiments with different parameters and reported a state-of-the-art result in that the system in Experiment 11 achieved the highest overall average accuracy of 92.81% for identifying the seven spoken languages considered in the selected corpus.  
The average precision when combining GTCC and MFCC in Experiment 11 for spoken LID in Arabic was 95.84%, Chinese 96.82%, English 93.85%, French 92.64%, German 81.87%, Russian 96.79%, and Spanish 94.46%. Furthermore, our experiments indicate that the proposed system gives the best results for identifying Arabic and Russian, discriminates among similar languages, and is extensible to new languages. Additionally, this article provides a good reference for people interested in developing Arabic-related ASR systems.  
In future research, we can extend the number of languages and compare our CRNN architecture with other variations of deep learning architectures. Many other speech corpora can also be used for evaluation. The immediate benefit of an effective multi-lingual spoken LID system is that it can be developed based on the system's output for different purposes, including multi-lingual automatic translation.  
Conceptualization, A.A.A., M.A.Q., A.H.M. and Y.A.A.; methodology, A.A.A. and M.A.Q.; software, A.A.A. and M.A.Q.; validation, A.A.A., M.A.Q., A.H.M. and Y.A.A.; formal analysis, A.A.A., M.A.Q., A.H.M. and Y.A.A.; investigation, A.A.A. and Y.A.A.; resources, A.A.A. and M.A.Q.; data curation, A.A.A.; writing--original draft preparation, A.A.A., M.A.Q. and A.H.M.; writing--review and editing, A.A.A. and Y.A.A.; visualization, A.A.A.; supervision, Y.A.A.; project administration, A.A.-A.; funding acquisition, Y.A.A. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Researchers Supporting Project number (RSP-2021/322), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors would like to thank Researchers Supporting Project at King Saud University.  
The authors declare no conflict of interest.
###### Abstract  
Indoor positioning systems have become increasingly important due to the rapid expansion of Internet of Things (IoT) technologies, especially for providing precise location-based services in complex environments such as multi-floor campus buildings. This paper presents a WiFi fingerprint indoor localization system based on AdaBoost, combined with a new access point (AP) filtering technique. The system comprises offline and online phases. During the offline phase, a fingerprint database is created using received signal strength (RSS) values for two four-floor campus buildings. In the online phase, the AdaBoost classifier is used to accurately estimate locations. To improve localization accuracy, APs that always appear in the measurement data are selected for applying the AdaBoost algorithm, aiming to eliminate noise from the fingerprint database. The performance of the proposed method is compared with other well-known machine learning-based positioning algorithms in terms of positioning accuracy and error distances. The results indicate that the average positioning accuracy of the proposed scheme reaches 95.55%, which represents an improvement of 5.55% to 16.21% over the other methods. Additionally, the two-dimensional positioning error can be reduced to 0.25 m.  
indoor localization; WiFi fingerprints; Adaboostreceived signal strength; probability-one access point (AP) filtering
###### Abstract  
The meter reading work in the power distribution room of a mine is traditionally carried out by manual inspection, which requires several workers and often lacks timeliness and efficiency. Owing to the widespread use of quadruped robots, this paper proposes a Mining Automatic Meter Reading System, named MAMRS, to identify and read pointer and digital meters in mine distribution rooms using quadruped robots. The new method consists of three stages. Initially, this technique identifies the type of meter using the ResNet18 convolutional neural network model, then proceeds to extract the dial area image using the YOLOv5 object detection algorithm, and finally reads the pointer and digital meters' data using the U2-net algorithm and SVM, respectively. Experimental results show that the recognition accuracy rates for meter classification, pointer meter reading, and digital meter reading are 99.87%, 85.35%, and 90.73%, respectively. The MAMRS fulfills the need for fully automated and intelligent inspection of mine distribution rooms, resulting in significantly enhanced accuracy, flexibility, and innovation in inspection processes. Moreover, it reduces labor costs and improves inspection efficiency. The findings of this study serve as a reliable reference for intelligent inspection practices in the power distribution rooms of metal mines.  
deep learning; mine power distribution room; quadruped robot; meter reading; fully automatic inspection 20241410949[[https://doi.org/10.3390/app142310949](https://doi.org/10.3390/app142310949)]([https://doi.org/10.3390/app142310949](https://doi.org/10.3390/app142310949))
# Learnable Nonlocal Contrastive Network for Single Image Super-Resolution
## 4 Experiment
### Visualization Results  
In Figure 6, we show the visual quality comparison of SR results with 4\(\times\)SR under the BI model, from which we can find that most SR models cannot clearly reconstruct the image texture information. Some early models, such as Bicubic, SRCNN, and FSRCNN, cannot even reconstruct the rough outline of the original image. Although some of the more popular methods can reconstruct the general outline of the image, they also suffer from serious artefacts and cannot reconstruct natural textures. Taking \"img 8006\" as an example,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Scale**} & **Set5** & **Set14** & **BSD100** & **Urban100** & **Manga109** \\  & & **(PSNR/SSIM)** & **(PSNR/SSIM)** & **(PSNR/SSIM)** & **(PSNR/SSIM)** & **(PSNR/SSIM)** \\ \hline Bicubic & & 33.66/.09299 & 30.24/.08688 & 29.56/.08431 & 26.88/.08403 & 30.80/.09339 \\ SRCNN [13] & & 36.66/.09542 & 32.45/.09067 & 31.36/.0879 & 29.50/.08946 & 35.60/.09663 \\ FSRCNN [40] & & 37.05/.09560 & 32.66/.09090 & 31.53/.08920 & 29.88/.09020 & 36.67/.09710 \\ VDSR [14] & & 37.53/.09590 & 33.05/.09130 & 31.90/.08960 & 30.77/.09140 & 37.22/.09750 \\ EDSR [28] & & 38.11/.09602 & 33.92/.09195 & 32.32/.09013 & 32.93/.09351 & 39.10/.09773 \\ DBPN [49] & & 38.09/.0960 & 33.85/.09190 & 32.27/.0900 & 32.55/.09324 & 38.89/.09775 \\ RDN [50] & & 38.24/.09614 & 34.01/.0212 & 32.34/.09017 & 32.89/.09353 & 39.18/.09780 \\ FPAN [51] & & 38.19/.09612 & 33.88/.09210 & 32.30/.0912 & 32.72/.09339 & 39.03/.09722 \\ RCAN [15] & & 38.27/.09614 & 34.12/.09216 & 32.41/.09027 & 33.34/.09384 & 39.44/.09786 \\ HNCT [34] & & 38.08/.09608 & 33.65/.09182 & 32.22/.09001 & 32.22/.09294 & 38.87/.09774 \\ SAN [20] & & 38.31/.09620 & 34.07/.09213 & 32.42/.09028 & 33.10/.09370 & 39.32/.09792 \\ HAN [52] & & 38.27/.09614 & 34.16/.09217 & 32.41/.09027 & 33.35/.09385 & 39.46/.09785 \\ NLSN [31] & & 38.34/.09618 & 34.08/.
## 6 Discussion
### Effectiveness of Class Balancing and Research Implications  
In the context of DDoS detection in IoT/Cloud environments, addressing class imbalance through the Synthetic Minority Over sampling Technique (SMOTE) was a critical factor in enhancing model performance. Specifically, SMOTE proved essential for improving recall in ensemble models such as Random Forest (RF) and XGBoost, as it allowed the classifiers to better recognize minority class instances, particularly benign traffic patterns, which are frequently underrepresented. This strategy significantly reduced the occurrence of false negatives, thus contributing to a more reliable anomaly detection framework. As noted by [19], uncorrected class imbalance can severely affect the detection capability of minority class events, generating critical blind spots in security systems.  
From an operational standpoint, the findings confirm that ensemble learning methods are highly suitable for real-time deployment in IoT/Cloud scenarios, especially where resource constraints and attack sophistication are key limitations. The models demonstrated not only high accuracy but also computational efficiency, making them attractive for environments with limited processing capacity. Furthermore, the inclusion of interpretability mechanisms, such as feature importance rankings and confusion matrices, reinforces the idea that the transparency and auditability of the models is an increasingly relevant requirement in industrial cybersecurity applications, as emphasized in [14].  
In contrast, the limited performance of the LSTM model underlines the challenges of deploying deep-learning architectures in constrained environments. The results suggest that LSTM requires not only extensive hyperparameter tuning but also the potential integration of hybrid or attention-based architectures such as CNN-LSTM or GRU LSTM to address its deficiencies in generalization and temporal pattern extraction, as recommended by [8]. These observations provide valuable insights for guiding future research on the application of deep learning in IoT/Cloud security, particularly in optimizing resource use without sacrificing detection quality.
## 4 Results and Discussions
### Critical Desorption Pressure Characteristics  
The critical desorption pressure (\(p_{cd}\)) of CBM refers to the pressure when desorption and adsorption reach the equilibrium; that is, the pressure when the gas adsorbed on the surface of coal pores and fractures begins the process of desorption [51]. In other words, the \(p_{cd}\) is the pressure corresponding to the measured gas content of the coal sample on the isothermal adsorption curve, and its calculation equation is as follows:  
\[p_{cd}=\frac{V_{a}p_{L}}{V_{L}-V_{a}}, \tag{2}\]  
where \(p_{cd}\) is the critical desorption pressure, in MPa; \(V_{a}\) is the measured gas content of coal seam, in m\({}^{3}\)/t; \(p_{L}\) is the Langmuir pressure, in MPa; and \(V_{L}\) is the Langmuir volume, in m\({}^{3}\)/t.  
The ratio of \(p_{cd}\) to reservoir pressure (\(p\)) directly determines the difficulty of the drainage and depressurization of CBM. The greater the ratio, the easier it is for CBM to  
Figure 11: The relationship between the (**a**) R\({}_{0,max}\), (**b**) depth, and Langmuir volumes (V\({}_{L}\)) of coals in the Dacun Block.  
Figure 12: The relationship between the (**a**) gas contents, (**b**) reservoir pressure, and gas saturations of coals in the Dacun Block.  
produce gas. The results show that the \(p_{cd}\) of coal reservoirs in the Dacun Block varies from 0.29 to 7.21 MPa, averaging 2.19 MPa. Figure 13 shows that the \(p_{cd}\) has a good positive correlation with gas content. The ratio of \(p_{cd}\) to reservoir pressure (\(p\)) ranges from 0.04 to 0.89, averaging 0.37. It can be seen that the ratio of \(p_{cd}\) to reservoir pressure (\(p\)) is relatively low, indicating that the CBM development in the Dacun Block needs a large depressurization of reservoir pressure to produce CBM.
# Efficient Transmit Antenna Subset Selection for Multiuser Space-Time Line Code Systems
## 4 Complexity Analysis
### Complexity of SINR-Greedy-Based TAS Selection Algorithm  
From Algorithm 1, the computational complexities of the SINR-greedy-based TAS selection scheme in terms of RMs and RSs, respectively, can be evaluated line by line as  
Line 1:  
* RM in \(C_{n}=\left\|\mathbf{H}(:,n)\right\|_{F}\Rightarrow 8 KN_{T}\),
* RS in \(C_{n}=\left\|\mathbf{H}(:,n)\right\|_{F}\Rightarrow 8 KN_{T}-2N_{T}\),
* Line 9:
* RM in \(\bar{\mathbf{\Phi}}=\bar{\mathbf{H}}_{temp}\bar{\mathbf{H}}_{temp}^{H}+(2 K\sigma_{z}^{2}/\sigma_{x}^{2})\mathbf{I}_{2K}\Rightarrow(16K^{2}+8K)(k+1)+3\),
* RS in \(\bar{\mathbf{\Phi}}=\bar{\mathbf{H}}_{temp}\bar{\mathbf{H}}_{temp}^{H}+(2 K\sigma_{z}^{2}/\sigma_{x}^{2})\mathbf{I}_{2K}\Rightarrow(4K^{2}+2K)(4k+3)+2K\),
* RM in \(\bar{\mathbf{\Phi}}^{-1}\Rightarrow 16K^{3}+24K^{2}\),
* RS in \(\bar{\mathbf{\Phi}}^{-1}\Rightarrow 16K^{3}+8K^{2}\),
* Line 10:
* RS in \(\mathcal{T}\left[\bar{\mathbf{\Psi}}_{k_{d}}\right]\Rightarrow 4K\),
* Thus, the overall complexities of the SINR-greedy-based TAS selection algorithm (called greedy) in terms of RMs and RSs, respectively, are given as  
\[\begin{array}{ll}N_{\text{greedy}}^{RM}=K^{3}\big{(}16N_{T}N_{S}+8N_{S}-16N_ {T}-8N_{S}^{2}\big{)}+K^{2}\Big{(}8N_{T}N_{S}^{2}+32N_{T}N_{S}+\frac{52}{3}N_{ S}-40N_{T}-12N_{S}^{2}-\frac{16}{3}N_{S}^{3}\Big{)}\\ \hskip 14.226378 pt+K\big{(}4N_{T}N_{S}^{2}+4N_{T}N_{S}+\frac{8}{3}N_{S}-\frac{8 }{3}N_{S}^{3}\big{)}+3N_{T}N_{S}+\frac{3}{3}N_{S}-3N_{T}-\frac{3}{3}N_{S}^{2} \end{array} \tag{51}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Expressions** & **CMs** & **CSs** \\ \hline
**AB** & \(NPM\) & \(NP(M-1)\) \\ \hline \(\mathbf{D}=\mathbf{A}\mathbf{A}^{H}\) & \(0.5 MN(N+1)\) & \(0.5(M-1)N(N+1)\) \\ \hline \(\mathbf{D}^{-1}\) & \(0.5N^{3}+1.5N^{2}\) & \(0.5N^{3}-0.5N^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity of complex multiplications and complex summations in three matrix-related operations.  
\[N_{\text{greedy}}^{RS}=K^{3}\big{(}16N_{T}N_{S}+8N_{S}-16N_{T}-8N_{S}^{2} \big{)}+K^{2}\Big{(}8N_{T}N_{S}^{2}+12N_{T}N_{S}+\tfrac{22}{3}N_{S}-20N_{T}-2N_{S }^{2}-\tfrac{16}{3}N_{S}^{3}\Big{)} \tag{52}\] \[+K\Big{(}4N_{T}N_{S}^{2}+8N_{T}N_{S}+\tfrac{14}{3}N_{S}-4N_{T}-2N_ {S}^{2}-\tfrac{8}{3}N_{S}^{3}\Big{)}-2N_{T}\]
# Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications
## 3 Method
### Phase 1--Definition of the \"What's\"
#### 4.1.4 Technology Acceptance When the Technology Is a Service/Platform/System  
The number of articles belonging to this category is significantly higher than the others, totaling thirty-one works. The recurrent TAM applications of this nature can be due to the extensive Internet use and its paramount role in developing new technologies [63] thanks to the advantages it offers, such as speed and reliability [64]. This categorization contains TAM applications to predict and evaluate the acceptance of specific software, systems, or platforms. The most customary TAM validation within the e-learning sector is to assess the acceptance of learning management systems (LMS) as in the works by [PERSON] and [PERSON], [PERSON], [PERSON] et al., [PERSON], [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] et al., [PERSON] and [PERSON] et al. [65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75]. Regarding learning environ ments, contributions on TAM research have been made by [PERSON], [PERSON] et al. [76; 77] and [PERSON] and [PERSON] [78] and authors like [PERSON] and [PERSON] and [PERSON] and [PERSON] [79; 80] have investigated the intention to benefit from online courses. Understanding users' motivations that trigger instructional resources acceptance is a key process in developing learning platforms and systems. Amongst the examined tools are Compliant Learning Object (SCLO) [81], educational wikis [82], web feeds [83], platforms supported by google sites and apps [84], as well as apps such YouTube for learning assistance [85]. Moving to the agricultural field, [PERSON] et al. [86] tested an Online Agro-technology Diploma program from the TAM perspective. Moreover, innovative technologies like mobile-based agricultural services and information systems were under the scope of analysis of the work by [PERSON] and [PERSON], [PERSON] and [PERSON] [87; 88] and [PERSON] and [PERSON] [16]. Some other interesting TAM implementations evaluated farmers' intention to use a microfinance platform [89], the acceptance of a QR Code for a food traceability system [8] and the usage of Cloud Computing technologies for farming in Ireland [90]. As for the VR sector, the contributions made by [PERSON] and [PERSON] and [PERSON] et al. [91; 92] explored the determinants of acceptance of augmented-reality interactive systems.
## 3 Model
### Method of Route Segment Construction  
In order to optimize the route in the available airspace of the terminal area, this paper adopts the method of route segmentation and searches optimal result in both horizontal and vertical space. The starting and ending points of the arrival and departure routes are set as follows:  
1. The starting point of the arrival route is the arrival fix at the boundary of the terminal area, and the ending point of the arrival route is the tangent of the glide path and the outer marker;
2. The starting point of departure route is the runway threshold, and the ending point of departure route is the departure fix on the terminal area boundary.  
The route is divided into several segments in airline route selection, the segment length is \(\left|p_{u,v}^{k}-P_{u,v}^{k-1}\right|=3\)\(km\). The candidate points of segments were constructed in horizontal and vertical planes respectively to form the segment search space. By combining the candidate points of any adjacent segments, the terminal area arrival and departure routes are finally formed.  
\(P_{u,v}^{k}\) is the candidate point of segment \(k\), and the search space of segment \(k\) is \(u\) column, \(v\) row, \(P_{u,v}^{k}=\left(x_{u,v}^{k},y_{u,v}^{k},z_{u,v}^{k},\lambda_{u,v}^{k},\varphi _{u,v}^{k}\right)\), \(x_{u,v}^{k}\), \(y_{u,v}^{k}\), \(z_{u,v}^{k}\) represents the 3D coordinates of the candidate point of segment, \(\lambda_{u,v}^{k}\), \(\varphi_{u,v}^{k}\) represents the heading angle and flight path angle of segment \(k\), as shown in Figure 4, \(P^{k-1}\) is the end point of segment \(k\)-1 and the starting point of segment \(k\).  
Figure 3: The ladder class times of routes network environment influences.  
When the candidate points of segments were constructed, according to aircraft performance, passenger comfort and flight safety requirements, the maximum deviation angle of the segment \(\omega\) is set as the minimum deviation angle of aircraft type \(i\) in the horizontal profile, and the horizontal angle between adjacent candidate points in the arrival and departure route is set as \(\Delta\theta=\frac{\omega}{4}\), therefore, the heading angle \(\lambda_{u,\varphi}^{k}=\lambda_{u,\varphi}^{k-1}+\tau\cdot\Delta\theta\), \(\tau\in\{-2,-1,0,1,2\}\), as shown in Figure 5.  
The flight path angle of arrival route should be \(0\sim 3.3^{\circ}\) in the vertical profile [26], so the vertical angle between adjacent segment candidate points of arrival route is set to \(\Delta\varphi_{A}=1.1^{\circ}\), as shown in Figure 5(a). The flight path angle of departure route should be \(0\sim 9.3^{\circ}\)[26], so the vertical angle between adjacent segment candidate points of departure route is set to \(\Delta\varphi_{D}=3.1^{\circ}\), as shown in Figure 5(b). Therefore, the pitch angle \(\varphi_{u,\varphi}^{k}=\varphi_{u,\varphi}^{k-1}+\sigma\cdot\Delta\varphi\), \(\sigma\in\{-3,-2,\cdots,2,3\}\).
###### Abstract  
This study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. This study's novelty lies in predicting the mortar's porosity considering the substrate's influence on which this mortar is applied. For this purpose, an experimental database comprising 1592 datapoints of industrial mortar applied to five different substrates (hollowed ceramic brick, solid ceramic brick, concrete block, concrete slab, and lightweight concrete block) was generated using an experimental program. The samples were characterized by bulk density, open porosity, capillary water absorption coefficient, drying index, and compressive strength. This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. The results indicate that it is possible to predict the open porosity of mortar with good prediction accuracy, and that both Random Forest (RF) and Support Vector Machine (SVM) algorithms (RF = 0.880; SVM = 0.896) are suitable for this task. Regarding the main characteristics that influence the open porosity of the mortar, the bulk density and open porosity of the substrate are significant factors. Furthermore, this study employs a straightforward methodology with a machine learning no-code platform, enhancing the replicability of its findings for future research and practical implementations.  
random forest; support vector machine; industrial mortar; substrate; prediction Article
## 4 Case Study
### Data Preparation
#### 4.2.4 Disaster Event Data  
Field investigations were conducted to collected geological disaster event data from a specific route into Tibet. The dataset includes information on the occurrence time, location, disaster type, scale, and clearance duration. Based on the 2021-2023 geological disaster survey data, the distribution of geological disaster events in Tibet is shown in Figures 9-12. The primary disaster types include rockfalls, landslides, debris flows, subsidence, and water damage.  
Figure 11: The degree of disruption for geological disasters.  
Figure 12: Average debris clearance duration of geological disasters.  
Figure 10: Level of geological hazards.  
It is important to clarify that road water damage refers to the destruction of road infrastructure caused by heavy rainfall, flooding, scouring, impact forces, seepage, and human activities within a short period. Based on the affected locations, road water damage can be classified into four categories: riverbank roadbed erosion, bridge pier scouring, bridge foundation erosion, and culvert blockage. In this study, water damage specifically refers to riverbank roadbed erosion, which primarily manifests as embankment collapse and slope protection failure, typically classified as shallow surface damage with a relatively short repair period.  
Further analysis of Figure 9 showed that collapses and debris flows are the most common types of geological disasters, accounting for 48.61% and 21.13%, respectively. Landslides occur at a moderate frequency, representing 13.82%. subsidence and water damage events are less frequent, accounting for 8.91% and 7.53%, respectively. From the perspective of disaster severity (Figure 10) and its impact on road operations (Figure 11), debris flows are most likely to cause complete road closure, accounting for 50% of such events, followed by water damage at approximately 20%, with water damage being the most severe type of geological disaster. Collapses have the least severity and are more likely to result in partial road closures, as the impact is mainly concentrated on one side of the road. Landslides tend to cause low-severity events, with a higher proportion of two-way traffic. Subsidence events are relatively evenly distributed across different road closure statuses, rarely causing complete closures, and more often resulting in partial closures or maintaining two-way traffic. Regarding the average debris clearance time caused by disasters (Figure 12), water damage has the longest clearance time, up to 70 h, followed by collapses, due to the extensive material removal and repair of damaged roadbeds and structures, requiring heavy machinery and high safety measures. Debris flows and landslides have shorter clearance times because the material is relatively loose, necessitating a rapid response.