esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Relate Work
### Related Work in the ASR System
Since the model structure of RNN-HMM has not significantly improved the recognition rate, scholars have further explored and developed a RNN structure model suitable for speech modeling. The LSTM emerges as the times require, eliminating the long-term dependence on the RNN. [PERSON] et al. [6] pointed out that using the LSTM as an acoustic model for speech recognition is better than the DNN, and proposed an LSTM-RNN acoustic model for large vocabulary speech recognition, which greatly reduces the amount of training calculations, and also proves that the frame overlap and reduction the frame rate can obtain a more accurate model and faster decoding. At present, deep learning for speech recognition is mainly divided into two main methods: one is the use of the deep neural network model to replace the Gaussian model part of the original GMM-HMM model--that is, the NN-HMM model; the other is based on the End-To-End deep speech recognition model. Although the NN-HMM greatly improves the accuracy and recognition rates of speech recognition, it is necessary to realize the alignment of the HMM structure and speech before deep neural network training. With the progression of End-To-End technology in the fields of machine translation and speech generation, speech recognition based on End-To-End technology has also attracted the attention of the academic community because of its superior performance. Different from traditional speech recognition methods, which split the main task into several sub-tasks and focus on feature extraction, the acoustic model and other aspects, the latest End-To-End speech recognition uses the Mel-Frequency Spectrum (MFC) as input and directly outputs comprehensible natural language text. Some End-To-End speech recognition technologiesare divided into Connectionist Temporal Classification (CTC) and the sequence-to-sequence method (Sequence-to-Sequence), and some are divided into the CTC method and attention method (Attention). Speech recognition technology has entered the End-To-End era. Inspired by the above research, the multi-layer Causal Hole Convolution model based on End-To-End technology described in this article, combined with Transformer's self-attention mechanism, which directly captures the correlation of different parts of an audio clip, can give the model better time or frequency-domain invariance and powerful characterization capabilities. The deep hole convolution layer increases the receptive field, so that the convolution can output more information; the residual neural network module can better control the error; the gated recurrent unit (GRU) can be used to effectively select the input information; CTC solves the mapping problem between the text sequence and the output of the neural network model; finally, the LSTM is used as the output to obtain the time feature to realize the domain recognition in acoustic communication.
# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 2 Evolution and Enablers of Industrial Internet of Things
### Industry 4.0-Evolution  
The industrial revolution with the passage of time has many phases according to the requirement and challenges of the respective era. Figure 3 gives an idea about evolution towards Industry 4.0 with its elements.  
**Industry 1.0:** At the end of the 18 th century, the 1 st industrial revolution started with the help of water and steam power, which systematizes the factory floor. First, the mechanical weaving loom was established in 1784, and the first mechanical system was built thorough mechanical production facilities.  
**Industry 2.0:** In the beginning of the 20 th century, the 2 nd industrial revolution started using electrical energy. The first assembly line using electrical energy was established in 1870. The introduction of mass production in industry 2.0 enhanced the industry.  
**Industry 3.0:** Beginning of the 1970s i.e., in 1969, the first control system using programming language was established. Industry was slowly shifted to automation using information technology and micro-electronics's applications. This is the 3 rd industrial revolution [26].  
**Industry 4.0:** This previous industrial revolutions give rise to the development of industry 4.0. Industry 4.0 contributes a revolution to all domains comprising economic, academic, research, industrial and manufacturing sectors. There is a huge impact of the industrial revolution on the manufacturing processes of many fields. Implementing industry 4.0 demands change in many technologies namely automation, identification, computer, network communication, digital manufacturing, production process, production control management, decision making, judgment, sensing and analysis [27]. In the future, the manufacturing industry is expected to change on a large scale because of all new generation networks and interfaces offered by the environment of industry 4.0. This transformation is already in process in many industrial sectors. Up till now, for the fourth industrial revolution, exponentially growing technologies are sensor technology, artificial intelligence,  
Figure 3: Evolution towards Industry 4.0/IIoT.  
machine learning, robotics, nanotechnology, and 3D printing [28]. These technologies were invented decades ago, but their minimum cost and exponential growth will shape industry 4.0. To change the industrial process, researchers are focusing on providing the evolved form of these technologies in terms of flexibility and fast computational process. All this automation in industry is very important for the economic growth of a country.
## 2 Problem Description  
### NMF Algorithm  
The NMF algorithm decomposes a nonnegative matrix \(\mathbf{V}\in\mathbb{R}_{+}^{K\times N}\) into two nonnegative matrices \(\mathbf{W}\in\mathbb{R}_{+}^{K\times R}\) and \(\mathbf{H}\in\mathbb{R}_{+}^{R\times N}\) as  
\[\mathbf{V}\approx\mathbf{W}\mathbf{H}, \tag{1}\]  
where \(\mathbb{R}_{+}^{K\times N}\) is the field of matrices with \(K\) rows and \(N\) columns that contain nonnegative elements. Alternatively, the NMF model can be expressed in terms of a noise matrix \(\mathbf{E}\in\mathbb{R}^{K\times N}\) as follows:  
\[\mathbf{V}=\mathbf{W}\mathbf{H}+\mathbf{E}. \tag{2}\]  
Algorithms for solving the NMF problem and for estimating the matrices \(\mathbf{W}\) and \(\mathbf{H}\) from the target matrix \(\mathbf{V}\) were developed in [3]. They comprise alternating estimation equations for each matrix:  
\[\mathbf{W} \leftarrow\arg\min_{\mathbf{W}}C(\mathbf{V}\parallel\mathbf{W} \mathbf{H}) \tag{3}\] \[\mathbf{H} \leftarrow\arg\min_{\mathbf{H}}C(\mathbf{V}\parallel\mathbf{W} \mathbf{H}) \tag{4}\]  
with the constraints that \(\forall W_{kr}\geq 0\) and \(\forall H_{m}\geq 0\), where \(C(\mathbf{A}\parallel\mathbf{B})\) is a distance measure between the matrices \(\mathbf{A}\) and \(\mathbf{B}\), \(W_{kr}\) is the element in the \(k\)th row and \(r\)th column of matrix \(\mathbf{W}\), and \(\forall W_{kr}\) means all of the elements of matrix \(\mathbf{W}\).  
Several \"distance\" measures, including the Euclidean distance and the Kullback-Leibler divergence, can be used to calculate \(C(\mathbf{V}\parallel\mathbf{W}\mathbf{H})\). If the Euclidean distance is used, \(C(\mathbf{V}\parallel\mathbf{W}\mathbf{H})\) is defined as follows:  
\[C(\mathbf{V}\parallel\mathbf{W}\mathbf{H})=\frac{1}{2}\|\mathbf{V}-(\mathbf{W }\mathbf{H})\|_{F}^{2}, \tag{5}\]  
where \(\parallel\ \ \|_{F}\) is the Frobenius norm [3; 22]. Through the application of the multiplicative update rule [3], \(\mathbf{W}\) and \(\mathbf{H}\) can be iteratively estimated as follows:  
\[\mathbf{W}\leftarrow\mathbf{W}\otimes[(\mathbf{V}\mathbf{H}^{T})\oslash( \mathbf{W}\mathbf{H}^{T})], \tag{6}\]  
\[\mathbf{H}\leftarrow\mathbf{H}\otimes[(\mathbf{W}^{T}\mathbf{V})\oslash( \mathbf{W}^{T}\mathbf{W}\mathbf{H})], \tag{7}\]  
where \(\otimes\) and \(\oslash\) indicate element-wise multiplication and division, respectively.
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## Architecture of the Real-Time Wide-Area Rain-Rate Measurement System
### Market Opportunities  
The technologies developed within the project will be used to obtain very detailed estimates of precipitation. The NEFOCAST Service Center is able to provide real time precipitation data for now-casting applications, since it is primarily thought of as a tool for monitoring and detect precipitating events, even severe events.  
In particular, it will be possible to obtain information with a temporal resolution much higher than what provided by other monitoring systems. These results will be valuable and significant for the prevention of natural disasters and for the continuous monitoring of basins and their precipitation status, as well as for other applications in the productive and civil fields. A considerable number of companies provide weather data in XML or CSV format at very convenient prices. Compared to existing market solutions, the main advantages of the NEFOCAST solution are:  
* Higher accuracy, frequency and detail for the provided information, especially when combined with other forecast systems;
* The availability of all information in one single data center;
* The possibility to serve a large number of professional users using the same technological platform.  
The NEFOCAST solution could provide interesting perspectives in reducing costs and having a high quality of the provided data at the same time. The prospective is to expand the covered  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Forward Link** & **Return Link** \\ \hline Satellite & EUTELSAT 10A & EUTELSAT 10A \\ Frequency & 11,345.8 MHz & 14,216.6 MHz \\ Polarization & Vertical & Horizontal \\ Physical Layer Protocol & DVB-S2 & F-SIM \\ Link Layer Protocol & IP-MPE & F-SIM \\ EIRP & 48 dBW & - \\ G/T & - & +4 dB/K \\ MODCOD & QPSK 4/5 & - \\ Spreading Factor & - & 16, 32, 64, 128, 256 \\ \hline \hline \end{tabular}
\end{table}
Table 1: NEFOCAST Space Segment.  
area from one single country in one year after the project's end to ten European countries in the forthcoming five years. The higher the number of installed SmartLNBs, the more accurate and detailed the precipitation estimation is. A very dense network of sensors, such as the one that could be available using home satellite receivers, could lead to a downscaling of the field's coverage. Such a resolution increase will be beneficial when dealing with extreme events such as sudden flooding in urban areas to mitigate the exposure to risk for the population.
# Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
## 1 Introduction  
The internet and modern web services have been increasing within the last few decades; a surplus of information is now accessible to everyone [1]. It can be challenging for users to filter through all this information and take away essential aspects. Many online e-commerce firms recommend products to their users, selling millions of products on one platform. For an everyday user, browsing through all possibilities can be overwhelming; this can cause information overload. Recommender systems aim to solve that information overload problem while personalizing the user experience by delivering accurate, personalized recommendations of items/products to users according to their preferences [2]. A recommendation system (RS) aims to predict if an item would be useful to a user based on given information [3]. The use of these systems has been steadily growing within the last few years, where they are used in retail and e-commerce firms like eBay and Amazon. These companies acquire massive users' data and tailor the RSs to meet the users' and business needs [4, 5]. RSs are widely utilized in e-commerce and retail; they are also used in many other industries such as healthcare, transportation, and agriculture [6, 7, 8]. High-quality RSs positively impact the users' experience and the overall enterprises' revenue or decision. RSs have attracted many researchers for the past years, and various literature reviews were conducted, addressing different RSs' features, algorithms, and challenges [1, 9, 10, 11, 12, 13, 14]. Yet, none of these reviews touched on all RSs' aspects holistically. In [9], authors have focused on categorizing RSs based on data they have used. A survey of RSs using only social networks is provided in [10] and location-based RSs using social networks [11]. RSs aresurveyed in [12] from their application point of view. A review in [13] has focused on algorithms of RSs [13] while the characteristics of RSs are summarized [14].  
In this paper, we present a comprehensive guide to RSs. We highlight different RSs categories and analyze the issues associated with current RSs, including cold start, data-sparsity, scalability, and diversity. Also, we demonstrate how to evaluate the RSs' performance using different metrics such as recall, precision, accuracy, ROC (Receiver operating characteristic) curve, and F-measures. Moreover, we outline the adoption of RSs in many industries and domains. This paper is structured as follows: Section 2 presents the recommendation categories, Section 3 highlights the main challenges in RSs, Section 4 explains the different evaluation metrics, Section 5 introduces the business adoptions of RSs, and Section 6 is for the conclusion and future directions.
## 3 Results  
### Raw Field Data Analysis  
Subsoil horizons in their in situ state are easily discernible, with associated structures delineated as hyperbolas in the GPR scan (Figure 2b). However, the GPR scan on the distorted soil layer region revealed less of the soil subsoil horizon, with an almost completely obliterated structure due to the dearth of multiple hyperbolas on the scan (Figure 2a),particularly in the area of the near-surface that has direct contact with the compaction device (from 0 to 20 cm depth, green rectangle outlines in Figure 2a,b).  
Both the mean and median attenuation attributes, which are related to the instantaneous power of the GPR-propagated signal, peaked at \(\sim\)14 ns at the unaltered soil layer (non-compacted) zones (Figure 2c), depicting the possibility of the contrasting dielectric permittivity of the topsoil horizon and the subsoil layer where the mean attenuation begins to decline. However, there is no sharp decline in the average attenuation curve in the GPR profile of the zone of the altered soil layer (Figure 2d), which may be due to the effect of the re-arrangement of the soil structure that merged the upper horizon and the subsoil layer, obliterating the contrast in the dielectric permittivity.  
The instantaneous power at the non-compacted zone decreases so sharply at time 15 ns, which may be due to attenuation effects imposed by the constituents (most likely the fluid contents) of the adjacent soil layer (Figure 2c). However, in the compacted zone where soil structure is altered, there is no sharp decline in the instantaneous power (Figure 2d), depicting less attenuation, perhaps due to the expulsion of the fluid contents of the adjacent layer.  
As shown in Figure 2c and 2d, respectively, the best exponential decay-fitting curve and power-law decay-fitting curve values for the unaltered soil structure zone (\(\sim\)e\({}^{-0.07646}/\)\(-\)t\({}^{-0.562}\)) are more than the altered zone (\(\sim\)e\({}^{-0.0093t}/\)\(-\)t\({}^{-0.007018}\)). It could be interpreted that attenuation is more in the unaltered zone than in the zone of reworked soil structure. Thus, this may be a diagnostic signature of the GPR signal at these different zones.  
The instantaneous power function points more to the contrast change in the properties of the propagation medium, perhaps because of the structure alteration caused by the normal stress induced by the tractor's compaction.  
Figure 2: GPR 2D profile sections and their signal analytic plots; (**a**) 2D section at the zone of soil alteration and (**b**) 2D section at the unaltered zone; (**c,d**) are attenuation attribute plots at both the unaltered and altered zones, respectively.
# The Effect of Bodyweight Support and Incline Running on Triceps Surae Electromyographic Activity
###### Abstract  
Body weight support (BWS) and incline running (IR) are commonly used either during rehabilitation or during training separately, with many positive effects on athletes' performance and rehabilitation. The aim of the present study was to investigate the interaction between bodyweight support and incline running on the electromyographic activity of the triceps surae and compare it to flat running. In eighteen healthy men (age: 20.3 \(\pm\) 1.2 years, body weight: 70.2 \(\pm\) 4.8 kg, body height: 179.6 \(\pm\) 5.4 cm), the changes in electromyographic activity (EMGA) during a 10 min run with BWS (15% or 30% of body weight; in different occasions) and IR at 7%, as well as jumping performance and gait spatiotemporal parameters, were evaluated. A lower Rating of Perceived exertion and a significant decrease in the size of the Vastus Lateralics (VL) (33.4%), Soleus (SOL) (17%), and Gastrocnemius Lateralis (GL) EMGA (28.5%, \(p\) < 0.05) but not in Gastrocnemius Medialis (GM) (10.5%, \(p\) > 0.05), was observed during BWS30% at 7% slope compared to flat running. Also, low-frequency fatigue of the quadriceps was induced only after running without BWS on a 7% slope (\(p\) = 0.011). No changes were found in jumping performance (\(p\) = 0.246) and gait spatiotemporal parameters (\(p\) > 0.05) except for flight time (\(p\) < 0.006). In conclusion, running with a slope of 7% and 30% of BWS can result in EMG activity comparable to that observed during level running. This method can also be used in prevention and rehabilitation training programs without creating fatigue.  
Keywords:injury prevention; body weight support; electromyography; fatigue; slope; uphill running +
Footnote †: journal: Applied Sciences
# Democratizing Deep Learning Applications in Earth and Climate Sciences on the Web: EarthAlHub
## 3 Results
### Earth and Climate Sciences Use Cases
#### 3.2.3 Flood Forecasting with NRM  
Flood forecasting is a vital task in hydrology. Beyond physical modeling, various approaches have been used to tackle this task with both conventional machine learning models and deep learning [56]. This model was introduced by [57] and utilizes long short-term memory (LSTM) [58] networks for 24 h of rainfall-runoff forecasting. The model was trained on Keras for a USGS streamflow station in Iowa, United States and then converted to a TensorFlow.js model. The config was created to read a CSV file for all the data that the  
Figure 6: (**a**) Low-resolution DEM, (**b**) high-resolution DEM, and (**c**) generated high-resolution DEM by DSRGAN.  
Figure 7: An example output of river image generation GAN [55].  
model expects. The config file for this model can be seen in Appendix B.7. An example forecast for 24 h of input and 24 h of output runoff is visualized on a line chart in Figure 8.
## 3 Experimental Results and Discussion
### Quantitative Real-Time Polymerase Chain Reaction (Q-PCR): Quality Assurance
#### 2.4.3 Intra- and Inter-Assay Variation: Reproducibility of the Real-Time PCR
The correlation between _TYMS_ and the regression grade was also revealed by the application of further statistical tests such as the t-test as shown in Figure 13.  
The interrelation between the different assays was also determined by the Chi\({}^{2}\)-test as listed below: \begin{tabular}{l l l} _ERCC1_ & \(\bullet\) & _RRM1 = p = 0.001_ \\ ERCC1 & \(\bullet\) & \(\bullet\) & TYMS = p = 0.30 \\ RRM1 & \(\bullet\) & \(\bullet\) & TYMS = p = 0.241 \\ \end{tabular}   
Therefore, a correlation between the RNA expression of _ERCC1_ and _RRM1_ in tumor tissue was statistically supported. The endogenous control gene allows the gene expression profile of the target gene to be normalized to the amount of input RNA or cDNA. It corrects for variation in possible RNA degradation, differences in sample handling, variation in reverse-transcription efficiency, presence of inhibitors in the RNA sample and RNA content or differences in sample handling. As described in the results section, the comparative method (ratio **2\({}^{\text{-}\text{AACT}}\)**) was used, including _\(\beta\)2-microglobulin_ (\(\beta\)2M) as endogenous control and the normal tissue as calibrator [14]. When considering the gene expression pattern, no correlations with sex or age of any patient were detected (Table 7). 5-FU targets folate metabolism through the inhibition of thymidylate synthase. It was obvious that all patients with high _TYMS_ expression levels showed no or only low response to 5-FU. This observation agrees with the literature which states that the _TYMS_ expression is linked to cellular sensitivity or resistance to the drug [15, 16]. Other studies found that patients suffering from metastatic cancer with amplified _TYMS_ genes have a shorter median survival than those without amplification [17]. As already mentioned, one patient with metastases and _TYMS_ amplification showed resistance to 5-FU and died of colorectal cancer within a short period of time.
## 3 Proposed Method
### Dataflow and Communication  
As illustrated in Figure 2\(c\), the data flow and communication between the base station and edge devices are designed to balance efficiency, accuracy, and scalability. The base station plays a central role in the system, predicting the target's trajectory based on previous position data and movement patterns, generating optimal schedules for activating and deactivating edge devices, and maintaining a centralized server database that stores information, such as the target's position, movement direction, and scheduling updates. On the other hand, the edge devices perform localized processing of image data, to detect the target and analyze its movement direction, thereby reducing the computational load on the base station and minimizing data transmission requirements. Each edge device maintains a local database, to store relevant tracking information, and it sends processed updates, including the target's position and direction, back to the base station. Communication between the base station and edge devices follows a structured approach with three types of updates: one-time updates for initial settings, on-demand updates to dynamically control the schedule of the edge devices, and per-iteration updates, which include real-time tracking results sent at the end of each iteration, to ensure synchronization and accuracy. This structured data flow reduces communication overhead while maintaining the system's responsiveness and accuracy in dynamic and resource-constrained environments. To ensure data consistency, the base station serves as the authoritative node for tracking metadata. When an edge device updates a re-identification feature, it sends these lightweight data to the base station, which then broadcasts the update to all active devices. This approach ensures that all devices share the same target identifiers.  
Furthermore, if an edge device loses connectivity, it continues to track locally and accumulates new detections and feature updates. Once connectivity is restored, these stored updates are transmitted to the base station and merged into the global tracking state during the next scheduling cycle. This mechanism maintains tracking consistency despite short-term network disruptions.