esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
The quantitative study reveals an average age of 6 years of the papers, indicating that the subject is recently studied in West African countries. There is an increase in published articles from 2014 and an effective growth from 2018, with a peak in 2020. The University of Abomey-Calavi (Benin) is the institution with the highest production, followed by Odafemi Awoolowo University (Nigeria) and Koforidua Technical University (Ghana). The countries in West Africa with the top production are, in descending order, Nigeria, Ghana, Benin, Senegal and Gambia. Overall, many international collaborations have been observed, mostly with the USA, followed by France, the UK, Luxembourg and Belgium. However, a key limitation lies in a lack of collaborations between West African countries themselves.  
From the qualitative study, we can note that 50% of the 20 case studies are related to environment, while 40% are related to agriculture and 10% to both environment and agriculture. Indeed, several articles present case studies in both fields simultaneously.  
The quantitative and qualitative studies give very interesting insights on the current state of the art concerning the adoption of IoT by West African researchers, to solve agricultural and environmental issues. The wide range of use cases clearly demonstrates the interest in this technology and its usefulness, even in the least developed countries. In the following, we discuss insights obtained from investigating the 20 selected studies, both for applications in agriculture and environment. More specifically, we detail the application domains, the sensor data collection techniques, the challenges and finally the opportunities and future directions.
## 2 Materials and Methods
### GC-FID e GC-MS Analysis  
GC-FID analysis of the volatile components was carried out using an Agilent 4890D instrument coupled to an ionization flame detector (FID). Compounds were separated on a HP-5 capillary column (5% phenylmethylpolysiloxane, 25 m \(\times\) 0.32 mm i.d.; 0.17 mm film thickness; J&W Scientific, Folsom, CA, USA), working with the following temperature program: 5 min at 60 \({}^{\circ}\)C, rising at 4 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C, then at 11 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C, then held for 15 min; injector and detector temperatures, 280 \({}^{\circ}\)C; carrier gas, helium (1.4 mL/min); injection volume, 1 \(\mu\)L; split ratio, 1:34. A mixture of aliphatic hydrocarbons (C8-C30; Sigma, Milan, Italy) in hexane was directly injected into the GC injector under the above temperature program in order to calculate the retention indices of each compound.  
GC-MS analysis was performed using an Agilent 6890N gas chromatograph coupled to a 5973N mass spectrometer equipped with a HP-5 MS capillary column (5% phenyl-methylpolysiloxane, 30 m \(\times\) 0.25 mm i.d., 0.1 mm film thickness; J&W Scientific). The same was programmed at 60 \({}^{\circ}\)C for 5 min, rising at 4 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C, then at 11 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C, then held for 15 min, and finally at 11 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C and held for 5 min; carrier gas, helium; flow rate, 1.0 mL/min; injector and transfer line temperatures, 280 \({}^{\circ}\)C; injection volume, 2 \(\mu\)L; split ratio, 1:50; scan time, 75 min; acquisition mass range, 29-400 \(m/z\). All mass spectra were acquired in electron-impact (EI) mode with an ionization voltage of 70 eV.  
The identification of volatile components was based on computer matching with the WILEY 275, NIST 05 and ADAMS libraries. A home-made library was used as well. Whenever possible, components were identified by comparing the retention times and mass spectra with those of authentic compounds using the program MSD Chemstation G1701 EA (Agilent).
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
### Objectives and Contributions  
This research aims to develop a classification system for correct and incorrect pronunciation of the Arabic short vowels. It is a novel research idea focusing on the subtle pronunciation differences in Arabic speech processing. The outcomes of this research can be useful in developing advanced systems that can autonomously classify words and sentences to effectively facilitate CA learning with accurate pronunciation. As there are 28 alphabets in the Arabic language and each alphabet has three possible vowel states, they make a total of 84 unique phonemes. Thus, given audio that utters any of these 84 phonemes, our task is to accurately map the audio to the correct phoneme.  
Although there are a few existing datasets related to Arabic pronunciation, there is no dataset that serves our purpose of containing the different possible vowel pronunciations. Thus, we created a new dataset of the recordings of the Arabic alphabets through an online audio recording system. After passing the data through various stages of preprocessing, we augmented the available audio data by generating synthetic audio to make a sufficiently bigger audio dataset. We trained a deep convolutional neural network (CNN) over this data for audio classification. The trained network can classify unseen audio data with a testing accuracy of 95.77% into one of the 84 classes. Our research is different from the previous works in terms of the dataset, features engineering, proposed architecture, and evaluation performance.  
The major contributions of this work are:  
1. Collection of an audio dataset for the Arabic alphabet focused on the three states of vowels for each alphabet.
2. Classification of Arabic short vowels by recognizing the correct short vowels from a recorded phoneme.
3. Constructing a general CNN architecture for phoneme classification. This allows replicating the architecture for similar tasks or a different number of classes.
4. Sharing our experience of model optimization and fine-tuning with the researchers and practitioners to aid their knowledge of building better models in the future.  
The remainder of the article is organized as follows. Section 2 discusses the related work. Section 3 describes the materials and methods used in this research. Section 4 explains the development of the CNN model for Arabic short vowel classification and describes the techniques of data augmentation and fine-tuning applied to improve the model's performance. Section 5 reports the results of the classification. Section 6 contains discussions in view of the related work and implications of our work. Section 7 concludes this article.
## 3 Proposed Method
### Message Embedding
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Illustration** \\ \hline \(\mathbf{C}_{um}\) & The distortion vector \\ \(\mathbf{V}_{\mathbf{sb}}\) & The floating-point distortion vector \\ \(\mathbf{d}_{\mathbf{du}}\) & A \(k\)-ary secret message vector \\ \(L\) & The length of vector \\ \(d_{\mathbf{zm}}\) & An \(m\)-ary secret message \\ \(\mathbf{d}_{\mathbf{sb}}\) & An \(n\)-ary secret message vector \\ \(W^{*}\) & The optimal weight for weighted color transfer \\ \(\mathbf{P}^{\prime\prime}_{s1}\) & The pixel value of the stego image 1 \\ \(\mathbf{P}^{\prime\prime}_{s2}\) & The pixel value of the stego image 2 \\ \(\mathbf{R}^{\prime\prime}_{ouct}\) & The restored image \\ \(\mathbf{R}^{\prime\prime}_{ouct}\) & The restored pixel value \\ \(\mathbf{F}\mathbf{P}\) & A single floating-point pixel \\ \(l_{iou}\) & The source image used for optimal weighted color transfer \\ \(\mathbf{I}^{\prime}_{ouct}\) & The stego OWCT image \\ \(\mathbf{P}^{\prime\prime}_{s1}\) & The stego pair of the red channel \\ \(\mathbf{P}^{\prime\prime}_{s2}\) & The stego pair of the green channel \\ \(\mathbf{P}^{\prime\prime}_{b3}\) & The stego pair of the blue channel \\ \(\mathbf{P}^{\prime}_{ouct}\) & The tentative stego pixel concealed with one secret message \\ \(\mathbf{P}^{\prime}_{ouct}\) & The tentative stego pixel concealed with two secret messages \\ \(l^{\prime}_{s1}\) & The stego image 1 \\ \(l_{s2}\) & The stego image 2 \\ \(l_{tar}\) & The target image used for optimal weighted color transfer \\ \(\mathbf{W}_{a}\) & The 3-tuple weight vector \\ \(\mathbf{W}_{b}\) & The 2-tuple weight vector \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The notations used in our MHDI-RDH algorithm. Figure 1: The flowchart of the message embedding in our proposed scheme. thereby producing dual stego pixels, \(\mathbf{P}_{s1}^{\prime\prime}\) and \(\mathbf{P}_{s2}^{\prime\prime}\). Similarly, we proceed with the message embedding in every pixel, so as to produce dual stego images, \(I_{s1}^{\prime\prime}\) and \(I_{s2}^{\prime\prime}\). The dual stego images enable the receiver to extract one part of the secret data, \(\mathbf{d}_{\mathbf{sb}}\), \(d_{\mathbf{z}m}\), and \(\mathbf{d}_{\mathbf{du}}\) independently, and restore the image \(I_{\text{succ}}^{\prime\prime}\), depending on the authorized three secret keys, \(key_{1},\;key_{2},\;key_{3}\). We detail these two phases as follows.
# Electrically Small Water-Based Hemispherical Dielectric Resonator Antenna
## Appendix B
### Sensitivity and Tunability  
Variation in the cable type of same characteristic impedance had a minimum impact on the radiated power. The same was the case for the length of the transmission line below the ground plane (\(l_{c}\)). However, variation of the length and position of the monopole (\(\Delta l_{m}\) and \(\Delta x_{m}\)) as well as the hemisphere radius and temperature of water (\(\Delta r_{w}\) and \(\Delta T_{w}\)) affected the radiated power. Analysis of the different parameters showed that changes of \(\Delta l_{m}\approx 1.7\) mm, \(\Delta x_{m}\approx 0.7\) mm, \(\Delta V_{w}\approx 1.4\) mL, and \(\Delta T_{w}\approx 0.4\)\({}^{o}\)C resulted in a 1% decrease in radiated power for the magnetic dipole antenna at 300 MHz. This information is important because it shows how sensitive the antenna is to fabrication imperfections. However, this can also be seen as a way to tune the antenna. This is illustrated in Figure 14 by the results of simulated temperature tuning and measurements of water extraction from the hemisphere for the magnetic dipole antenna. In Figure 14a, the total efficiency as a function of frequency and temperature is shown. Clearly, the resonance frequency was shifted by a change in temperature. From 0 to 100 \({}^{\circ}\)C, the peak of the efficiency was blue-shifted linearly (as indicated by the black tendency line) from 286 to 358 MHz, corresponding to a shift of 25%. This blue-shift was obviously due to the permittivity of water changing from \(87.8-j2.8\) to \(55.8-j0.22\), effectively bringing a change of 25% to the wavenumber in water. Furthermore, the temperature growth decreased the losses in water, and the total efficiency was above 50% from around 55 \({}^{\circ}\)C.  
The reflection coefficient was moreover measured for different water fillings extracting 1% (\(\approx\)3.5 mL) volume of water at the time from the completely filled hemisphere. During the measurements, the ground plane was kept in horizontal level. The results are shown in Figure 14b, and it can be seen that there was a linear blue-shift and increment of the minimum in the reflection coefficient as water was extracted, demonstrating yet another means of tunability in the water-based antenna.  
The tunability properties of the antenna can, in principle, also be used for sensing. Several antennas designed for sensing temperature changes of up to 1000 \({}^{\circ}\)C have already been demonstrated [31; 32; 33].
# Norovirus Detection in Ready-To-Eat Salads by Propidium Monoazide Real Time RT-PCR Assay
###### Abstract  
Ready-to-eat (RTE) salads have recently been associated with food-borne norovirus outbreaks, although these infections are mainly related to shellfish and berry consumption in the EU. A total of 135 bagged RTE vegetables were analyzed in order to investigate the occurrence of norovirus (NoV) genotype I (GI) and II (GII) RNA and to differentiate between infectious and non-infectious viruses by using propidium monoazide (PMAX) coupled with the real time Reverse Transcription (RT) PCR method. Initially, the PMAxx real time RT-PCR assay was optimized on NoV GI and GII suspensions, and proved capable of detecting significant (\(p<0.05\)) differences between infectious and inactivated viruses. Our analysis conducted on RTE salads samples showed the presence of norovirus GII in 74.8% of samples, of which 37.6% were infectious. The samples tested for viral contamination came from only two RTE vegetable-processing plants. The findings in this study could also be due to virally-contaminated water used in food production, processing, or preparation. This study stresses the need for effective real-time RT-PCR tools capable of qualitative and quantitative detection of NoV RNA, as well as being able to measure virus infectivity, for risk assessment, which is crucial in several public health measures and food regulations.  
NoV GI and GII; RTE salads; molecular assay; food safety; PMAxx Real Time RT-PCR; foodborne viruses; public health Article
# A Survey on MLLMs in Education: Application and Future Directions
## 4 Discussion and Future Directions
### Future Directions: Towards a Unified AI-Powered Educational Ecosystem
Engaging a diverse group of stakeholders--including educators, learners, policymakers, and ethicists--in the development process can help align the technology with educational values and societal norms [119]. The unified ecosystem should be envisioned as augmenting, rather than replacing, the role of educators. AI agents can handle administrative tasks, provide supplemental instruction, and offer personalized support, freeing educators to focus on higher-order teaching activities such as facilitating critical discussions, fostering creativity, and mentoring students. This synergy between AI and human educators enhances the educational experience by combining technological efficiency with human insight and empathy [3]. Moreover, the unified ecosystem can foster global collaboration and resource sharing. By connecting educational platforms across institutions and borders, learners and educators can engage in cross-cultural exchanges, collaborative projects, and peer learning opportunities. Such global connectivity enriches the educational experience, preparing learners for participation in an increasingly interconnected world [120]. To support continuous learning and adaptability, the ecosystem should incorporate mechanisms for ongoing feedback and improvement. AI agents can learn from interactions, outcomes, and user feedback, refining their models over time to enhance effectiveness. Additionally, professional development programs for educators are essential to equip them with the skills to leverage AI tools effectively and to adapt pedagogical approaches in response to technological advancements [73]. In conclusion, transitioning towards a unified AI-powered educational ecosystem addresses the limitations of standalone MLLM applications and maximizes the benefits of AI in education. By integrating various AI agents into a cohesive platform that emphasizes interoperability, ethical practices, and collaboration between technology and human educators, the educational landscape can be transformed. This unified approach promises to create a more connected, equitable, and effective educational system that is responsive to the needs of learners in the era of AI. Figure 4: Future unified AI-powered educational ecosystem, supported by external modules and infrastructures.
# Review on Rail Damage Detection Technologies for High-Speed Trains
## 3 Non-Destructive Testing Methods for High-Speed Train Rails
### Laser Ranging Detection Methods  
Laser rangefinders typically operate based on the principle of laser propagation time. The laser emitter emits a laser beam towards the surface of the rail and measures the time required for the pulse to reflect off the rail surface and return to the sensor. The measurement principle is shown in Figure 11. At the same time, corresponding laser heads and laser emitters can be arranged according to the arbitrary shape of the rail, such as longitudinal horizontal, alignment, transverse horizontal, and torsion of the rail. Multiple laser rangefinders can be used for measurement, which is called the chord  
Figure 10: Principle diagram of new eddy current testing.  
method. Figure 11 shows the principle of the chord method for measuring the longitudinal horizontal length of the rail. This method usually uses the distance between the front bogie and the rare bogie as the reference chord and measures the main vector at 10 midpoints [56]. Extracting specific objects plays an important role in laser ranging data processing.  
Wuhan University has developed a system for measuring the external dimensions of steel rails using laser ranging sensors [57], which integrates sensor technology, laser measurement technology, multi-sensor integration and control technology, computer software technology, and network communication technology. Laser ranging sensors belong to a type of point structured light, which utilizes advanced laser triangulation technology to achieve fast measurement response speed and high measurement accuracy.  
In terms of the detection principle, the line structured light measurement method uses optical technology for 3D measurement, which has the advantages of non-contact deployment, fast speed, simple implementation, high accuracy, and good anti-interference ability and currently makes it one of the most widely used methods in 3D measurement. The point structured light rail shape measurement method developed by Wuhan University has been applied to Wuhan Welding Rail Factory and has achieved certain results, confirming the superiority of optical technology in rail shape detection. Line structured light is superior to point-by-point scanning and slow-point structured light while avoiding the complex algorithm of encoding and decoding required for multiline structured light, making it more suitable for the development of this system. Using a pulse laser rangefinder, the ranging accuracy of the rail surface can reach \(\pm\)0.05-0.1 mm. A 1550 nm fiber laser is used in conjunction with a high-precision time measurement chip.  
Laser sensors are currently highly mature products in the industrial sector, widely used in machine tools, production lines, the rubber industry, and the steel industry. Their measurement accuracy can reach 0.019 mm. At the same time, the displacement sensor accuracy is 0.015 mm, and the guide rail accuracy is 0.012 mm, ensuring the accuracy of the system in all aspects.
# Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM
## 4 Hand Posture Feature Extraction
### Handcrafted Skeleton Features
#### 4.1.2 Hand Skeleton Shape Extraction  
The role of hand shape in the dynamic hand gesture recognition determines the movement of the joints and components of the local motion. Therefore, we represent the hand skeleton shape \(\mathbf{S}_{kshape}\) with two components: the movement of the joints with respect to their neighbors \(\mathbf{S}_{knnov}\) and the angle of the joints with respect to two neighboring joints \(\mathbf{S}_{krot}\), as shown in Figure 4:  
\[\mathbf{S}_{kshape}=\left\{\mathbf{S}_{kmnov},\mathbf{S}_{krot}\right\} \tag{28}\]  
Regarding the shape descriptor of the movement of a hand joint with respect to its neighbors, we use all displacements at a point with respect to the remaining points with no overlap between the two points as follows:  
\[\mathbf{S}_{kmnov}^{t} =\left\{\mathbf{x}_{j}^{t}-\mathbf{x}_{i}^{t}\left|\right.i<j\text{ and }i,j\in\left[1,N_{j}\right]\right.\right\} \tag{29}\] \[\mathbf{S}_{kmnov} =\left\{\mathbf{S}_{kmnov}^{t}\right\} \tag{30}\]  
In this study, with \(N_{j}=22\), there are in total \(C_{N_{j}}^{2}=231\) elements in \(\mathbf{S}_{kmnov}^{t}\). Besides the local movement features, we also suggest the angles between one joint and two distinct joints as the features to exploit the local rotation in the dynamic gesture. This is calculated as:  
\[\mathbf{S}_{krot}^{t} =\left\{\frac{\left(\mathbf{x}_{j}^{t}-\mathbf{x}_{i}^{t}\right).\left( \mathbf{x}_{k}^{t}-\mathbf{x}_{j}^{t}\right)}{\left\|\left(\mathbf{x}_{j}^{t}-\mathbf{x}_{i}^ {t}\right)\right\|\left\|\left(\mathbf{x}_{k}^{t}-\mathbf{x}_{j}^{t}\right)\right\|} \right.\left|i<j<k\text{ and }i,j,k\in\left[1,N_{j}\right]\right.\right\} \tag{31}\] \[\mathbf{S}_{krot} =\left\{\mathbf{S}_{krot}^{t}\right\} \tag{32}\]  
Similarly, the number of angle features at time \(t\) is \(C_{N_{j}=22}^{3}=1540\).  
Figure 4: Hand skeleton shape calculated by the movement and rotation of joints with respect to their neighbors. There are 22 joints in the hand skeleton data numbered from 0–21: 0 (wrist), 1 (palm), 2–5 (thumb), 6–9 (index), 10–13 (middle), 14–17 (ring), and 18–21 (pinky).
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 3 Proposed Augmented Lagrangian-Based Reinforcement Learning
### Definition of State, Action and Reward in RL
#### 3.2.3 Reward Function  
In (8), we have multiple constraints. For the association constraint, i.e., (10), and the requirement constraints of the devices, i.e., (11)-(13), we directly map the actions to the corresponding ranges. As for bandwidth constraints (9), we consider an augmented Lagrangian method to constrain the total bandwidth since it cannot be handled at the time of action selection.  
Motivated by the augmented Lagrangian method, we introduce the penalty into the design of the reward function. In more detail, that is, when the bandwidth constraints of all BS are satisfied, the reward function equals the original weighted overall utility, i.e., (8), which can be calculated as  
\[r=w_{\text{eM}}\!\!\sum_{n\in\mathcal{N}_{\text{eM}}}\!\!U_{n}+w_{\text{uR}}\! \!\sum_{n\in\mathcal{N}_{\text{uR}}}\!\!U_{n}+w_{\text{mM}}\!\!\sum_{n\in \mathcal{N}_{\text{mM}}}\!\!U_{n}, \tag{20}\]  
Otherwise, the reward equals the penalty item similar to the augmented Lagrangian method, that is,  
\[r=\sum_{m\in\mathcal{M}^{\prime}}(B_{m}-G_{m}), \tag{21}\]  
where the set \(\mathcal{M}^{\prime}\) just involves BSs that do not satisfy the bandwidth constraint, and \(G_{m}=\sum_{n\in\mathcal{N}}x_{nm}b_{nm}\) represents the overall bandwidth used in \(m\)-th BS. In (21), it means when the total bandwidth constraint is not satisfied, the reward is a negative value related to the exceeded bandwidth.