esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Methods
#### 2.2.4 Three Machine Learning Methods for Metrics Integration  
After determining the relationships between each remote sensing metric and species richness, we selected the high-explained-variance metrics to develop a multivariate integration index using machine learning models. In this study, we compared the capability of support vector machine (SVM), random forests (RF), and neural networks (NN) to integrate various metrics. Species richness was trained on 50% of the randomly extracted pixels and cross-validated on the remaining 50%. The same number of pixels was used for training and predicting in order to avoid under- or over-fitting due to an unbalanced dataset [80].  
Support vector machine (SVM) is a supervised machine learning model with associated learning algorithms that analyze data used for classification and regression analysis [81]. SVM uses kernel functions implicitly to map training data into a higher-dimensional feature space. The maximum separation hyperplane is defined by a set of support vectors, which are a function of the training data that lie on/the closest to the separating margin [82]. In this study, the _e1071_ library of the R statistical package was utilized to optimize the SVM parameters [83].  
Random forests (RF) is a regression model that grows an ensemble of trees [84; 85]. Each tree casts a unit vote for the most popular class according to the input variables. The growing process of RF is by bootstrap aggregating (or bagging), where a tree is randomly grown from the dataset, and this process can provide substantial gains in the accuracy of predicting models [86; 87], and it requires no pruning. In this study, we used the RF algorithm implemented in the _randomForest_ R package, with the parameter values for the algorithm, (i.e., number of trees to grow (ntree)) equaled to 500, and the number of descriptors randomly sampled at each split (mtry) equaled to the total number of descriptors in the dataset divided by three.  
Neural networks (NN) is a non-linear statistical data modelling tool used for prediction and regression. We selected the multilayer feed-forward neural network, which is one of the most popular approaches to neural networks (NNs) [88; 89; 90]. The multilayer feed-forward NN is a backpropagation network that trains the data using a backpropagation algorithm [84; 91]. This network comprises three layers: an input layer, one or more hidden layers, and an output layer. Neurons from one layer are in one direction linked to all neurons in the subsequent layers [92]. This study used the _nnet_ library provided by the R package in order to develop the model [36]. The _nnet_ package is the library for establishing multiple feed-forward NNs with more than one hidden layer [93]. This study constructed the model by carrying out the learning for a total of 30 times with the maximum number of iterations at 100.
## 3 Contemporary 3D TLS and the Methodology of Their Use for Documenting Archaeological Artefacts
### TLS Scanners
Depending on the solution used in the device, the altitude above sea level is obtained on the basis of readings from GNSS data or by using an electronic barometer built with MEMS (MicroElectroMechanical Systems) technology. The MEMS technology also creates a magnetometer, used by the scanner as an electronic compass. The compass reading is used to pre-align the obtained scan with respect to the north direction. In order to measure the inclination (inclinometer), a MEMS accelerometer is used, which measures the changes in induction caused by the inclination of the mass in the sensor in response to changes in the position of the device in relation to the gravitational field of the earth. The tilt reading is used to level the scan. Modern TLS scanners are therefore able, during a single measurement session, to obtain clouds of object surface measurement points, data on their levelling, height above sea level, GPS position and orientation towards the sides of the world (Figure 4), as well as photographic images of high resolution and colour quality of the scanned surfaces. Performing a series of such automated measurements in the field allows for quick and highly accurate acquisition of geometric and visual data that allow archaeological artefacts to be documented. Figure 3: Occurrence of an error in assigning a colour to a specific position of an object’s surface. Designated surface position—P, colour reading location—C.
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 2 System Model
### Multiplexing Model for eMBB-URLLC Services  
Usually, eMBB transmission is allowed to span multiple time domains to improve spectral efficiency. In contrast, URLLC transmission can span multiple frequency channels to meet its time delay requirements [19]. In this paper, the URLLC user is scheduled with  
Figure 1: System Model for eMBB and URLLC Coexistence Services.  
a short transmission time interval (sTTI), while the TTI size used to schedule the eMBB user is longer (slot duration is 1 ms). As shown in Figure 2, when URLLC services pass in and puncture the second (CB2) and sixth code block (CB6) of eMBB user 2, it degrades the eMBB quality of service. That is a serious problem faced in eMBB and URLLC coexistence scenarios [20], so appropriate mechanisms should be introduced to guarantee the quality of ongoing eMBB transmissions.
# The Role of AI in Warehouse Digital Twins: Literature Review +
## 5 Results
### What AI Technics Are Most Used for Warehouse Management under the DT Paradigm?  
#### 5.1.1 Artificial Intelligence  
The literature reviewed in this study has shown a wide range of ML applications in WDTs, primarily focused on classification, and forecasting tasks. Neural networks (NNs), especially convolutional NNs, have been extensively used for classification tasks. For example, ref. [30] used a convolutional NN to monitor fruit freshness in stores by analyzing thermal images of bananas. In contrast, ref. [31] used a sparse autoencoder for abnormal stationary differentiation in cold storage warehouses. Object detection for inventory and asset inspection applications in buildings has been successfully accomplished using YOLOv2, as demonstrated by [26; 32]. In addition, location estimation has been performed using deep learning techniques such as long short-term memory (LSTM) networks, as demonstrated by [33; 34]. Ref. [25] utilized black hole optimization-based clustering to group the available supply demands based on time-frame-related objective function.  
Forecasting tasks have been addressed with a wider range of ML techniques, with NNs still being the most widely used. Ref. [35] used a neuro-fuzzy model to forecast the future arrivals of stock keeping units (SKUs), while [40] used backpropagation NNs for sales predictions. Gradient-boosting decision tree (GDBT) has been used for anomaly detection and maintenance monitoring, as demonstrated by [36], and proximal policy optimization (PPO) has been used for inventory predictions by [38]. Ref. [41] used a combination of time-weighted linear regression method (TWMLR) and non-dominated sorting genetic algorithm (NSGA-II) for process time prediction and optimal allocation of trolleys for material handling tasks. A DT framework can curate multiple algorithms and subject problems to a set of optimization tools. Numerous ML techniques have been implemented in the \"algorithm center\" by [42], where the appropriate algorithm is selected to match the problem.  
Supervised learning has been the most used ML method, with 11 papers utilizing it on its own or in combination with other types of learning (cf. Figure 5). Unsupervised learning has been used three times, while reinforcement learning has been used only once. Deep learning techniques, particularly deep neural networks, have received significant attention due to their increased computational power and compatibility with big data [26; 30; 31; 38; 43]. Some algorithms have been approved through testing and are envisioned to be used in a DT framework. In contrast, others have already been applied in a case study demonstrating a united DT/AI embedded system. The natural and strong relation between AI and DT could also be noticed in the bibliometric networks that showcase keywords such as \"Artificial Intelligence\", \"Machine Learning\", \"Deep learning\" and \"Big Data\". However, they were not explicitly targeted in both Figures 2 and 3.
###### Abstract
Diagnosis may be confirmed with imaging methods, including sialography, sialoendoscopy, ultrasonography (US), computed tomography (CT), and magnetic resonance imaging (MRI). US is in common use and may reveal hypoechoic inclusions in the affected gland, as well as possibly enlarged cervical lymph nodes [13; 14]. Currently, there is no absolute consensus on a precise treatment guideline for JRP [15]. Common treatments are antibiotics and analgesics, sialogogues, hydration, and gland massage, which are in common use for the acute phase [9; 16]. For the chronic phase, diagnostic sialography was shown to have a therapeutic effect, which was credited to the irrigation and the potential anti-bacterial activity of the iodine-based contrast material [17; 18]. This was the origin of the idea of implicating sialoendoscopy for therapeutic purposes; like sialography, sialoendoscopy enables the dilation and irrigation of the ductal system and the application of anti-inflammatory solutions. The hypothetic idea of the therapeutic effect of sialoendoscopy is irrigation, which may cut the inflammatory cycle by cleaning the duct of debris and mucous plugs [19]. In JRP, the inflammation of the gland reduces salivary flow, which aggravates inflammation, causing ductal metaplasia, further decreased salivary flow, and progressive parotid atrophy, thus creating a vicious cycle [20]. Factors that might increase the probability of this inflammatory cycle are reduced salivary flow due to dehydration, inflammation caused by infection, and ductal abnormalities of a congenital or autoimmune origin [20]. Although many reports and publications show success in treating JRP with sialoendoscopy [8; 21; 22], the superiority of this treatment modality in comparison with other techniques is not statistically proven [22]. The aim of this article is to present the experience of the Maxillofacial Department of the Tzafon Medical Center, Poriya, Israel in the treatment of JRP with sialoendoscopy, to assess the procedure of sialoendoscopy as a treatment modality for JRP, and to discuss the relevant literature on this topic.
## 1 Introduction
Moreover, different research groups have utilized Q-learning because it is an optimal solution for designing energy-efficient MAC and routing protocols for UWSNs [21; 36; 37; 38]. As opposed to conventional protocols based on predefined rules, Q-learning leverages the nodes to adaptively learn optimal strategies to conserve energy using reinforcement learning [39]. This is especially useful in UWSNs, wherein the environmental conditions of high latency, low bandwidth, and unpredictable link qualities make communication very difficult [40; 41; 42]. Q-learning iteratively reinforces the decision-making process of the node from past experiences to yield better performance with reduced energy consumption and an elongated lifetime of the network without extensive prior knowledge regarding network dynamics [43]. Prior works have investigated several approaches to address these issues, such as employing directional antennas to enhance the transmission distance and minimize interference [44]. Some have incorporated scheduling policies for sensor nodes to avoid collision, enhancing the energy efficiency of the overall system/network [45]. To address these challenging issues, as mentioned earlier, we propose an adaptive Q-learning-based energy-efficient and low-collision MAC protocol to improve the performance of UWSNs. We use a reinforcement machine learning (ML) methodology to design an adaptive, energy-efficient, low-collision MAC protocol. By this method, we adapt transmission energy levels by observing the interference and noise levels of the network on the go, without prior knowledge about the interference sources. In addition, our proposed methodology has the potential to increase energy efficiency while enhancing the collision avoidance mechanism, which is a major problem of UWSNs. The detailed overview of the designed paradigm is given in Figure 1. The fundamental contributions of the research work presented by us are as follows:  
* We have developed a new low-collision method and combined it with an energy-efficient MAC protocol using Q-learning. Our chosen methodology improves the system's overall throughput. * Our chosen methodology also improves the collision avoidance of data packets. * We have proposed a new multi-cluster network for UWSNs to prove the theoretical analysis of our proposed system. The rest of the paper is as follows: Section 2 describes the system model, with collision design and analysis of our proposed system, including collision avoidance, avoiding collisions between clusters, the problem of spatial-temporal uncertainty, the problem of hidden terminals, problems with exposed terminals, and the system model. Furthermore, Section 2 presents an in-depth overview of our proposed MAC protocol: enhancing network efficiency and performance. Section 3 presents an improved Q-learning algorithm for better management of channel utilization in UWCNs. Section 4 briefly describes a performance evaluation, including simulation design, performance metrics, and results evaluation, as well as a comparison with recent and advanced MAC protocols, while the last section is the Conclusions.
# Black PEO Coatings on Titanium and Titanium Alloys Produced at Low Current Densities
## 3 Results
### Morphology and Composition of the Black PEO Coatings  
Surface SEM micrographs are reported in Figure 3. In general, the characteristic porous structure of PEO coatings can be seen in accordance with the literature [46,47]. Pores have a diameter of a few nanometers to micrometers in size. In fact, with the prolonged PEO process time, from 30 min to 60 min, a significant change in the morphology of the sample's surface is noticeable. PEO coatings obtained after 30 min have more pores of a smaller diameter, while with an increase in the processing time, there is also an increase in pores dimensions from nanometer to micrometer sizes, creating a channel structure. [PERSON] et al. [9] reported similar results, where after extending the reaction time, the nano-pores were stacked and connected to form the micro-pores and particular channel structure.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **Absorptance (\(\alpha\))** \\ \hline G2-30 & 0.84 \\ G2-60 & 0.86 \\ G5-30 & 0.84 \\ G5-60 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The absorptance value of the PEO-coated samples.  
Figure 1: Stereo microscope images of PEO-coated samples on two different titanium alloys and with two different PEO treatment times: (**a**) G2-30, (**b**) G2-60, (**c**) G5-30, and (**d**) G5-60.  
Cross-section SEM micrographs are presented in Figure 4. In the micrographs, the titanium substrate is shown in gray, the epoxy resin in black, and there is a PEO coating between them. From the resulting images, the change in the compactness of the outer layer can be noticed by increasing the processing time. Likewise, after 30 min of the PEO processes, coatings contain many pores full of grooves and empty space. Although there is no significant change in the general thickness between the PEO coatings, a difference in the shape of the PEO coating is observed.  
Figure 3: Surface SEM micrographs of black PEO coatings of the sample: (**a**) G2-30, (**b**) G2-60, (**c**) G5-30, and (**d**) G5-60.  
Figure 2: Reflectance spectra of PEO-coated samples.  
the barrier layer can be observed. The 30-minute coatings have an almost invisible inner barrier layer, while with the 60-minute coatings, it can be seen that this space is almost completely filled (indicated by arrows), suggesting a thicker inner barrier layer.  
Based on the EDS analysis of the surface and the cross-section, coatings consist of the following elements: O, Na, P, Mo, Ti, Fe, V, and Al, as shown in Table 3. In particular, coatings are rich in oxygen and phosphorus, with less iron, molybdenum, and sodium. The presence of molybdenum, iron, phosphorus, and sodium is due to the electrolyte used to prepare PEO coatings, while titanium, vanadium, and aluminum are from the substrate. Small amounts of vanadium and aluminum are present only in G5 titanium alloy, which is expected considering the chemical composition of titanium G5 [48]. Sodium is present only in the EDS analysis of the sample's surface, which may suggest that sodium compounds do not go in-depth in the coating.
## 3 Mathematical Formulation of the CPV
### RADTIRC as the Reference for the Model
Any CPV will focus the solar irradiance from a large area into a smaller area where a solar PV is placed [55]. The relationship between the value of irradiance with the inclusion of CPV, \(\mathrm{G_{con}}\) and the opto-electronic gain, \(\mathrm{C_{opto-e}}\), the solar irradiance, \(\mathrm{G}\), the irradiance at reference 1000 W/m\({}^{2}\), \(\mathrm{G_{ref}}\) and the incidence angle, \(\theta\) is presented in Equation (1). Note that for a non-CPV, the value of \(\mathrm{C_{opto-e}}\) is equal to 1. \[\mathrm{G_{con}}=\mathrm{C_{opto-e}}\bigg{(}\frac{\mathrm{G}}{\mathrm{G_{ref}} }\bigg{)}\cos\theta \tag{1}\]
###### Abstract
Since GaN-based LLC converters are employed at very high frequencies, even higher than 500 kHz, full ZVS should be guaranteed because even a portion of hard switching could lead to shoot-through, severe thermal stress, and, finally, the device failure [6]. Ultra-high power density achieved by utilizing GaN HEMT switches in the LLC converter makes it an ideal choice for commercial applications such laptop adaptors and flat screen TVs, all-in-one desktops, and cell-phone fast chargers [25]. However, the most important problem with GaN HEMTs is the inherent normally on state of typical HEMT devices; this has been solved in most current available switches available in the market [33]. Also, there are some challenges GaN HEMTs face in LLC resonant converters, like digital control system design, PCB winding for MHz range switching frequency, flux cancellation techniques, EMI noise cancellation, and the current conductive limitation of a single GaN switch [11; 30; 34; 35; 36; 37; 38; 39; 40; 41].
## 5 Simulation Results and Discussion
Of course, the analysis of the performance of turbo codes, LDPC codes, and polar codes in the condition in which the impulse response of the channel was considered to be unitary (meaning the ideal situation) was carried out in the papers [19; 20], where the BER vs. SNR curves for turbo codes, LDPC codes, and polar codes, using an AWGN channel without ISI, were drawn and where a much lower level of BER for a given SNR was observed. Although, currently, OFDM is predominantly used, this fact should not impede/stop comparisons of the performances of turbo codes, LDPC codes, and polar codes using BPSK modulation. Indeed, the BER level is higher if the equalization is performed separately, before the decoding process, such as in this paper, and not within the decoding process, such as in turbo equalization (which is not the topic of this paper). In this paper, the transfer function was chosen arbitrarily, and of course, depending on the response of the channel to the impulse, each code can be optimized to obtain the maximum performance. In addition, in this paper, we did not propose any very innovative ideas regarding the three codes, but we tried to test their performance in a certain arbitrarily chosen situation. We believe that these results can be useful to other researchers, considering that we have not found any works in which the three codes were compared under the same conditions, simultaneously.
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 4 CNN Model for Arabic Short Vowels Classification
### Data Augmentation  
Data augmentation is a technique for increasing the number of records in the dataset by slightly modifying the copies of original data for producing synthetic data using statistical methods and algorithms. Many data augmentation techniques exist in the literature [37], so we applied a few techniques one-by-one. Each time an augmentation technique was applied, it resulted in the gradual increase of the audio instances in the dataset; we also kept evaluating the validation accuracy of the base model, which was also improving. We used the augmentation techniques of noise injection, time-shifting, and changing the audio speed using time stretch, as shown in Figure 2. Other audio data augmentation techniques, such as pitch shift, were not ideal for our dataset because it required randomly changing the pitch, thus, distorting the pronunciation. The techniques are described as follows.  
Noise injection: This technique introduces white noise in the audio dataset [38] as a ratio between the signal and noise. This method is appropriate for our model as we can assume that given environmental variations, user input is not noise-free in most circumstances. We applied random noise augmentation by adding two types of noise values X to audio files using the NumPy library in Python, with X = 0.005, and X = 0.0005. Figure 2 illustrates the noise injection results of an audio file, where noise is injected at the rate of X = 0.005.  
Time-shifting: The method of time-shifting is applied to shift the audio forward or backward [38]. We used the roll method in Python's Numpy library to shift the start of an audio file S milliseconds. If \(D_{a}\) is audio data, and \(D_{a}=[x_{1},x_{2},\ldots,x_{n}]\), by applying Silent \(S_{t}=[S_{1},S_{2},\ldots,S_{m}]\), it becomes \(D_{shifted}=[S_{1},S_{2},\ldots,S_{m},x_{1},x_{2},\ldots,x_{n-m}]\), Where \(S_{t}\leq D_{s}\). Figure 2 presents the result of the time-shifting function; the audio file is shifted forward for 2 milliseconds at the beginning of the graph by replacing it with silence.  
Changing speed: This technique allows adjusting the speed of audio signal \(S\) by a certain rate \(R\) as \(S=S\div R\). We used the values of \(R\) = [1.25, 1.4, 1.5, 1.6]. Figure 2 illustrates the result of changing the audio speed where \(R\) = 1.5. It is investigated in [39] that short vowels can be varied on shorter and longer duration.  
After applying the data augmentation techniques, we ended up with a total of 49,829 audio files. With the augmented dataset, we achieved a validation accuracy of more than 85%.  
Figure 1: A two-phase approach of developing a CNN model for classifying Arabic short vowels.