esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical and Experimental Characterization of a Coanda-Type Industrial Air Amplifier
###### Abstract  
The performance of an industrial air amplifier is assessed through experimental and numerical characterization, with a focus on examining the influence of various operating conditions (isolated, \"blowing,\" and \"suction\" modes) and direct geometric scaling of the device within the specified range of the injection gap (\(\delta\)) and the inlet pressure characteristic values. The findings underscore the presence of a linear trend of the entrained mass flow and a nonlinear decay of the amplification factor, both with notable sensitivity to the gap width. Numerical RANS simulations validate the experimental data, characterize the asymmetric flow downstream from the device, and facilitate the exploration of more complex scenarios. In this regard, scaling the device's dimensions reveals an optimal aspect ratio between the minimum diameter (\(D_{m}\)) and \(\delta\) to maximize the entrained mass flow. This research provides valuable insights into the behavior of air amplifiers, offering guidance for their design and application across various industrial contexts.  
experimental aerodynamics; CFD; compressible flow; flow manipulation Article
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Mobile Networks
#### 4.5.3 4G/LTE/WiMAX  
The dawn of 4G provided the internet with a major boost, as greatly improved data rates became ubiquitous. Throughput of well over 100 Mbps became achievable with  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **1G** & **2G** & **3G** & **4G** & **5G** \\ \hline Approximate deployment date & 1970–1980 & 1990–2004 & 2004–2010 & 2010 & 2020 \\ Theoretical data & 0.002 & 0.384 & 56 & 1000 & 10,000 \\ rate (Mbps) & N/A & 629 & 212 & 60–98 & \textless{}1 \\ Technology & Analog cellular & GSM (Digital) & CDMA 2000, WCDMA, UMTS & WiMAX, LTE & \\ Service & Voice telephony & Digital voice, SMS, data packet & High quality audio, video and data & Wearable devices, IoT with AI capabilities & Wearable devices with AI capabilities \\ Multiplexing & FDMA & FDMA, TDMA & CDMA & MC-CDMA, OFDMA & OFDMA \\ Switching & Circuit & Circuit, packet & Circuit, Packet & All packet & All packet \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Evolution of Mobile Networks [20; 114].  
4G. 4G uses Long-Term Evolution (LTE) technology with the WiMAX protocol based on the IEEE 802.16 standard, which were developed to provide long-distance broadband wireless data connectivity. One major high point of 4G is the use of Orthogonal Frequency Division Multiple Access (OFDMA), which provides good spectral efficiency for more data rates, mitigation of frequency-selective fading, and narrow band interference. 4G uses Multicarrier CDMA (MC-CDMA) as an access technique. LTE, LTE-Advance (LTE-A), and the preceding generation (3G) have to a great extent improved spectral efficiency, throughput, coverage, and latency, with LTE and LTE-A superseding their respective predecessors. LTE technology has found many applications in telemedicine. The research work of [116] considered the handover mechanism between LTE and Wi-Fi and proposed a Vertical Hand-Over (VHO) algorithm between LTE and Wi-Fi wireless technologies.  
Despite the numerous gains of 4G technologies in terms of throughput, coverage, and latency, there exist challenges that hamper the full benefits of its application, for example the high demands of IoT communications such as Machine-to-Machine (M2M) and real-time telemedicine applications. Among the challenges with 4G are [115]:  
1. Optimizing the allocation of spectrum resources;
2. High energy consumption, leading to high CO2 emissions;
3. The difficulty of significantly improving spectral efficiency.
# Performance Evaluation of Sequential Rule Mining Algorithms
## 3 Methods of Applying Comparative Algorithms
### The Operation of the Non-Redundant with Dynamic Bit Vector Algorithm  
The main idea of the NRD-DBV algorithm is to mine more compact data without losing any information or distorting the final result. It is based on mining closed patterns that depend on a vertical format. It performs efficiently in large datasets since it generates a smaller number of rules. Additionally, it utilizes pruning techniques to enhance overall performance.  
Figure 2: Framework of TNS algorithm.  
The NRD-DBV Application Algorithm  
The implementation of the NRD-DBV is performed in the following steps, as shown in Figure 3:  
1. Transforming the dataset to a bit vector by removing zeroes from the front and the end of the sequence. It is followed by using a dynamic bit vector structure to determine the position of each item.
2. Setting the support threshold to find all items that achieve the threshold value and storing them in a prefix tree structure as the parent node of the tree.
3. Performing the check downward closer checking means removing all prefixes that do not expand the rule. For instance, item c ordinarily happens after item b with a similar support value of b; at that point c ought to be ingested such as A(BC) = 60% and A(BC)DE = 60%. By applying this approach, we will remove sequence A (BC).
4. Setting all found frequent sequences as a sub-node and analyzing it, whether prefix generator or closed patterns.
5. Applying the sequence extension technique for every sub-node by applying two methods. The first is called item extension. It expands the patterns by including new items at the final item-set by regarding that item as larger than the last element of the item-set. The second one is called a sequence extension item-set expansion by inserting the item as a new item-set after the final existing, as displayed in Algorithms A3 and A4.
6. Producing the NRD-DVB by applying a condition to halt producing rules that do not achieve the confidence threshold. This is indicated as, in the case (\((\frac{\sup(Sequence\ of\ Sn)}{\sup(Sequence\ of\ pre)})\)\(\geq minconf\)), the point nr-SeqRule, even with nr-SeqRule union with R. Additionally, cease to produce the rules for the sub-nodes. Conjointly, cease producing the rules when support (n1) < support (n) and if \(\frac{sup(n1)}{sup(n2)}<\) minConf, then \(\frac{sup(n2)}{sup(n)}<\) minConf.  
The NRD-DVB algorithm helps to compact the dataset by eliminating any super sequence with a similar support value to the root. Therefore, it is more proficient, as:  
1. It diminishes the memory utilization, and the runtime demanded to mine large sequence datasets.
2. Furthermore, it embraces the prefix tree to store all sequences that produce more profici  
Figure 3: The NRD-DBV algorithm framework based on [31].
# Software Integration of Power System Measurement Devices with AI Capabilities
## 5 Real-Time Application: Machine Learning-Based Load Identification  
As indicated in the Introduction, there are a large number of applications of machine learning techniques for processing the data obtained by measurement devices [19; 20; 21]. Therefore, a software solution such as the one presented in Section 3.2 must be able to work in real time by applying machine learning techniques. An example of this type of applications is the so-called Non-Intrusive Load Monitoring (NILM) that uses machine learning techniques to identify connected loads in a residential [16] or industrial environment (see [17] and the references therein), generally using an SM to detect events with power variations and current harmonics. On the other hand, it is known that different  
Figure 12: Frequency measures in both plants.  
Figure 13: Voltage measures in the TC of each plant.  
types of LED lamps produce different harmonic distortions in the grid [60], in addition to the fact that this behavior may vary with the temperature of the lamp [61].  
Therefore, to validate the capabilities of the developed tool for software integration shown in Section 3.3, several machine learning techniques for real-time load identification were implemented and validated experimentally, specifically, to identify the connected LED lamp (among three different models) using the voltage harmonics measurements provided by a PQM. Figure 14 shows the setup carried out, as well as the software tools integration, which is similar to the one studied at Section 4.1.  
An open-source tool for machine learning in Python, Scikit-learn 1.6 [62], was used to process the data and implement the algorithms. Three different machine learning techniques were applied: Neural Networks (NNs), Decision Tree (DT), and Random Forest (RF).  
Next, we will show the data collection for learning, its preprocessing, the offline training of different detection methods, and, finally, the experimental results of the real-time application of the trained models to identify connected and disconnected lamps, focusing on the analysis of their performance and robustness.
## 2 Methodology
### Increasing of Pointing Accuracy  
In order to increase the pointing accuracy in order to obtain a pointing error of less than 100 \(\upmu\)rad, an additional motion may be added to the ATP system's Body and Pointer to increase the possibility that the tracking phase takes place (Figure 13).  
The operation illustrated by the control diagram is the same as that described in Section 2.5, with two additional blocks (Figure 13 bottom left part) introducing an additional motion to the ATP system's DoFs.
## 2 Background and Proposed Method
### Proposed Method Overview  
This study proposes a speaker verification method for real-work scenarios: (1) It employs a self-supervised learning-based pseudo-labeling approach for speech corrupted by noise and reverberation. Contrary to HuBERT, pure speech is corrupted with a strong additive noise component to produce corrupted speech for subsequent input. Additionally, HuBERT incorporates a random masking operation into the input, which represents the second phase of interference addition. This is done to mimic the actual acoustic characteristics of a noisy environment. The ultimate objective is to predict pseudo-labels for pure speech. Additionally, the prediction of pseudo-labels is extended to scenarios involving strong reverberation, targeting reverberant speech. (2) To integrate the pre-trained model with the robust speaker verification architecture, the extracted audio features from the last Transformer encoder are adapted through a 1-dimensional convolution layer to a compatible format for the verification network. (3) ECAPA-TDNN is enhanced in this study through the integration of a convolutional aggregation mechanism inspired by U-Net, which facilitates the fusion of contextual features within the TDNN sub-network. The integration of attentive average pooling into the Squeeze Excitation module enables the TDNN subnet to adaptively and discriminately learn channel-wise weight information. This modification enhances the network's capability to extract speech features even under strong noise interference across varying time intervals. Lastly, we incorporate a multi-heads attentive statistics mechanism into the pooling factor \(\alpha\), enhancing the network's resilience to interference from low and medium-frequency noise after pooling.
# A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
## 4 Evaluation
### Evaluation Metrics  
We chose five evaluation metrics to assess our method: accuracy, balanced accuracy, precision, recall, and \(F1\) score. The calculation formulas are shown in (2)-(6) as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
\[Balanced\_accuracy=\frac{1}{2}(\frac{TP}{TP+FN}+\frac{TN}{TN+FP}) \tag{3}\]  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
\[F1=2\frac{Precision\times Recall}{Precision+Recall} \tag{6}\]  
where \(TP\) denotes actual true and classifier predicted true; \(TN\) denotes actual false and classifier predicted false; \(FP\) denotes actual false and classifier predicted true; and \(FN\) denotes actual true and classifier predicted false.
# A Method for Modeling and Testing Near-Real-Time System Scenarios
## 5 Sample Scenario
### Generating Tests for a Handover Scenario
For each node \(i\) with \(b(i)>0\) we add a source arc \((S^{*},i)\) with capacity \(b(i)\), and for each node \(i\) with \(b(i)<0\) we add a sink arc \((i,T^{*})\) with capacity \(-b(i)\). Figure 12 shows this; \(b(i)\) is the difference between two sums--the sum of the minimum flows entering the vertex \(i\) and the sum of the minimum flows leaving the vertex \(i\). Figure 11: The network after reducing the number of vertices. The vertices that must be flow elements are divided into two. The arcs between these vertices have the minimum flow values of 1. Then, the problem of maximum flow in this transformed network must be solved. If the maximum flow saturates all source and sink arcs, then the initial problem has a feasible solution. Otherwise, the problem is not feasible. It is worth paying attention to an important element related to determining the maximum (or allowable) flow in a situation where there are network arcs with minimum flows greater than 0. In such a case, before starting the calculations, the values of the minimum flows of the arcs flowing in and out to each of the vertices should be corrected so that the principle of flow conservation is met. This situation is shown in Figure 12. An arc with a minimum capacity of 1 exits from the \(S5^{\prime}\) vertex and an arc with a capacity of 0 enters. The modification of the capacity value in this case consists in increasing the value for the entering arc. One of the paths in the flow network from vertex \(S^{*}\) to \(T^{*}\) is the path \((S6-S14)^{\prime\prime},T,S,(S0-S4)(S5),(S6-S14)^{\prime}\). After joining the vertices into vertex \((S6-S14)\) and sorting starting from vertex \(S\), one of the two minimum coverage paths in the network from Figure 12 is determined. This path is as follows: \((S,S0,S1,\ldots,S13,S14,T)\). An alternative method to the method based on flows determined in graphs is a flow method defined based on mixed-integer linear programming (MILP) tasks. In the literature, there are many examples of algorithms for determining network flows based on integer problems ([PERSON] and [PERSON] [39]). The tasks basically concern determining the maximum flow, but after transforming the network in accordance with the model presented by [PERSON] and [PERSON] [36], the task of determining the minimum flow can be solved. However, the minimum flow problem is not the same as the minimum cost network flow task, which should be emphasized. Figure 12: A modified flow network on which the maximum (or feasible) flow is determined. For selected network vertices, the principle of flow conservation is not met when the values of required minimum flows on the arc are taken into account.
## Abbreviations  
ASI  
Agenzia Spaziale Italiana (Italian Space Agency)  
CAM  
Criteri Ambientali Minimi (Minimum Environmental Criteria)  
CNN  
Convolutional Neural Network  
DSM  
Digital Surface Model  
DTM  
Digital Terrain Model  
EnMAP  
Environmental Mapping And Analysis Program  
F1  
F1 Score  
GCPs  
GSD  
Ground Sampling Distance  
GTs  
Ground Truths  
HS_DS  
Hyperspectral Dataset with 205 bands  
HSI  
Hyperspectral Imagery  
HSI1  
Hyperspectral Imagery with GSD = 30 m  
HSI2  
Hyperspectral Imagery with GSD = 5 m  
Mask of Hyperspectral Dataset  
K  
Cohen's Kappa Coefficient
## 2 Spatiotemporal Characteristics of Diversified Loads in Multiple Functional Areas
### Characteristics of the Loads Suitable for Transmission across Functional Areas
#### 2.2.2 Characteristics of the Charging Loads of EVs  
The energy storage capacity of EV batteries allows their charging demand to be met within a certain period of time, which means that the charging loads of EVs could be shifted on the time scale. The owners of EVs have certain flexibility in the choice of charging time, and the charging loads have certain controllability.  
At the same time, as a means of transportation, the spatial distribution of EVs in each functional area is closely related to its travel law. The transferability of EVs in different places could be utilized toestablish links between functional areas. Similar to a virtual power network, the charging demand of EVs could be adjusted according to the different functional areas and time periods. The orderly charging of EVs through reasonable dispatching could effectively improve the consumption rate of renewable energy and suppress the volatility of renewable energy.  
According to the results of a parking demand survey in Zhejiang Province of China [25], daily dispatching is divided into three time periods in this paper, which are represented by \(k\) = 1, 2, 3, respectively. It is assumed that EVs are located in all functional areas of a region during the dispatching period. The distribution probability of EVs in each time period for different functional areas is shown in Table 2.  
The charging loads of \(R\) functional areas and \(T\) dispatching periods can be regarded as virtual energy storage. The relationship between the charging power and charging capacity of EVs can be described as  
\[E_{r,t+1}=E_{r,t}+\eta_{\text{ch}}P_{\text{ch},r,t}\Delta t \tag{7}\]  
\[\alpha_{r,k}^{\min}E_{r,k}^{av}\leq\sum_{t\in k}E_{r,t}\leq\alpha_{r,k}^{\max }E_{r,k}^{av} \tag{8}\]  
\[P_{\text{ch},r,t}^{\min}\leq P_{\text{ch},r,t}\leq P_{\text{ch},r,t}^{\max} \tag{9}\]  
\[(1-\lambda)\sum_{r=1}^{R}\sum_{t=1}^{T}\eta_{\text{ch}}P_{\text{ch},r,t}^{0} \Delta t\leq\sum_{r=1}^{R}\sum_{t=1}^{T}\eta_{\text{ch}}P_{\text{ch},r,t} \Delta t\leq(1+\lambda)\sum_{r=1}^{R}\sum_{t=1}^{T}\eta_{\text{ch}}P_{\text{ch},r,t}^{0}\Delta t \tag{10}\]  
The constraint of continuous change of charging capacity is described in (7). The constraint of the upper and lower limits of charging capacity of EVs in the same time period of area \(r\) is described in (8). The constraint of the upper and lower limits of charging power of EVs can be expressed as (9). It is ensured that the total charge loads after optimal dispatching of EVs in the whole areas meet the forecasted charging loads within a certain range in (10).
## 3 Results and Discussion
### Inhibitor Releasing Characteristics and Mechanism of OIMs@PAM
#### 3.2.2 Long-Term Release Behavior of Solid Corrosion Inhibitor at Different Temperatures  
The OIM concentration and cumulative release of GE-EP@OIMs in NaCl solution at 20 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C were studied. At the initial immersion stage, the OIM release from GE-EP@OIMs is very high, with the concentration of OIMs released in 20 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C environments reaching 313 mg/L, 497 mg/L, and 703 mg/L, respectively. However, the OIM concentration gradually decreases over time as the immersion period increases. After immersing the sample for 60 days in 20 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C environments, the OIM concentration declines to 9 mg/L, 99 mg/L, and 19 mg/L, respectively.  
The continuous decrease in the released concentration of OIMs can be attributed to two main factors: (1) The release path of OIMs gradually shifts from the surface to the interior of the material, and (2) the ongoing reduction in the OIM content within GE-EP@OIMs lowers the driving force for further OIM release. Over the same release time, the OIM release concentration increases with temperature from 20 \({}^{\circ}\)C to 80 \({}^{\circ}\)C within the first 1-30 days. This may be due to the higher temperatures promoting the thermal motion of OIMs, which accelerates their migration between the molecular chains formed by the water-based epoxy resin and the curing agent [32]. After 30-60 days, the OIM release concentration follows the trend, 50 \({}^{\circ}\)C \(>\) 80 \({}^{\circ}\)C \(>\) 20 \({}^{\circ}\)C.  
This is because the cumulative release of OIMs from GE-EP@OIMs in the 80 \({}^{\circ}\)C solution reaches 81% within the first 30 days, leading to a significant decrease in the residual content of the OIM corrosion inhibitor in the sample, which drops from 34.75% to 6.60%. The cumulative release of GE-EP@OIMs after immersion in 80 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 20 \({}^{\circ}\)C solutions for 60 days is 96%, 86%, and 36%, respectively. After 30 days of release in the 20 \({}^{\circ}\)C solution, the cumulative release no longer increases significantly, indicating that the release of GE-EP@OIMs has stopped, with an effective release time of about 30 days at this temperature.  
In the 50 \({}^{\circ}\)C and 80 \({}^{\circ}\)C environments, GE-EP@OIMs is nearly completely released. In the 50 \({}^{\circ}\)C environment, the cumulative release continues to increase over the entire immersion period (1-60 days), suggesting stable release of OIMs from GE-EP@OIMs. In contrast, in the 80 \({}^{\circ}\)C environment, the release slows down after 40 days, which is attributed to the reduction in release kinetics due to the decreasing residual content of OIMs. The differences in the cumulative release curves at various temperatures suggest that the release mechanisms of the GE-EP@OIMs solid corrosion inhibitor may vary depending on the temperature.
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 2 Materials and Methods
### Data
#### 2.2.2 Auxiliary Data  
In conjunction with satellite imagery, this study incorporated a land use dataset provided by ESRI. This dataset was generated by ESRI using Sentinel imagery with a resolution of 10 m. It was trained using deep learning methods on images labeled by people from the National Geographic Society [32]. The dataset includes 11 land use categories. After downloading the data, preprocessing steps such as cropping and resampling were applied to convert it into binary data representing cropland and non-cropland.