esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sentiment Analysis Mechanisms  
The sentiment analysis mechanism is based on text analysis and natural language processing (NLP), which aims to identify, extract, and analyze the polarity of sentiments from different sources and languages. The first process for identifying the sentiments is to discriminate between subjective and objective sentences. Subjective sentiments contain polarity terms that reflect the users' attitudes in social media reviews. In contrast, objective sentiments are based on general facts or information that do not reflect the user's orientation.  
The sentiment polarity can be verified based on several weights and scales. Most research methods depend mainly on analyzing the sentiments, whether they are positive, negative, or neutral. Positive polarities express the positive orientation of the users towards a service or a topic under consideration. In contrast, negative polarities denote the opposite meaning to express the user's negative orientation towards the service. Neutral orientations mean that the detected positive and negative polarities are equal; therefore, the user orientation towards a topic or a service is fair. In addition, the sentiment classified as neutral may contain neither positive nor negative terms to be detected. In Figure 1, the overall sentiment analysis mechanism is explained. The main process for analyzing user reviews and tweets centrally depends on lexicon-based and machine-learning (ML) approaches. Each approach has its advantage, methodology for implementation, and methods for dealing with input data and user reviews. In addition, each conducted approach must be measured based on data processing, accuracy, and performance. The methodology's performance can also be changed according to the level of analysis of the polarity terms. There are three main analysis levels: aspect, sentence, and document. In addition, analyzing Arabic sentiments with highly complex terms with different linguistics is considered a challenging process.  
Figure 1: Sentiment analysis approaches and algorithms.
# Leveraging Explainable AI to Support Cryptocurrency Investors
## 2 Comparison with Prior Works  
Table 1 summarizes the main characteristics of the existing approaches to eXplainable AI (XAI) in finance, including the Cryptocurrency-based Machine Learning Explainer (CryptoMLE) presented in this paper. We analyze the current and prior works under the following aspects:  
1. The considered assets, which encompass specific stocks, cryptocurrencies, or a combination of the above (e.g., the stocks belonging to the Standard&Poor500 U.S. index).
2. The features under analysis, which describe the environmental and market characteristics considered by the classification models (including the blockchain-related features for cryptocurrency assets).
3. The availability of a user interface to support domain expert decisions.
4. The main model used to explain ML-based decisions (e.g., SHAP [13] for the proposed CryptoMLE tool).
5. The resolution of the analyzed data (typically, one sample per trading day).
6. The goal of the approach (e.g., support decision making with data-driven insights for CryptoMLE).  
The main goal of this work is to present a visual analytics tool providing AI-based explanations for cryptocurrency investors. Notice that our goal is not to propose a new, more effective trading system but rather to provide experts with an interactive tool, based on XAI, to explain the decisions of algorithmic trading approaches and make appropriate decisions.  
Similar to [16; 17; 18], CryptoMLE provides domain experts with a graphical interface. Unlike all the prior works on algorithmic trading, it also allows them to interactively collect, analyze, and compare data models trained in multiple time periods. Analogously to [18], CryptoMLE analyzes a large number of cryptocurrencies. Unlike [18], it also considers blockchain-related data.  
CryptoMLE relies on SHAP [13], whereas other approaches (e.g., [17; 19]) adopt simpler explainable models such as partitional clustering and decision tree, which are known to be less robust to noise and model bias than SHAP. The work recently proposed in [20] is, to the best of our knowledge, the first attempt to use SHAP in algorithmic trading. Unlike [20], this work (1) Addresses short-term cryptocurrency trading instead of long-term portfolio management. Hence, it compares the outcomes of classification models predicting next-day cryptocurrency price; (2) Presents a graphical tool for supporting decision making. It also allows experts to interact with the tool and gain insights into specific market trends; (3) Analyzes a significantly larger set of cryptocurrencies (21 vs. 8).
## 4 Methods
### Infrastructure  
The physical architecture of the laboratory combines long-range and short-range communication systems to reflect the diversity of technologies found in rural deployments. As illustrated in Figure 1, the laboratory integrates LoRaWAN and WiFi devices, allowing for simultaneous testing of different configurations and use cases.  
As mentioned before, LoRaWAN scenarios include commercial sensors such as Dragino LHT65, LGT-92, and LDS02, which communicate using long-range, low-power transmissions with Mikrotik LR8 kit and Dragino LPS8 gateways. Depending on the scenario, sensor data can be sent to The Things Network [53] or processed locally using Eclipse Mosquito MQTT broker v2.0.18 [37], installed on the laboratory's backend.  
To complement this setup, WiFi-based scenarios employ ESP32 microcontrollers connected to various sensors (motion, temperature/humidity, vibration, and distance) using MQTT, HTTP, UDP, or CoAP for communication.  
Figure 2 presents the logical topology of the network, detailing how data flow between devices and storage systems. This diagram is essential for understanding the integration between communication technologies and the software stack.  
The laboratory operates within a dedicated virtualized environment with Docker [47], hosting core IoT services such as Mosquito MQTT Broker, Node-RED, InfluxDB, MongoDB, and Grafana for data handling and visualization. A Raspberry Pi 3 Model B is also deployed as a gateway and passive traffic capture node, playing a key role in WiFi scenarios by enabling passive monitoring, without interfering with normal operations. In addition, a Windows 11 workstation was used to flash firmware on ESP32 devices and also to interact with the backend through web interfaces and file transfer tools.
## 4 Experimental Analysis
### Experimental Results and Analysis
#### 4.3.6 Distance Metric Performance  
In practical analysis, understanding the magnitude of differences between individuals is crucial for evaluating their similarities and categories. Similarity measurement is a method for assessing how data samples are interrelated or close to each other. Typically, this involves calculating the distance between the features of entities. Similarity measures are usually represented as numerical values, with higher values indicating greater similarity between data samples. These values are often transformed to a scale between 0 and 1, where 0 signifies low similarity (data objects are dissimilar), and 1 indicates high similarity (data objects are very similar). In other words, as the correlation between data objects strengthens, the distance decreases, and the similarity score increases. In this context, Euclidean distance is considered optimal and valuable for distance computation.  
To clarify this issue, we take the example of four pairs of conjugate bone stick images from the test set, where the upper image A_01 corresponds to the lower image A_02, and their similarity scores are presented in Table 8. The closer the distance measurement, the higher the similarity score for bone stick images. In Table 8, the average similarity score among the four pairs of bone stick images is above 0.8, indicating a high correlation between the matching results of the bone stick images and the distance measurements.
## 4 Experiments
### Ablation Study
#### 4.3.2 Effect of CESSC  
SSC only processes convolution in the foreground covered by the mask ratio \(\mathbf{H}_{j}\), which sharply decreases model complexity. Compared to ARH2000, VisDrone2019 has more targets. Therefore, when CESSC is used in the detection head, the inference accelerates by 91.3% and 83.8% on the former and latter datasets, respectively. A precise mask ratio, combined with CE-GN in enhancing the global context, compensates for the loss induced by extracting only foreground features due to SSC. Therefore, the CESSC component further promotes detection accuracy with the two datasets. The above experimental results demonstrate the capability of CESSC to balance accuracy and efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Baseline** & **ResNet** & **MFF** & **CESSC** & **Focal Loss** & **mAP (\%)** & **Parameters/MBFPS/s** \\ \hline \multirow{5}{*}{**VisDrone2019**} & ✓ & & & & & 19.9 & 22.9 & 55 \\  & ✓ & ✓ & & & & 25.2 & 39.8 & 37 \\  & ✓ & ✓ & ✓ & & & 28.6 & 46.4 & 31 \\  & ✓ & ✓ & ✓ & ✓ & & 31.1 & 28.2 & 57 \\  & ✓ & ✓ & ✓ & ✓ & ✓ & **33.1** & **28.4** & **56** \\ \hline \multirow{5}{*}{**ARH2000**} & ✓ & & & & & & 78.9 & 22.9 & 138 \\  & ✓ & ✓ & & & & 81.7 & 39.8 & 103 \\  & ✓ & ✓ & ✓ & & & 84.2 & 46.4 & 92 \\  & ✓ & ✓ & ✓ & ✓ & & 93.6 & 22.2 & 176 \\  & ✓ & ✓ & ✓ & ✓ & ✓ & **94.3** & **22.3** & **176** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation studies on VisDrone2019 and ARH2000.  
Figure 3: Visualized thermal comparison of UAV images before and after MFF processing.
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
###### Abstract  
In order to solve the congestion problem caused by the dramatic growth of traffic in data centers, many end-to-end congestion controls have been proposed to respond to congestion in one round-trip time (RTT). In this paper, we propose a new congestion control mechanism based on backpressure feedback (BFCC), which is designed with the primary goal of switch-to-switch congestion control to resolve congestion in a one-hop RTT. This approach utilizes a programmable data plane to continuously monitor network congestion in real time and identify real-congested flows. In addition, it employs targeted flow control through backpressure feedback. We validate the feasibility of this mechanism on BMV2, a programmable virtual switch based on programming protocol-independent packet processors (P4). Simulation results demonstrate that BFCC greatly enhances flow completion times (FCTs) compared to other end-to-end congestion control mechanisms. It achieves 1.2-2\(\times\) faster average completion times than other mechanisms.  
datacenter networks; congestion control; flow control; programmable switches Article
# Spectrum Allocation Based on an Improved Gravitational Search Algorithm
## 3 Technologies
### Critical Technologies  
#### 3.1.1 Encoding and Decoding  
The encoding maps the position of a particle to a solution, which is an important concept in IGSA. Interference-free allocation matrix \(A\) is subject to the availability matrix \(L\) which contains many zero elements. Therefore, considering the computational complexity of spectrum allocation, this paper encodes only nonzero elements in matrix \(L\). Following the column-priority principle, these nonzero elements are encoded into a binary row vector. After encoding, we can obtain a row vector \(P\) with length \(D\). \(P\) represents the position of a particle in the proposed algorithm.  
Decoding is the reverse process of encoding. The reverse process remaps the row vector \(P\) to an allocation scheme \(A\). Figure 1 illustrates the process of encoding and decoding.  
Figure 1: The mapping process.
## 6 Proposed Model
### NMT Decoding for Arabic Dialects Sentence  
Neural machine translation NMT maps all words of source sentence for Arabic dialect into a continuous space through an embedding layer. The word embeddings are processed by a bidirectional layer implemented with LSTM that will encode each source word together with its right and left context in a sequence of hidden states. Once the hidden states of the Bi-LSTM encoder are computed, the decoder starts generating the target string one word at a time. The decoder is implemented as a Bi-LSTM layer. As shown in Figure 2, a shared language-invariant layer and Bi-LSTM decoders are used for all the translation tasks. The model has two decoders: LA decoder and MA decoder that capture more information from Modern Standard Arabic (MSA) that benefits to the internal representation of Levantine Arabic (LA) and Maghrebi Arabic dialects. Each decoder is followed by a dense layer and output layer. Each output layer is composed of SoftMax layer. Figure 2 describes the shared layers depending on the architecture of the model. The hard parameter sharing was applied in the proposed multitask NMT model by sharing the shared-layer and the language-invariant layer across the related tasks.
# Thermal Characteristics Study of the Bump Foil Thrust Gas Bearing
###### Abstract  
In this paper, a thermo-hydrodynamic model of the bump foil thrust gas bearing is developed, which solves the coupled gas film three-dimensional energy equation, non-isothermal Reynolds equation, and the foil deformation equation. The effects of bearing speed, thrust load, and external cooling gas on the bearing temperature field are calculated and analyzed. The test rig of foil thrust gas bearing was built to measure the bearing temperature under different working conditions. Both simulation and experiment results show that there exist temperature gradients on the top foil both in the circumferential and radial directions. The simulation results also shows that the top foil side of the gas film has the highest temperature value in the entire lubrication field, and the position of highest temperature moves radially inward on the thrust plate side as the rotor speed increases. The gas film temperature increases with the increasing rotor speed and bearing static load, and rotor speed has greater effects on the temperature variation. Cooling air flow passing through the bump foil is also considered in the simulations, and the cooling efficiency decreases as the mass of gas flow increases.  
bump foil gas bearing; thermo-hydrodynamic model; temperature field; thermal characteristics analysis +
Footnote †: journal: Applied Sciences
# Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition
## 4 Experimental Setup and Evaluation
### Model Interpretability
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Sensors\_Feature** & \multicolumn{3}{c}{**Feature Importance**} & \multicolumn{3}{c}{**F1-Score of \(N\) Runs**} \\ \hline
**Type\_Location\_Place of Sensors** & **LSTM** & **Rank** & **1D CNN** & **Rank** & **Mean \(\pm\) SD of LSTM** & **Mean \(\pm\) SD of CNN** \\ \hline PIR\_Shower\_Bathroom & 9.56 & 2 & 7.62 & 9 & \(62.79\pm 1.95\) & \(61.96\pm 1.25\) \\ PIR\_Basin\_Bathroom & 11.62 & 1 & 5.66 & 11 & \(60.73\pm 1.80\) & \(63.92\pm 1.61\) \\ PIR\_Door\_Kitchen & 8.53 & 9 & 10.28 & 3 & \(63.82\pm 1.70\) & \(59.30\pm 1.26\) \\ PIR\_Door\_Bedroom & 8.10 & 11 & 6.81 & 10 & \(64.25\pm 1.48\) & \(62.77\pm 1.35\) \\ PIR\_Door\_Living & 8.65 & 7 & 9.39 & 7 & \(63.70\pm 1.48\) & \(60.19\pm 1.63\) \\ Magnetic\_Maindoor\_Entrance & 8.12 & 10 & 9.60 & 6 & \(64.23\pm 1.90\) & \(59.98\pm 1.41\) \\ Magnetic\_Fridge\_Kitchen & 6.65 & 12 & 11.43 & 2 & \(65.70\pm 1.68\) & \(58.15\pm 1.26\) \\ Magnetic\_Cupboard\_Kitchen & 9.06 & 5 & 5.03 & 12 & \(63.29\pm 1.78\) & \(64.55\pm 1.05\) \\ Electric\_Microwave\_Kitchen & 8.71 & 6 & 12.41 & 1 & \(63.64\pm 1.34\) & \(57.17\pm 1.90\) \\ Pressure\_Bed\_Bedroom & 8.63 & 8 & 9.68 & 5 & \(63.72\pm 1.24\) & \(59.90\pm 2.13\) \\ Pressure\_Seat\_Living & 9.40 & 3 & 10.20 & 4 & \(62.95\pm 2.18\) & \(59.38\pm 1.15\) \\ Flush\_Toilet\_Bathroom & 9.33 & 4 & 9.35 & 8 & \(63.02\pm 2.70\) & \(60.24\pm 2.38\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Permutation feature importance of Ordonez Home B. Dinner, and snack. Hence, the proposed joint learning takes advantage of the complementary features to improve the performance of the minority classes.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 5 Use Cases Deployment
### Network Service Instantiation  
Once the function and services descriptors, as well as the slice, are available, the last step is the instantiation of the virtualized service over the given slice. In this step, the application is deployed in the form of VMs or containers. More in detail, the virtualized functions are placed over the compute chunk of the slice and connected to the network chunk, providing end-to-end connectivity with the access chunk of the slice. The successful instantiation of the network services related to the considered media vertical over the neutral host framework are reported by the platform Dashboard and can be also corroborated by checking underlying systems (such as OSM and OpenStack).  
Additionally, to complement the programmability principles of the proposed framework, a DNS server is automatically deployed by the platform to support the service operation in terms of IP addresses and domain names resolution.
## 4 Results and Discussion
The location of interest, i.e., Kookmin University, is adjacent to the mountain, and the 1 \(\times\) 1-km\({}^{2}\) COMS pixel corresponding to the location is likely to include both buildings and forest. Consequently, the combined effects of vegetation, soil, and building properties resulted in the ground albedo, \(C_{\text{min}}\). In winter, this issue becomes even more evident due to snow cover, which is often disguised as cloud. If the ground albedo change is considered, the solar irradiance model can improve prediction accuracy. The Rigollier model consistently produced the largest error, and it only performed slightly better than the [PERSON] model during April and September 2018. Contrarily, the Hammer model produced smaller rRMSE and rMBE values than the [PERSON], [PERSON], and Rigollier models throughout the 12 months. The lowest errors gained by the Hammer model were 18.8% for the rRMSE and 0.6% for the rMBE in April. The accuracy of the [PERSON] model followed that of the [PERSON] model, with the best rRMSE and rMBE being 22.6% in February and 3.7% in September, respectively. The hybrid model consistently had a better accuracy than any of the four semi-empirical models for each month, with the lowest rRMSE and rMBE values at 17.1% and \(-\)0.1% during April, respectively. The hybrid model also showed higher accuracy compared with the other models during winter, as can be observed from the rRMSE values of 19.9%, 21.4%, and 18.5% for December, January, and February, respectively. So far, the hybrid model has turned out to be more accurate than any other model. However, the development of such models is cumbersome and awkward because the hy  
Figure 8: Comparison of monthly accuracy based on rRMSE (%). Figure 9: Comparison of monthly accuracy based on rMBE (%). brid model in this study was created after using trial-and-error combinations of constituent formulas. Therefore, to replace the combination process in the hybrid model, an LSTM model was attempted, and its results are summarized in Table 7. As the hybrid model produced the best accuracy among the semi-empirical models, the input variables of the hybrid model (\(C_{sat}\), \(\theta_{\text{z}}\), \(T_{L}\), and \(\psi\)) were selected for those of the LSTM model. The LSTM model revealed a slight improvement in RMSE compared to the hybrid model. Its MBE slightly increased, but it was comparable to that of the hybrid model and still better than those of the other models. Consequently, it was demonstrated that the machine learning method can replace the hybrid model.
## 2 Derivation of Reliability Evaluation Theory
Identical deformation of the above formula can be expressed as:  
\[ \begin{array}{ll}R&=\text{Pr}\left(\frac{\overline{X}\!-\!\overline{Y}}{S\sqrt{ (1/n)+1}}<\frac{\overline{X}\!-\!L}{S\sqrt{(1/n)+1}}\right)\\ &=\text{Pr}\left(\frac{\overline{X}\!-\!\overline{Y}}{S\sqrt{(1/n)+1}}<\sqrt{ \frac{n}{n+1}}K\right)\\ &\qquad=\text{Pr}\left(T<\sqrt{\frac{n}{n+1}}K\right)\end{array}  \tag{5}\]  
Combining Formula (3) and Formula (5), the reliability can be described as:  
\[R=t\left(\sqrt{\frac{n}{n+1}}K,n-1\right) \tag{6}\]  
where, \(t(x,n)\) represents the cumulative distribution function of \(t\) distribution at \(x\) with \(n\) degrees of freedom. Regarding this reliability as the lower confidence limit of the reliability of the product, according to the Chinese standard GB/T 4885-2009 [15], the corresponding confidence can be calculated as:  
\[\gamma=F(\sqrt{n}K,n-1,\sqrt{n}\omega_{L}) \tag{7}\]  
where, \(F(x,n,\delta)\) represents the cumulative distribution function of the non-central \(t\) distribution at \(x\) with \(n\) degrees of freedom while the non-central parameter is \(\delta\), and \(\omega_{L}=\Phi^{-1}(R)\), where \(\Phi^{-1}(\cdot)\) is the inverse function of the cumulative distribution function of the standard normal distribution.
# Removal of Contaminants in Water with Scallop Shell Waste
## 2 Materials and Methods
### Caffe Acid Removal Tests  
The removal percentage of caffeic acid and arsenic(III) was evaluated using different particle size ranges of scallop shell waste reported in Table 1. To a borosilicate glass batch reactor was added 250 mL of 10 ppm caffeic acid (CA) solution. Subsequently, 500 ppm of scallop shell waste with different diameter ranges of BS, MS, and SS (see Table 1) were added. The suspension was stirred in the dark until 180 min for BS, MS, and SS, respectively.  
The reactor's temperature was maintained at room temperature by recirculating tap water through the reactor double jacket while stirring was continuously provided. Samples of 10 mL were taken every 10 min until 180 min were complete. All samples were filtered using a nylon membrane with a pore of 0.22 \(\upmu\)m to remove scallop shell waste.  
The removal percentage of caffeic acid was determined using a UV-visible Evolution 220 Thermo Scientific, Mundelein, IL, USA. Absorbance spectra measures were obtained between 200 and 500 nm. In addition, the CA removal percentage was determined using high-resolution liquid chromatography (HPLC) using an Agilent Infinity 1260 chromatograph equipped with a UV-vis diode array detector and a C-18 reverse column. A mixture of acetonitrile:water/formic acid (%) (13:87) was used as a mobile phase, with a flow rate of 0.7 mL min\({}^{-1}\) by 12 min. Caffeic acid was determined at a holding time of 8.9 min, setting 324 nm as the wavelength to calculate the pollutant (CA) removal percentage by this technique. Tests with each diameter of scallop shell waste (BS, MS, and SS) were performed three times.