esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
### Related Studies  
There have been a number of other surveys/review papers on wireless positioning systems in the last few decades. A comparative analysis of the state of the art is presented in Table 1, with respect to the different signal measurements and localization techniques covered, the environments (indoors, outdoors) and the wireless technologies considered. From the results, it becomes evident that there is not a single review that adequately covers all the different aspects considered. In particular, the majority of the surveys mainly concentrate on the indoor environment, while completely omitting the outdoors. In general, the range-free localization techniques are less discussed, as compared to the range-based (i.e., lateration and angulation). Furthermore, from the wireless technologies, Wi-Fi is mostly discussed, with the rest to be either completely omitted or discussed briefly.  
In more detail, in [4], a distinction is made between localization techniques and technologies aiming to provide a conceptual structure. In [5], the WSN localization algorithms are categorised as centralised, distributed, anchor-based, anchor-less, range-based, and range-free. A comprehensive survey is provided in [6], focusing on the localization and positioning of human users and their devices. The localization of the IoT is also analysed, together with numerous emerging IoT technologies. In [7], a comparative study is provided between the following range-free localization techniques: Centroid, Amorphous, APIT, DV-Hop, and DV-HopMax. The pedestrian indoor positioning systems (IPSs) for mass market applications are reviewed in [8], which are classified into network-based, inertial-based, and hybrid-based systems and are analysed in terms of infrastructure and methodology deployed in the mass market. Many of the studies mainly focus on specific localization techniques, such as fingerprinting [2], pedestrian indoor positioning systems [8], mmWave frequencies [9], Internet of Things (IoT) applications and communication technologies [10], narrowband Internet of Things (NB-IoT) [11], signal processing techniques on Wi-Fi sensing [12], acoustic localization systems [13], wireless acoustic sensor networks (WASNs) [14], and visible light communications (VLC) [15]. On the other hand, some of the existing surveys concentrate on a specific domain, such as WSNs [5,16], range-free localization techniques [7,16], and smartphones [17]. A broad overview of Bluetooth, UWB, ZigBee, and Wi-Fi wireless technologies is provided in [18]. On the other hand, in some reviews, the wireless technologies are not included at all, as in [5,7,14,16,19].
# Institutional Adoption and Implementation of Blended Learning in the Era of Intelligent Education
## 4 Results and Discussion
### Improvement of Students' Learning Performance
#### 4.3.2 Students' Satisfaction Rate of the BL Courses during School Closure Period  
On average, more than 20,000 students responded to the course evaluation form per week during the school closure period in the spring semester of 2020. Descriptive analysis results are depicted in Figure 3.  
Figure 3: Student perception of the BL courses during the school closure period.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Categories**} & \multicolumn{3}{c}{**Fall Semester of 2019**} & \multicolumn{3}{c}{**Fall Semester of 2020**} \\ \cline{2-7}  & **BL (M)** & **Non-BL (M)** & **Sig.** & **BL (M)** & **Non-BL (M)** & **Sig.** \\ \hline Innovation & 4.618 & 4.611 & 0.828 & 4.682 & 4.612 & 0.002 * \\ Higher-order & 4.6095 & 4.597 & 0.694 & 4.6495 & 4.617 & 0.223 * \\ Challenge & 4.577 & 4.543 & 0.321 * & 4.697 & 4.6425 & 0.045 * \\ \hline Total & 4.6025 & 4.5855 & 0.593 & 4.6725 & 4.623 & 0.032 * \\ \hline \hline \end{tabular}
\end{table}
Table 5: Course evaluation of S University (\(n=60\)).  
As is seen in Figure 3, more than 95% of the students were able to finish their study tasks on time every week and the student satisfaction rate increased steadily from 86.83% in Week 1 to 93.03% in Week 10.  
This result agrees with previous research findings that students hold positive attitudes and express great satisfaction towards BL [31; 32]. Furthermore, this result supports [PERSON] et al.'s claim that students may develop a greater sense of satisfaction with the BL course experience [33].
## 5 Simulation Experiments
### Simulation Design  
In this simulation experiment, 100 sensor nodes are distributed randomly in a simulation area of 100 m \(\times\) 100 m. A sink node is at the location of coordinates (x = 50, y = 175). The bandwidth is set to 1 Mbps. The length of messages is 500 bytes. The head length of all messages is 25 bytes. The initial energy of each node is 2J and in isomorphism (the same performance).  
In the simulation experiment, the wireless communications system model is composed of transmitting circuits, power amplifiers and receiving circuits. The power consumption of transmitting and receiving circuits is \(E_{elec}=50 nJ/bit\). When the sender transmits kbit to the receiver with the distanced, the power consumption of the sender and the receiver is the following respectively:  
\[E_{n_{1}}(k,d)=E_{n_{elec}}(k)+E_{n_{elec}-amp}(k,d)=E_{a_{inc}}k+\varepsilon_{ amp}kd^{a} \tag{10}\]  
\[E_{n_{1}}(k)=E_{n_{elec}-acc}(k)=E_{a_{inc}}k \tag{11}\]  
where \(n\) is determined by the transmission path. If the transmission is in free space, \(n=2\). If inside clusters, \(\varepsilon_{amp}=10\)\(pJ/bit/m^{2}\). If there exists multi-path fading, n = 4, cluster heads communicate with Sink nodes and \(\varepsilon_{amp}=0.0013\)\(pJ/bit/m^{4}\).
###### Abstract  
This study compares the effectiveness of fine-tuning Transformer models, specifically BERT, RoBERTa, DeBERTa, and GPT-2, against using prompt engineering in LLMs like ChatGPT and GPT-4 for multi-class classification of hotel reviews. As the hospitality industry increasingly relies on online customer feedback to improve services and strategize marketing, accurately analyzing this feedback is crucial. Our research employs a multi-task learning framework to simultaneously conduct sentiment analysis and categorize reviews into aspects such as service quality, ambiance, and food. We assess the capabilities of fine-tuned Transformer models and LLMs with prompt engineering in processing and understanding the complex user-generated content prevalent in the hospitality industry. The results show that fine-tuned models, particularly RoBERTa, are more adept at classification tasks due to their deep contextual processing abilities and faster execution times. In contrast, while ChatGPT and GPT-4 excel in sentiment analysis by better capturing the nuances of human emotions, they require more computational power and longer processing times. Our findings support the hypothesis that fine-tuning models can achieve better results and faster execution than using prompt engineering in LLMs for multi-class classification in hospitality reviews. This study suggests that selecting the appropriate NLP model depends on the task's specific needs, balancing computational efficiency and the depth of sentiment analysis required for actionable insights in hospitality management.  
t 2024  
capabilities. These LLMs, coupled with the technique of prompt engineering, have opened new possibilities for tackling various NLP tasks, including sentiment analysis and multi-class classification. This paper compares the effectiveness of two approaches for multi-class classification in hospitality review analysis: fine-tuning selected models specifically for the task and using prompt engineering in LLMs. The main hypothesis is that fine-tuning models can achieve better results and faster execution than prompt engineering in LLMs due to the latter's complex architecture and computational requirements.  
The paper first reviews relevant research in the Section 2. The Section 3 provides insight into the basics of machine learning in NLP, focusing on Transformer architecture, various models, and multi-task learning. The Section 4 describes the techniques, datasets, and evaluation criteria. The Section 5 provide details of the experiments conducted and an analysis of the results, while the Section 6 summarizes the key findings of this work.
# Viscoelastic Model and Synthetic Seismic Data of Eastern Rub'Al-Khali
###### Abstract
In this study, we build a 2D viscoelastic model of the eastern Rub' Al-Khali, which is close to the border of the UAE (Figure 1B) and generate the associated dual-component seismic data sets. Similar models and synthetic seismic data sets from the northwest, eastern, and central parts of Saudi Arabia have been made publicly available by the authors [21, 22, 23]. Models of P-wave velocity (V\({}_{\rm p}\)), S-wave ve  
Figure 1: (**A**) Satellite image showing the location of the Rub’ Al-Khali basin. (**B**) Top basement depth structure map [20]. locity (V\({}_{\text{s}}\)), Density (\(\rho\)), P-wave quality factor (Q\({}_{\text{p}}\)), and S-wave quality factor (Q\({}_{\text{s}}\)) that cover Neoproterozoic basement to Quaternary sand are first compiled from available literature [24]. These models are subsequently utilized in generating the 2D synthetic viscoelastic dual-component seismic data sets that include vertical component data and horizontal component data recorded by surface geophones using a conventional 2D acquisition geometry. The horizons, models, and synthetic seismic data sets are made publicly available at the following link: ([[https://www.dropbox.com/sh/9t099](https://www.dropbox.com/sh/9t099) hhn1h8 nea8/AAChpqVg3R_km2 MIEMJ0 SuxL2?dl=0]([https://www.dropbox.com/sh/9t099](https://www.dropbox.com/sh/9t099) hhn1h8 nea8/AAChpqVg3R_km2 MIEMJ0 SuxL2?dl=0)). The synthetic seismic data sets are saved in SEGY files, while horizons and faults are saved in a CSV text file.
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 4 Results and Discussions
### Discussion of Results
Sharply contrasting with the consistency in the Oniru values is the Ikorodu set of measurements. Having the highest range, it is clear that the best and worst RSRP values and the effect of such values are common in Ikorodu. In Figure 3, the consistency in values is still maintained by the Oniru VI values. As expected, Ikorodu values are also the most varied, and stability is mostly found in the UNILAG values. Technically, this confirms our initial inference that cell reselection and handover would be more seamless in UNILAG than in Ikorodu and worst in Oniru VI. Regarding signal strength, it is clear from Figure 4 that UNILAG and Ikorodu record better value ranges. However, the most consistent values in Oniru may be an advantage here as users across different areas within the network reach will experience almost similar signal strengths when available. Surprisingly, Figures 5-7 indicate that high data rates are more common in the UNILAG and Oniru areas compared to Ikorodu. Although more erratic, the throughput signal levels indicate that more data speeds are recorded in UNILAG than in Ikorodu. As stated in Section 4.2, this may be due to actual MBB differences or owed to the lapses in the measurement data obtained. While executing the PSO in Algorithm 2, it is important to highlight at this point that in assigning the upper and lower boundaries, the least and highest values obtained during the test were used so that the swarm was forced to search within the sample space of the 3086 rows of data recorded during the test for the RSRP, RSRQ, and RSSI optimization procedures. The SINR values were not measured, and as such, a prior initialization of data was performed using an appropriate objective function. As presented in Table 6, we can see that the optimized values, though not exactly the values set by the NCC, are much closer to the specified values. Nonetheless, these values have considered the environmental factors and the hardware limitations of the MNO in operation.
## 5 Experimental Results  
### Two Sources  
Let us consider two sources emitting the following signals that are received by three sensors.  
\[s_{1}(t)=e^{(0.1\pi jt+aj\pi t^{3})} \tag{21}\] \[s_{2}(t)=e^{(0.45\pi jt-aj\pi t^{3})}, \tag{22}\]  
where \(a=1.8311\times 10^{-5}\). The sources are placed at angles \(-5^{\circ}\) and \(5^{\circ}\). The signal duration is from 0 to 128 s with sampling frequency equal to 1 Hz. The signals are received by uniform linear array of 4 sensors such that spacing between two sensors in one half of the wavelength. The estimated IF of the signal using the proposed algorithm vs. the original IF is shown in Figure 4 and the estimated DOA vs. the original DOA are shown in Figure 5. The length of the Gaussian window used in this study is 63 with \(\sigma=1\) and \(\Delta f=2\).  
Figure 4: Estimated IF (solid line) vs. original instantaneous frequency (IF) (dashed line).  
Let us compare the proposed algorithm with the conventional TF-MUSIC algorithm, the ADTFD based ridge tracking algorithm [25], and spatial adaptive TFD based Viterbi algorithm [24], for the signal to noise ratios (SNR) ranging from \(-10\) dB to \(10\) dB, using the mean square error (MSE) as a criterion. The MSE between the original angle, i.e., \(\theta\) and estimated angle, i.e., \(\hat{\theta}\) is computed as:  
\[MSE=10\log_{10}(\theta-\hat{\theta})^{2} \tag{23}\]  
To estimate MSE 100 simulations are performed. The estimated MSE curves are plotted in Figure 6. The results demonstrate that the lowest MSE is obtained by the proposed algorithm for majority SNR.  
Figure 5: Spatial spectrum.  
Figure 6: Mean square error (MSE) curves of DOA estimates for over-determined scenario.  
Let us now repeat the experiment for an under-determined scenario, i.e., for the number of sensors is equal to 2. In this scenario, the conventional TF-MUSIC algorithm cannot be employed, so the performance comparison is only made with the ADTFD based ridge tracking algorithm [25] and spatial adaptive TFD based Viterbi algorithm [24]. Figure 7 illustrates that the proposed method has the lowest MSE for SNR greater than \(-5\) dB. The code to reproduce the aforementioned results can be downloaded from [[https://github.com/nabeelalikhan1/DOA-using-FAST-IF](https://github.com/nabeelalikhan1/DOA-using-FAST-IF)]([https://github.com/nabeelalikhan1/DOA-using-FAST-IF](https://github.com/nabeelalikhan1/DOA-using-FAST-IF)) (accessed on 3 February 2021).
## 3 Hardware Implementation
### Resources Utilisation and Power Consumption  
In this section, resource utilisation after carrying out logic synthesis, implementation and place and route on different space-grade FPGAs are presented. In particular, the number of Look-Up-Tables (LUTs), D-edge triggered flip-flops (DFF) and Random Access Memory (RAM) blocks necessary to implement one R-SpaceFibre CODEC are reported. Table 1 shows the resource utilisation for the R-SpaceFibre CODEC on a Xilinx Zynq-7000, on a Xilinx Virtex 5, on a Microsemi RTG4 and on a Microsemi RTAX2000. These numbers refer to a R-SpaceFibre implementation with a single 256 words VCB and 62.5 MHz clock frequency, achieving a data-rate of 2.5 Gbps (SpaceFibre, and consequently R-SpaceFibre, shall transmit one 40 bit word per clock cycle). Figure 4: Architectural diagram of testbed. Finally, Table 2 shows a comparison between hardware resources of R-SpaceFibre and IngeniArs SpaceFibre CODEC IP on a Microsemi RTAX2000 and on a Microsemi RTG4. The CODECs implemented still has a single 256 words VCB, and the target frequency is 62.5 MHz for RTG4 and only 40 MHz for the RTAX2000, due to the lower performances of the FPGA, with a maximum data-rate of 1.6 GHz. This is acceptable as the aim of this paper is to demonstrate the possibility to reduce hardware resources needed by a SpaceFibre end-node. It is clearly observable that R-SpaceFibre considerably reduces (more than 40%) the hardware resources needed for its implementation on both RTAX2000 and RTG4. In particular, RTAX2000 has limited number of hardware resources and, if compared with RTG4 and R-SpaceFibre implementation, allows saving a consistent part of synthesisable logic. Post-layout simulations were carried out on a Xilinx Zynq-7000 to estimate the power consumption of both R-SpaceFibre and SpaceFibre IP cores. The results were obtained with 200 \(\upmu\)s simulations, corresponding to the transmission of 520 kBit of data (divided in 36 bit data words). The CODECS were implemented with one VC and the operating frequency was set at 62.5 MHz. The results are shown in Table 3.  
Power consumption results similar for the two implementations. In fact, R-SpaceFibre does not implement broadcast service and the retry mechanism. However, in a standard SpaceFibre \begin{table}
\begin{tabular}{c c c c c c c} \hline
**FPGA** & **VC** & **LUT** & **util\% LUT** & **DFF** & **util\% DFF** & **BlockRAM-FIFO** \\ \hline Zynq-7000 & 1 & 1675 & 0.73\% & 1791 & 0.4\% & 1.5 \\  & 2 & 2248 & 1\% & 2345 & 0.5\% & 2 \\ Virtex 5 & 1 & 1682 & 2.05\% & 1103 & 1.35\% & 2 \\  & 2 & 2662 & 3.25\% & 1653 & 2.02\% & 4 \\ Microsemi RTG4 & 1 & 2561 & 2.07\% & 1227 & 1.19\% & 16 \\  & 2 & 4122 & 3.47\% & 1892 & 2.00\% & 32 \\ Microsemi RTAX2000 & 1 & 3479 & 16.18 \% & 1530 & 14.23\% & 4 \\  & 2 & 5356 & 24.91\% & 2641 & 24.56\% & 8 \\ \hline \end{tabular}
\end{table} 
Table 1: R-SpaceFibre CODEC-DECoder (CODEC) resource utilisation on different Field Programmable Gate Arrays (FPGAs).
## 6 Conclusions  
In this paper, we propose a method called MDAR, the multi-head relation-fused graph attention network and DeepFM with an attention mechanism, to address the issue of data underutilization in original KGRSs. Firstly, we leverage a DeepFM with an attention mechanism (DeepAFM) in the RS module to distinguish the importance of different feature interactions, extracting low-order linear interaction features crucial for target prediction. Secondly, we employ deep neural networks (DNNs) in the cross-compression unit to extract complex interaction features, facilitating the exchange of deep semantic information between the RS modules and KGE modules. Finally, in the KGE module, we utilize a relation-fused multi-head graph attention network (RMGAT), aggregating multi-relation neighborhood features of a given entity into the KG network to learn a comprehensive entity representation, effectively addressing the problem of knowledge graph completion. The experimental results on real-world datasets demonstrate that MDAR outperforms state-of-the-art baselines in terms of recommendation performance and model efficiency.  
In future research, we intend to evaluate the model on more datasets to validate the applicability of MDAR. Meanwhile, we also plan to enhance MDAR in the following aspects:  
* Incorporating reinforcement learning and meta-learning into recommendation systems [43; 44] to improve the adaptive capability of our system;
* Exploring different attention mechanisms [45; 46] in the sharing units to better distinguish feature contributions, advancing research in the recommendation field;
* Building temporal knowledge graphs (TKGs) by adding timestamps to the KG [47; 48; 49] to complement the knowledge graph information, offering significant insights into knowledge graph enhancement.  
Overall, we aim to utilize various learning methods to continually enhance our recommendation system's performance and broaden its application to diverse fields.  
Conceptualization, Q.X.; methodology, Q.X.; software, Q.X.; validation, Q.X.; formal analysis, S.L.; investigation, P.W. and S.L.; resources, P.W.; writing--original draft, Q.X.; writing--review and editing, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China under Grant No. 2021 YFB1714400.  
Not applicable.  
Data Availability Statement: The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 4 Results and Discussion
### Discussion
#### 4.3.2 BiGRU Model  
Figure 8 shows that the accuracy achieved with BiGRU at 20 epochs in the training dataset is (99.34%); the model undergoes significant growth until reaching a final accuracy of (98.60%) with the validation dataset. This increase in accuracy is accompanied by a steady decrease in loss, indicating a continuous improvement in the model's predictive ability. Although some fluctuations in performance are observed during training, the model manages to stabilize towards the end of the process, demonstrating effective learning and an ability to generalize well to unseen data.  
The F1-score generally performed well in most classes, ranging from (92.00%) to (99.00%). Specifically, classes 1 and 3 achieved F1 scores of (99.00%) and (97.00%), respectively, indicating high precision and recall in classifying those classes. Class precision was (100%), suggesting no false positives for any class. However, recall varied between classes, with values ranging from (85.00%) to (98.00%), indicating that the model had difficulty recovering all instances of classes 2 and 4 compared to the other classes. Overall, the model showed good performance in most classes, although there could be room for improvement in the recovery of some specific classes.  
The total model training time was 626 s. During this process, (45.00%) of the GPU was utilized, indicating a moderate but significant use of available hardware resources to accelerate model training. This percentage of GPU utilization suggests efficient resource allocation.  
Figure 8: BiGRU accuracy.
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 3 Results  
### Data Transfer Rate and Quality  
A key parameter determining the accuracy and resolution of the sensor network is its data transfer rate and quality. Bell angle data is sent from each sensor node as packets, and a measure of data transfer quality is the number of lost packets. The distribution of the lost packets, measured by sending a set number of packets at a set data rate for different sensor node arrangements, is shown in Figure 7,with each line representing an individual node. Plots a-e show how data transfer quality varies as the number of simultaneously transmitting nodes increases from 1 to 5 when a Google Nexus 6 Plus mobile phone is used as a router. The more than 1 order of magnitude increase in packet loss for five sensor nodes indicated that the capacity of the phone was reached. This was confirmed by repeating the experiment with an O2 wireless box IV router, shown in plot f, and observing the same minimal packet loss as in plots a-d. Table 5 shows that the packet loss rate is \(\geq\)0.05% independent of the number of boards as long as data transfer rate is within the maximum capacity of the router. No pattern emerged in the lost packet distribution indicating that packets were lost due to random variations in noise from external sources.