esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method  
We propose a new end-to-end neural network model called the multi-scale channel spatial attention network (MSCSA-Net) for semantic segmentation of remote sensing images. The architecture of MSCSA-Net is depicted in Figure 2a and was mainly inspired by the encoder-decoder architecture.  
Figure 2: (**a**) The overall architecture of MSCSA-Net. (**b**) The CBRB module in MSCSA-Net. (**c**) The SCBLR module in MSCSA-Net.  
In the encoder, ResNet-50 is used as our backbone to extract four different scales of feature maps, which are obtained after maxpooling layers. We name the four different scales of feature maps as C2, C3, C4, and C5, as shown in the figure. The higher-level feature map C5 is passed through the separable convolution batch normalization leaky ReLu (SCBLR) block to obtain feature map C6. The architecture of the SCBLR block is shown in Figure 2c. The input feature map C5 is passed through the 2D separable convolution of kernel size \(7\times 7\) and then goes to the batch normalization and leaky ReLu layers to generate output feature map C6.  
For the decoder, we can divide it into two parts. The first part uses our proposed local channel spatial attention (LCSA) module on four of the feature maps (C2 to C5). The LCSA module is depicted in Figure 3 and introduced in detail in Section 3.1.  
The feature map C5 is passed through LCSA module and the LCSA output LC5 is concatenated with feature map C6. The concatenated feature map is then passed through the convolution batch normalization ReLu block (CBRB) for output CB5, where the kernel size of the CBRB is \(3\times 3\). The architecture of the CBRB is shown in Figure 2b. The CBRB output CBS is upsampled and concatenated with the LCSA output LC4, and then it is passed through the CBRB for output CB4. The CBRB output CB4 is upsampled and concatenated with the LCSA output LC3, and then it is passed through the CBRB for output CB3. The CBRB output CB3 is upsampled and concatenated with the LCSA output LC2, and then it is passed through the CBRB for output CB2.  
In the second part of the decoder, we use our proposed multi-scale attention (MSA) module, which can be treated as another decoder. The CBRB outputs CB5, CB4, CB3, and CB2 are passed through MSA modules to generate MSA outputs M5, M4, M3, and M2, respectively. The feature map C6 goes to the channel attention module (CAM) and its output is concatenated with the MSA output M5 and then passed through the CBRB and upsampled for output U5. The MSA output M4 is concatenated with the upsampled output U5, and then it is passed through the CBRB and upsampled for output U4. The MSA output M3 is concatenated with the upsampled output U4, and then it is passed through the CBRB and upsampled for output U3. The MSA output M2 is concatenated with the upsampled output U3, and then it is passed through the CBRB and 16-LCSA and upsampled for the final prediction output.
## 3 Results
### Models Performance and Driving Factors of NEE Prediction
Gain measures the improvement in the predictive power of the model after splitting the node; cover refers to the number of samples covered by a node; frequency represents the number of samples for different categories within a node. Input factors are air temperature (TA), incoming shortwave radiation (SW_IN), vapor pressure deficit (VPD), atmospheric pressure (PA), wind speed (WS), soil water content (SWC), normalized difference vegetation index (NDVI), and the temperature difference between the previous day and the current day (DeltaTA). Figure 4: Scatter plots of the observed and predicted NEE at seven rice paddy sites using the XGBoost and RF models. Red points represent the observed and predicted NEE using the XGBoost model. Blue points represent the observed and predicted NEE using the RF model. Figure 5 shows the importance of different factors at different sites to NEE through the XGBoost model (a higher value of gain implies greater importance). It can be seen from Figure 4 that except for the JP-Mse site NETRAD data missing, the gain of NETRAD at other sites exceeded 0.2, and the contribution of NETRAD to the prediction of NEE ranked top. After averaging the gain of these sites, the gain of NETRAD was 0.38, ranking first. Followed by NDVI (0.26), LW (0.15), and SWC (0.12), the rest of the factor gain values were less than 0.10. TA was important at only one site (IT-Cas).
## 2 Background and Related Work
### Related Work
#### 2.2.6 Research Gap  
The review of related work highlights a significant gap in the current literature regarding the effective integration of concurrent purchase data into e-commerce recommendation systems. While recent advancements in deep learning and transformer-based models, such as BERT, have shown promising results in various recommendation tasks, their application to leveraging concurrent purchase data remains largely unexplored.  
Our research aims to address this gap by proposing a novel approach that harnesses the power of a transformer-based BERT model to effectively incorporate concurrent purchase data into e-commerce recommendation systems. Through extensive experiments on real-world e-commerce datasets, we demonstrate the effectiveness of our proposed method in capturing the complex dynamics of concurrent purchasing behavior and providing more accurate recommendations compared to traditional approaches. Furthermore, we showcase the significance of integrating concurrent purchase information by comparing the performance of our BERT-based model with and without the inclusion of such data.  
It is important to note that while our study focuses on the comparison between traditional models and BERT, as well as BERT and SLLM (TinyLlama), and the impact of incorporating concurrent purchase information, we do not delve into comparisons with other transformer-based models or large language models (LLMs) that are not SLLMs. Future research could explore the potential of these advanced models in the context of concurrent purchase data, building upon the foundation laid by our work.  
By successfully leveraging concurrent purchase data with a BERT-based model, our research takes a crucial step toward developing more sophisticated and effective e-commerce recommendation systems. We believe that our findings will inspire further investigations into the integration of complex purchasing behaviors, ultimately leading to enhanced personalization and user satisfaction in e-commerce platforms.
# A 2.4-GHz Fully-Integrated GaAs pHEMT Front-End Receiver for WLAN and Bluetooth Applications
## 4 Conclusions  
In this paper, a fully-integrated GaAs pHEMT front-end receiver including a SP3T switch and a LNA with bypass function with four operating modes at 2.4 GHz, fabricated in a 0.25 \(\upmu\)m pHEMT process for an IEEE 802.11 b/g standard is presented. An asymmetrical SP3T switch architecture is realized to provide signal transmission path for LNA, and extra PA and Bluetooth modules. A compromise between maximum gain and minimum noise is implemented based on inductively source-degenerated cascode topology. The LNA also incorporates a bypass module to handle high power signals. An NF of 1.7 dB, a power gain of 20 dB and IIP3 of 6 dBm drawing 11 mA of current from 5 V power supply at 2.4 GHz are realized in LNA high gain mode. In bypass mode, the LNA has an IL of 7.5 dB. The measured IL of transmit switch is 0.6 dB at 2.4 GHz while 0.8 dB of Bluetooth switch. With critical on-chip matching to the antenna, the proposed front-end receiver is suitable for WLAN and Bluetooth applications.  
Conceptualization, R.Y. and Z.Z.; methodology, R.Y. and Z.Z.; software, R.Y. and Z.Z.; validation, R.Y., Z.Z. and H.X.; formal analysis, R.Y. and Z.Z.; investigation, R.Y. and Z.Z.; resources, Z.Z. and G.Z.; data curation, R.Y. and Z.Z.; writing--original draft preparation, R.Y.; writing--review and editing, R.Y., Z.Z., H.X. and G.Z.; visualization, R.Y. and Z.Z.; supervision, Z.Z. and G.Z.; project administration, Z.Z. and G.Z.; funding acquisition, Z.Z. and G.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China (Grant No. 2018 YFB1802000), the National Natural Science Foundation of China (Grant No. 61974035), and the Guangdong Local Inno. Research Team of Pearl River Talent Program (Grant No. 2017 BRT01X168).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** The data may be obtained from the authors upon request.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Ref.** & [3] & [5] & [30] & [31] & **This work** \\ \hline
**Topology** & **SP3T + LNA** & **SP3T + LNA** & **SP3T + LNA** & **SP3T + LNA** & **SP3T + LNA** \\ \hline Freq. (GHz) & 2.4 & 2.4 & 2.4 & 2.45 & 2.4 \\ Gain (dB) & 13 & 14.8 & 11.5 & 12 & 20 \\ Si (dB) & –7 & –7 & –11 & –11 & –12 \\ NF (dB) & 3 & 3.6 & 1.9 & 1.9 & 1.7 \\ Input \(P_{\mathrm{1 dB}}\) & (dBm) of SP3T & –6 & N/A & –0.5 & N/A & –3 \\ \(+\) LNA & (dBm) of & 7 & –2.7 & N/A & 10 & 6 \\ SP3T + LNA & (dBm) & 7 & 8 & N/A & 8 & 11 \\ \(P_{\mathrm{DC}}\) (mW) & 23.1 & 26.4 & N/A & 26.4 & 55 \\ Technology & Bulk CMOS & Si CMOS & GaAs pHEMT & GaAs pHEMT & GaAs pHEMT \\  & 0.18 \(\upmu\)m & 0.13 \(\upmu\)m & 0.5 \(\upmu\)m & 0.5 \(\upmu\)m & 0.25 \(\upmu\)m \\ FOM & 4.34 & 0.47 & N/A & 10.94 & 15.11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with previous studies.
# Use of Indoor Location Technologies in Healthcare Contexts: A Scoping Review
## 1 Introduction
Nonetheless, current research appears not to reflect the opportunities this sector presents fully [9; 10], highlighting the need to understand the state of research and the direction required for the broader adoption of RTLS in healthcare. Thus, this article aims to characterise the current research on indoor location technologies in healthcare. This characterisation includes identifying the most commonly used technologies, the healthcare contexts in which they are applied, the methodological approaches adopted in literature, and the primary outcomes, barriers, and success factors reported. In addition, we analyse the first authors, countries, and institutions associated with the selected studies. This enables the identification of leading research groups, regional and institutional patterns, and broader trends in developing and applying indoor location technologies in healthcare. Understanding where and by whom the research is being conducted can offer valuable insights into the maturity and focus areas of the field across different contexts. The following research questions guide this scoping review:What are the characteristics and methodological approaches of existing research on indoor location technologies in healthcare? Who are the primary authors, and which countries and institutions contribute significantly to this topic? 2. Which technologies are predominant, and in which healthcare context are they typically adopted? 3. Do published works discuss factors contributing to success or failure? If so, what are these factors? These research questions were selected because understanding the research landscape, technology adoption, and influencing factors is essential for guiding future development and implementation efforts. By concentrating on these core areas, this review aims to identify key trends, common application domains, and critical success factors, while also revealing gaps in the literature. Although other aspects of RTLS in healthcare, such as detailed technical specifications or economic evaluations, are important, they were not emphasised to maintain a comprehensive yet focused overview that facilitates targeted research and practical applications. The following section will present the methodology of the review. This will be followed by the presentation and discussion of the results obtained from the analysis of the included articles, concluding with final remarks and considerations.
## 7 Solution of The Digital Beamforming Matrix and The Analog Beamforming Matrix  
In this section, we aim to investigate the design of digital beamforming matrix \(\mathbf{F}_{BB}\) and analog beamforming matrix \(\mathbf{F}_{RF}\) in a hybrid beamformer. After obtaining \(\mathbf{W}\), we need to further solve \(\mathbf{F}_{BB}\) and \(\mathbf{F}_{RF}\). The solution method of \(\mathbf{F}_{BB}\) and \(\mathbf{F}_{RF}\) can be divided into two steps:  
* The first step: we adopt the EVD algorithm to solve the hybrid beamforming matrix \(\mathbf{F}\) from \(\mathbf{W}\).
* The second step: we propose the MM-AltOpt algorithm to obtain \(\mathbf{F}_{BB}\) and \(\mathbf{F}_{RF}\).
# Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar
###### Abstract  
The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE) base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system's capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications.  
passive forward scattering radar sensor; target detection; target classification +
Footnote †: journal: Sensors
## 2 Setup and Modeling
### Modeling
#### 2.2.3 BFL Response  
The BFL field transfer function, \(T_{BFL}(\lambda)\), is given by [24]  
\[T_{BFL}(\lambda)=\exp[-\gamma\Psi(\lambda)/2]+j\sin[-\Psi(\lambda)/2] \tag{5}\]  
where  
\[\Psi(\lambda)=2\pi BL_{BFL}/\lambda+\Delta\lambda BL_{BFL}/\lambda^{2} \tag{6}\]  
In Equations (5) and (6), \(B\) and \(L_{BFL}\) are the BFL's birefringence and polarization maintaining (PM) fiber total length, respectively, which define the Free Spectral Range (\(FSR\)) of the BFL response, \(FSR=\lambda^{2}/BL_{BFL}\), and the BFL detuning defined as \(\Delta\lambda=\lambda_{enc}-\lambda_{netch}\), where \(\lambda_{enc}\) is the spectral position of the input CW beam to be encoded and \(\lambda_{netch}(\equiv FSR/2)\) is that of the nearest notch. Since by physical principle \(\lambda_{enc}<\lambda_{notch}\), this implies that \(\Delta\lambda<0\), with \(|\Delta\lambda|\in[0,FSR/2]\).
## 2 Electrical Equipment and Assembly to Power the Transducer
### Signal Generator Specification  
* Digital Signal Generators provide maximum flexibility like
* Dual independent signal output channels.
* Digitally synthesized waveforms for maximum flexibility.
* Uses 12-bit DACs running up to 2 MHz sampling frequency.
* Selection of any frequency (20-200 kHz)
* Sweep mode, any rate and frequency range.
* Pulse mode with duty cycle control for all signals generated.
* Arbitrary signal generation by importing synthesized signals from a file (useful for research purposes).
* Bespoke firmware supported by BIC and can be upgraded with new features and bug fixes.
## 4 User Study
### Experiment Procedure  
To verify the hypothesis, an experiment and survey were carried out at a university in South Korea (Busan) from 20 January to 24 January, 2020. In performing the experiment, all the necessary safety measures were taken so as to reduce the possibility of users experiencing injury or simulator sickness. In this regard, the experiment's plan was approved by the Institutional Review Board (IRB).  
According to the experiment results from the previous study [3], simulator sickness is relieved when moving around with actual walk within virtual space, compared to moving around using a controller. Therefore, it is very unlikely that simulator sickness will occur in this experiment. Nevertheless, in case of simulator sickness, the experiment was conducted by ensuring and providing a space for participants to sit down or lie down for a while, and under conditions in which an emergency center could be called in case of a more urgent situation.  
The experiment was conducted in three stages in total. First of all, the practice stage for training VR controllers and Treadmill was implemented before starting the fire safety education game in earnest. The Virtuix-Omni Treadmill used in the experiment works in a way that the slippery soles of the shoes slip over the Treadmill to guide walking. Because it is a VR mobile device that none of the participants in the experiment have used, it is difficult for them to walk on the Treadmill naturally from the beginning. Therefore, the participants had to practice the basic steps of walking. Although there were differences among them, most participants were generally able to walk naturally to a certain degree within at least three minutes.  
After the completion of the practice stage, the fire safety education game started. The participants escape the virtual fire environment while learning how to deal with fires in real time according to the voice of the guide in VR. The learner can choose one of three situations: escape to the ground, evacuate to the rooftop, escape using an air mattress, and escape using a rescue helicopter. If he or she takes an elevator, which is a prohibited action in the event of a fire, he or she will die and have to start again on the corridor stage. It took five to ten minutes for each participant to play the learning game.  
After completion of the experiment, participants fill out a questionnaire made by the empirical recognition scale of [PERSON] (2020) [10] and the short form of STAI of [PERSON] and [PERSON] (1992) [40]. Finishing the questionnaire took approximately ten to fifteen minutes.
## 4 Conclusions  
In this study, concrete containing MWCNTs was fabricated, and self-sensing concrete specimens were tested under a fatigue setup. Tests were performed to check the possible correlation between different stress levels and damage-sensing abilities during concrete fatigue life. Based on the results obtained, the following conclusions can be drawn:  
* The concept of the G-value was defined as the slope of the electrical response baseline of the self-sensing concrete as a function of the number of cycles. It reflects the rate of the damage created in the concrete per each cycle at the specific stress level in the fatigue test.
* Higher absolute values of the G-value indicate that more severe damage growth conditions occur, and more destructive external effects affect the concrete member.
* A novel damage curve, log (G)-log (N), was developed as an alternative to traditional fatigue curves, S-log (N), based on the electrical response of self-sensing concrete estimated through a linear regression approach.
* The log (G)-log (N) diagram can be used to determine the level of damage in concrete infrastructure members based on the continuous monitoring of the electrical response in time. This result can be useful to monitor the infrastructure behavior under different loading conditions and any other destructive effects. This highlights the potential of using this type of material as an embedded sensor for estimating the remaining life of concrete infrastructure and helping to decide the proper structural maintenance action.  
Conceptualization, M.A.; Data curation, M.A. and P.A.; Formal analysis, M.A. and P.A.; Funding acquisition, G.L. and M.A.; Investigation, M.A., J.-M.T., G.L., and P.A.; Methodology, M.A.; Project administration, M.A.; Resources, M.A., J.-M.T., G.L., and P.A.; Writing--original draft, M.A.; Writing--review and editing, J.-M.T., G.L., and P.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Iran National science foundation: INSF under contract number 99022755, also this work was supported by Shahid Rajaee Teacher Training University under contract number 181931.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Hydrographic Observation  
The water temperature and light intensity at A2 and A5 were continuously observed with a water temperature logger (HOBO TidbiT v2 UTBI-001, Onset, Bourne, MA, USA) installed right above the bottom and a light quantum meter (COMPACT-LW, JFE Advantech, Hyogo, Japan) deployed 3.0 m above the seafloor. To observe the flow speed and direction near the seafloor, an electromagnetic flowmeter (INFINITY-EM, JFE Advantech, Hyogo, Japan) was also installed at 3.0 m above the seafloor of A5. All the observation devices were moored to an anchored rope at each station. The observation period was from 25 July 2018 to 7 August 2018. Vertical profiles of environmental variables (temperature, salinity, density, and chlorophyll fluorescence) were also measured with CTD (RINKO-Profiler ASTD102, JFE Advantech, Hyogo, Japan) at all the stations on 29 July 2018.  
Figure 1: Location of the study area and details of the artificial reef (AR) system: (**a**) location of the AR system (black star), (**b**) detailed structure of a tower reef (20 m high), and a cube reef (3 m high) deployed in the AR system, (**c**) the distribution of tower reefs and cube reefs in the AR system detected by side-scan sonar, and (**d**) location of the sampling stations (A1, A2, and A5); an open circle represents a tower reef.