esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 3 Proposed Classifier
### System Model  
As depicted in Figure 1, we assume a satellite communication network in which a GEO satellite terminal \(S\) aims to send a signal to a ground mobile terminal, which, in our case, acts as a receiver \(R\). Line-of-Sight (LOS) communication from satellite terminals is not always accessible; as a result, an IRS unit is installed on the building's wall to help transmit the satellite signal to the receiver. The IRS has \(K\) reflecting components, and the receiver receives numerous beams. We assume the receiver receives the signal from the satellite terminal using a single antenna. The receiver end considers the channel state information (CSI) entirely known.  
The channels of different links in the proposed system model are \(S\to IRS\) and \(IRS\to R\). The channel gain from \(S\to IRS\) is denoted by \(\zeta_{q}\in\mathbb{Z}^{Q\times 1}\), with \(\zeta_{q}=[\zeta_{1},\zeta_{2},...,\zeta_{q},...,\zeta_{Q}]\), where \(\zeta_{q}\) is the channel gain from \(S\) to \(IRS\). The channel gain \(\zeta_{q}\) can be defined as [43]:  
\[\zeta_{q}=e^{-i\varphi}\frac{\sqrt{G^{S}}}{4\pi\frac{d_{\zeta}}{\lambda}} \tag{1}\]  
where \(G^{S}\) is the gain of \(S\), \(d_{\zeta}\) is the distance from \(S\) to \(IRS\), and \(\psi\) is the phase of \(S\). The channel from \(IRS\to R\) is denoted by \(\kappa_{q}\in\mathbb{Z}^{Q\times 1}\), with \(\kappa_{q}=[\kappa_{1},\kappa_{2},...,\kappa_{q},...,\kappa_{Q}]\), where \(\kappa_{q}\) is a channel from \(IRS\) to \(R\). The channel gain is defined as:  
\[\kappa_{q}=cos^{2}(\phi)\frac{\sqrt{G^{R}}\kappa_{IRS}\beta_{IRS}}{d_{\kappa}} \tag{2}\]  
where \(G^{R}\) is the antenna gain at the receiver, and \(\phi\in[0,\pi/2]\) is the angle of incidence. \(d_{\kappa}\) is the distance from \(IRS\to R\), and \(\alpha_{IRS}\) and \(\beta_{IRS}\) denote the width and length of the \(IRS\). The signal received at the receiver end is expressed as follows:  
\[\mathbf{y}=(\zeta_{q}\Theta\kappa_{q})\mathbf{x}+\mathbf{\eta} \tag{3}\]  
where \(\Theta\in\mathbb{Z}^{Q\times Q}\), and \(\Theta\) is defined as:  
\[\Theta=diag(\omega_{1}e^{j\gamma_{1}},\omega_{2}e^{j\gamma_{2}},...,\omega_{ Q}e^{j\gamma_{Q}}) \tag{4}\]  
Figure 1: System model.  
where \(\gamma=[0,2\pi]\) is the phase shift, \(\omega=[0,1]\) is the amplitude of the IRS, \(\eta\) is the additive white Gaussian noise, and \(\mathbf{x}\) is the PSK- or QAM-modulated signal.
# Improvements of the xAAL Home Automation System
## 5 The xAAL Communication Protocol
### Alternative Home Automation Protocols over IP
#### 5.7.2 xAP and xPL  
The eXtensible Automation Protocol (xAP) [40], is a pragmatic ad hoc simple textual home automation protocol, which uses IPv4 broadcast for message passing between senders and receivers. xAP has also the notion of schemas, which defines message format classes, and somehow device types. The main criticism is about its ad hoc nature: schemas are not so formal, and it is concretely rather difficult to interoperate with a device that one has not coded by oneself. The other difficulty is about its addressing principles by 'logical name', 'device id', 'instance id','sub-addressing endpoints', which are commonly replaced by the wildcard mark '*'. The last surprising point is about the architectures: hosts are communicating with each other by IPv4 broadcast, but software on the same host are locally connected via a UDP unicast socket to a 'hub' component in this host which is in charge of forwarding those UDP packets to other hosts by broadcast.  
The xPL protocol [41] is a fork of the xAP protocol. It defines itself as a 'glue' to tie together home automation technologies. It tried to do things in a more formal way: the UDP port in use was claimed and officially registered by IANA, schemas were written in XML, unfortunately without clarifying the behavior of devices. Finally, it suffers from the same criticisms as xAP.  
xAP and xPL are ten-year-old protocols and are now closed projects. However, they have participated founding basements of home automation protocols over IP in terms of need expression and in terms of solutions basis for interoperability: distributed systems, device-to-device communications, schemas for devices' definition, lightweight communication stack.
# Outage Probability Analysis in Relaying Cooperative Systems with NOMA Considering Power Splitting
## 3 Performance Analysis
### Average Bit Error Probability (ABEP)  
In principle, the average bit error probability (ABEP) is known as the probability used to examine the performance of wireless applications. Considering various modulations, i.e., BPSK, BPSK with orthogonal signaling, and \(M\)-ary square QAM alphabet, which transmits data by changing the amplitude of two carrier signals.  
For the sake of simplicity, the ABEP, \(P_{b}(e)\) for the end-to-end SNR, \(\gamma_{e2e}\) can be expressed by  
\[P_{b}=\int_{\gamma=0}^{\infty}P_{b}\left(e|\gamma\right)f_{e2e}\left(\gamma \right)d\gamma, \tag{26}\]  
where \(P_{b}\left(e|\gamma\right)\) is averaging the conditional BEP over the PDF.  
Using BEP in an AWGN channel [29], the \(k\)-th bit ABEP of Gray bit-mapped \(M\)-ary square QAM is given by  
\[P_{b}\left(e|k\right)=\tfrac{2}{\sqrt{M}}\sum\limits_{i=0}^{\left(1-2^{-4} \right)\sqrt{M}-1}\left\{\left(-1\right)^{\left[\frac{4^{2}-1}{\sqrt{M}} \right]}\left(2^{k-1}-\left[\frac{i2^{-1}}{\sqrt{M}}+\frac{1}{2}\right]\right) \times\varepsilon\left[Q\left(2i+1\right)\sqrt{\frac{3\gamma}{M-1}}\right] \right\}, \tag{27}\]  
where \(\varepsilon[.]\) denotes the statistical expectation operator, and \(Q(x)\) is the Gaussian Q-function defined as \(Q\left(x\right)=\tfrac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-t^{2}/2}dt\), and \(x\leq 0\).  
Eventually, the ABEP of \(M\)-ary square QAM can be written as  
\[P_{b}\left(e\right)=\frac{1}{\log_{2}\sqrt{M}}\sum\limits_{k=0}^{\log_{2} \sqrt{M}}P_{b}\left(e|k\right). \tag{28}\]  
In order to compute the ABEP as in [29], the following expression is given  
\[\mathcal{B}=E\left|pQ\left(\sqrt{2q\gamma}\right)\right|, \tag{29}\]  
where \(\left(p,q\right)=\left(1,2\right)\) for BPSK, and \(\left(p,q\right)=\left(1,1\right)\) for QPSK. Consequently, before obtaining the ABEP, the distribution function of \(\gamma\) must be expected.  
Then, the expression for ABEP derived in (29) is rewritten over the CDF as  
\[\mathcal{B}=\frac{p\sqrt{q}}{2\sqrt{\pi}}\int_{\gamma=0}^{\infty}\frac{e^{-q \gamma}}{\sqrt{\gamma}}F_{\gamma_{e2e}}\left(\gamma\right)d\gamma. \tag{30}\]  
The total ABEP of \(x_{1}\) is given by  
\[\mathcal{B}_{sum,x_{1}}=\frac{p\sqrt{q}}{2\sqrt{\pi}}\int_{\gamma=0}^{\infty} \frac{e^{-q\gamma}}{\sqrt{\gamma}}\left(F_{\gamma_{D,1,x_{1}}}\left(\gamma \right)+F_{\gamma_{e2e,x_{1}}}\left(\gamma\right)\right)d\gamma, \tag{31}\]  
where \(F_{\gamma_{D,1,x_{1}}},F_{\gamma_{e2e,x_{1}}}\) are respectively presented in (14b) and (18).  
Likewise, the ABEP of \(x_{2}\) can be derived based on (30) as \(\mathcal{B}_{x_{2}}=\frac{p\sqrt{q}}{2\sqrt{\pi}}\int_{\gamma=0}^{\infty} \frac{e^{-q\gamma}}{\sqrt{\gamma}}F_{\gamma_{D,2,x_{2}}}\left(\gamma\right)d\gamma\), by using the same method to the CDF of \(x_{1}\) in (14b).
# Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
## 2 Contextualisation
### Active Learning Scenarios  
The idea of AL is to enable the selection of fewer training instances (e.g., images or patches) to achieve a higher performance when they are labelled by an oracle (i.e., human expert). It is an iterative process, repeated over time, that combines data sampling and model training. The number of learning cycles or iterations \(K\) is defined according to the  
Figure 3: Object detection road map since 2001. Image taken from [21]. Similar evolution was presented in [22].  
following equation \(K=B/b\), where \(B\) is the total budget for labelling and b is the labelling budget per cycle, also known in some works as the batch size. Another condition that allows stopping the iteration process is related to a performance goal, which is calculated via inference on the validation and test sets that are selected in the beginning of the process and kept until it finishes.  
The choice of AL technique to be applied depends on the type of application. Generally, there are three main scenarios in AL, as discussed in [11; 35]:  
* Membership query synthesis: this scenario involves generating new unlabelled instances to be queried. The AL algorithm assesses the usefulness of creating new images that need to be labelled and added to the annotated image dataset.
* Stream-based selective sampling: in this scenario, unlabelled instances are observed one at a time, and query selection is performed sequentially. The algorithm determines whether each instance should be selected for labelling or not.
* Pool-based sampling: this scenario involves generating a query by evaluating the entire \"pool\" of unlabelled instances. Since deep neural network training is typically batch-based, there is a need for Batch Active Learning (BAL) techniques. BAL selects multiple instances from the pool to be labelled [9; 36; 37]. In [8], it is also referred to as Batch Mode Deep Active Learning (BMDAL), which is considered one of the most efficient strategies for OD.  
The pool-based scenario assumes three pools of images or patches: (i) unlabelled (\(U_{0}\)), (ii) selected (\(S_{0}\)), and (iii) labelled (\(L_{0}\)). On each iteration \(I_{(0..K)}\), a set of images \(b\) from \(U_{i}\) is included in \(S_{i}\) according to an acquisition function \(f\), and the images in \(S_{i}\) are labelled by the external oracle and included into \(L_{(i+1)}\). Also, models are trained using the \(L_{i}\) set of images and producing AI models \(M_{i}\) that can be used as part of the acquisition function \(f(U_{0},M_{i})\). At the end of each iteration [35]:  
\[L_{(i+1)}=S_{i}\cup L_{i} \tag{1}\]  
\[U_{(i+1)}=U_{i}-S_{i}. \tag{2}\]  
The process ends when a desired performance is achieved, or the annotation budget is spent. Pool-based sampling strategies are very time-consuming since they require the evaluation and ranking of all the instances of unlabelled data in order to select the top \(N\) samples for model training [38]. To reduce the processing time needed to rank all the instances, some methods apply a sub-pooling strategy [39].
## 5 System Implementation and Field Testing
### Implementation Details
#### 5.1.4 Central Subsystem  
The central subsystem was implemented using Visual Studio C#, hosted on a desktop PC running Windows. Microsoft SQL Server served as the system database on the same desktop PC.  
As for the communication between the subsystems, the mobile app subsystem may communicate with the central subsystem via Wi-Fi or 3G/4G communications systems through the Internet, and the detecting/counting subsystem may communicate with the central subsystem via Ethernet or Wi-Fi systems.
## 3 Results and Discussion
### Determination of the Crack Initiation Criterion Based on Strain  
The research results described in Section 3.1 show that the frost-heave propagation of cracks can be studied by measuring the strain at the crack tip. In this section, theoretical analysis of crack growth based on this strain is carried out. The crack propagation of a specimen after the experiment is shown in Figure 9a. From this figure, it can be seen that the initial crack extends vertically, which conforms to the characteristics of a mode I crack propagation. Figure 9b illustrates the full field of the surface strain distribution by means of the DIC technique. From this figure, it can be seen that the range of main tensile strain on the surface of the concrete specimens is distributed along the crack tip. The AE localization event on the surface of the specimen is shown in Figure 9c. It can be seen that AE localization event is densely distributed along the initial crack direction. For more detail see Section 3.1.2. Comparing Figure 9a-c, it is found that the process of frost-heave crack propagation can be determined according to the principal tensile strain, which is of profound significance for crack prevention and the monitoring of concrete structures. As concrete is a typical quasi-brittle material, the propagation progress of cracks can be subdivided into micro-crack initiation, stable propagation, and unstable propagation [38; 39]. An accurate and applicable crack propagation criterion is essential in the analysis of concrete fracture processes. Most of the existing research uses parameters such as cracking toughness, maximum tensile stress, and stress intensity factor at the crack tip to determine the crack propagation state [40; 41; 42; 43]. The value of the stress-intensity factor at the crack tip depends not only on the magnitude of the external load on the crack, but also on the magnitude of crack cohesion. Additionally, initiation toughness is also controlled by the crack initiation load and the equivalent length of the crack. The value of the frost-heave force measured by different test methods differs greatly [18; 19]. Therefore, the application of the existing theory of the analysis of frost-heave crack propagation process has certain deficiencies. Therefore, there is an urgent need to establish a strain model in order to evaluate the crack-propagation state under frost heave.
# Buffeting Response Prediction of Long-Span Bridges Based on Different Wind Tunnel Test Techniques
## 3 Test Preparation
### Turbulence Field Simulated  
The taut strip model test was conducted in a large-scale wind tunnel, and passive simulation devices such as spires, baffles and rough elements were used to simulate the turbulent flow field, as shown in Figure 4.  
Because the turbulence integral scale simulated in the spire turbulence field is usually larger than that in the grid turbulence field, the test identification of the aerodynamic admittance and integrated transfer function of the structure were conducted in the simulated spire turbulence field, which can increase the ratio of the turbulence integral scale to the model width in order to minimize the influence of the turbulent three-dimensional effect. It can also simulate the shear layer characteristics, which is more convenient for analysis and comparison with the taut strip model test results. Therefore, the sectional model test was also conducted in a spire turbulence field, as shown in Figure 5.  
Figure 4: Spire turbulence field installed in XNJD-3 wind tunnel.  
To study the buffeting response under the turbulence field, it is necessary to first measure the flow field quality to ensure the accuracy of the study. It should be noted that, according to the buffeting theory of long-span bridges, it is not difficult to find that the fluctuating components of the turbulence field along the transverse wind direction do not contribute to the calculation of the buffeting response, so the transverse turbulent fluctuating characteristics were not repeated here. Among many turbulent fluctuating wind velocity spectrum models, the von Karman spectrum [41], which is widely used in aviation and structural wind engineering, is more suitable for describing atmospheric turbulence higher above the ground and the turbulent characteristics simulated in a wind tunnel [42; 43]. To ensure the quality of the simulated turbulence field in the test, it is usually fitted to the von Karman spectrum. Figures 6 and 7 show the longitudinal and vertical fluctuating wind velocity spectrum of the above turbulence fields, and the von Karman spectrum was used to fit the results. The results show that the simulated fluctuating wind velocity spectra fit well with the von Karman spectrum, and met the test requirements.  
In addition, Table 1 gives the other two important parameters of the turbulence field: turbulence intensity and turbulence integral scale. In Table 1, \(I\) and \(L\) represent the turbulence intensity and turbulence integral scale, respectively, and the subscripts \(u\) and \(w\) represent the longitudinal and vertical directions of the turbulence field, respectively.  
Figure 5: Spire turbulence field installed in XNJD-1 wind tunnel.  
Figure 6: Longitudinal and vertical turbulence spectra in XNJD-1 spire turbulence field. (**a**) Longitudinal; (**b**) vertical.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 2 Related Works
### Exemplar Transformer  
Exemplar Transformer [17] is a Transformer structure based on a single-instance-level attention layer. Despite the success of Swin Transformer in reducing the computational complexity of attention during the feature extraction stage by dividing the window, there is still room for improvement in the efficiency of self-attention computation during the feature fusion stage. The Exemplar Transformer points out that in natural language processing, each feature represents a specific word or token, during self-attention computation, it is necessary to merge all features. However, in visual tasks, adjacent spatial locations often correspond to the same object. When tracking a single object, the attention computation process can be optimized. exemplar attention achieves this by spatial aggregation and compression, utilizing globally generated query tokens from the input image to focus attention on a single instance. It eliminates the need for a similarity function within thesample, providing a unique perspective on spatial relationships in visual tasks. Exemplar Transformer reduces the complexity of self-attention, improves computational efficiency, and has been applied in E.T.Track, yielding significant results.
# Improving RE-SWOT Analysis with Sentiment Classification: A Case Study of Travel Agencies
## 5 Discussion, Implications, and Conclusions
### Discussion
With such thoughts, this study aimed to propose a way to cope with a lack of user ratings when using RE-SWOT. Sentiment classification is a process of automatically recognizing opinions in text and determining their emotional polarity according to the emotions expressed by users. To a certain extent, user ratings express the emotional polarity of comments. Therefore, this study employed sentiment classification to generate sentiment polarity when user ratings are not available. The sentiment classification tool used in this study was VADER, because it is especially suitable for emotions expressed in social media. Given a sentence, VADER gives scores in four categories: positive, negative, neutral, and compound. In order to better adapt to the initial RE-SWOT method, the VADER scores were transformed into number on a scale of \(-2\) to \(2\). Consequently, we were able to smoothly incorporate sentiment classification into the RE-SWOT method without changing the original procedures. To sum up, RE-SWOT provides an easy way to automatically generate a SWOT matrix, and the results of this research help to enable RE-SWOT to be used for any kind of product. Traditionally, SWOT analysis required interviews to be conducted with stakeholders and market research to be performed to gather necessary information to generate a SWOT matrix. With the advent of social media, more and more users are expressing their opinions online. Online user reviews provide valuable information that can be used for SWOT analysis. With sentiment classification, SWOT analysis can be conducted for any product with online user reviews by means of RE-SWOT. Using the proposed method, we conducted SWOT analysis for OTAs with data collected from the TripAdvisor forum. Even though the forum lacks user ratings, the experimental results show that the proposed method was able to generate a SWOT matrix successfully.
# Experimental Study on Microstructure of Unsaturated Expansive Soil Improved by MICP Method
## 2 SWCC Test Materials and Methods
### Sample Preparation of MICP Improved Expansive Soil
#### 2.2.2 Preparation of Cementation Liquid  
The cementation liquid is a source of calcium for the MICP process and provides the raw material and hydrolytic environment for the metabolic processes of the microorganisms. Based on the chemical reaction equation for the MICP process, it can be calculated that for every molecule of calcium carbonate produced, 1 molecule of calcium chloride and 1 molecule of urea are required [28]. Therefore, the ratio of calcium chloride to urea in the cementation liquid was determined to be 1:1 and the concentration of the cementation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\leq\)**0.005** & **(0.005,0.075)** & **(0.075,0.025)** & **(0.025,0.5]** & **(0.5,2]** & **(2,20)** \\ \hline
15.4 & 62.9 & 7.1 & 6.9 & 5.2 & 2.6 \\ \hline \end{tabular}
\end{table}
Table 1: Particle size distribution of expansive soil.  
Figure 1: _Bacillus pasteurii_ used in testing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Liquid** & **Plastic Limit** & **Plastic Index** & **Natural** & **Optimal** & **Maximum Dry** \\
**Limit (\%)** & **(\%)** & **(\%)** & **Moisture** & **Content (\%)** & **Content (\%)** & **Density (\%)** \\ \hline
58.1 & 22.3 & 35.8 & 21.8 & 23.3 & 1.75 \\ \hline \end{tabular}
\end{table}
Table 2: Indicators of physical property of expansive soil.  
liquid was 1 mol/L. Each 100 mL of cementation liquid contained 21.91 g of \(CaCl_{2}\)-6\(H_{2}O\) and contained 6.01 g of urea.
## 3 Results and Discussion  
### Visual Observation of the Composite PCM  
Figure 1 shows photos of the sintered composite PCM samples with different formulations (Table 2). Samples S1 to S3 (with less than 60 wt.% salt) present good shape stability after sintering with no observable defects. No PCM leakage was observed from these samples during the solid-liquid phase transition. Some cracks can be observed on Sample S5, which has the highest percentage of salt (80 wt.%). Deformation may be observed on Sample S4 after sintering, which contains 70 wt.% salt. Therefore, Sample S3, with 60 wt.% of salt, was taken as the optimal formulation, having the highest amount of PCM without any deformation or leakage after sintering.  
To enhance the charging and discharging rates, SiC particles were added to the formulation to increase the overall thermal conductivity of the PCM composites. Such an additive is chemically stable at high temperatures (up to \(\sim\)2000 \({}^{\circ}\)C [35]) and has a thermal conductivity as high as 270 Wm\({}^{-1}\)K\({}^{-1}\)[33; 34]. Carbon materials such as graphite have been used as a thermal conductivity enhancer in the past but they are chemically unstable at high temperatures [5]. Samples with different percentages of SiC were prepared using 60% salt as the base formulation; see Sample S6 to Sample S10 in Figure 1. Little effect on the shape stability of the samples can be seen due to the addition of SiC.  
Figure 1: Visual observations of composite PCMs after sintering.