esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Comparative Analysis  
Our comparative analysis of FID scores distinctly highlighted the superiority of our method over current state-of-the-art approaches in the field, as summarized in Table 3. Our technique achieved a remarkably low FID score of 29.36, significantly better than the scores reported in other notable studies. For instance, [PERSON] [35] reported an FID score of 111.33 with only three categories, and [PERSON] [2] achieved a score of 95.20 across five categories. [PERSON] had two studies listed; one achieved an FID score of 69.50 with two categories [34], and another had an improved FID score of 37.04 with nine categories [37]. Additionally, [PERSON]'s work [36] also reported an FID score of 69.50 for two categories. The notable reduction in our FID score, despite the increased complexity of handling 35 categories, underscores our method's effectiveness in producing high-quality images that more closely resemble real X-ray images. This result not only validates our innovative approach but also establishes a new benchmark for fidelity in image synthesis, facilitating the progression of future developments in this field.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 3 Interleaver
### Proposed Interleaver
#### 3.7.4 Correlation Criterion  
To decrease the correlation between the information from the first encoder and the information at the input of the second decoder, a circular displacement is applied at the output of the interleaver. Let \(j_{1}\) and \(j_{2}\) be the information data, and \(\pi(j_{1})\) and \(\pi(j_{2})\) be the locations obtained after interleaving so that the interleaver can ensure that if \(|\;j_{1}-j_{2}|<S\) then \(|\pi(j_{1})-\pi(j_{2})|>\) S. However, some input data may be addressed in the output vector, and the index mapping \(j\rightarrow\pi(j)\) and \(j=\pi(j)\) could occur. This could be a valid mapping for the interleaver. However, this type of mapping worsens the performance of the iterative decoding of turbo codes [40].  
The minimum distance is defined as  
\[D_{min}=|j-\pi(j)|_{min} \tag{25}\]  
for a higher value \(D_{min}\) the correlation between the extrinsic information of the decoders decreases. If the circular travel distance \(d\) is applied, which maximizes \(D_{min}\) when a circular displacement is made, it is described by  
\[0<d<\frac{S_{min}}{2} \tag{26}\]  
The value \(d\) that maximizes \(D_{min}\) is calculated as  
\[d=\frac{S_{min}}{2}-1 \tag{27}\]  
and the attainable values are  
\[D_{min}=S_{min}/2 \tag{28}\]
# A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments
## 4 Results
### Range Errors under Different Environments  
GNSS measurements under environments such as urban canyons, overpasses and vegetation usually suffer from poor signal reception, low gain, large multipath errors, and noises, which result in variation and increases in range errors. To investigate this issue, the range errors under three scenarios are analyzed in this subsection to fathom the impact of the environment on it.  
As shown in Figure 8, three typical epochs are selected for detailed analysis: (a) in a parking lot, and can be regarded as an open sky area; (b) in a suburban area, where the  
Figure 7: Error bars of range errors changing with SNR (left) and elevation angles (right) for smartphone (**a**) and ZIM2 (**b**).  
car was driving close to tall buildings; (c) in a vegetated area, where the car was close to trees on one side of the road. The sky plots on the right show the corresponding epoch, the second of the day (SoD), and range errors for different satellites are presented by different colors and sized dots (the larger the dots, the larger the range errors). It can be seen from Figure 8 that, owing to nearby buildings and trees limiting sky visibility, GNSS signals were blocked significantly, reducing satellite visibility. As expected, the range errors are at a low level when driving in the parking lot, except for satellite R17, whose elevation angle is under 5\({}^{\circ}\). In contrast, range errors increase significantly when the vehicle was in the suburban area, even for signal azimuths without building blockage. While driving in the vegetation area, it seems that the signal blockage by the tree has limited impacts on the range errors of other tracked satellites.  
To better understand the range errors under different environments, the whole trajectory of the smartphone data are classified into three scenarios, namely open sky, vegetation, and sub-urban, Table 2 summarizes the overall statistics of the range errors and the 95 th per denotes the range errors of exact the 95 th percentile. It can be noted that, in the sub-urban environment, the mean values of range errors and corresponding STDs for P1 and P5 are significantly larger than that in open sky and vegetation, which is expected because of the signal blockage and higher-level multipath in the sub-urban. Under the vegetation environment, the range errors are slightly larger than those in the open sky and are consistent with  
Figure 8: Sky plot of satellite range errors under (**a**) open sky, (**b**) sub-urban, and (**c**) vegetated environments.  
the analysis above, for which the vegetation does not have a significant impact on range errors in comparison to sub-urban.
## 4 Proposed Protocol
### Establishing the Feasible Routing Set  
The hierarchical network structure \(\mathcal{T}(\mathcal{V}_{T},\mathcal{E}_{T})\) is the basis of establishing the feasible routing set for each sensor node. Each feasible routing set is composed of a great number of valid routes that can be constructed by adding sensor nodes, which could participate in routes layer by layer until the BS \(v_{0}\) is added. Next, we give an example that displays this process from the transmitter \(v_{i}\) to the BS \(v_{0}\). Assume that \(v_{i}\in\mathcal{V}_{k(i)}\) and \(k(i)\) is the hierarchical number of \(v_{i}\) in \(\mathcal{T}(\mathcal{V}_{T},\mathcal{E}_{T})\) and is determined by the location of \(v_{i}\) regarding \(v_{0}\). In order to establish a feasible routing set \(\mathcal{R}_{i}\) for the transmitter \(v_{i}\), we first build a hierarchical network structure \(\mathcal{T}_{i}(\mathcal{V}_{T}^{(i)},\mathcal{E}_{T}^{(i)})\) on the basis of \(\mathcal{T}(\mathcal{V}_{T},\mathcal{E}_{T})\). The hierarchical network structure \(\mathcal{T}_{i}(\mathcal{V}_{T}^{(i)},\mathcal{E}_{T}^{(i)})\) for the transmitter \(v_{i}\) is similar to \(\mathcal{T}(\mathcal{V}_{T},\mathcal{E}_{T})\) and is denoted as  
\[\left\{\begin{array}{l}\mathcal{V}_{1}^{(i)}=\{v_{0}\}\\ \cdots\cdots\\ \mathcal{V}_{j}^{(i)}=\left\{v_{p}\in\mathcal{V}_{T}^{(i)}|v_{q}\in\mathcal{ V}_{j-1}^{(i)},m_{pq}=1,\;e_{pq}\in\mathcal{E}_{T}^{(i)}\right\},\\ \cdots\cdots\\ \mathcal{V}_{k(i)}^{(i)}=\{v_{i}\}\end{array}\right. \tag{7}\]  
where \(\mathcal{V}_{j}^{(i)}\subset\mathcal{V}_{j}\), \(\mathcal{V}_{T}^{(i)}=\mathcal{V}_{1}^{(i)}\cup\cdots\cup\mathcal{V}_{j}^{(i) }\cup\cdots\cup\mathcal{V}_{k(i)}^{(i)}\), and \(\mathcal{E}_{T}^{(i)}\subset\mathcal{E}_{T}\).  
The hierarchical network structure \(\mathcal{T}_{i}(\mathcal{V}_{T}^{(i)}\mathcal{E}_{T}^{(i)})\) is the optimal network structure for \(v_{i}\), as well as the foundation for establishing the feasible routing set \(\mathcal{R}_{i}\). Each time we select one sensor node from the sensor node subset \(\mathcal{V}_{j}^{(i)}\), we also perform the same operation for all other layers of \(\mathcal{V}_{T}^{(i)}\). These selected sensor nodes will construct a valid route from the transmitter \(v_{i}\) to the BS \(v_{0}\), i.e., the \(p\)th valid route of the feasible routing set \(\mathcal{R}_{i}\), denoted by  
\[r_{p}^{(i)}=\left[v_{(p,k(i))}^{(i)}\rightarrow\cdots\rightarrow v_{(p,f)}^{(i )}\rightarrow\cdots\rightarrow v_{(p,2)}^{(i)}\rightarrow v_{(p,1)}^{(i)}\right] \tag{8}\]where \(v_{(p,j)}^{(i)}\in\mathcal{V}_{j}^{(i)}\).  
To unify formation, let \(v_{(p,k(i))}^{(i)}\) represent \(v_{i}\) and \(v_{(p,1)}^{(i)}\) represent \(v_{0}\). This process repeats, and all valid routes such as \(r_{p}^{(i)}\) establish the feasible routing set \(\mathcal{R}_{i}\) that is shown as  
(9)  
where \(N_{i}=\left|\mathcal{V}_{1}^{(i)}\right|\times\cdots\times\left|\mathcal{V}_{ j}^{(i)}\right|\times\cdots\times\left|\mathcal{V}_{k(i)}^{(i)}\right|\) denotes the number of valid routes in \(\mathcal{R}_{i}\).
## 2 Materials and Methods
### Swab Collection and ATP Bioluminescence Assessment  
The dental chair light and the operator's face shield were the surfaces analyzed. The surfaces were cleaned with 70% alcohol disposable gauze just before the dental procedure. The amount of viable splatter produced during the procedure was assessed by swabbing standardized 5 cm \(\times\) 10 cm areas of the aforementioned surfaces with sterile cotton swabs (Copan Diagnostic, Murrieta, CA, USA) before and after treatment.  
The sole difference between the control and the D-DART group was the static device placed during a dental hygiene four-hands procedure. For the swab analysis, the investigator was blinded to the study groups' allocation. The study's primary outcome was a comparison of the total relative light unit (RLU) counts in two items in the dental environment: the dentist's face shield and the dental chair light. Each swab was placed in a sterile 15-mL tube containing 1 mL sterile PBS and subjected to vigorous agitation for 5 min to elute cells from the swabs. The eluates were subsequently subjected to adenosine triphosphate (ATP) bioluminescence assays.  
The ATP assay was used to evaluate the spread of potential contaminants; ATP bioluminescence was measured using the BacTiter-Glo ATP detection reagent (Promega, Madison, WI, USA). An equal volume of the ATP reagent was added to eluates from the cotton swabs and incubated at room temperature for 5 min. The amount of luminescence was determined using a GloMax 20/20 luminometer (Promega, Madison, WI, USA). Contaminating ATP levels, expressed in relative light units (RLUs), were recorded.
# Evaluating the Effectiveness of Designs for Low-Cost Digital Manufacturing Systems
## 6 Discussion
### Comparison with Existing Approaches for Evaluating Architectural Designs  
This study has performed a structured implementation comparison to quantify the effectiveness of designs for low-cost digital systems that are directly based on reference architectures. The effectiveness is evaluated in terms of the effort required to design a low-cost digital system. Compared to the studies that focus on a feature comparison and mapping and alignment of reference architectures [14; 40; 41; 42; 43], this study concentrates on the designs as a result of adopting a reference architecture and analyses their effectiveness based on a set of measures and metrics. In contrast to [PERSON] [44], we focus on reference architectures and develop metrics which are relevant to the chosen design approach but independent from a particular system design. Similar to [PERSON] [45], this study relies on the LOCs metric to assess the design effort of architectural designs. Compared to [PERSON] and [PERSON] [46], the evaluation conducted in this study is centred on the design and does not capture the performance of resulting digital systems. Contrary to [PERSON] et al. [47], this study does not analyse the coupling or cohesion since these metrics can only be applied to software systems and the types of systems considered in this study also consist of hardware components. Finally, similar to [PERSON] et al. [48], we count the number of interfaces and interactions among components in this study for assessing the system design. It is noteworthy to mention that the evaluation approach does not necessarily need to involve metrics being applied to the final design. For example, an alternative approach may involve applying the metrics before and during the design process of a digital system to facilitate making decisions early in the design process [56].
## 4 Discussion
### Comparisons of Fusion Results with Different Methods  
We evaluated the proposed fusion method using ten image pairs from the TNO and M3 FD datasets. For comparison, we selected eight state-of-the-art techniques: six traditional methods (CBF [48], DRTV [49], MLGCF [50], MSID [12], PM [51], and RGFTV [17]) and two deep learning-based approaches (DenseFuse [32] and SeAFusion [17]). All methods were implemented according to their original papers. The experiments were conducted on a desktop equipped with a 3.6 GHz Intel Core CPU and 64 GB of RAM. To objectively assess performance, we employed eight evaluation metrics: VIF [52], SF [53], AG [54], SD [55], CC [56], PSNR [57], EN [58], and QABF [59]. These metrics cover both reference and non-reference indices, providing a comprehensive assessment of information content, human perception, and image characteristics.
## 2 Experimental Programme
### Test Method and DIC Measurement  
In this study, in order to measure static in-plane tensile residual strengths of the GFRP laminates after impact, tension after impact tests of the impacted tensile specimens were carried out using a servo-hydraulic machine (MTS 810, MTS Systems, MN, USA) with a 100-kN load capacity. Figure 1a represents the geometry of the specimen. Tensile loads were imposed on the specimen in displacement control at a rate of 1 mm/min. by clamping both end tabs of the specimen with hydraulic wedge grips.  
Figure 1: Geometry and dimension of (**a**) tensile specimen, (**b**) pyramidal impactor and (**c**) hemispherical impactor.  
Before performing the tensile tests, load train alignment was adjusted with the maximum bending strain of less than 50 \(\mu\varepsilon\). During tensile loading, the full-field in-plane strain distribution around the impact indent was measured using DIC (Digital Image Correlation) technique. In addition, tensile properties of the non-impacted UD and TRI GFRP laminates were measured according to ASTM D3039M and ISO 527-4, and those are presented in Table 1. Here, the tensile strain was obtained from the strain gages attached on the front and back face of the specimen. These tensile strength values were used in determining reduction in the residual strength due to impact damage  
The DIC measurement was carried out with a commercial 3-dimensional DIC system (GOM, ARAMIS 5M) which was composed of two Charge Coupled Device (CCD) cameras, equipped with 35 mm Schneider lenses, with a resolution of \(2448\times 2050\). In this study, the angle between these two cameras was setup as 25\({}^{\circ}\), and the deformed surface images of the impacted laminate specimen were captured at a rate of 1 frame/s. The camera was calibrated with a 2.0 mm spaced black dot grid, which provided a resolution of 14.1 \(\mu\)m/pixel. Figure 2 shows the DIC system configuration for this measurement. In this DIC measurement, high contrast speckle pattern of white background and black dots was directly applied on the impacted surface of the specimen. The image was analyzed with the facet size and step size of 24 and 19 pixels, respectively. Here, the spatial resolution was 0.272 mm.
# Adaptive Relay Selection Scheme by Using Compound Channel
###### Abstract  
As a cellular network of the fifth generation (5G) is commercialized, mobile devices and data throughput have rapidly increased. According to the spatial density for communication increases, the nodes of cell are overloaded. Therefore, the heterogeneous ultra dense network (UDN) is suggested. Furthermore, the techniques for selecting a relay have been proposed in the Heterogeneous Net (HetNet). The relays are needed to improve communication performance and mitigate overload of nodes. In this paper, an adaptive relay selection scheme is proposed to obtain the diversity gain from multiple relays. To enhance the reliability of communication, the proposed scheme suggests a new algorithm considering outage probability and diversity gain of compound channel. Furthermore, the selected relays use an antenna selection algorithm to improve the channel capacity. Simulation results show that the proposed scheme improves the bit error rate (BER) and the data throughput.  
relay selection; cooperative systems; MIMO; outage probability; compound channel; antenna selection +
Footnote †: journal: Applied Sciences
# An Optical Differential Method for Underwater Wireless Communication in Turbid Environments
## 1 Introduction
modeled the effect of turbulence changes caused by ocean salinity and temperature on BER [13]. Based on the channel estimation and error correction technique of a multiple-input, multiple-output orthogonal frequency division multiplexer, [PERSON] et al. simulated the channel capacity, BER, signal-to-noise ratio, data rate, received power, and mean square error under different water quality conditions, including pure seawater, seawater, nearshore seawater, and turbid water, in the MATLAB environment, and verified the results [14]. [PERSON] et al. investigated the relationship between the scintillation index and ocean depth and anisotropic inclination and showed that the scintillation index is strongly dependent on these two parameters [15]. Despite progressive advancements achieved in terms of, e.g., longer distances, higher signal-to-noise ratios, and lower BERs, in recent years, current underwater optical communications have mostly relied on the techniques of intensity modulation and direct detection, which largely resort to more powerful light sources on the transmitting side and more sensitive detectors on the receiving side. However, increasing the transmission power undoubtedly increases system energy consumption, and increasing receiver sensitivity raises system costs. Also, such a technique almost reaches the system's physical limits. For example, water on the transmitting side starts boiling if the laser power increases to a critical value (several Ws) [16]. In this study, we propose a new method to further extend the communication distance in turbid underwater environments. Similar to the concept of \"differential signals\" in electronics, instead of detecting individual intensity, the intensity ratio between different transmitting beams is our target. For this reason, this method is termed an \"optical differential communications method\" (ODCM). We will introduce the theoretical background and experimental setup and demonstrate the validation of the ODCM in the following sections. For simplicity, the turbulence and communication rate limit will not be considered for the present proof-of-concept demonstrations.
## 2 Geometric Error of Five-axis CNC Machine Tool
### Establishment of Geometric Error Model of Rotary Axis
\begin{array}{l}p^{\prime}=R(-c)\cdot\left[\begin{array}{cc}f_{x}(x,y,z,a) &f_{y}(x,y,z,a)&f_{z}(x,y,z,a)&1\end{array}\right]^{T}\\ v^{\prime}=R(-c)\cdot\left[\begin{array}{cc}f_{i}(a)&f_{j}(a)&f_{k}(a)&1 \end{array}\right]^{T}\end{array}  \tag{9}\]  
For position \(p^{\prime}=(X^{\prime},Y^{\prime},Z^{\prime},1)^{T}\) and posture \(v^{\prime}=(I^{\prime},I^{\prime},K^{\prime},0)^{T}\), we obtained Equations (9) and (10) as:  
\[\left\{ \begin{array}{l}X^{\prime}=\cos c\cdot f_{x}(x,y,z,a)+\sin c\cdot f_{ y}(x,y,z,a)\\ Y^{\prime}=-\sin c\cdot f_{x}(x,y,z,a)+\cos c\cdot f_{y}(x,y,z,a)\\ Z^{\prime}=f_{z}(x,y,z,a)\\ I^{\prime}=\cos c\cdot f_{i}(a)+\sin c\cdot f_{j}(a)\\ J^{\prime}=-\sin c\cdot f_{i}(a)+\cos c\cdot f_{j}(a)\\ K^{\prime}=f_{k}(a)\end{array} \right. \tag{10}\]  
\[\left\{ \begin{array}{l}f_{x}(x,y,z,a)=x+y\cdot\varepsilon_{yA}-z\cdot \varepsilon_{BA}-\delta_{xA}+\varepsilon_{yC}\cdot(y\cos a+z\sin a)+ \varepsilon_{pC}\cdot(y\sin a-z\cos a)-\delta_{xC}\\ f_{y}(x,y,z,a)=-x\cdot\varepsilon_{yC}+(y-x\cdot\varepsilon_{yA}+z\cdot \varepsilon_{aA}-\delta_{yA}+z\cdot\varepsilon_{aC})\cdot\cos a+(z+x\cdot \varepsilon_{BA}\\ -y\cdot\varepsilon_{aA}-\delta_{xA}-\delta_{xA}-\delta_{yC}\\ f_{z}(x,y,z,a)=x\cdot\varepsilon_{pC}+(x\cdot\varepsilon_{yA}-z\cdot \varepsilon_{aC}-y-z\cdot\varepsilon_{aA}+\delta_{yA})\cdot\sin a+(x\cdot \varepsilon_{BA}-y\cdot\varepsilon_{aC}\\ -y\cdot\varepsilon_{aA}+z-\delta_{xA})\cdot\cos a-\delta_{xC}\\ f_{i}(a)=-\varepsilon_{BA}+\sin a\cdot\varepsilon_{YC}-\cos a\cdot \varepsilon_{pC}\\ f_{j}(a)=\cos a\cdot\varepsilon_{aA}+\sin a+\cos a\cdot\varepsilon_{aC}\\ f_{k}(a)=-\sin a\cdot\varepsilon_{aC}-\sin a\cdot\varepsilon_{aA}+\cos a \end{array} \right. \tag{11}\]  
Equations (9) and (10) were mathematical expressions of the tool tip position and tool posture obtained by homogeneous coordinate transformations after introducing the position error parameters of the rotary axis. When the position error parameters of two rotary axes tended to zero, error Equations (9) and (10) became equal to Equation (5) of the position and posture of the tool in an ideal state. Spatial geometric errors of machine tools were obtained by subtracting the theoretical position and posture of the tool from its actual position and posture, as stated in Equation (11):  
\[\left\{ \begin{array}{l}E_{p}=p^{\prime}-p\\ E_{v}=v^{\prime}-v\end{array} \right. \tag{12}\]  
where \(E_{p}\) denotes spatial position error of tool tip and \(E_{v}\) denotes spatial posture error of tool. The comprehensive error of space point \(\Delta E\) was obtained as Equation (12):  
\[\Delta E=\sqrt{(X^{\prime}-X)^{2}+(Y^{\prime}-Y)^{2}+(Z^{\prime}-Z)^{2}} \tag{13}\]
# Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation
## 3 Results
### Data Transmission
#### 3.1.2 Quality Evaluation Indexes of Signal Recovery  
Besides SNR, we introduced scale-invariant signal-to-noise ratio (SISNR), normalized correlation coefficient (NCC), bit error rate (BER) and echo return loss enhancement (ERLE), to more sufficiently assess the applicability of the LMS adaptive filter and DPRNN model for echo cancellation. SISNR might remodify the clean signal using a scaling factor, ensuring that the distorted portion and the clean signal vector are orthogonal. This index eliminates substantial distortion resulting from the SNR when the generated and true signals hold parallel directions and different amplitudes but share similar waveforms. SISNR is formulated as below:  
\[SISNR=10\log_{10}\frac{||x_{target}||^{2}}{||e_{noise}||^{2}} \tag{15}\]  
\[x_{target}=\frac{(\hat{x},x)x}{||x||^{2}}e_{noise}=\hat{x}-x_{target} \tag{16}\]  
where, \(x_{target}\) denotes the norm of the clean signal, and the \(e_{noise}\) represents the norm of the difference between the clean signal and the estimated signal.  
NCC is a parameter to quantify the overall waveform similarity before and after signal denoising, which is expressed as follows:  
\[NCC=\frac{\sum_{n=1}^{N}A_{s}(n)A_{d}(n)}{\sqrt{(\sum_{n=1}^{N}A_{s}(n))((\sum _{n=1}^{N}A_{d}(n)))}} \tag{17}\]  
where \(A_{s}(n)\) is the reference signal and \(A_{d}(n)\) is the separated signal. In this study, a higher NCC means a better efficiency of echo cancellation.  
BER is the probability of code transmission error and is calculated as:  
\[P_{e}=\frac{1}{4}erfc\bigg{(}\sqrt{\frac{r}{4}}\bigg{)}+\frac{1}{2}e^{-\frac{r }{4}} \tag{18}\]  
where \(r=20\lg(V_{s}/V_{n})\), \(V_{n}\) is the amplitude of received signals, and \(V_{n}\) represents the amplitude of the noise in received signals.  
ERLE is a metric used to evaluate the performance of echo cancellation in acoustic systems, indicating the degree of improvement in the audio signal after echo elimination. ERLE is defined as below:  
\[ERLE=10\log_{10}\bigg{(}\frac{E\{y^{2}(n)\}}{E\{s^{2}(n)\}}\bigg{)} \tag{19}\]  
In the equation, \(y(n)\) represents the desired signal and \(s(n)\) represents the estimated signal. A higher ERLE value indicates a better echo cancellation.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline Channel Length/mm & 30 & 40 & 50 & 50 & 50 & 50 & 60 \\ Offset Distance/mm & 0 & 0 & 0 & 6 & 12 & 18 & 0 \\ \hline SNR/dB & 12.36 & 10.76 & 13.44 & 9.54 & 7.87 & 4.10 & 8.67 \\ \hline \end{tabular}
\end{table}
Table 1: SNRs of received signals \(S_{R}(t)\) under different channel conditions.