esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 2 System Model  
This section describe a two-stage downlink (DL) communication system, where the transmitter (TX) sends messages to user equipment (UE) through a dedicated relay, as indicated in Figure 3. The TX has a large-scale antenna array (URA or CA) for high spectral-efficient massive access, whereas the UEs and relay station (RS) each have a single antenna. Unlike the CR-NOMA model in Figure 2, where the channel from TX to UEs through RS are theoretical channel models (CBSM), the proposed model replaces the CBSM channel in Figure 2 with GBSM channel model, as shown Figure 3. The strong user equipment (UE\({}_{1}\)) in the proposed system of Figure 3 is near the RS, whereas the weak user equipment (UE\({}_{2}\)) is farther away from the RS.  
Figure 3: Proposed model of the downlink cooperative relay NOMA network with mMIMO.  
The communication link from TX to UEs through RS is modeled using a 3D GBSM channel model with a single bounce and single cluster. In this context, URA and CA is considered as the transmitters. The proposed system uses two-time slots to achieve a seamless transmission from the TX to UEs through the RS. During the initial time slot, the TX sends signals to the relays using superposition coding. The combined signal (\(\mathbf{x}\)) of the two UEs with superposition coding is given by  
\[\mathbf{x}=\sum_{i=1}^{2}\sqrt{\alpha_{i}P_{\mathrm{s}}}s_{i} \tag{1}\]  
where the \(i\)th user transmits power allocation factor, and the source signal is \(\alpha_{i}\) and \(s_{i}\), respectively. Noticeably, \(\sum_{i=1}^{K}\alpha_{i}=1\leq P_{\mathrm{s}}\) and \(\mathbb{E}\|x_{i}\|\leq P_{\mathrm{s}}\), where \(K=2\) user equipment and total transmit power is given as \(P_{\mathrm{s}}\).  
Using NOMA concepts implies that power allocation factors are arranged according to users' proximity to the RS, that is \(\alpha_{1}<\alpha_{2}\) where \(\alpha_{1}\) and \(\alpha_{2}\) are the power allotment coefficient of the strong or near user equipment (UE\({}_{1}\)) and the weak or far user equipment (UE\({}_{2}\)) respectively. Hence, without loss of generality, the channel gains of the two UEs are arranged as \(\left|\mathbf{h}_{\mathrm{R},1}\right|^{2}\geq\left|\mathbf{h}_{\mathrm{R},2 }\right|^{2}\). We exploit channel reciprocity through a conventional time division duplex mode [98]. We presume that MIMO downlink (DL) pilots with an \(N_{t}\) transmitting antenna allow the TX to estimate the DL channel. Generally, for a single-hop communication involving a transmitter and two users, the received signal (\(y\)) by the \(i\)th user at the destination with DL data transmission can be expressed as,  
\[y_{i}=\mathbf{H}\mathbf{x}+n_{i} \tag{2}\]  
where \(\mathbf{x}\) is an \(N_{t}\times 1\) data signal, \(i=1,2\), \(\mathbf{H}\) is the communication channel matrix such as the 3D channel realization matrix presented in (12) and (15), \(n_{i}\) is the additive Gaussian noise with zero mean and variance, \(\sigma^{2}\).  
The following subsection present the 3D GBSM channel models for CA and URA transmitters.
# Complex Processing of Saponite Waste from a Diamond-Mining Enterprise
## 6 Granulometry Investigation of Saponite Suspension  
According to the technological scheme, the recycled water supply system consists of a concentrating plant, tailing dump, and concentrating mill. Water is reserved after the tailings and fed first to the grinding mill, then to the classifiers. To study the granulometric composition of suspensions, a Horiba LA-950 express universal laser particle size distribution analyzer was used to determine the granulometric composition of the discharge from the classifiers after the saponite ore dressing process (Figure 3).  
According to the granulometry results, the average particle size of saponite sludge is 2.6 \(\upmu\)m. In this case, the deposition rate decreases significantly, and the water flow turbulence dominates the particle movement. Saponite ore particles are highly dispersed and hardly precipitate at all, due to the chaotic motion of part of the water.  
The deposition rate for given particle size is calculated using Equation (1):  
\[w_{\text{oc}}=\frac{d^{2}(\rho-\rho_{\text{c}})g}{18\mu_{\text{c}}} \tag{1}\]  
The deposition rate for the particle size is calculated and equals 5.7 \(\times\) 10\({}^{-8}\) m/s, suggesting that the saponite pulp is close to a colloidal solution with its sedimentation rate and the particles do not settle under the action of gravity in a reasonable amount of time. In laboratory conditions, saponite precipitation without the application of coagulants and flocculants is about 49 mm per day.
## 2 Failure Rate Modeling in Ice Disaster Scenarios  
### Ice Disaster Trajectory Prediction  
To facilitate pre-disaster prevention and control planning, this study predicts the potential trajectory and impact areas of ice disasters based on data from the 2008 severe ice event in Hunan Province [23] and relevant meteorological data, as illustrated in Figure 2. In this figure, the green nodes denote community locations, the black nodes denote general system nodes, and the blue ellipses represent the predicted trajectory and affected regions of the ice disaster. This spatial prediction not only highlights the vulnerable areas within the network but also provides a critical basis for subsequent failure rate modeling and emergency-resource dispatch planning.  
Figure 1: Ice disaster resource scheduling optimization.  
Figure 2: Community network based on ice disaster trajectory prediction.  
As an empirical model, it offers only a first-order approximation, and does not ingest real-time atmospheric updates or provide probability bounds. Future studies will refine this layer by coupling high-resolution numerical weather prediction output with machine learning ensembles so that forecast uncertainty can be quantified and incorporated into dispatch decisions.
## 6 Summary of Results  
Our work provides threefold solution for an efficient and cost effective Toll system.  
* Antenna installment: Proposed two-ray model optimized the signal strength considering the concrete ground and provides the solution of antenna installation at the toll gate. A 15 m antenna height and 45.50 tilt angle established the detection efficiency at a distance of (35-38) m.
* Reader collision: Modified Pulse protocol with modified contention window length ensured fair transmission distribution among the readers and reduced the collision between them. The proposed protocol achieved higher query rates 2509 queries/sec, while minimum four reader are actively working and led to 92% utilization of the system. Further maximum execution time for highest reader 20 is 0.008 s.
* Tag collision: Proposed M-ary trimming query tree algorithm expanded the queries to M element for reducing identification time and removed no-response node or idle node to enhance the throughput. It achieved 98% throughput, considering M = 8 over the conventional method. This way it offers a cost effective, low latency and higher throughput ETC model.
# An Approach of Automatic SPARQL Generation for BIM Data Extraction
###### Abstract  
Generally, building information modelling (BIM) models contain multiple dimensions of building information, including building design data, construction information, and maintenance-related contents, which are related with different engineering stakeholders. Efficient extraction of BIM data is a necessary and vital step for various data analyses and applications, especially in large-scale BIM projects. In order to extract BIM data, multiple query languages have been developed. However, the use of these query languages for data extraction usually requires that engineers have good programming skills, flexibly master query language(s), and fully understand the Industry Foundation Classes (IFC) express schema or the ontology expression of the IFC schema (ifcOWL). These limitations have virtually increased the difficulties of using query language(s) and raised the requirements on engineers' essential knowledge reserves in data extraction. In this paper, we develop a simple method for automatic SPARQL (SPARQL Protocol and RDF Query Language) query generation to implement effective data extraction. Based on the users' data requirements, we match users' requirements with ifcOWL ontology concepts or instances, search the connected relationships among query keywords based on semantic BIM data, and generate the user-desired SPARQL query. We demonstrate through several case studies that our approach is effective and the generated SPARQL queries are accurate.  
Keywords:building information modelling (BIM); ifcOWL; data extraction; semantic; SPARQL generation +
Footnote †: journal: Applied sciences
## 2 Blind Source Separation Techniques
### BSS Model  
The objective of BSS is to extract the source signal from the measured values \(\mathbf{x}(t)\) of \(m\) sensors, which are assumed to be instantaneously mixed with each other. The mathematical expression of this problem is as follows:  
\[\mathbf{x}(t)=\mathbf{A}\mathbf{s}(t)=\sum_{i=1}^{n}a_{i}s_{i}(t). \tag{1}\]  
The objective of BSS is to recover the mixing matrix \(\mathbf{A}\) and the \(n\) sources \(\mathbf{s}(t)=[s_{1}(t),\)\(\dots,\)\(s_{n}(t)]^{\mathrm{T}}\) from the \(m\) measured mixtures \(\mathbf{x}(t)=[x_{1}(t),\)\(\dots,\)\(x_{m}(t)]^{\mathrm{T}}\), \(a_{i}\) is the \(i\)-th column of \(\mathbf{A}\) associated with \(s_{i}(t)\). BSS techniques, such as ICA, SOBI, CP, and GED, impose the condition \(m=n\), where the number of mixtures is equal to the number of sources. This implies that the matrix \(\mathbf{A}\) is square, and a reverse transformation is available.  
\[\mathbf{s}(t)=\mathbf{A}^{-1}\mathbf{x}(t). \tag{2}\]  
This significantly simplifies the BSS problem, because, once \(\mathbf{A}\) is estimated, the source can be easily recovered by Equation (2). Additionally, the overdetermined BSS problem (\(m\)\(>\)\(n\)) can be resolved by transforming it into a square problem for dimensionality reduction through principal component analysis (PCA).  
BSS algorithms assume that the source signals are statistically independent, thus allowing the blind source separation model to be estimated. To achieve this, the CP algorithm searches for the de-mixing matrix \(\mathbf{W}\) such that the components \(\mathbf{y}(t)=[y_{1}(t),\)\(\dots,\)\(y_{n}(t)]^{\mathrm{T}}\) recovered by Equation (3) approximate the simplest sources \(\mathbf{s}(t)\), and \(\mathbf{W}=\mathbf{A}^{-1}\).  
\[\mathbf{y}(t)=\mathbf{W}\mathbf{x}(t) \tag{3}\]  
Furthermore, the GED algorithm can be used to calculate \(\mathbf{A}\) and \(\mathbf{s}(t)\) through the generalized eigen decomposition technique.
# Variable Responses to a Marine Heat Wave in Five Fringing Reefs of Southern Taiwan
## 3 Results
### Benthic Community Changes in Response to the 2020 Marine Heatwave  
Prior to the 2020 bleaching event (April 2020), the coral/algal cover of the four shallow reefs--H] (Figure 3F), HBH (Figure 3G), OL (Figure 3H), and ILI (Figure 3I)--averaged 47/50% (0.9:1; Figure 3K), 37/47% (0.8:1; Figure 3L), 55/43% (1.3:1; Figure 3M), and 43/38% (1.2:1; Figure 3N), respectively (mean coral & algal covers of 3-m reefs of 47 & 44%, respectively; 1.1:1). By the time of the during-bleaching survey (September), \(>\)65% of these corals had bleached (Figure 4A-D), and the algal cover had risen to 54, 48, and 44% at HBH (Figure 3G), OL (Figure 3H), and ILI (Figure 3I), respectively; algal cover remained similar at H] (\(\sim\)47%; Figure 3F). Univariate trends in overall hard coral cover and bleaching over space and time can be found in Table S1; the site x time interaction effects are instead discussed herein.  
Upon resurveying these reefs in December 2020, 53, 80, 60, and 74% decreases in coral cover were documented at HJ, HBH, OL, and ILI, respectively, and the coral covers of these reefs were only 22 (Figure 3F), 7.5 (Figure 3G), 22 (Figure 3H), and 11% (Figure 3I), respectively. Note that at this time, there were still live bleached corals on these four reefs (6, 3, 12, & 17% of total benthic cover, respectively). Over the course of 2021, coral coverremained 36 and 49% lower than in April 2020 at HJ (30% cover in December 2021, of which 2% were still bleached) and HBH (19% in December 2021, of which 1% were still bleached), respectively. In contrast, coral cover nearly reached the pre-bleaching level of 55% at OL (51% in September 2021), and coral cover was actually higher at ILI: 47% in Sept. 2021 (46% in Figure 3I since 1% of these 47% were still bleached.) vs. 44% in Apr. 2020. The 7-m site, ILO, showed a different trend (Figure 3J): healthy coral and algal covers were 36 and 49%, respectively, in April 2020 (0.7:1; Figure 3O), yet only 9% of the coral tissue area was bleached in September 2020 (Figure 4E). Hard coral cover (unbleached corals) actually increased to 38% in September 2020, with algal cover staying relatively consistent (~50%). By the time the heat wave had passed (December 2020), coral cover had risen to nearly 50% at ILO, reaching ~52% by the final survey time (September 2021).
## 3 Technical Background
### Accelerator Coherency Port (ACP)  
In a heterogeneous SoC-FPGA such as Xilinx Zynq, the ACP is defined as a slave interface. It is used by hardware accelerators to access the external memory with optional cache coherency. Figures 4 and 5 show that the ACP is connected to the snoop control unit (SCU) [17] that controls cache coherency between the master interfaces of hardware accelerators, the L1 and L2 cache. From a system point of view, the ACP allows the FPGA to compete with the ARM cores for memory access using the following process:  
* During an ACP write request issued by a master interface, the SCU checks the existence of the targeted data in the different levels of the cache memory. If they are present, the SCU cleans and invalidates the appropriate cache line and sends a request to update the data in memory.
* During an ACP read request, if the data reside in the cache memory, whether they are invalidated or not, the data are returned from the cache memory to the master interface. Otherwise, the data are transmitted directly from the external memory to the master interface.  
In general, a master interface connected to the ACP can read coherent memory directly from the L1 and L2 caches but cannot write directly to the L1 cache. The coherency of an ACP request is controlled using _AxCache[3:0]_ and _AxUser[4:0]_ signals of the ACP that are detailed in the following section.
# An Efficient Interface for the Integration of IoT Devices with Smart Grids
###### Abstract  
The evolution of computing devices and ubiquitous computing has led to the development of the Internet of Things (IoT). Smart Grids (SGs) stand out among the many applications of IoT and comprise several embedded intelligent technologies to improve the reliability and the safety of power grids. SGs use communication protocols for information exchange, such as the Open Smart Grid Protocol (OSGP). However, OSGP does not support the integration with devices compliant with the Constrained Application Protocol (CoAP), a communication protocol used in conventional IoT systems. In this sense, this article presents an efficient software interface that provides integration between OSGP and CoAP. The results obtained demonstrate the effectiveness of the proposed solution, which presents low communication overhead and enables the integration between IoT and SG systems.  
internet of things; smart grids; communication protocol; interoperability; CoAP; OSGP 2020, 2849; doi:10.3390/s20102849
## 5 Experimental Validation  
### Experimental Design  
To verify the effectiveness of the proposed method, test experiments were carried out in an FL-13 acoustic wind tunnel at the China Aerodynamic Research and Development Center (CARDC), and the actual test diagrams are shown in Figure 10. The FL-13 is a large-scale, low-speed wind tunnel with direct current and closed series dual test sections. The test section size is 15 m (length) \(\times\) 8 m (width) \(\times\) 6 m (height).  
A civil aviation standard high-lift configuration CAE-AVM-HL aircraft standard model with a scale of 1:5.6 is placed in the open test section of the airflow for testing, with the aircraft model facing the incoming airflow. Three Bluetooth sound sources with the same frequency response are installed at the bottom of the leading edge of the wing. The two equal-strength speakers on the left are separated by 0.3 m to verify the resolution performance of the sound source imaging. At the same time, the one on the right is the speaker whose sound pressure level is 20 dB lower than that on the left side, which is used to verify the dynamic range performance of sound source imaging. The locations of the three sound sources remain consistent with Section 4 above. At the same time, a large microphone array is arranged 7 m directly below the aircraft standard model. The microphone array is a large circular array with a diameter of 8 m, containing nine rings  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Time (s)** \\ \hline SBL & 1.51 \\ OMP & 0.04 \\ FISTA & 16.84 \\ Off-grid-\(GL_{1}\) & 28.88 \\ RPCA-SBL & 1.71 \\ VG-SBL & 7.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average computation time for different methods at \(f\) = 500 Hz, SNR = 20 dB.  
and a total of 193 array elements, and the microphone distribution is also consistent with that in Section 4 above. The microphones used are GRAS 40 PH type 1/4 inch free-field microphones, which can measure a frequency range from 10 Hz to 20 kHz, dynamic response range from 33 dB (A) to 135 dB, and sensitivity of 50 mV/Pa, which can meet the measurement requirements. A 256-channel dynamic data acquisition system based on NI.PXle-4499 is used for multi-channel data synchronous acquisition.  
Two working conditions are set up for the test, the stationary no-flow condition and the 40 m/s flow condition, and three sound sources simultaneously generate 300 Hz and 500 Hz frequency signals under the two test conditions. The actual sound pressure levels of the loudspeakers at different frequencies are collected separately based on separate microphones, as shown in Table 6. The aerodynamic noise generated by the aircraft model in the presence of flow velocity can be regarded as the background noise in the loudspeaker identification process.
## 2 Materials and Methods
### Data Preprocessing Methods
#### 2.2.2 detect_faults Algorithm  
The detect_faults algorithm (Algorithm 3) is designed to detect potential \"faults\" in a one-dimensional signal. It uses the find_peaks algorithm (see Algorithm 2) to identify peaks in the absolute value of the signal, which are interpreted as indicators of damage.  
```
1:Input:signal--one-dimensional array
2:Input:\(min\_threshold,max\_threshold\)--limits for the prominence parameter
3:Output:Tuple (\(peaks\_\)), where peaks--an array of peak indices
4:\(peaks\leftarrow\) find_peaks( \(|signal|\), \(prominence=(min\_threshold,\ max\_threshold)\)) \(\triangleright\) See Algorithm 2
5:return peaks
```  
**Algorithm 3** detect_faults  
Basic Steps of the Algorithm  
Input data: The algorithm takes as input a one-dimensional array signal and parameters min_threshold and max_threshold, which define the bounds for the prominence parameter when searching for peaks.  
Finding peaks with find_peaks: The algorithm calls the find_peaks function (Algorithm 2) to search for peaks in the absolute value of the input signal \(|\)_signal_\(|\). Parameters prominence=(min_threshold, max_threshold) are passed to find_peaks to specify the criteria for selecting peaks based on their prominence.  
Output: The algorithm detect_faults returns a tuple whose first element is the array peaks. The array peaks contains indices of peaks detected by the find_peaks algorithm, which, in this context, are interpreted as indices of potential faults.  
Remarks  
The detect_faults algorithm relies on the assumption that faults in the signal appear as peaks or abnormal outliers. By using the absolute value of the signal, both positive and negative outliers can be detected. The effectiveness of fault detection depends on the proper selection of the prominence parameters (min_threshold, max_threshold), which must be adjusted according to the signal characteristics and expected types of faults.
## 4 Performance Evaluation
### Simulation Setup and Parameters  
To evaluate the performances of the proposed protocol in terms of average delay, packet delivery ratio and energy consumption, we have carried out simulations under NS3 simulator on Linux workstation. Base Station is located at the origin of the coordinate system. The smart devices are placed from 1 to 100 m from the BS (we place a device every two meters).  
Communication is considered across the IEEE 802.15.4 LWPAN and 2.405 GHz (channel 11). The energy consumption for transmitter and receiver electronics is 50 nJ/bit and for transmitter amplifier is 100 pJ/bit/m\({}^{2}\). The other parameters are assumed to be at their default values as defined in NS3.  
The simulation parameters are presented in Table 5.  
From the experiments, considering an IEEE 802.15.4 M2M device network as case study, the average delay overhead, Packet Delivery Ratio (PDR) and energy overhead consumption of the network, obtained from the simulation are shown in Figures 12 and 13.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Platform & NS3.33/Ubuntu 20.04 \\ \hline Programming & C++ \\ \hline Transmit Power (dBm) & 0 dBm \\ \hline Elec & 50 nJ/bit \\ \hline Eamp & 100 pJ/bit/m\({}^{2}\) \\ \hline Maximum distance & 100 m \\ \hline Number of devices & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameters.  
The simulation results illustrated by the graph in Figure 12 show that the overhead delay increases with the evolution of the distance. In our experimentation, 92 m is the maximum distance covered by the device radio; over longer distances (**d** > 92 m), the packet delivery ratio (PDR) becomes equal to zero. The reason is that the transmission power of 0 db corresponds to a communication range of approximately 87 to 92 m.  
The proposed protocol delivers comparable communication and storage overheads and PDR as the other studied protocols, but with relatively less energy consumption by wireless devices as shown in Figure 13. This leads consequently to an improvement of the lifetime of the M2M device network.  
Figure 12: Average delay overhead, packet delivery ratio vs. distance.  
Figure 13: Energy overhead consumption.
# Examining Pipe-Borehole Wall Contact and Pullback Loads for Horizontal Directional Drilling
## 2 Analysis of Pipe-Borehole Wall Contact
### HDD Pipe-Borehole Wall Contact Model  
The contact pressure distribution can be expressed as follows [33]:  
\[p_{c}=p_{0}\bigg{(}1-\frac{x^{2}}{l^{2}}\bigg{)}^{n}. \tag{13}\]  
There are two parameters that need to be determined in Equation (13): the maximum contact pressure \(p_{0}\) and the pressure distribution exponent \(n\). The following sections will explain how to determine these two parameters.