esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Overall Hardware Implementation
### Bit-Width Definition  
In digital implementation, it is essential that the proposed model bit-width is described in detail for reducing the final hardware costs. As a first step, based on all parameters and variables of the proposed model, the number of integers and fraction parts must be calculated. In this way, for the proposed DSSN model, the maximum and minimum values are \(16\) and \(-1.3177\), respectively. These values require the bit number of \(4\) and \(3\) for the integer and the fraction parts, respectively. Thus, in the first state, the number of \(7\) can be required. Moreover, based on the scheduling diagrams of the proposed DSSN neuron model (Figure 9), in the generating paths of the output signals (\(V\) and \(n\)), the signals may be shifted to the right and left. In this condition, the bit-width is significantly varied. Based on the final calculations, by shifting the signals to the right in the semi-final steps of the basic variables, the number of \(8\) is added to the fraction part of the bit-width. Moreover, one bit must be calculated for the sign bit of the proposed system. Consequently, the bit-width of the final system is calculated as \(16\). In this calculation, the number of \(4\) for the integer part, the number of \(11\) for the fraction section, and \(1\) bit for sign bit are required.
# Transient Analysis of a Selective Partial-Update LMS Algorithm
## 5 Results
### Tracking Analysis  
The results presented in Figure 5 shows the ability of the proposed stochastic model to predict the tracking ability of the SPU-LMS-M-min algorithm. The plant to be emulated contains the first 32 coefficients from Model 2 of [28]. The additive noise, as well as the perturbation \(\mathbf{q}(k)\), are distributed according to a white Gaussian process. Table 2 presents the parameters used in the simulations.  
The results shown in Figure 5 reveal the asymptotic MSD performance of the algorithm as a function of \(\beta\). The excellent adherence between the experimental results and the theoretical forecast stands out. Note that there is a specific value of \(\beta\) that optimizes the asymptotic performance, which is duly estimated by the elaborated theoretical analysis. This value of \(\beta\) is high enough to allow the filter to follow the non-stationarity of the plant varying in time, but at the same time, it is not high enough to generate a lot of variability in the estimation process. Such high variability has repercussions on the second-order statistics (such as variance), which are captured by performance metrics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Figure 5** \\ \hline \(\beta\) & \(10^{-2}\) \\ \(\sigma_{v}^{2}\) & \(10^{-6}\) \\ \(H(2)\) & \(1-0.8z^{-1}+0.2z^{-2}\) \\ \(\sigma_{q}^{2}\) & \(10^{-15}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in tracking simulations scenario.  
Figure 4: Theoretical (red dashed line) and empirical (blue solid line) evolution of the adaptive filter coefficients for \(B\in\{1,2,3,4\}\). (**a**) MSD (dB); (**b**) MSE (dB).  
In the following scenario related to Figure 6, the target is to identify ability of the proposed stochastic model in following the evolution of the MSD and MSE under a non-stationary \({\sigma_{v}}^{2}\). The plant to be identified contains the first 32 coefficients of Model 2 of [28]. The total number of blocks is \(M=8\). To evaluate the accuracy of the behavior of the proposed model before a colored input signal, the input signal is generated by passing a Gaussian white noise of unitary variance through the filter \(H(z)=1-0.8z^{-1}+0.6z^{-2}-0.1z^{-3}\). The parameters of SPU-LMS-M-min are: \(\beta=8\times 10^{-3}\) and the formula used for calculating \(\sigma_{v}^{2}(k)\) is  
\[\sigma_{v}^{2}(k)=\overline{\sigma}_{v}^{2}+A\sin(2\pi f_{s}k), \tag{46}\]  
where \(\overline{\sigma}_{v}^{2}=10^{-2}\), \(A=10^{-1}\) and \(f_{s}=2\times 10^{-3}\). Empirical results were obtained through \(2\times 10^{4}\) independent Monte Carlo trials. The results shown in Figure 6a reveal adequate correspondence between the theoretical and experimental MSD curves, both in steady-state and transient regimes. The results shown in Figure 6b, which take advantage of the same parameters as those of Figure 6a, reveal adequate correspondence between the theoretical and experimental MSE curves, both in steady-state and transient regimes.
# OpenCare5G: O-RAN in Private Network for Digital Health Applications
## 3 Proposed Solution
### OpenCare5G Project Architecture
room and ultrasound equipment examination room at HCFMUSP. Its implementation alleviates network congestion and efficiency in the face of high traffic demand;
* The Fronthaul Transport Network of gNB-RU is connected by glass fiber optics to obtain broadband with synchronous ethernet interface, using wave-length division multiplexing (WDM) connected to RU to improve the efficiency/throughput of the medium with an enhanced common public radio dynamic interface (eCPRI PDU) encapsulated in ethernet for VLAN, class 2;
* Between RU/DU, O-RAN Alliance (Open RAN) of User Plan (UP) traffic service points, time synchronization via Global Positioning System (GPS) [25; 31]. At this stage we have the Fronthaul packet network carrying the eCPRI PDU traffic so that it is virtually lossless. Depending on the use case in 5G, the latency in the Fronthaul network is not more than tens of microseconds;
* The DU/CU must combine to allow dual connectivity between the Sub-6 GHz and 5G network bands, as well as coordinated transmission to eliminate interference to the minimum possible [29];
* In the \"Subnetwork/Sync\" we have the switch and the GPS connection with the gNB-RU (Fronthaul);
* The DU/vDU of the gNB-DU (Midhaul) of the distribution unit (DU) mounted on a COTS server with docker system for vDU containerization;
* Midhaul 1 Transport Network from gNB-DU (Midhaul) with glass fiber optics and split 2.0 interface for LAN-to-LAN connection Midhaul 2 Transport Network from gNB-DU (Midhaul) to Unit Centralized (5G-O-CU);
* The CU/vCU of the gNB-CU (Backhaul) of the Centralized Unit (5G-O-CU) mounted on a COTS Server performing the containerization and virtualization of the vCU in the native cloud connected to the 5G Core (5 GC) server, which is connected to the datacenter in Itala, 9 km from HCFMUSP. The remote unit (RU) hosts the L1 layer and the lower PHY-Low sublayer in real time, and the vCU is responsible for non-real-time functions such as RAN resource management, encryption and retransmission. Normally, a single vCU can manage many DUs, and a single DU can connect to multiple RUs [24]. In 5 GCN, the Service Based Architecture (SBA) defines the network components with a set of network functions interconnected by containers and non-containers, as shown in Figures 2 and 3.
## 2 Design Algorithm  
The application of machine learning techniques, particularly neural networks, has shown considerable promise across various domains. For example, in structural engineering, machine learning algorithms have been employed to analyze and predict the behavior of complex structures subjected to different loads and environmental conditions. By leveraging extensive datasets and learning patterns, these algorithms facilitate accurate predictions of structural responses, fatigue life, and failure modes. In the realm of microwave circuit design, the complexities associated with nonlinear phenomena and intricate engineering issues necessitate the development of efficient and accurate modeling techniques. This paper aims to introduce a fast surrogate model-based algorithm that utilizes multilayer perceptron neural networks specifically for microwave circuit design. By incorporating machine learning techniques, this algorithm seeks to address the design challenges in microwave circuits and provide valuable insights for design optimization [44; 45].  
In this section, as shown in Figure 1, the proposed design algorithm is presented to help to design complicated devices within microwave and communication systems, particularly, filters and power dividers. In Step 1 of the proposed design algorithm, the preliminary LPF is presented, in which the dimensions are not finalized. The presented LPF may have any arbitrary structure. In Step 2, the ANN structure is proposed for the presented LPF. Then, the proposed ANN model is trained using an electromagnetic simulation of the preliminary resonator in Step 3. The dimensions of the preliminary LPF may be finalized by the ANN, based on the LPF desired parameters and the predicted data, as shown in Step 4; the final LPF can be created in Step 5. After applying the proposed final LPFs to the two main branches of the divider (Step 6), the final WPD may be achieved, as detailed in Step 7.  
Figure 1: The presented design algorithm used to design the proposed power divider, which is described in seven steps.
# Utilization of Partially Liquefied Bark for Production of Particleboards
## 2 Materials and Methods
### Characterizations
#### 2.4.4 Mechanical Properties of Particleboard  
The bending test for measuring the modulus of elasticity (MOE) and modulus of rupture (MOR) of the particleboard panels manufactured with PLB was performed according to EN 310:1993 [35] by using a universal testing machine (Zwick/Roell Z005, Zwick/Roell GmbH, Ulm, Germany). Six samples per board measuring 210 \(\times\) 25 mm\({}^{2}\) were tested using a span of 160 mm and a cross-head speed of 7 mm min\({}^{-1}\) (time to break was between 45 and 75 s). MOE was determined between 10 and 40% maximum load.  
Six samples measuring 210 \(\times\) 25 mm\({}^{2}\) (width \(\times\) length) were cut from each panel and tested for their tensile strength parallel to the surface by using a universal testing machine (Zwick/Roell Z005, Ulm, Germany).  
The internal bond (IB) strength test was conducted following EN 319:1993 [36]. Six samples per particleboard measuring 50 \(\times\) 50 mm\({}^{2}\) were bonded with hot-melt glue and the test perpendicular to their surfaces were performed using a universal testing machine (Instron 4466, Darmstadt, Germany). A loading speed of 0.75 mm\(\cdot\)min\({}^{-1}\) was used for testing (time to break was between 50 and 70 s).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Panel** & **Description** & **Density (kg/m\({}^{3}\))** & **Thickness (mm)** \\ \hline I & 4.7\% PLB & 399 \(\pm\)19 & 7.98 \(\pm\) 0.21 \\ II & 9.1\% PLB & 397 \(\pm\) 65 & 8.05 \(\pm\) 0.54 \\ III & 20.0\% PLB & 554 \(\pm\) 18 & 8.32 \(\pm\) 0.27 \\ IV & 33.3\% PLB & 540 \(\pm\) 74 & 8.18 \(\pm\) 0.21 \\ V \({}^{*}\) & 10.0\% MUF & 755 \(\pm\) 17 & 7.63 \(\pm\) 0.09 \\ VI & 10\% MUF + 4.7\% PLB & 662 \(\pm\) 23 & 7.70 \(\pm\) 0.05 \\ VII & 10\% MUF + 9.1\% PLB & 734 \(\pm\) 22 & 7.60 \(\pm\) 0.15 \\ VIII & 10\% MUF + 20.0\% PLB & 686 \(\pm\) 62 & 7.79 \(\pm\) 0.11 \\ IX & 10\% MUF + 33.3\% PLB & 632 \(\pm\) 16 & 8.03 \(\pm\) 0.12 \\ \hline \end{tabular} \({}^{*}\) as a reference board made from melamine–urea–formaldehyde (MUF) adhesives and wood particles.  
\end{table}
Table 1: Density and thickness of the manufactured particleboard.
## 4 Map Spectrum-Based Spatiotemporal Clustering
### Similarity Measurement of the Map Spectrum Model  
The similarity measurement between spectrum lines is the key component of the map spectrum-based spatiotemporal clustering model. In this study, the cosine of two vectors consists of node variables of the spectrum line, as shown in Equation (7)  
\[\cos\varnothing=\frac{\sum_{i}^{n}(a_{i}\times b_{i})}{\sqrt{\sum_{i}^{n}a_{i}^ {2}}\times\sqrt{\sum_{i}^{n}b_{i}^{2}}} \tag{7}\]  
where vectors \(\textbf{a}=\{a_{1},\ a_{2},a_{3},\cdots,a_{n}\}\) and \(\textbf{b}=\{b_{1},\ b_{2},b_{3},\cdots,b_{n}\}\) represent nodes of spectrum lines that are involved in similarity measurements. The range of the cosine value between two vectors is [0,1]. The close the value is to 1, the higher the similarity of the two spectrum lines.
# Performance Evaluation of LoRa Considering Scenario Conditions
###### Abstract  
New verticals within the Internet of Things (IoT) paradigm such as smart cities, smart farming, or goods monitoring, among many others, are demanding strong requirements to the Radio Access Network (RAN) in terms of coverage, end-node's power consumption, and scalability. The technologies employed so far to provide IoT scenarios with connectivity, e.g., wireless sensor network and cellular technologies, are not able to simultaneously cope with these three requirements. Thus, a novel solution known as Low Power - Wide Area Network (LP-WAN) has emerged as a promising alternative to provide with low-cost and low-power-consumption connectivity to end-nodes spread in a wide area. Concretely, the Long-Range Wide Area Network (LoRaWAN) technology is one of the LP-WAN platforms that is receiving greater attention from both the industry and the academia. For that reason, in this work, a comprehensive performance evaluation of LoRaWAN under different environmental conditions is presented. The results are obtained from three real scenarios, namely, urban, suburban, and rural, considering both dynamic and static conditions, hence a discussion about the most proper LoRaWAN physical-layer configuration for each scenario is provided. Besides, a theoretical coverage study is also conducted by the use of a radio planning tool considering topographic maps and a precise propagation model. From the attained results, it can be concluded that it is necessary to evaluate the propagation conditions of the deployment scenario prior to the system implantation in order to reach a compromise between the robustness of the network and the transmission data-rate.  
Keywords:LP-WAN; IoT; Smart cities; LoRa; LoRaWAN +
Footnote †: journal: Sensors
## 3 Obstacle Avoidance Scenario
### Obstacle Detection Technologies  
A system can get various information about its surrounding obstacles from light or soundwave reflection via different sensors. From the previous section, we know these non-contact sensors have different information-sensing capacities. These pieces of information contain the obstacle's size, distance, shape, color, and direction. After selecting the farmland for spraying, farmland obstacles are more likely a no-fly or no-spraying zone for the UAV. Thus, the primary concerns for the UAV are the shape and position of the obstacle. To achieve obstacle-detection capacity, the sprayer UAV can choose a single or multiple fusion detection methods. Due to the emerging development and future demand of image processing and 3D mapping, obstacle detection using image processing has attracted the attention of several researchers [161; 162; 163; 164; 165; 166; 167; 168; 169; 170; 171; 172]. Moreover, because of the rapid response and low computational requirement, many detection and ranging-based or active sensor-based research also have been used for the detection of the surrounding obstacle's position, as well as its coordinates and mapping [173; 174; 175; 176; 177; 178; 179; 180]. Other studies on such detection systems use the fusion of active and passive sensing [181; 182; 183]. For autonomous navigation, the obstacle detection method is an important part. From analyzing previous studies, we found some of the major obstacle detection methods, which are given in the next section.
## 3 Testing Set-Up and Data Acquisition  
### Experiments to Measure Horizontal Displacement  
#### 3.1.1 Precast Concrete Walls Information  
Reinforced concrete walls were erected to assess the feasibility of real-time lateral displacement monitoring with LiDAR equipment. The study employed two types of precast (PC) walls in its construction, as illustrated in Figure 4. Figure 4a showcases Wall A, consisting of four PC walls each measuring 1550 mm (B) \(\times\) 800 mm (H) \(\times\) 200 mm (T), together resulting in a total height of 3200 mm. Shear reinforcement bars were exclusively installed on the PC wall located on the first level, which encountered the greatest degree of loading. The manufactured PC wall was fastened to a foundation of 2400 mm (B) \(\times\) 550 mm (H) \(\times\) 800 mm (T) at the bottom and a force beam of 1550 mm (B) \(\times\) 400 mm (H) \(\times\) 350 mm (T) at the top. Figure 4b showcases Wall B, which features a 450 mm (B) \(\times\) 600 mm (H) opening between a 350 mm (B) \(\times\) 800 mm (H) wall and a 750 mm (B) \(\times\) 800 mm (H) wall. This opening enables a 200 mm high beam to connect the asymmetric left and right walls. Wall B was constructed  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Types** & **Values** \\ \hline Horizontal resolution & -0.96 mm \\ \hline Vertical resolution & -0.97 mm \\ \hline Distance between specimen and LiDAR & 800 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Resolution of LiDAR measurement in this study.  
Figure 3: An algorithm designed to extract edges from a point cloud and a diagram illustrating the measurement data discrepancies with the actual concrete surface.  
by stacking four separate PC walls, with the foundation and force beam matching the size of Wall A. An actuator was situated in contact with the right center of the force beam of each experimental unit and we applied a lateral load at a height of 3375 mm from the foundation. A primary LVDT labeled as number 1 was installed at the same height as the actuator of each experimental unit to measure the displacement of the wall structure due to lateral load forces.  
The lateral displacement applied was planned as depicted in Figure 5. The force displacement's absolute value ranged from 0.9 mm to 200 mm for approximately three hours. Three cycles were performed in both the compression and tension directions for the same displacement, and a displacement 135% larger than the previous displacement was repeated until the maximum displacement was reached.
# A Linear Fitting Algorithm Based on Modified Random Sample Consensus
###### Abstract  
When performing linear fitting on datasets containing outliers, common algorithms may face problems like inadequate fitting accuracy. We propose a linear fitting algorithm based on Locality-Sensitive Hashing (LSH) and Random Sample Consensus (RANSAC). Our algorithm combines the efficient similarity search capabilities of the LSH algorithm with the robust fitting mechanism of RANSAC. With proper hash functions designed, similar data points are mapped to the same hash bucket, thereby enabling the efficient identification and removal of outliers. RANSAC is then used to fit the model parameters of the processed dataset. The optimal parameters for the linear model are obtained after multiple iterative processes. This algorithm significantly reduces the influence of outliers on the dataset, resulting in improved fitting accuracy and enhanced robustness. Experimental results demonstrate that the proposed improved RANSAC linear fitting algorithm outperforms the Weighted Least Squares, traditional RANSAC, and Maximum Likelihood Estimation methods, achieving a reduction in the sum of squared residuals by 29%, 16%, and 8%, respectively.  
Keywords:Locality-Sensitive Hashing; random sample consensus; linear fitting; outlier removal
## 5 Asphalt Mixture Production
The modeling results indicate that the ANN can successfully predict the temperature of aggregate piles during production and storage as part of the entire asphalt mixture production process. \begin{table}
\begin{tabular}{c c c} \hline
**Reference** & **Year and Area of Analysis** & **Result** \\ \hline [PERSON] and [PERSON] [57] & 2016 Effect of variable insulation thickness on the use of solar panels. & Solar aggregate piles preheat the mineral mix and reduce moisture content, reducing the energy required to produce warm mix asphalt. \\ \hline [PERSON] et al. [6] & 2017 Use of the stockpiles of solar aggregates—influence of the variable surface area of the stockpiles of solar aggregates exposed to the sun. & Influence of the variable solar surface area of a solar pile on the heat accumulation results of mineral mixtures in different seasons (summer–autumn). For every 10 \({}^{\circ}\)C increase in temperature, a potential energy saving of 3.7 kWh can be calculated. \\ \hline [PERSON] et al. [58] & 2019 Utilization of solar aggregate stockpiles—storage of recycled materials. & Influence of time spent in sunlight, type of material stored, method of storage, and time stored. \\ \hline [PERSON] et al. [39] & 2019 Predicting the temperature of stored materials—using solar thermal storage. & Temperature prediction of stored materials using solar aggregate reserve storage. Lower temperatures were predicted for mixtures stored at 0 \({}^{\circ}\)C air temperature and higher values at 30 \({}^{\circ}\)C air temperature. \\ \hline [PERSON] et al. [33] & 2019 The energy consumption of asphalt mixes is significantly influenced by the moisture content of the aggregates. & Removing 1\% water from a mineral mixture requires 7.34 kWh of thermal energy. \\ \hline [PERSON] and [PERSON] [59] & 2023 Effect of aggregate moisture content on greenhouse gas emissions. & Total greenhouse gas emissions are 47.1 kg CO\({}_{2}\)e/t natural aggregates with a moisture content of 5\% (0\%: 36.6 kg CO\({}_{2}\)e/t). \\ \hline [PERSON] et al. [60] & 2024 Unit drying/heating reduces fuel oil consumption. & Drying/heating units cut PM, Nox, and VOC emissions by around 18 percent, 22 percent, and 35 percent, respectively. \\ \hline \end{tabular}
\end{table} 
Table 1: Heating and drying of materials in the hot mix asphalt production process as studied in previous research.