esa-sceva/satcom-chunk-collection huggingface dataset:
# Switchable Filter with Four Operating Modes Implemented on a 50-Ohm Microstrip Line
## 5 Conclusions  
In this paper, a compact switchable filter supporting four modes is designed and implemented. The proposed filter provides DB-BPF centered at 1.4 GHz/5.1 GHz and 2.4 GHz/4.2 GHz, a wideband BPF from 2.05 to 4.41 GHz, and an APF operating from 0 to 7 GHz. The bias circuit is integrated into the 50 \(\Omega\) line to improve space efficiency, and the simulation and measurement results are generally in good agreement. In particular, the proposed filter satisfies the miniaturization and multifunctionality required for next-generation communication systems such as 5G and IoT and demonstrates excellent performance.
## 6 Conclusions  
This study conducted a series of push-out tests on group stud connectors in steel-concrete composite beams for straddle monorail applications. Four sets of specimens were designed, with variables including the number of studs, arrangement patterns, and specimen size. The key findings from the experiments are as follows:  
1. Ductile Failure Modes: The group stud connectors exhibited ductile failure modes. Analysis of the load-displacement curves reveals that all full-scale specimens achieved displacement ductility coefficients exceeding 12. At failure, a significant slip occurred between the high-strength concrete and the steel beam due to the shear deformation of the studs, highlighting the strong deformation capacity of the straddle monorail composite beam.
2. Group Stud Effect: The group stud effect significantly reduced the shear capacity of the studs due to uneven shear force distribution. The outer studs experienced higher loads, while the central studs carried less load. The degree of reduction in shear capacity was proportional to the density of the stud arrangement.
3. Size Effect: When specimen size was reduced, the mechanical behavior and failure modes of the group stud connectors remained consistent, but the group stud effect was diminished, and deformation capacity was enhanced.
4. The shear capacity obtained from four specifications is larger than the test results with a difference of almost 25% for the full-scaled specimens and 10-25% for the scaled -specimen.
5. The finite element analysis and parameter analysis of the structure will be conducted to improve the generalizability. In addition, the long-term performance of group stud connectors will be considered to replicate the performance of the connections under real conditions.  
L-TJ.: conceptualization, data curation, methodology, writing--original draft, and funding acquisition. Z.-H.W.: investigation. Y.-F.Z.: software and writing--editing. J.-Z.Z.: conceptualization. S.-K.W.: supervision. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (2024 YFF0505400), the China Postdoctoral Science Foundation (2024M763871), and Fundamental Research Funds for the Central Universities (2024 CDJYXTD-005).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Optical Amplifiers for Access and Passive Optical Networks: A Tutorial
## 3 Optical Amplifiers in Telecommunications Networks
### Raman Amplifiers
The difference between the saturation power for the 10 mW pump power and the saturation power for the 300 mW pump power is approximately 2.5 dB. Advantages of Raman amplifiers:  
* high gain and saturation power,
* compatible with installed SM fibers,
* usable on any wavelength in telecommunication bands,
* low noise figure in comparison with those of SOAs and EDFAs,
* wavelength conversion,
* large transmission capacitance,
* able to be used to extend EDFAs. Disadvantages of Raman amplifiers:  
* high pump power requirements,
* lower efficiency for a specific wavelength then EDFAs (for the same pump power),  
Figure 14: Dependence of the saturation power on the pump power. Figure 13: Measurement scheme with a Raman amplifier. * sophisticated gain control is needed.
# Gathering Big Data in Wireless Sensor Networks by Drone +
## 6 Algorithm for Big Data Gathering during Drone Movement
### The Algorithm to Create the Subsets
#### 6.2.1 Check for Overlapping  
Given a path between two consecutive hovering points in a tour, it is possible that during the drone flight over this path, the drone will be inside of areas covered by more than one sensor node at the same time. In other words, during the drone flight, it can cross regions with _overlapping_ of radio ranges. To avoid packet collisions in this scenario, only one node at a time can send data to a drone. Figure 10 exemplifies this overlapping. It shows a path between the hovering points \(p_{i}\) and \(p_{i+i}\), the sensor nodes \(s_{1}\) and \(s_{2}\), and their radio ranges. Since these nodes are close to each other, the area covered by their radios on the tour is almost the same.  
The proposed algorithm avoids overlapping by defining how the sensor nodes transmit to the drone and remove some nodes when the overlapped region is not large enough for the drone to receive data from all sensor nodes. Lets consider \(d_{i\leftrightarrow i+1}\) the subset of sensor nodes able to send data to drone flying on the path between \(p_{i}\) and \(p_{i+1}\). First, the algorithm sorts the nodes in \(d_{i\leftrightarrow i+1}\) according to the sequence of nodes perceived by the drone during its flight between \(p_{i}\) and \(p_{i+1}\). On this path, the algorithm defines where each sensor node can communicate with the drone. Then, it allocates part of the path \(p_{i}-p_{i+1}\) to the first sensor node in \(d_{i\leftrightarrow i+1}\). This part starts where the drone enters  
Figure 10: (**a**) Example of overlapping of radio ranges. (**b**) Part of the path where each node has to send data to the drone.  
inside of the radio range of this node and has length \(trs\), according to Equation (4). Hence, it verifies if the next node in \(d_{i\leftrightarrow i+1}\) can transmit data after the first node, that is, after the end of the first part. If so, the algorithm allocates this second part to the second node. If not, it verifies the next sensor node. This task is repeated until the last sensor node in \(d_{i\leftrightarrow i+1}\). Then, the algorithm verifies the next subset in \(D_{mov}\). Figure 10b presents the path \(p_{i}-p_{i+1}\) with the first part allocated to sensor node \(s_{1}\) and the second part allocated to sensor node \(s_{2}\).
# Analytical Computation of the Spatial Resolution in GNSS-R and Experimental Validation at L1 and L5
## 4 Discussion  
To the authors' best knowledge, this is the first analytical study of the response of a GNSS-R system to a reflectivity step. The analytical expression of the measured reflectivity \(\hat{\Gamma}\) can be derived from Equation (7):  
\[\hat{\Gamma}=\left|F(v)\cdotp(\varepsilon_{r,1},\ \theta_{i})+F(-v)\cdotp( \varepsilon_{r,2},\ \theta_{i})\right|^{2}, \tag{11}\]  
with \(v\) given by Equation (5). It shows a smooth transition between \(\left|\rho(\varepsilon_{r,1},\ \theta_{i})\right|^{2}\) and \(\left|\rho(\varepsilon_{r,2},\ \theta_{i})\right|^{2}\), and ripples associated to different Fresnel zones passing from one area to the other one. The width of the transition, as measured from the 10% above the lowest reflectivity values to 90% of the highest reflectivity value, has been found to be dependent on the amplitude of the reflectivity step itself, although it is quite flat for a wide range of reflectivity steps (Figure 10): \(\Delta v\approx 1.5\)  
\[\Delta x=\Delta v\sqrt{\frac{\lambda\cdot h_{R}}{2\cdot cos(\theta_{i})}} \approx 1.5\sqrt{\frac{\lambda\cdot h_{R}}{2\cdot cos(\theta_{i})}}. \tag{12}\]  
The spatial resolution and the position of the peaks of the ripples was validated using airborne data at the L1 and L5 bands.  
Figure 15: Land-to-water transition during an MIR at L1 test flight over Point Nepan, Melbourne (38.35\({}^{\circ}\)S, 144.74\({}^{\circ}\)E). (**a**) Geo-located reflectivity, (**b**) time evolution of the reflectivity, and a zoom-in of the step transition on the (**c**) left and (**d**) right.  
It is worth mentioning that the theoretical analysis assumes that coherent scattering is the dominant mechanism, which is the case when transitioning from land to/from calm water. However, when transitioning into open ocean, where incoherent scattering is more important, the presence of the ripples is not that evident, and the reflectivity exhibits random fluctuations associated mostly to the speckle noise.  
These results should allow us to improve the determination of the extent of the flooded areas using GNSS-R observations as in [12; 13; 14; 15], and they can also be used to dynamically optimize the coherent and incoherent integration times in future GNSS-R instruments (airborne or spaceborne) depending on the application or target area, as opposed to the approach implemented in past and current missions (1 ms coherent integration time, and 1 s incoherent integration time) such as UK TDS-1 [16] and NASA CyGNSS [17] missions.
## 5 Implementation
### Recognition and Tracking Module
#### 5.3.3. Blobs Classification  
Blobs classification takes the set of rectangles as input and classifies them into _useful blobs_, i.e., those containing objects of interest, or as _not useful blobs_ when not. Not useful blobs are discarded. This stage takes as input the set of blobs and not the entire image and avoids processing image areas without particular interest, therefore, resulting in a better performance. Three different techniques are implemented, which can be applied isolated or in combination with each other, to improve the results of the classification:  
* _Aspect ratio_ (AR) classifies blobs based on the relation (ratio) between their width and height. If the ratio is close to the average value of the objects of interest, AR indicates that the blob contains at least one object of interest. The major benefit of AR is its low computational cost. However, it tends to be inaccurate because the reference aspect ratio often varies significantly for different data sources. AR is not useful for discarding blobs (i.e., the fact that a blob fulfills the relation does not mean that it contains an object of interest) and wrongly discards blobs that do not comply with the established aspect ratio criterion due to the fact that they contain multiple objects of interest (e.g., objects close enough one of each other that conforms a unique blob).
* _Computational intelligence_ uses the _default people detector_, a pre-trained learning algorithm included in OpenCV. [PERSON] and [PERSON] [37] demonstrated that a combination of Histograms of Oriented Gradients (HOG) for feature extraction, and Support Vector Machine (SVM) for the classification of the feature vectors, allows obtaining accurate detection results. The method is based on moving a gridded window all over the image, extracting the vectors of features (using HOG) and classifying them (using SVM) to decide if the image contains a person. Considering that the proposed system studies the movement of persons, the blob classification technique processes just those areas where movement was detected. Thus, the default people detector algorithm is applied just over each detected blob, reducing the computational cost of the processing. This method returns a set of rectangles that contains persons, some of them overlapped. To reduce and unify the number of rectangles, the _Non-Maximum Suppression_ algorithm [38] is used.
* _Aspect ratio frequency_ filters blobs depending on the frequency that similar blobs were filtered by computational intelligence algorithms. In this way it is possible to simulate a behavior close to the computational intelligence algorithms without having to execute them in each iteration. Aspect ratio frequency can be applied in two different ways: (i) directly filtering those blobs in which the frequency is greater than a threshold, or (ii) categorizing the blobs frequencies into three different levels and depending on that level, discarding the blob, requesting a computational intelligence method to process the blob, or keeping the blob.
# Sustainable Development of Concrete through Aggregates and Innovative Materials: A Review
## 2 Recycled Materials
### Recycled Tire Rubber
[34] proposed using fibers coated with crumb rubber for precast flat-roofs. Table 4 shows weight analysis of the main components of the two types of recycled rubber used in [34]. In addition, Table 5 indicates the mix proportions of the different concrete mixtures considered in [34]. Finally, Table 6 depicts the bulk density, bulk porosity, compressive strength, Young's modulus, bending strength, toughness and toughness index (TI) of concrete specimens with CR an FCR. Deviation factor (%) is indicated between brackets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Replacement \%** & **Cement (kg)** & **Water in Liters** & **Fine Aggregates** & **Coarse** & **Rubber** \\  & **(W/c = 0.50)** & **(kg)** & **Aggregates (kg)** & **Aggregates (kg)** \\ \hline
**0\%** & 364.81 & 225.17 & 610.43 & 1239.64 & - \\
**5\%** & 437.77 & 224.2 & 590.03 & 1177.65 & 23.30 \\
**10\%** & 437.77 & 224.2 & 590.03 & 1115.67 & 46.73 \\
**15\%** & 437.77 & 224.2 & 590.03 & 1053.69 & 70.101 \\
**20\%** & 437.77 & 224.2 & 590.03 & 991.71 & 93.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mix proportion (kg/m\({}^{3}\)) of the rubber concrete [32]. Copyright © 2017, Elsevier B.V. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Specific Gravity** & **Bulk Density (kg/m\({}^{3}\))** \\ \hline
**Rubber aggregates** & 1.10 & 650 \\
**Fine aggregates** & 2.6 & 1650 \\
**Coarse aggregates** & 2.8 & 1720 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Physical properties of materials used as aggregates in the concrete [32]. Copyright © 2017, Elsevier B.V. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**\% of Replacement** & **Ultimate Load (kN)** & **Compressive Strength** (MPa)** & **Weight of Cubes** (kg)** & **Density (kg/m\({}^{3}\))** \\ \hline
**0\%** & 713.25 & 31.7 & 8.15 & 24,141.81 \\
**5\%** & 657.67 & 29.23 & 7.56 & 2240.702 \\
**10\%** & 576.22 & 25.61 & 7.23 & 2143.14 \\
**15\%** & 480.05 & 21.34 & 6.51 & 1928.88 \\
**20\%** & 373.72 & 16.61 & 6.34 & 1879.933 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Compressive strength and density of concrete [32]. Copyright © 2017, Elsevier B.V. Although several studies have been carried out with rubberized concrete, more investigations are necessary that consider its microstructure and influence in corrosion behavior of reinforcement steel bars [35]. Furthermore, the mechanical behavior of rubberized concrete depends on the size range and percentage of the rubber particles. Figure 3 shows a comparison of several tire rubber particles with different size ranges and other aggregates used in rubberized concrete by [35]. It depicts small and large rubber particles from car tires with size up to 10 mm and 20 mm, respectively.
## Results  
### Land Use Change and Its Impact on Ecosystem Services  
A significant change of LULC patterns has taken place in the study area during the period of the first round of the GGP. The GGP involves four types in this study area, namely, conversion of farmland to forest (TFF), conversion of farmland to shrub (TFS), conversion of unused land to forest (TUF) and conversion of unused land to shrub (TUS). Between 2000 and 2015, the GGP-driven conversion area is 4209.7 ha, accounting for 14.5% of the overall LULC. The area of TFF was the major type of LULC change, with an area of 3459 ha, accounting for 82.16% of the GGP area.  
The GGP areas are mainly distributed around riverbanks and western parts of the study area (Figure 2). In this mountainous study area, unexpectedly, most of converted farmlands distributed relatively flat area, where the degree of slope was less than 15\({}^{\circ}\). The GGP area that degree of slope ranges 0\({}^{\circ}\) to 8\({}^{\circ}\) and 8\({}^{\circ}\) to 15\({}^{\circ}\) accounted for 44.99% and 42.41%, respectively, while the area where the degree of slope ranges \(>\)15\({}^{\circ}\) only accounted for 12.60%. Steep-sloped farmlands with a total area of 6156.5 ha were still cultivated.  
Figure 2: Land-use conversion type of the Gain for Green Program between 2000 and 2015 and their slope distribution.  
The concerned regulating services in the GGP area was greatly improved in the first round (Figure 3). The flood mitigation, water purification, soil retention, and carbon storage increased by 58.72%, 82.31%, 55.95%, and 504.77% over the 15-year period, respectively. These results revealed that implementation of the GGP in mountain areas can significantly contribute to an improvement in the regulating services.
## 2 Basic Theories  
### Structural Modal Theory  
In this research, the diverted gas impingement muffler is treated as a multi-degree-of-freedom system, as illustrated in Figure 2.  
The vibration equation for a linear, time-invariant system with multiple degrees of freedom can be represented as follows:  
\[[M]\big{\{}\ddot{x}\big{\}}+[C]\big{\{}\dot{x}\big{\}}+[K]\big{\{}x\big{\}}= \{f(t)\} \tag{1}\]  
Figure 1: The working principle of the split-stream rushing muffler.  
Figure 2: Multiple-degree-of-freedom system.  
In the equation, \([M]\) represents the mass matrix of the multi-degree-of-freedom system, \([C]\) denotes the system damping matrix, \([K]\) denotes the system stiffness matrix, \(\{f(t)\}\) is the excitation vector at each point of the system, and \(\left\{\breve{x}\right\}\), \(\left\{\breve{x}\right\}\), and \(\left\{x\right\}\) correspond to the acceleration vector, velocity vector, and displacement vector, respectively. Their definitions are provided below.  
\[\left\{x\right\}=\left[x_{1},x_{2}\cdots x_{\text{N}}\right]_{\text{N}\times 1 }^{\text{T}} \tag{2}\]  
\[\left\{f(t)\right\}=\left[f_{1},f_{2}\cdots f_{\text{N}}\right]_{\text{N} \times 1}^{\text{T}} \tag{3}\]  
Given a small damping coefficient in the structural system, the muffler is treated as an undamped-free system, with the external structural system being free from external forces. Consequently, under free vibration, both its damping matrix and the excitation vector at each point of the system become zero, i.e., \(\left[C_{\text{f}}\right]=0\), \(\left\{F_{\text{f}}\right\}=0\), and the differential equation in Equation (1) simplifies to  
\[[M]\left\{\breve{x}\right\}+[K]\left\{x\right\}=0 \tag{4}\]  
Upon further analysis of Equation (4) and assuming the free vibration to be harmonic motion at this point, the subsequent equation is derived:  
\[\left\{x\right\}=\left\{\varphi\right\}\sin(\omega t+\theta) \tag{5}\]  
\[\left\{\breve{x}\right\}=-\omega^{2}\left\{\varphi\right\}\sin(\omega t+\theta) \tag{6}\]  
Substituting the aforementioned Equations (5) and (6) into (4) yields  
\[\left\{\varphi\right\}([K]-\omega^{2}[M])=0 \tag{7}\]
# A Novel QoS-Aware ARQ Scheme for Multi-User Transmissions in IEEE802.11 ax WLANs
## 1 Introduction
The major contributions of this article are listed below:  
* A novel ARQ scheme for the MU transmission in the aggregation-enabled WLANs is discussed. * The proposed scheme is QoS-aware and protocol-friendly. * The full utilization of the channel resource originally used for padding bits and the adequate sliding of the aggregation window are considered together in the proposed scheme. * The measures to reduce the computational load and processing time are given to guarantee timely processing when the algorithm is implemented. The remainder of the paper is organized as follows. In Section 2, the background and related work are introduced. In Section 3, we present the BP-ARQ scheme for MU transmissions in IEEE802.11 ax WLANs. In Section 4, we illustrate the progress of the proposed scheme through a comparison with other schemes. We conclude the paper in Section 5.
# A Multiprotocol Wireless Sensor Network for High Performance Sport Applications
## 2 Materials and Methods
### Design of the Master Node  
The master node is composed of an MCU, a GPS module, a 9-axis inertial measurement unit (IMU), an nRF24L01P+PA+LNA module, and a Wi-Fi module (Figure 5a).  
The GPS module has a sampling rate of 20 Hz. Since the maximum stroke rate of an athlete during a sprint is about 180 strokes per minute (i.e., 3 Hz), the module can measure speed fluctuations at each stroke (intracyclic speed). The module communicates with the MCU via a serial port at a speed of 230.4 Kbps. The IMU has a sampling frequency of 50 Hz and is connected to the MCU by an I2C (inter-integrated circuit) serial bus link (up to 400 kHz).  
The management of the e-kayak WSN is computationally rather demanding. The MCU is tasked with acquiring data from GPS and IMU, communicating with the slave nodes via nRF24L01+ modules and with the user via Wi-Fi, calculating real-time parameters and storing the data in a micro SD memory card. For this reason, the chosen MCU is Pjrc's Teensy 3.6. It is arguably the most powerful Arduino clone available on the market, featuring a 32-bit 180 MHz ARM Cortex-M4 processor with a floating point unit. It also features an on-board micro secure digital (SD) slot and several serial  
Figure 4: The webpage acts a communication interface.  
Figure 5: Block scheme of the master node (**a**) and slave node (**b**). IMU = Inertial measurement unit, MCU = Microcontroller unit, GPS = Global positioning system, SPI = Serial peripheral interface, PPS = Pulse per second.  
ports for communication with peripherals. Since GPS data, IMU data, and sensors data have different sampling frequencies and time scales, they are synchronized using the GPS receiver PPS (pulse per second) signal.  
A custom-made board has been designed (Figure 6a) to accommodate all the main components, as well as a charge circuit and all the necessary interfaces with the commands (switch, button, leads, etc.). The node is placed in a waterproof case for on-board use.  
The master node is powered by a 3.7 V, 1200 mAh LiPo battery. It can also be connected to a PC via USB cable for system diagnostics.