esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Advances in Active Impulsive Noise Control with Focus on Adaptive Algorithms
## 4 Other Transformation Algorithms
[PERSON] derived a filter-x affine projection algorithm based on the minimum variance distortionless response (MVC) by solving an inequality-constrained problem [67]. This algorithm demonstrates a fast convergence when dealing with active impulsive noise control. [PERSON], inspired by convex combination methods, combined the FxLMS algorithm with the FxMVC algorithm, developing the C-FxMVC algorithm using an amplitude constraint approach [68]. Simulation results indicate that the C-FxMVC algorithm performs well in terms of convergence speed and noise reduction. [PERSON] theoretically analyzed the stability of VersoriaLMS-type algorithms based on the maximum skewness line criterion under Gaussian and non-Gaussian noise conditions [69]. [PERSON] proposed a set of adaptive algorithms belonging to the SigmoidLMS type by embedding conventional cost functions into the sigmoid function [70]. Leveraging the amplitude compression feature of the sigmoid function, this algorithm resists impulsive noise interference. The algorithm introduces a free parameter, aside from the step size, controlling the compression amplitude and influencing convergence speed and steady-state error. The sigmoid function transformation algorithm exhibits some similarity to the hyperbolic tangent transformation. [PERSON] introduced a robust semi-quadratic criterion (HQC) adaptive filtering algorithm using convex cost functions [71]. The proposed HQC introduces a more efficient performance surface, leading to substantial improvements in dealing with impulsive noise. [PERSON] proposed a robust algorithm and confirmed through simulation studies that the arctan algorithm family achieves a higher performance compared to standard algorithms [72]. The filter coefficient update formulas for the algorithms are shown in Table 6.  
In summary, in practical applications, the first consideration involves determining whether a prior estimate of \(\alpha\) is necessary to guide the selection of the appropriate algorithm. Additionally, factors such as the computational complexity and hardware feasibility of the algorithm must be taken into account. The final step is to choose the appropriate algorithm based on the severity of the pulse noise. It is worth noting that in real life, pulse noise often coexists with
## 4 Experimental Results and Analysis
### Model Performance Evaluation  
In this section, we use the proposed LCANet for feature extraction and damage localization of the guided wave data. First, the model uses the training set to optimize parameters, and then to identify the damage on the test set. In this paper, the number of training epochs is set to 100.  
As shown in Figure 9, the loss of the proposed LCANet is gradually decreasing with the increase of training epochs. The loss decreases rapidly when the epoch is less than 10 and then remains stable. It indicates that the model has converged after repeated learning.  
Furthermore, as shown in Figure 10, it is the accuracy variation between the proposed model and the contrastive models during the training process. It is obvious in Figure 9 that LCANet is better than other contrastive models. LCANet does not learn enough during the first few training sessions, so the accuracy was only 20%. However, after the sixth training stage, the the accuracy rate improved significantly. After only eight training stages, the accuracy rate reached 100%. This shows that LCANet has a powerful feature extraction and learning capability. In particular, the loss is not minimized at this point, but the damage is accurately identified. The 2D-CNN-based SHM methods also have superior performance,  
Figure 9: Training loss of LCANet.  
followed by ResNet, which both use 2D data as input. ResNet accuracy is slow to improve, because the model was too deep while the training samples are few. Moreover, it also affects the speed of learning. 1D-CNN required at least 40 iterations for accurate recognition and the rate of increase in accuracy is also slow. Therefore, compared with other SHM contrastive methods, the proposed LCANet has outstanding performance.
Sensors_**2015**, _15_, 13805-13838; doi:10.3390/s150613805  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**Interference Mitigation Schemes for Wireless Body Area Sensor Networks: A Comparative Survey**  
**[PERSON]. [PERSON] and [PERSON] ***  
Department of Computer Engineering, Chosun University, 309 Pilmun-daero, Dong-gu,  
Gwangju 501-759, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-62-230-6032; Fax: +82-62-233-6896.  
Academic Editor: [PERSON]  
_Received: 20 April 2015 / Accepted: 8 June 2015 / Published: 11 June 2015_  
**Abstract:** A wireless body area sensor network (WBASN) consists of a coordinator and multiple sensors to monitor the biological signals and functions of the human body. This exciting area has motivated new research and standardization processes, especially in the area of WBASN performance and reliability. In scenarios of mobility or overlapped WBASNs, system performance will be significantly degraded because of unstable signal integrity. Hence, it is necessary to consider interference mitigation in the design. This survey presents a comparative review of interference mitigation schemes in WBASNs. Further, we show that current solutions are limited in reaching satisfactory performance, and thus, more advanced solutions should be developed in the future.  
**Keywords:** wireless body area sensor network; interference mitigation; coexistence; power control; medium access control; cognitive radio
## 3 Laboratory Study
### Sample Preparation and Blending Protocols  
The main aim of this research study is to investigate the influence of Kraft lignin as a bitumen modifier on the chemo-thermal characteristics of the lignin-modified bitumen, as well as the influence of different blending conditions. To achieve this, Kraft lignin was added to the bitumen at three (3) different percentages of 5, 10, and 20% (Table 4) by weight of the lignin-modified bitumen (lignin + bitumen). According to the recently published article by our colleague, the maximum mass percentage of this specific lignin (Kraft lignin: acidic and derived from softwoods) with an unmodified bitumen (PG 588\(-\)28 or PG 52S\(-\)34) and mixed with a mechanical mixer is 30%; otherwise, the storage stability and homogeneity of the bitumen-lignin blend are affected negatively [20]. In addition, two (2) types of mixers were utilized in this study, an IKA RW16 Basic Overhead Stirrer, with a maximum rate of 1200 rpm, referred to as the mechanical mixer (MM), and a Silverson L4 RT high-shear mixer (HSM), with a maximum rate of 8000 rpm, as shown in Figure 3. This study focuses on the effect of the shear rate as the research parameter, with two (2) different rates investigated: 1000 and 5000 rpm. The blending time was fixed at 60 min, and the blending temperature was maintained at 150 \(\pm\) 5 \({}^{\circ}\)C throughout the blending process by an oil bath. Prior to the blending process, the bitumen samples were placed in an oven at 150 \({}^{\circ}\)C to achieve a uniform blending temperature. Subsequently, the beaker containing the bitumen was suspended in an oil bath for 20 min to stabilize the bitumen temperature before the addition of Kraft lignin. Due to the sticky nature of Kraft lignin, the addition process of the additive was carried out gradually within 15 min, followed by 45 min of blending time, for a total of 60 min. In order to enhance the accuracy and comparability of test results, two (2) virgin bitumen specimens were processed using the same blending protocols without the addition of Kraft lignin, referred to as VB-MM for the mechanical mixer and VB-HSM for the high-shear mixer. The samples in this study were named according to Table 4.  
Figure 2: Particle size distribution of Kraft lignin determined by laser diffraction technique.
## 6 Methodology
### Description of the Experiments
#### Scenario 2  
The objective of Scenario 2's experiments was to assess the network behavior of a linear topology in terms of latency and PLR. The experimental evaluation of this topology, subject to different values of PIR, would validate the _software-in-loop_ approach developed in this work.  
For measuring both PLR and latency, each transmitter node (from 1 to 9) wrote to a log file the information associated to every packet it generated. This information included a timestamp and a unique packet ID. Since that information was included into the packet payload, it could be retrieved at the receiver side, and ultimately written into a reception log. All transmitters' and receivers' logs were compared after the execution of the scenario finished. PLR was calculated as in Scenario 1, taking into account that all messages were intended to reach Node 0. Moreover, latency or end-to-end delay was estimated considering the packet-embedded timestamp. It must be taken into account that all the nodes share a common OS clock reference, since the emulated STANAG 5066 stacks are independent processes of the same machine. If a multi-computer simulation approach had been used instead of concurrent execution, the latency would have been impossible to measure, and round-trip time would have substituted it, since no synchronization is needed between nodes.  
Different simulations were run to test how the network behaved subject to different values of PIR. For each value of PIR, every node generated packets with a period \(t_{gen}=L\cdot PIR^{-1}\), with \(L\) the network cardinality.  
For all the simulations using this scenario, STANAG 5066 ARQ service was used. This means that a STANAG 5066 link is necessary to be created for packet transmissions between nodes. STANAG 5066 link is a handshaking mechanism in which STANAG 5066 standard ensures that the destination node is available to receive data. An initial message was sent from Node 9 to Node 0 prior to start the simulation itself. This strategy ensures that all the links are properly established before generating traffic. The daisy-chain network configuration will make this initial packet to pass through all the nodes of the network, forcing the creation of STANAG 5066 links among neighbour nodes.  
Table 5 summarizes the used PIR values and their corresponding \(t_{gen}\). For each PIR configuration, the simulator ran for 48 h. The total amount of handled packets is also specified in the table.
## 3 Simulation Results
#### Case Study  
Consider now three different cases, with \(r_{f}=r_{\mathcal{G}}=0.723\,\mathrm{au}\), \(r_{f}=r_{\mathcal{G}^{\prime}}=1.524\,\mathrm{au}\), and \(r_{f}=r_{\mathcal{V}_{\mathrm{b}}}=5.2\,\mathrm{au}\), which model a simplified two-dimensional transfer from Earth to Venus, Mars, and Jupiter, respectively. Using the mathematical model described in the preceding section and a diffractive sail with a characteristic acceleration \(a_{c}=1\,\mathrm{mm}/\mathrm{s}^{2}\), the minimum flight time is about 189 days for the Venus case, 365 days for the Mars case, and 2420 days for the Jupiter case. In addition, according to Figure 9, for a reflective sail-based scenario of equal characteristic acceleration, the minimum flight times are about 408 days for the Mars case, 205 days for the Venus case, and 377 days for the Jupiter case. A comparison between the optimal performance of a reflective and a diffractive sail is summarized in Table 1, where the last column highlights that the flight time saving with a diffractive sail is considerable (about 36%) in the Earth-Jupiter transfer scenario. Note that the performance improvement is about 10% for the Mars and Venus case, which is due to the reduced value (when compared with the Jupiter trans) of the typical flight time for a solar sail with \(a_{c}=1\,\mathrm{mm}/\mathrm{s}^{2}\).  
The optimal transfer trajectories are sketched in Figure 12 along with a comparison of the optimal transfer trajectories for the ideal reflective case, while Figure 13 shows the time variation of the control parameter \(\tau\). In particular, there exists a substantial difference between the two optimal transfer trajectories in the Earth-Jupiter mission case. The interesting aspect that emerges from Figure 13 is that in the Earth-Jupiter scenario, the control parameter is nearly constant (\(\tau=-1\)) along the entire flight, and the state of EOCPs is equal to 2 during the transfer; see Equation (12). Such a particular behaviour suggests a potential approach to the trajectory optimization, which could exploit an analytical, albeit approximate, method to evaluate the minimum flight time by assuming a constant value of the control parameter \(\tau\). The latter analysis point is beyond the scope of the current paper and represents the natural extension of the mathematical model discussed in the preceding section.  
Figure 12: _Cont._Figure 12: Optimal transfer trajectory for the three mission scenarios. Black circle \(\rightarrow\) start, black square \(\rightarrow\) arrival, blue line \(\rightarrow\) parking orbit, red line \(\rightarrow\) target orbit, black line \(\rightarrow\) optimal transfer trajectory.  
Figure 13: _Cont._
# GIS-Based Wind and Solar Power Assessment in Central Mexico
## 3 Results
### Solar Global Resources Assessment  
According to data from AWSs, the average solar irradiation in Central Mexico is 6.16 kWh/m\({}^{2}\)/day. Figure 9 shows the solar average irradiance by month.  
Figure 9: Average solar irradiation in Central Mexico.  
Figure 9 shows the average global irradiance of Central Mexico. It can be observed in which months there is greater solar potential; these months are March, April, and May, with more than 1000 W/m\({}^{2}\). However, February, June, July and August also present around 900 W/m\({}^{2}\).  
Solar irradiation has been modeled using ArcGIS with the kriging interpolation model, and will be presented annually and in blocks of four months, for the reader's convenience. Figure 10a-d shows the average solar irradiation on January, February, March, and April.  
In these first months of the year, the most intense solar radiation or quantity of solar radiation occurs in the states of Guanajuato, Queretaro, Hidalgo, State of Mexico, Mexico City, Morelos, and in the northern part of Puebla.  
The irradiation in May, June, July and August is presented in Figure 11a-d.  
Figure 10: Average solar global irradiation in Central Mexico in (**a**) January, (**b**) February, (**c**) March, and (**d**) April.  
During the months of Figure 11, the solar irradiation with the highest magnitude occurs in the states of Hidalgo, southern Guanajuato, southern Queretaro, State of Mexico, Mexico City and Morelos.  
The irradiation in September, October, November and December is presented in Figure 12a-d.  
Figure 11: Average solar global irradiation in Central Mexico in (**a**) May, (**b**) June, (**c**) July, and (**d**) August.  
In the last months, shown in Figure 12, the solar irradiation with the highest magnitude is only present in the states of Hidalgo, southern Guanajuato, southern Queretaro and northern Mexico State.  
Figure 13 shows the annual average solar irradiation in Central Mexico.  
Figure 12: Average solar global irradiation in Central Mexico in (**a**) September, (**b**) October, (**c**) November, and (**d**) December.  
According to the map in Figure 13, the average global solar irradiation levels are between 5.60-5.89 kWh/m\({}^{2}\)/day; the highest magnitudes are present in the states of Queretaro, southern Guanajuato, Mexico City, Hidalgo, Estado de Mexico, and Morelos.  
The figures on solar irradiation show the areas with the highest solar potential for each month of the year, and can be used for the installation of photovoltaic panels in these areas.  
If 1000 photovoltaic panels with a capacity of 300 W each were installed, we would have a capacity of 300 kW; therefore, with a solar irradiation of 7.17 kWh/m\({}^{2}\)/day in May, we would be producing 2151 kWh.
# Thermal Interaction and Cooling of Electronic Device with Chiplet 2.5D Integration
## 2 Thermal Interaction Model of Chiplet 2.5D Integration  
### Chiplet 2.5D Integration  
Figure 1 illustrates the typical schematic of chiplet 2.5D integration, whereby two chiplets are mounted to an interposer via microbumps, and the interposer is bonded to the substrate using C4 bumps. The underfill is filled in between the chiplets and the interposer, as well as between the interposer and the substrate. A Cu heat sink as lid is attached to the top surface of the chiplets and the substrate via TIM and adhesive. This type of cooling scheme is called near-chip cooling. In contrast to remote cooling, near-chip cooling  
eliminates the spreader and a layer of TIM, resulting in a shorter heat transfer path. In contrast to embedded cooling, near-chip cooling has one more layer of TIM, yet it simplifies the fabrication process.
## 3 Results and Discussion
### Mechanism of Lithium Entrapment into Solid Phase
Further analysis was carried out by combining scanning electron microscopy (SEM) and XRD results, which confirmed that some of the small granular crystals observed in SEM were Li\({}_{2}\)SO\({}_{4}\) crystals. This implies that Li\({}_{2}\)SO\({}_{4}\) crystals are entrained in MgSO\({}_{4}\) crystals and adhere to the surface of MgSO\({}_{4}\) in the form of small granules. Consequently, during the brine cooling process, lithium loss occurs in the form of Li\({}_{2}\)SO\({}_{4}\cdot\)H\({}_{2}\)O crystal entrainment into MgSO\({}_{4}\) crystals. Figure 8: Solid-phase SEM and EDS results: **(a,b)** represent the solid phase obtained by cooling crystallization of brine; **(c)** represents the crystals obtained by cooling crystallization of a pure lithium sulfate solution; and **(d)** represents the crystals obtained by cooling crystallization of a pure magnesium sulfate solution. Therefore, during the variable-temperature enhanced magnesium-lithtium separation process, the change in the MgSO\({}_{4}\) complex structure in solution due to a decrease in temperature results in a rearrangement of the hydration layers and promotes MgSO\({}_{4}\) crystallization. This alteration in water structure within brine increases the hydrated layer distance between Li\({}^{+}\)-SO\({}_{4}\)\({}^{2-}\) clusters and enhances the generation of Li\({}_{2}\)SO\({}_{4}\) crystals, leading to lithium loss during the magnesium-lithtium separation process.
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 2 Theoretical and Technical Foundations
### System Architecture of Wi-Fi Data Acquisition  
The system architecture is shown in Figure 1, and mainly includes the Wi-Fi data acquisition module, data upload module, and background servers. The Wi-Fi data acquisition module uses a TZ Wi-Fi probe. The data upload module uses commercially available mobile hotspot devices to provide Wi-Fi probes with the network required for data upload in various scenarios and ensures the time synchronization of different probe devices. The background server is an efficient and stable Ali Cloud server that can meet the needs of multiple Wi-Fi collection devices to upload data simultaneously. The proposed devices do not require calibration. The primary role of wireless devices is to detect time and estimate location, which are achieved through algorithms.
# A Modular Web-Based Software Solution for Mobile Networks Planning, Operation and Optimization
## 6 Case Studies  
One of the main purposes of the solution is to provide access to a set of high-quality RAN algorithms as a backbone for network simulation and optimization. These algorithms support a user to monitor each network element performance, to conduct processes of Root-Cause Analysis (RCA) for network faults, as well as to identify efficient actions for network performance optimization. For all these tasks, the access to accurate, complete and relevant network data is of utmost importance. The data layer fulfils these requirements and aggregates several network data sources, as network topology, DT data, CM data, PM data, and EM.  
We now show two typical use cases for the solution. In both cases, the dataset to be used is composed by a subset of a real MNO topology data, with the respective CM and PM data, and a set of DT campaigns. Moreover, the dataset is bounded geographically to a urban/suburban area of interest and it provides information about 2G, 3G and 4G technologies.  
The CM information included both configuration parameters and information about the network topology (sites and cells). Overall, for each site, its location, type (e.g., macro, micro, indoor), and the vendor are provided. For each cell, the available information includes the cells' antenna height, azimuth, band, frequency, and bandwidth. The CM parameters encompass the MDT, EDT, maximum transmission power, and handover configurations.  
The PM data, which was not available for all network cells, included several KPIs from three groups. A group of KPIs reporting the distribution of UE CQIs, considering the CQI indexes (from 0 to 15), another group measuring the cell downlink throughput, and the last group indicating the number of successful establishments, both globally and by the establishment cause (e.g., emergency, high priority, mobile originating data). Additionally, all the available KPIs report to a period of two months with a temporal resolution of 15 min.  
The available DT measurements include information about the received power (Received Signal Strength Indicator (RSSI) for 2G, Received Signal Code Power (RSCP) for 3G, and Reference Signal Received Power (RSRP) for 4G), and the interference level (Reference Signal Received Quality (RSRQ) for 4G and Energy per Chip on Spectral Noise Density (Ec/No) for 3G). Additionally, each DT measurement includes the cell identification (Broadcast Control Channel (BCCH) and Base Station Identity Code (BSIC) for 2G, the Primary Scrambling Code (PSC) for 3G, and the Physical Cell Identity (PCI) for 4G), and the measurement frequency, besides the respective measurement location.  
Notice that both use cases underlie very interactive workflows, which makes difficult to truly depicting them in this document. Anyhow, the goal is to provide a glimpse about the potentialities of the solution.
## 3 Materials and Methods  
### Sentinel-2 Data Extraction  
The Sentinel-2 L2A data include surface reflectance computed using the Sentinel-2 correction (Sen2 cor) algorithm [54]. Analysis primarily focused on MSI bands 1 (443 nm) to 8 (842 nm) that have been useful for Chl-a retrieval for Case-1 and Case-2 waters. We resampled band 1 and bands 5-7 from their original 60 m GSD and 20 m GSD, respectively, down to 10 m to match the spatial sampling of the highest resolution bands using a nearest neighbor resampling method.  
We wrote JavaScript in GEE to select and extract Sentinel-2 L2A data based on the NYS boundary, within the temporal window that matched the in situ data. We initially used a QA60 bitmask approach [55], which is the ESA-provided cloud mask layer and is suggested for Sentinel-2 data in GEE. We smoothed the imagery using a 3 \(\times\) 3 window to mitigate possible registration issues between the in situ data and the imagery. With a goal of correlating spectral and in situ data, rather than downloading imagery, we generated an Excel file that contained the DNs of the spectral data corresponding spatially and temporally to the location of the CSLAP sampling sites.