esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Reinforcement Learning to Optimize Fusion Parameters
#### 3.3.3 Q-Learning in RL-Fusion
\begin{table}
\begin{tabular}{c c c c} \hline
**Action Category** & **Action Description** & **Parameter Range** & **Specific Adjustment Method** \\ \hline \multirow{4}{*}{Weight adjustment} & Reconfigure the weights of & & Increase or reduce the weight of a \\  & each base model in the fusion & & base model, the step size is \\  & model to enhance or attenuate & & \(\mathrm{w}_{\mathrm{i}}\in[0,1],\sum\mathrm{w}_{\mathrm{i}}=1\) & \(\Delta\mathrm{w}=0.05\), For example, the current \\  & the impact of a particular & & weight \(\mathrm{w}_{\mathrm{1}}=0.5,\mathrm{w}_{\mathrm{2}}=0.5\), the action \\  & model. & & will adjust \(\mathrm{w}_{\mathrm{1}}\) to \(0.55\) and \(\mathrm{w}_{\mathrm{2}}\) to \(0.45\). \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Fusion method \\ selection \\ \end{tabular} } & Different model fusion & & Choose a new fusion method. For \\  & methods were chosen, Slerp, & & example, switch from the current \\  & TIES & & TIES to Slerp. \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Learning rate \\ adjustment \\ \end{tabular} } & The learning rate during the & & & A linear or exponential adjustment \\  & model fusion is dynamically & & based on the current learning rate, for \\  & adjusted to control the pace of & & example, from \(\eta=0.01\) was increased \\  & parameter updates. & & & to \(\eta=0.02\) \\  & & & Or reduced to \(\eta=0.005\). \\ \hline \end{tabular}
\end{table} 
Table 1: Action description. At each time step, the agent selects an action based on the current state of the model. The Q-function is subsequently updated using the current Q-value and the observed reward, refining the estimates of different parameter configurations. Through repeated iterations, Q-learning progressively optimizes the base model parameters to maximize long-term rewards, thereby improving the performance of the final fused model. This update is governed by the following Equation (12):  
\[\text{Q}(\mathbf{s_{t}},\mathbf{a_{t}})\leftarrow\text{Q}(\mathbf{s_{t}}, \mathbf{a_{t}})+\alpha[\mathbf{r}_{t+1}+\gamma\text{max}\text{Q}(\mathbf{s_{t+1 }},\mathbf{a})-\text{Q}(\mathbf{s_{t}},\mathbf{a_{t}})], \tag{12}\]  
where \(\text{Q}(\mathbf{s_{t}},\mathbf{a_{t}})\) represents the Q value of the at taking the action in state st; \(\alpha\) is the learning rate and controls the magnitude of the Q value update; \(\mathbf{r}_{t+1}\) is the immediate reward after executing the action in the current state; \(\gamma\) is the discount factor indicating the effect of the future reward on the current decision; \(\mathbf{s_{t+1}}\) is the new state after the action is executed; \(\text{max}\text{Q}(\mathbf{s_{t+1}},\mathbf{a})\) represents the maximum Q value of all possible actions in the new state. As the Q-values are updated, the agent can progressively optimize the model fusion performance by continuously adjusting the model parameters (model weights, fusion methods, etc. ), ultimately converging to the optimal configuration.
## 4 VSG Applications of Power Electronics-Based Units
### Demand Response  
Demand-side management (DSM) was introduced in the late 1970s in response to the oil crisis, which spurned awareness of the need for energy conservation [52]. During consumption fluctuation, load management can improve energy efficiency; this is one of the central advantages of smart grids, and improves the system's network dynamics, stability, line losses, and operation costs [53; 54; 55; 56]. Most DSM is based on information technology (IT) and authorization from customers to disconnect selected appliances in order to perform a set of functions such as generation/demand forecasting, demand scheduling, and peak shaving, according to the demands of the market. Demand-side response inherently consists of mathematical optimization problems that need to be calculated in real  
Figure 11: Framework for vehicle-to-grid (V2G) service.  
Figure 10: Schematic of a vulnerable AC system with an auxiliary VSG energy storage system (ESS).  
time. These occur through integer programming formulations that find time-variable solutions for every single appliance. Then, the power access for each single appliance is approved or denied by the DSM control. However, since portions of the load can tolerate low power for some time, charging EVs or heating can be extended under continuous low-power consumption. Thus, it is better to perform continuous control for extendable loads in order to improve service quality by reducing rate of demand rejection.  
Synchronous machines can adjust their output autonomously according to the status of the system and shift their operation mode from the generators to the motors. Regarding the inner magnetic fields of synchronous machines, the stator magnetic field rotates behind the rotor and tries to align with it while converting the generator mode from mechanical power to electric power. The opposite is the motor mode, which pulls the rotor to rotate [16]. Such conversion can also be implemented by remodifying the control channels of virtual synchronous machines to operate as rectifiers that consume power. In this way, large-scale DC extendable loads can perform as virtual synchronous motors that regulate power in a continuous way, as shown in Figure 12. If a large regional load on a VSG-controlled converter station performs DSM, then considerable operation reserve can be achieved.
## 2 Literature Review
### Tag Movement and Direction
(2017) utilized XGBoost to classify the movement and direction of tagged products. Several movement types such as move in, move out, move close, static and turn back were considered in this study. The statistical features were extracted from RSS and timestamps of the single reader with two antennas. The result showed that the proposed model outperformed other machine learning models with an accuracy of up to 93.5%. Finally, [PERSON] et al. (2016) propose the state detection of multiple RFID tags using a single antenna placed at a certain angle. The states detected are stay still, forward, and backward movement. The purpose of the angled placement of the antenna is to obtain different values of received signal strength indication (RSSI) and phase angle according to the state of RFID tags. Those values are then evaluated using a random forest algorithm to define the state. The proposed method was evaluated through the experiment with 70 RFID tags and the better performance was shown by the angled antenna compared to the normal placement (without angle).
# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 3 Proposed Congestion Control Prediction Approach  
The congestion control algorithm is the essential mechanism of the SCTP and functions through the transport layer. Many tasks, comprising the transmitting and drawing of messages, error detection, and the retrieval of misplaced messages, can be achieved provided that the SCTP engages throughout this layer. These functions are adjusted to the obtainable utilization of the network. The number of terminals required to accomplish these functions is examined precisely as a result of the terminals-area-used units.  
In the data transportation method, a transmitter must be linked to a recipient. The transmitter institutes a connection with a particular endpoint. It transmits the initial message and waits for acknowledgement (ACKed) to observe cwnd and ssthresh. When the recipient sends ACKed to the transmitter, cwnd and ssthresh are identified. Hence, congestion control is accomplished. If the recipient does not conduct ACKed, then the retrieval of the misplaced phase is done. If cwnd is less than or adequate to ssthresh, then the slow-start phase is established. When cwnd is larger than ssthresh, the congestion avoidance phases are executed. This process continues until all messages are delivered.
## 3 Dataset Construction of Landslides in Yiyang City
### Extraction of Landslide Predisposing Factors
#### 3.2.3 Environmental Factors  
Among the environmental factors, vegetation coverage indirectly influences slope stability through its impact on soil erosion [26]. The Normalized Difference Vegetation Index (NDVI) maps serve as indicators widely utilized for assessing vegetation coverage. By calculating the NDVI values of vegetation coverage before and after the landslide during the heavy rainfall, we derived the NDVI value from the Landsat 8 OLI satellite  
Figure 5: (**a**–**e**) Topographical factors.  
remote sensing image acquired on 30 July 2016, as illustrated in Figure 6a,b. Subsequently, normalization was performed utilizing the following calculation formula:  
\[\text{NDVI}=(\text{NIR}-\text{R})/(\text{NIR}+\text{R}) \tag{1}\]  
The abbreviation NIR refers to the near-infrared band, while R denotes the red light band. We also took into consideration the effect of anthropogenic activities, with a focus on road network density and distance to roads as two key factors for comprehensive assessment. Higher road network density and closer proximity to roads are associated with increased frequency of human activities, potentially exerting an influence on slope stability through anthropogenic engineering interventions. In addition, we derived the spatial distribution of soil types in the study area from the soil type data in Hunan Province and subsequently utilized it for a statistical analysis of landslide occurrences in each predominant soil type, aiming to investigate the specific patterns of landslides associated with different soil types. Figure 6a-d display the remotely sensed NDVI, soil type, distance to roads, and road network density, along with other environmental variables.
## 4 Results and Discussion  
### Field Tests and Parameter Determination  
(1) Permeability coefficient of the unsaturated zone  
The test is to raise the water head artificially by injecting water into the test pit. It is an in-situ test method for measuring the permeability of loose rock and soil. The permeability coefficient of the unsaturated zone is measured by a water injection test of double ring. Two permeability tests were conducted in this study with the numbers of T1 and T2. The coordinates of T1 are 104\({}^{\circ}\)54\({}^{\prime}\)36.51\({}^{\prime\prime}\) E and 25\({}^{\circ}\)12\({}^{\prime}\)12.59\({}^{\prime\prime}\) N with the elevation of 1089.90 m. In addition, the coordinates of T2 are 104\({}^{\circ}\)55\({}^{\prime}\)16.01\({}^{\prime\prime}\) E and 25\({}^{\circ}\)11\({}^{\prime}\)18.07\({}^{\prime\prime}\) N with elevation of 1026.7 m. The data of tests are shown in Figure 4. The final steady infiltration rate at the end of T1 was 4.95 cm\({}^{3}\)/s. The area of the ring was 490.625 cm\({}^{2}\). The water level in the pit is 10 cm, and infiltration depth of groundwater was 40 cm. Hence, the calculated permeability coefficient of the unsaturated zone was 0.0081 cm/s. The infiltration rate for T2 was 14.1 cm\({}^{3}\)/s and the infiltration depth was 35 cm. The calculated permeability coefficient was 0.022 cm/s. The average value of 0.015 cm/s was regarded as the permeability coefficient of the unsaturated zone.  
(2) Division of Permeability coefficient zone  
According to the characteristics of lithology and discontinuity in the dam site, the media were divided into three zones in the vertical direction. They are strong, weak, and slight weathering zones from top to bottom, respectively. The media were also influenced by river erosion. There existed weathering, unloading, and fracture dissolution in a certain range of valley slopes on both banks. The adit data showed that the integrity of rocks is better in the mountain body than near the valley. Therefore, the mountain bodies of both banks are divided into three zones longitudinally. They are strong, weak, and slightly  
(3) Calculation of hydraulic conductivity for karst conduit  
Five karst pipes were considered in the study area (Figure 5). According to the measuring data from Table 4 and Equations (6) and (10), the calculation results of the main hydraulic conductivities for karst conduits are listed in Table 6.
## 3 Design of the Pressure Control Algorithm
### Design of the Adaptive Fuzzy PID Algorithm  
#### 3.2.1 The Adaptive Fuzzy PID Control Algorithm  
The principle of the adaptive fuzzy PID controller for the pressure control system is shown in Figure 5. The system pressure deviation e and the deviation change rate \(ec\) are used as the input of the adaptive fuzzy PID controller. The PID parameters \(\Delta K_{p}\), \(\Delta K_{i}\), and \(\Delta K_{d}\) are outputs of the controller. The PID parameters were adaptively adjusted online by the fuzzy inference method, which can meet different requirements of the different deviation \(e\) and the deviation change rate \(ec\) so that the controlled object achieves the quantitative dynamic and static performance.  
According to the adaptive fuzzy PID control principle, the adaptive fuzzy PID controller with compensation correction could be defined as follows:  
\[u(t)=(K_{p0}+\Delta K_{p})e(t)+(K_{i0}+\Delta K_{i})\int_{0}^{t}e(x)d(t)+(K_{d0 }+\Delta K_{d})\frac{de(t)}{dt}+\frac{dx_{\rm L}}{dt}G_{c}(s) \tag{26}\]  
where \(K_{\rm p0}\), \(K_{i0}\), and \(K_{\rm d0}\) are the initial set values of PID parameters.
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## 3 Proposed Method for Signal Detection
### Convergence Analysis of OAOR Method
: _If \(\rho(\mathbf{M})<1\), then the convergence rate of the OAOR method is linear, with_  
\[\mathbf{e}^{(k+1)} =\mathbf{\hat{x}}-\mathbf{x}^{(k+1)} \tag{30}\]3  
_for some constant \(C>0\)._  
Proof. : From the error equation derived in the previous theorem,  
\[\mathbf{e}^{(k+1)} =\mathbf{\hat{x}}-\mathbf{x}^{(k+1)} \tag{30}\]4  
Taking norms and using the triangle inequality,  
\[\mathbf{e}^{(k+1)} =\mathbf{\hat{x}}-\mathbf{x}^{(k+1)} \tag{30}\]5 \[\mathbf{e}^{(k+1)} =\mathbf{\hat{x}}-\mathbf{x}^{(k+1)} \tag{30}\]6  
For any matrix norm \(\|\cdot\|\) compatible with the vector norm, we have  
\[\mathbf{e}^{(k+1)} =\mathbf{\hat{x}}-\mathbf{x}^{(k+1)} \tag{30}\]7  
Therefore, for any \(\epsilon>0\), there exists a constant \(C_{1}\) such that  
\[\mathbf{e}^{(k+1)} =\mathbf{\hat{x}}-\mathbf{x}^{(k+1)} \tag{30}\]8  
Choosing \(\epsilon\) small enough such that \(\rho(\mathbf{M})+\epsilon<1\), and letting \(C=\max\{C_{1}\|\mathbf{e}^{(0)}\|\), \(C_{1}\|(\mathbf{I}-\mathbf{M})^{-1}\|\cdot\|\mathbf{c}\|\}\), we obtain  
\[\mathbf{e}^{(k+1)} =\mathbf{\hat{x}}-\mathbf{x}^{(k+1)} \tag{30}\]9As \(k\to\infty\), this approaches \(C\rho(\mathbf{M})^{k}\), proving linear convergence. To understand the complete signal detection implementation using the Proposed OAOR method, a numerical example is given in Appendix B.
# Versatile High-Throughput Platform for Focused Ultrasound In Vitro Application
###### Abstract  
For the more efficient application of ultrasound in future therapies, fundamental research is needed on the mode of action of ultrasound on biological systems using therapeutic frequencies. To address this need, a new versatile high-throughput platform for focused ultrasound in vitro application was designed, developed, and characterized. The applicator was aligned with the dimensions of a 96-well plate and frequencies commonly used in the therapeutic ultrasound range (0.5-2.0 MHz). Two different platform configurations were developed: (a) a low-intensity version with 96 individual transducers allowing dry coupling of the well plate; and (b) a high-intensity version with water cooling, supporting parallel sonication of 32 out of 96 wells. The platforms were characterized by performing an analysis of the homogeneity of the sound pressure and intensity, the impact of filled volume per well, the cross-coupling effect between the wells, and the influence of the well plate. The low-intensity design delivers pressure levels up to 605 kPa inside the well with maximum I\({}_{\text{SPPA}}\) values between 0.78 and 12.38 W/cm\({}^{2}\). In contrast, the high-intensity system achieves pressures up to 1460 kPa and a maximum I\({}_{\text{SPPA}}\) of 72 W/cm\({}^{2}\) inside the wells. The successfully developed high-throughput platform supports parallelized sonication in standard, well-plate formats and is suitable for focused ultrasound applications in vitro.  
high-intensity focused ultrasound; in vitro setup; low-intensity focused ultrasound; therapeutic ultrasound; versatile high-throughput platform
## 2 Software-Defined GNSS Receiver  
A GNSS software-defined receiver consists of three major components: RF front-end unit, a signal processing unit, and a navigation processing unit. The RF front-end module is responsible for signal amplification, noise filtering, down-conversion, automatic gain control, and analogue-to-digital conversion. The front-end module converts the received analog data to digital Intermediate Frequency (IF) data at a rate which is several times more than the code chipping rate. A 26 MHz sampling frequency is used to generate the raw digitized IF samples in all the experimented cases of this work.  
The digital IF data are then processed by a signal processing unit whose main responsibilities include signal acquisition, code and carrier tracking and data demodulation. The demodulated data and the resulting pseudorange measurements are then utilized by a navigation processing unit in order to offer a Position, Velocity and Timing (PVT) solution, along with some other relevant information. The software-defined receiver differs from a conventional receiver in the sense that the functions of the processing and navigation units, including correlation/tracking and navigation tasks, are delivered by software, leading to a more flexible design with potential savings in cost and power.  
A software-defined GNSS receiver platform, named as FGI-GSRx (Version 2), has been developed in Finnish Geodetic Institute for the analysis and validation of novel algorithms for an optimized GNSS navigation performance. The basic version of FGI-GSRx is based on an open-source software receiver platform [13], and it has been adapted to be BeiDou-compatible with a dual-frequency front-end from Nottingham Scientific Limited (NSL) [18]. The NSL front-end,'stereo v2' is used to capture the BeiDou data. The stereo front-end configuration is presented in Table 1. The BeiDou B1I signal spectrum, time-domain plot and bin distribution of the digitized IF samples are shown in Figure 1.
# Windowed Joint Detection and Decoding with IR-HARQ for Asynchronous SCMA Systems
### Main Contribution  
This paper focuses on exploring the performance of an RC-LDPC code-based IR-HARQ scheme for SCMA systems. In [23], [PERSON] et al. explain the fundamental principle of this scheme and present a joint detection and decoding algorithm. However, the incremental decoder utilized in their work is solely related to the detection in the current transmission round, and not across different rounds. Consequently, there is room for optimization of the decoding algorithm's efficiency and accuracy. To address this, we propose the transmission mechanism of the asynchronous IR-HARQ scheme, in which information can be sent during different transmission rounds based on each user's channel conditions. This approach enables the incremental decoders and MUD to consider not only the extrinsic information at the current time unit but also at previous time units. Moreover, the windowed joint detection and decoding algorithm to simulate this scenario is introduced. The simulation indicates that the proposed scheme can enhance the bit error ratio (BER) performance and achieve better throughput over a wide range of SNRs.  
The novelty and contributions of this paper can be summarized as follows:  
* We build an asynchronous uplink SCMA system with the RC-LDPC code-based IR-HARQ scheme and present the transmission mechanism of the asynchronous IR-HARQ scheme.
* We propose a novel windowed joint detection and decoding algorithm with IR-HARQ for asynchronous SCMA systems.
# Antioxidant and Antimicrobial Activity of Porcine Liver Hydrolyates Using Flavourzyme
## 2 Materials and Methods
### Antioxidant Activity
#### 2.5.4 Ferric Ion-Reducing Antioxidant Power (FRAP)  
The determination of the reducing power was effectuated applying a few adjustments to the original method [32]. The ability of extracts to reduce ferric ions (Fe\({}^{3+}\)) to ferrous iron (Fe\({}^{2+}\)) is the basis of this antioxidant assay. A navy-blue coloration is generated due to the formation of a Fe\({}^{2+}\)-TPTZ complex, which color intensity is proportional to the reducing capacity of the sample. The reaction is conducted in an acid medium to maintain iron solubility. Fresh FRAP reagent was constituted by the mixing of 300 mM acetate buffer (pH 3.6), 20 mM FeCl\({}_{3}\):6H\({}_{2}\)O, and 10 mM TPTZ in 40 mM HCl in a ratio of 10:1:1 (_v:v:v_). Nine hundred microliters of this solution, 30 \(\upmu\)L of appropriately diluted samples, and 90 \(\upmu\)L of distilled water were mixed. Subsequently, they were heated at 37 \({}^{\circ}\)C in the absence of light. Absorbances were read at 593 nm after 20 min of reaction and compared with the FeSO\({}_{4}\) standard curve and results were expressed as \(\upmu\)mol Fe\({}^{2+}\)/100 g pork liver.
## 3 Results
### Data Portal  
All regional datasets can be viewed and downloaded using a customized web application found at [[http://caribbeanmarinemaps.tnc.org/](http://caribbeanmarinemaps.tnc.org/)]([http://caribbeanmarinemaps.tnc.org/](http://caribbeanmarinemaps.tnc.org/)) (accessed on 19 October 2021; Figure 15). This Google Earth Engine app was developed as a tool for sharing the resulting map layers to non-technical stakeholders. Google Earth Engine (GEE) is an open-source remote sensing tool that provides free access to satellite imagery and analysis and allows geospatial developers to run complex geoprocessing and remote sensing functions [62]. GEE apps provide users with the ability to query, filter, visualize, and download datasets without technical expertise, software licenses, or extensive storage capacity. It also allows for exploration of habitat composition statistics developed from this map. For example, pie charts can be automatically generated that show the area totals for each benthic habitat class by selected geography. This app was embedded into the Caribbean Marine Maps site (CaribbeanMarineMaps.tnc.org, accessed on 19 October 2021), an ArcGIS Online StoryMap, that facilitates easy access to The Nature Conservancy's suite of coral and marine data resources in the Caribbean. The site includes data visualization tools, access to data downloads, training videos, and scientific information explaining the development process and utility as well as limitations of various datasets.  
Recognizing the need for additional refinement to the accuracy of these maps, an online tool was developed and can be accessed at the above site that permits expert feedback to be collected using both the 'Public Data Collection' feature in an ArcGIS Online web map and the Survey123 form. Experts can locate errors and suggested geolocated corrections to the habitat classes throughout the region. This feedback is critical to adjust and fine-tune the accuracy of the benthic habitat classes and is being shared with local experts, such as marine park managers, coral reef researchers, and divers, to collect iterative feedback  
Figure 14: Percentage of coral reef and seagrass within the shallow zone (<30 m) under protection or management regardless of management objectives by jurisdiction. The shaded areas represent the Exclusive Economic Zone (EEZ), and percentages are calculated not on total marine space for each jurisdiction, but the total area of shallow coral reef and seagrass area within each jurisdiction’s declared protected or managed area boundaries.  
on the accuracy of the map at specific locations. The collection of expert knowledge is an on-going effort, and all spatial feedback is compiled and used to manually adjust and improve future versions of the product. These datasets will also be used to train future benthic habitat class mapping algorithms.