esa-sceva/satcom-chunk-collection huggingface dataset:
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### Polynomial Regression
#### 3.6.2 Noise Figure vs. Gain  
The target gain is a crucial parameter of an EDFA. As shown in Equations (9) and (13), channel gain significantly affects the noise figure. It appears explicitly in the formulas and also influences the value of \(P_{ASE_{AMP}}\). Specifically, SSE remains constant with respect to channel gain since it depends solely on input signal parameters and not on the amplifier's characteristics. In contrast, ASE noise exhibits a linear increase (in dBm units, which implies exponential growth) with respect to gain, as expected. This behavior is depicted in Figure 8a, which also includes a linear fit of the curve.  
In Figure 8b,c it is possible to observe that both ASE and amplified SSE exhibit linear behaviors with respect to gain, albeit with similar slopes. Consequently, \(P_{ASE_{AMP}}\) also demonstrates a linear behavior, with values lying between ASE and amplified SSE. At lower input power levels, the curve closely aligns with ASE, reflecting the significantly lower level of amplified SSE. As input power increases, the impact of \(P_{ASE_{AMP}}\) becomes more pronounced.  
In all test scenarios, \(P_{ASE_{AMP}}\) consistently rises with channel gain, with its linear approximation slope always below 1, nearing this limit as gain increases. As a result, the difference in \(P_{ASE_{AMP}}-G\), as indicated in Equation (9), decreases as channel gain rises. Given that the third term in the equation remains constant with respect to gain (depending solely on frequency), we can deduce that the noise figure will exhibit a similar trend, showing lower values at higher target gains. These findings are depicted in Figure 9. The noise figure declines more sharply at lower gains because \(P_{ASE_{AMP}}\) changes less with gain,  
Figure 8: (a) ASE vs. gain, with linear fitting; (b) \(P_{ASE_{AMP}}\) vs. channel gain at low input power and (c) at high input power.  
Figure 7: Noise figure vs. input power, at various input parameter configurations.  
but the slope flattens as gain increases. A third-degree polynomial effectively models the tested curves, resulting in a very low Root Mean Square Error (RMSE).
## 4 Discussion
### SARA Is More Suitable Than IHA for Kinematic Analyses Based on Noisy Markers' Trajectories  
As pointed out in the literature [40], the intrinsic variability of the helical axis approach also affects its sensitivity to noise. As confirmed by the results presented in Section 3.4, in which different levels of additive noise were considered to alter the markers' trajectories as provided by the multi-body model, the performance of the IHA algorithm considerably worsen when SNR\({}_{\text{cmn}}\) values lower than 80 dB are selected. In particular, the AoR estimation accuracy seems to be mainly affected by the source of the common noise signal, which is used to simulate non-rigid deformations of the marker clusters such as the one related to the soft tissue artifact. Oddly, the rms distance measured at the maximum level of the common noise signal decreases while lowering SNR\({}_{\text{md}}\). This result might be due to the high level of random noise, which could contribute to the overall noise signal by subtracting to the common one. The SARA algorithm, on the other hand, can benefit from the lack of numerical differentiation and filtering that IHA methodology requires [34, 37]; therefore, it might be more suitable for any application based on position-based rather than velocity-based datasets, such as those provided by motion capture analysis. Numerical differentiation can indeed intensify the effect of noise on marker coordinates and compromise the reconstruction of joint kinematics. In addition, unlike the IHA methodology, the SARA algorithm does not require the calculation of the foot DoFs relative to the shank LCS, thus removing the impact of the errors associated with coordinate transformations. In support of these considerations, the AoR estimation performed by the SARA algorithm was always more accurate than the one obtained by application of the IHA methodology, even for high levels of noise.
# An Optimized Framework for WSN Routing in the Context of Industry 4.0
## 3 Proposed Framework
### Network Design  
Let us assume deployment of mobile sensor nodes have multiple sensing diodes, a high-end micro controller and one or more transceiver as per the requirement of the application of Industry 4.0. As the size of the node is very small in terms of memory, processing unit and energy, 6 LoWPAN is preferred for computation and execution process. The network is also assumed to work without a base station. All nodes are homogeneous in terms of functions. However, the head of the nodes requires control of the control interval of the messages. They are assigned weights with respect to their battery power, localization, mode of work (routers, wake or sleep), etc. Network setup can be observed from Figure 3, where it has 2 source nodes, 3 destination nodes, multiple relay nodes (one hop or two hop neighbor nodes) and multi point relay nodes. Coverage scalability is achieved in the graph G (with vertices V and edges E) with conditions:  
\[G=\{V,E\}=\{Si\}=\{Sm,Sd,Ss,Sr\} \tag{3}\]  
(\(Si=\) set of nodes n, \(Sm=\) set of multipoint nodes, \(Ss=\) set of source nodes, \(Sr=\) relay nodes, \(Sd=\) destination nodes)  
In \(Si\), there are four subgraphs composed of \(\{Sm,Sd,Ss,Sr\}\), which helps in transmission and reception of data with the help of multipoint and relay nodes. The mathematical analysis is followed in the same approach as in [26; 27]. It is performed to separate the nodes from each other in terms of their functions for smooth execution and to avoid unnecessary burden on the nodes. For optimized control intervals, it is necessary to divide the graph into subgraphs with particular functionality of the nodes. After division of nodes into different subsets with \((Sd=(V_{d},E_{d})\), \(Ss=(V_{s},E_{s}))\) one to one nodes will be connected for the initiation of the session. Wireless links are of three types: (1) symmetric links where the protocol of transmission and reception of data is same, (2) asymmetric links where the protocol for transmission and reception is different, and (3) missing links, which indicates the missing routes due to early death of nodes or environmental conditions. The sensor nodes are elected by proactive routing protocol OLSR, but optimized control interval is implemented during the transmission of messages (proposed approach) and the shortest path based on cooperative communication as was proposed in [22] is selected to transmit the data. Afterward, a reactive method is followed to manage the ad hoc nature of nodes.  
Figure 2: Distance measurements of mobile nodes.
## 3 Methodology
### Remote ID and UAV Hardware  
The Remote ID hardware used was two DroneTag Mini modules produced by DroneTag s.r.o. from the Czech Republic. It transmitted a broadcast Remote ID using Bluetooth technology and the network Remote ID used IoT 4G Chunghwa Telecom cellular network with a cloud-supporting database. The hardware size was 54 \(\times\) 35 \(\times\) 15 mm with a weight of 32 g equipped with internal GNSS, LTE, and Bluetooth antennas. Sensors for the GNSS, a barometer, and accelerometers were included for flight data measurement. It is a standalone module with a battery life of up to 14 h [35].  
During the flight test, the remote ID modules were mounted inside the fixed-wing UAVs, as shown in Figure 7. There were 5 fixed wings UAVs used, which were 2 Sky Surfers, 2 Mini Talons, and 1 Lanyu E-Fair. The UAV detail specification is described in Table 1. Each UAV was controlled by an experienced UAV pilot from our laboratory with a radio frequency remote controller.  
Figure 6: UTM monitoring application user interface (Note: Chinese characters are the road names and numbering).  
Figure 7: Fixed-wing UAVs used in the flight test with a Remote ID attached (red circle): (**a**) Sky Surfer 1, (**b**) Sky Surfer 2, (**c**) Mini Talon 1, (**d**) Mini Talon 2, and (**e**) Lanyu E-Fair with the Remote ID inset.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 2 Materials and Methods
### U-Net  
The U-Net is a fully convolutional neural network (FCN) which was first used for segmentation of biomedical images and was later adapted to space-based applications such as crater detection [28]. The network architecture in this paper is chosen to be a slightly modified version of the original one as shown in Figure 1, and it has a symmetric encoder-decoder structure: the encoding, downsampling path is a stacked sequence of two ReLU 3 \(\times\) 3 convolutional layers followed by a 2 \(\times\) 2 max pooling layer. At each level, the number of filters doubles, reaching its maximum value at the bottom.  
The decoding upsampling path contains 2 \(\times\) 2 up-convolutional layers concatenated with layers coming from the corresponding downsampling level in order to preserve already learned features. The concatenated layers are followed by dropout and a sequence of two 3 \(\times\) 3 convolutional layers up to the output layer in which a final sigmoid-activated 1 \(\times\) 1 convolution produces data ready to be binarized. The present U-Net will be fed with 512 \(\times\) 512 images and their corresponding masks.  
Configuration parameters used for this network to prevent overfitting are as follows:  
* Learning Rate: It is an optimizer's parameter that fixes the step size at each iteration during minimization of the loss function.
* Regularization Factor: It is a parameter needed for the l2 regularization based on penalization of the cost function.
* Dropout Rate: It regulates the percentage of inactive network elements in the dropout layers during training and the validation process.
* Kernel initializer: It sets the weight initialization method; in this case, it is set on he_normal [30].  
In this work, Adam was chosen as the optimizer and Binary Crossentropy was chosen as the loss function.
## 3 Results
### Diagnostic Accuracy of Salivary IL-6, IL-1\(\beta\), and CRP  
Based on the ROC curve analysis, salivary IL-1\(\beta\) and CRP showed good diagnostic accuracy (0.8 \(\leq\) AUC < 0.9) and IL-6 showed fair diagnostic accuracy (0.7 \(\leq\) AUC < 0.8), as categorized by [PERSON] et al. (2022) [23]. Statistical significance was found to distinguish children with moderate PIG from children with a severe degree of PIG (Table 2, Figure 4). At the optimal cut-off values, based on the largest [PERSON]'s index, IL-6, IL-1\(\beta\), and CRP demonstrated sensitivity values ranging from 75% to 87.5% and specificity values ranging from 63.16% to 83.33% (Table 3). Sensitivity for IL-6, CRP, and IL-1\(\beta\) was 87.5% (\(p=0.0384\)), 87.5% (\(p=0.0093\)), and 75% (\(p=0.0071\)), respectively, and specificity was 63.16% (\(p=0.0384\)), 78.95% (\(p=0.0093\)), and 83.33% (\(p=0.0071\)), respectively. IL-6 and CRP showed the highest sensitivity (87.5%) and IL-1\(  
We estimated the fair diagnostic accuracy (0.7 \(\leq\) AUC < 0.8) of the investigated salivary pro-inflammatory biomarkers in distinguishing children without PIG from those with a severe degree of PIG (ns) (Table 4) [23]. IL-6 and CRP showed the highest sensitivity (87.5%) and IL-1\(\beta\) showed the highest specificity (83.33%), with \(p\) = 0.1968 (Table 5). Salivary IL-6, IL-1\(\beta\), and CRP showed poor diagnostic capacity (0.6 \(\leq\) AUC < 0.7) to discriminate between children without PIG and those with a mild or moderate degree of PIG (ns) (Table 4) [23]. Our future research has to include a larger number of participants in all of the studied groups.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Control**} & \multicolumn{2}{c}{**Control**} & \multicolumn{2}{c}{**Control**} \\ \multicolumn{2}{c}{**Group—Mild**} & \multirow{2}{*}{_p_**-Value**} & **Group—Moderate Degree of Gingviitis** & \multirow{2}{*}{_p_**-Value**} & **Group—Severe Degree of Gingviitis** & \multirow{2}{*}{_p_**-Value**} \\ \multicolumn{2}{c}{**AUC (95\% CI)**} & & **AUC (95\% CI)** & & & **AUC (95\% CI)** & \\ \hline IL-6 & 0.5139 & 0.9254 & 0.6711 & 0.2148 & 0.7083 & 0.1968 \\ \hline IL-1\(\beta\) & 0.5139 & 0.9254 & 0.6435 & 0.3015 & 0.7083 & 0.1968 \\ \hline CRP & 0.5139 & 0.9254 & 0.6667 & 0.2267 & 0.7083 & 0.1968 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ROC logistic regression analysis of salivary IL-6, IL-1\(\beta\), and CRP levels comparing the control group with the groups with mild, moderate, and severe degrees of plaque-induced gingivitis.  
Figure 4: ROC curves of (**a**) IL-6, (**b**) IL-1\(\beta\), and (**c**) CRP as potential diagnostic biomarkers for differentiating children with a moderate degree of PIG from children with a severe degree of PIG. Significance level established at \(p\) < 0.05. Abbreviations: ROC—receiver operating characteristic; AUC—area under the curve; IL-6—interleukin-6; IL-1\(\beta\)—interleukin-1\(\beta\); CRP—C-reactive protein.
# Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures
## 3 Materials and Methods
### Hardware  
The glove used in the experiment was made of 10 piezoresistive flex sensors: 4 with a length of 112 mm and 6 shorter ones with a length of 73 mm. They were placed on the glove in two rows on each of the five fingers: the first at the level of metacarpophalangeal joints and the second covering interphalangeal joints. The choice of sensor placement assumed that independent flexion of the distal interphalangeal (DIP) joints does not occur in sign languages and that it does not belong to any group of gestures used in everyday life. Additionally, only a small number of people can perform such a flexion naturally.  
The sensors were attached to the textile surface of the glove using Velcro. The motion path of the sensors was stabilized by 3D-printed guides which are also attached to the glove using Velcro. Movable sensor mounts allowed us to adjust their placement and motion path to the user's finger spacing. In this way, the axis of the sensor always coincided with the finger axis during measurements, making them as accurate as possible. A photograph of the glove is shown in Figure 2. The sensors were connected to the measurement unit using conductive threads embedded in the glove. The ends of the thread were attached to the sensors with detachable connectors so that the sensors could be easily replaced. The other end of the thread was soldered to the PCB of the measurement system. On the PCB, there are also 15 k\(\Omega\) resistors to which piezoresistive sensors are soldered so creating a voltage divider.  
Linearity of the resistance characteristics of the sensor with respect to the bending angle was confirmed with a simple experiment measuring the resistance of the sensor in the bending range from 0\({}^{\circ}\) to 90\({}^{\circ}\) in 5\({}^{\circ}\) steps; it was carried out using Axiomet AX-8455 multimeter manufactured by Shanghai Yi Hua V&A Instrument Co. in Shanghai, China.  
The measurements of glove sensors are controlled by a microcomputer Raspberry Pi Zero W, which is a single-board computer running on a Broadcom BCM2835 1 GHz processor with 512 MB of built-in RAM. It consists of 4 ADS1115 16-bit analog-to-digital converters working at 860 Hz sampling rate (see Figure 3).  
The operation of the glove was controlled by the Linux operating system in a distribution designed for embedded systems with the program controlling the operation of the acquisition system written in Python.  
Figure 3: Block diagram of the glove.  
Figure 2: The glove used in the study.
## 4 Numerical and Anechoic Chamber Experiments
### Comparisons with other algorithms
From the figure, we can see that YOLOv3 has poor performance with misrecognition for both satellites and parabolic antenna; and YOLOv11 recognizes all the satellites but misses all parabolic antennas; YOLOv5 loses the recognition of two parabolic antennas and also duplicates the recognition of parabolic antennas; YOLOv6 and YOLOv8 lose the recognition of three parabolic antennas, and the latter has a false recognition, showing a weak generalizability. The proposed algorithm is able to recognize both satellites and parabolic antennas without any omissions or misjudgments, which fully demonstrates that the addition of modules can adaptively detect various scales and can solve the issue of sample imbalance between different types of samples. Figure 16: Comparison of performance between different networks on anechoic chamber data. Figure 15: Anechoic chamber experiment and satellite mock-up presentation. (**a**) Terahertz radar technology system. (**b**) Satellite model for anechoic chamber experiment. E. The limitations of the Improved-YOLOv8  
Although the proposed algorithm performs well on electromagnetic simulation datasets and can correctly identify some dark room data, exceeding most YOLO models and to some extent demonstrating the algorithm's superiority, it has not been compared with other one-stage or two-stage models. In the design phase of the algorithm, we mainly focused on data processing under ideal conditions. While it can successfully identify some dark room measurement data, it has not yet conducted in-depth analysis and optimization for the noise interference that may be encountered in practical applications. Moreover, this paper has fully analyzed the imaging characteristics of smooth parabolic antennas under non-polarized conditions and achieved good recognition results. However, the study has not explored other types of parabolic antennas, such as those equipped with feed sources or with grid-shaped parabolic antennas; therefore, it cannot be guaranteed that the generalizability to other structures of parabolic antennas remains. In addition, the improved-YOLOv8 model proposed in this study mainly optimizes the final detection process of the model by modifying the head part, thereby improving the model's performance. However, since the model has not been improved in the backbone and neck parts, it has not further optimized the model's feature extraction and enhancement process. Therefore, in the future, we will focus on enhancing the model's multi-scale feature extraction capabilities.
## 3 Results and Discussion
### Hardness Distribution
#### 3.2.1 Machine Learning Prediction Models of Hardness Distribution  
Figure 8 presents the results obtained by modeling the AMC's hardness. The ANN was successful where other algorithms failed in their attempts to accurately capture the trend of the model. According to Table 5, the optimized network's RMSE and R\({}^{2}\) values achieved for the hardness parameter were 0.818 and 0.996, respectively, for the training set; for the testing set, these values were 2.19 and 1. Consequently, this model inference is appropriate for predicting the hardness of brand-new, unidentified input data under various experimental setup configurations. Table 6 presents an illustration of the differences in error between the experimental and predicted results for the hardness of AMC, which are very minimal.
## 4 Discussion
### Limitations and Uncertainties  
Although this study benefits from continuous, ground-based observations of both SIF and GPP in a semi-arid evergreen needleleaf forest, several limitations and uncertainties remain that should be addressed in future work.  
First, the issue of footprint mismatch between flux and spectral observations may affect the accuracy of the observed SIF-GPP relationship. While both the EC system and the automated tower-based spectral system were installed on the same tower and the SIF field of view was largely within the EC footprint, this does not fully resolve the potential discrepancy in spatial representativeness. In heterogeneous or semi-structured ecosystems, such as semi-arid woodlands, even slight misalignments between the observation footprints can introduce inconsistencies in data interpretation [47, 48]. A more detailed analysis of spatial matching, including high-resolution footprint modeling, is recommended to further constrain this source of uncertainty.  
Second, this study focused on a single vegetation type within a semi-arid biome. While the ENF ecosystem is a representative aforestation type in northern China, the applicability of our findings to other semi-arid ecosystems, such as desert steppe, shrubland, or mixed savanna, remains unclear. Future studies should consider the deployment of coordinated multi-site observational networks that encompass diverse ecosystem types, thereby enabling cross-site comparisons of SIF-GPP relationships under different structural and climatic regimes.  
Third, our results highlight the high sensitivity of SIF to incident radiation, particularly under varying sky conditions. SIF values measured only on clear days may overestimate the seasonal mean value, especially during periods with frequent cloudy or rainy weather. This suggests that solar radiation conditions play a critical role in shaping the observed SIF-GPP relationship, at both diurnal and seasonal scales. As such, future studies should incorporate the classification of sky conditions (e.g., clear vs. overcast) into the analysis framework to better interpret the SIF response. Distinguishing between the influence of diffuse versus direct radiation on SIF could also help reveal mechanisms underlying its variability and decoupling from GPP [49].  
Taken together, these limitations suggest that although SIF holds strong potential as a proxy for photosynthetic activity, its interpretation requires careful consideration of the observational scale, ecosystem diversity, and the environmental context. Addressing these uncertainties will be essential for improving the robustness of SIF-based GPP estimation models, particularly in complex, semi-arid landscapes.
Moreover, CR has been utilized in many IIoT-based applications [24], e.g., improving the Quality of Service (QoS) in Wireless Sensor Network (WSN)-based smart grid applications [25]. Cognitive radio has also made its mark to mitigate interference in industrial WSNs (IWSNs) and fulfills the QoS requirement by improving the latency, transmission, and frame losses challenges [26]. This analysis aims to research the literature on cognitive radio in LPWAN for industrial IoT. We investigate different studies providing different backgrounds for study and their relationships. The following research questions (RQ): RQ1: \"How cognitive radio engage with the industrial IoT?\", RQ2: \"What are the Proposed architectures that Support Cognitive Radio LPWAN based IIoT?\", and RQ3: What key success factors need to comply for reliable CIIoT support in the industry?\". The main contribution of this paper is a systematic literature assessment on a long variety of extensive location (LoRa), Sigfox era, long time evolution class m (LTE-m) [27], and narrowband internet of things (NBIoT) in low electricity extensive location LPWAN technology with an unlicensed spectrum [28; 29]. Cognitive LPWAN allows industrial IoT applications for smart city services to access different wireless connectivity and selects appropriate communication technologies to achieve the best interaction experience [30; 31; 32]. The article is organized as consisting of three sections. Section 2 presents a comprehensive study on methods, material and methods, where it discusses the data collection, searching, and selecting the papers, and covers a depth review on cognitive-enabled industrial IoT applications, methods with pros and cons. Section 2 also answered each of the research questions and highlighted the deployment's significance, the determinant factors for designing, and modeling cognitive-enabled industrial IoT frameworks. Section 3 presents the concluding remarks, with future scope for this work being suggested.