esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 3 Signaling Schemes
### PAS Receiver  
At the receiver, the log-likelihood ratio (LLR) \(L_{j}(i)\) of the \(j\)th bit in the \(i\)th transmitted symbol is computed by a soft demapper as  
\[L_{j}(i)=\log\left(\frac{\sum_{x\in\mathcal{X}_{j,0}}P_{X}(x)P_{\gamma|X}(y_{ i}|x)}{\sum_{x\in\mathcal{X}_{j,1}}P_{X}(x)P_{\gamma|X}(y_{i}|x)}\right) \tag{11}\]  
for \(j=1,2,\ldots,m\) and \(i=1,2\ldots,n\), where \(\mathcal{X}_{j,u}\) denotes the set of \(x\in\mathcal{X}\) which have \(b_{j}=u\) in their binary labels for \(u\in\{0,1\}\). We emphasize that the nonuniform a-priori information on the symbols is used in (11). Instead of symbol-wise probabilities \(P_{X}(x)\), bit-wise probabilities \(P_{B_{j}}(b_{j})\) for \(j=1,2,\ldots,m\) can be used to compute (11) as in (Equation (60) in [28]) or (Equation (3.29-32) in [8]). Then a bit-metric decoder uses the LLRs in (11) as the decoding metrics, i.e., treats different coded bits in a given symbol as independent [7], and estimates the bits that were encoded by the FEC code. In the case of uniform signaling, these bits are the estimates of the information bits. For the PAS architecture shown in Figure 3 (middle), the output of the decoder consists of the estimates of the amplitude bits. Then these are mapped back to the information bit estimates using the inverse functions of the blocks in the shaper (green box), i.e., the corresponding bit-to-amplitude mapper followed by the corresponding amplitude deshaper. In addition to this, for the PAS architecture shown in Figure 3  
Figure 3: Signaling options: (**top**) uniform signaling with rate \(R=k/n\) bit/1-D, (**middle**) PAS with rate \(R=k/n\) bit/1-D (all information is on amplitudes), (**bottom**) modified PAS with rate \(R=k/n+\gamma\) bit/1-D (extra data is carried on signs).  
(bottom), the decoder also outputs the estimates of the \(\gamma n\) extra data bits which were used as some of the signs. A bit-metric decoder achieves the rate \(R_{\text{BMD}}\) for any input distribution \(P_{X}(x)\),  
\[R_{\text{BMD}} = \left[\mathbb{H}(X)-\sum_{j=1}^{m}\mathbb{H}(B_{j}|Y)\right]^{+} \tag{12}\]  
where \([\cdot]^{+}=\max\{0,\cdot\}\). In [84; 85], the achievability of (12) is derived using random coding arguments based on strong typicality (Chapter 1 in [86]). Later in (Lemma 1 in [87]), it was shown that (12) is an instance of the LM rate [41; 88]. AIRs of PAS have been studied in [89; 90; 80; 91]. It is demonstrated that the mutual information \(I(X;Y)\) and the rate \(R_{\text{BMD}}\) in (12) are achievable with PAS using symbol-metric and bit-metric decoding, respectively.
## 3 Experimental Results and Analysis
### Ablation Study  
To further assess the effectiveness of our proposed framework, we conducted an ablation study. All experimental data and parameter settings remained consistent throughout the study. The ablation study was designed as follows:  
* Assessment of the Proposed Framework's Effectiveness: We evaluated the performance improvement achieved by incorporating four popular networks (i.e., U-Net, ViT, TransUNet and ConvNeXt V2) as global feature extraction modules in our proposed framework.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Settings** \\ \hline Optimizer & AdamW optimizer \\ Initial learning rate & 0.0001 \\ Decay rate & \(1\times 10^{-8}\) \\ Momentum & \(\beta\)1 = 0.9, \(\beta\)2 = 0.999 \\ Batch size & 4 for steps 1 and 2, and 2 for step 3 \\ Epoch & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters.  
* Evaluation of Different Modules' Effectiveness: Using TransUNet as the global feature extraction module, we added feature aggregation module and lightweight convolution module sequentially to form three cases, aiming to verify the role and effectiveness of these modules.
## 4 Results and Discussion  
For the 32 sample points in Table 5, the analysis examined the temperature distribution on the disc's friction surface, with deviations quantified across 11 radial regions. Table 5 presents the FEA results for the maximum temperature and temperature deviation. Figure 8 illustrates the temperature distribution analysis based on the design variable locations. Deviation values ranged from 4.099 to 7.399, corresponding to maximum temperatures of 137.9 \({}^{\circ}\)C to 154.5 \({}^{\circ}\)C. Notably, Case 9 exhibited a 7.399 deviation and a maximum temperature of 151.8 \({}^{\circ}\)C. Figure 9 further highlights the correlation between the deviation and maximum temperatures.  
During ANN training, the Levenberg-Marquardt algorithm iteratively updated the weight and bias values, with performance assessed by the mean square error. Training, validation, and testing continued until we achieved the minimal mean square error [34]. Figure 10 compares the experimental and predicted datasets for training, validation, testing, and combined sets, confirming the ANN's accuracy [34].  
The ANN predicted the optimal brake pad positions, with Table 6 detailing the final design variable positions. Figure 11 shows a comparison of the locations of the nine circular friction blocks between the original pad and the ANN-predicted pad. Using the ANN, the friction block positions were optimized for reduced temperature deviation. Table 6 compares the analysis results between the original and optimized brake pad locations using the ANN. As shown in Table 7, the original pad's maximum temperature of 480.6 \({}^{\circ}\)C was reduced to 394.7 \({}^{\circ}\)C with the ANN (17.9% reduction). The original pad's temperature  
Figure 6: Schematic illustration of the ANN.  
Figure 7: Training processes of the ANN.  
deviation was improved from 8.128 to 3.333 with the ANN (55.4% reduction). The original pad experienced the maximum thermal stress of 721.4 MPa, lowered to 637.8 MPa with the ANN (11.6 % reduction).  
Figure 12 illustrates the temperature distribution during the braking process. The optimized pad with the ANN reveals a larger hot band, yielding a more uniform temperature distribution and reduced maximum temperature. Figure 13 shows the thermal stress distribution, highlighting the optimized ANN model's reduced thermal stress on the disc due to the expanded hot band, compared to the original model.  
Figure 11: Location comparison for original and ANN pads (yellow: original pad, red: ANN pad).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \begin{tabular}{c} **Friction Block \#** \\ \end{tabular} & \
\begin{tabular}{c} \(x\) \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} \(y\) \\ **(mm)** \\ \end{tabular} \\ \hline
1 & 24.0 & 196.5 \\
2 & 67.5 & 188.0 \\
3 & 112.0 & 188.0 \\
4 & 160.0 & 190.5 \\
5 & 34.0 & 246.5 \\
6 & 28.0 & 296.0 \\
7 & 77.0 & 275.5 \\
8 & 116.0 & 246.5 \\
9 & 165.0 & 239.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Final position of the friction blocks on the brake pad.  
Figure 10: Precision levels for training, validation, and test data of the ANN.
# Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems
## 3 The Proposed Methodology  
This study utilized a BN-based fault diagnosis reasoning framework, which is illustrated in Figure 1, and the research content is depicted in Figure 2. The BN model was constructed using the following steps: (1) determine the model scope and develop the fault tree; (2) transform the fault tree into a BN [19]; (3) acquire priority probabilities through expert knowledge; (4) determine CPT with the leaky noisy-OR model; (5) construct the BN model in GeNle software; and (6) conduct fault diagnosis using the BN model.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Uncertainty Type** & **Uncertainty Source** & **Reasoning Framework** & **Ref.** \\ \hline Knowledge uncertainty & Multi-state fault & BN; fuzzy logic & [9] \\ Knowledge uncertainty & Historical information & BN; Monte Carlo method & [10] \\ data uncertainty & evidence information & BN; distributed decision & [11] \\ Knowledge uncertainty & IoT monitoring data & making & [12] \\ Data uncertainty & Multi-sensor data & BN; fuzzy logic & [12] \\  & Sensor data and & BN; human in the loop; & [14] \\ Multi-form data & unstructured & data fusion & [14] \\  & naturallanguage & & \\ Data uncertainty & Sensor data and & BN; data fusion & [15] \\  & sub-system & & \\ Knowledge uncertainty & Capability of single BN & BN; ensemble learning & [16] \\ Knowledge uncertainty & FD structure & Probability graph; & [17] \\  & Cause-effect relationship & machine learning & \\ Data uncertainty & between system faults & BN; quantitative causal & \\  & and sensor measurements & graph & [18] \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of previous works.  
The accuracy of fault diagnosis using Bayesian networks (BNs) is heavily reliant on the precision of fault priority probabilities assigned to the root node and conditional probability tables (CPTs). These probabilities are commonly formulated based on expert knowledge and experience. Consequently, to address uncertainty in fault diagnosis, effective methods must be identified to determine the probabilities of BN nodes with uncertainty. In this research, fault priority probability values were obtained through the linguistic evaluation of a group of experts, as described in Section A, and the leaky noisy-OR model was used to obtain the CPT of BN nodes, which could simplify the determination of CPT and consider uncontrolled factors in IoT data, as described in Section C.
# Pipeline Damage Identification Based on Additional Virtual Masses
## 4 Test of the Pipeline Damage Identification
### Damage Degree Identification  
To verify the application effect of the method in the actual pipeline structure, the damage degree of substructure 5 was identified based on the laboratory pipeline model. A hole was dug inside substructure 5 to simulate damage. Figure 17a,b show the cross-section and size of the hole. The damage degree can be calculated using the material mechanics formula. As elastic modulus \(E\) is constant, the damage of stiffness \(EI\) is a reduction of the moment of inertia \(I\). The calculated moment of inertia of the cross-section is reduced to 0.83 [18].  
Damage to substructure 5 is found facing the ground and the acceleration sensor is disposed on the upper surface of the pipe facing the hole. The excitation point of the hammer is near the sensor, as shown in Figure 18. The positions of the acceleration sensor and hammer excitation are sequentially moved to measure the response of the 10 substructures. Figure 19 shows the frequency response of the damage to substructure 5 where a comparison of the undamaged pipe and damaged structure with additional 8 kg of virtual masses is shown as well. The first frequency is seen to decrease from 181.9 Hz to 177.3 Hz after the damage. With the addition of 8 kg virtual mass, the frequency changed to 115.1 Hz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order of Natural Frequency** & **Measured Frequency/Hz** & **Modeling Frequency/Hz** & **Error/\%** \\ \hline
**1 st** & 181.93 & 182.24 & 0.17 \\
**2 nd** & 473.68 & 476.79 & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The pipe: comparison of the measured and modeling frequencies.  
Figure 16: Frequency response.  
Figure 17: Substructure 5: damage size (unit: cm). (**a**) Damage section; (**b**) Hole size.  
The measured responses of the 10 substructures are substituted into the additional virtual mass formula to obtain the first frequency with the additional optimal masses. Then, the damage factor of the substructure can be iterated according to Equation (10), which is combined with the sensitivity matrix of theoretical FEM, as shown in Figure 20. In the experimental results, the damage to substructure 5 is reduced to 0.80 by the iteration, which is close to the theoretical calculation value of 0.83. This scenario indicates that the damage identification method is successful. At the same time, the iterated results of other substructures are close to 1.0, indicating that the structure is intact and consistent with the experimental model.  
Figure 19: Sub-structure 5: The measured frequency response.  
Figure 20: The test damage identification.  
Figure 18: Actual damage and measured response. (**a**) Damage hole; (**b**) Hammer excitation.
# Demonstration of High-Speed Optical Transmission at 2 \(\upmu\)m in Titanium Dioxide Waveguides
###### Abstract  
We demonstrate the transmission of a 10-Gbit/s optical data signal in the 2 \(\upmu\)m waveband into titanium dioxide waveguides. Error-free transmissions have been experimentally achieved taking advantage of a 23-dB insertion loss fiber-to-fiber grating-based injection test-bed platform.  
integrated optics; optical gratings; titanium dioxide; optical communications at 2 \(\upmu\)m wavelengths +
Footnote †: journal: Applied Sciences
## 2 Survey for Outdoor Agricultural UAV Applications
### Remote Sensing  
Remote sensing covers applications for evaluating the progress of production at different times. In order to perform a low-cost and straightforward evaluation of crop, soil and environmental conditions, the data obtained and the applications made are generally related to remote sensing. Remote sensing applications are the most applied precision agriculture application due to their necessity and importance. Thanks to remote sensing technologies, potential problems in agriculture are detected early, and measures can be taken to solve these problems promptly. This ensures crop efficiency. In this context, remote sensing covers a wide range of applications. All of the studies aimed at increasing the productivity of agricultural products by using remote sensing can be included under this category [4, 46]. Manual methods, in contrast to remote sensing, to obtain the phenotypic characteristics of crops directly measure data such as biomass, leaf area index (LAI) and chlorophyll content. However, manual methods require device operators to work intensively in the field and are therefore difficult and time-consuming. To eliminate these disadvantages, technological solutions have provided significant developments in this field [34]. Remote sensing attracts more attention today due to developments in robotic technology, the development of sensors and advances in data processing. Most applied remote sensing studies are carried out using aerial images taken by satellites, UAVs and manned aircraft. With these tools, data can be collected at spatial, temporal and spectral resolutions. These tools can collect large area data and are nondestructive. Remote sensing by satellites and aircraft provides the ability to collect data on a large spatial scale, but long revisit periods, cloud congestion and high costs limit the use of these tools in remote sensing. In addition, satellite images have low spatial resolution [47]. For this reason, recently, UAVs have become much more popular than other vehicles, with advantages such as flexibility of movement, ease of use, low cost and high spatial resolution [48]. An example study for remote sensing using a UAV was performed by [PERSON] et al. [49]. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [49] aimed to detect areas of banana infected or uninfected with fusarium wilt using multispectral images acquired with a UAV. Figure 5 shows the fusarium wilt disease distribution obtained as a result of the study. Green areas indicate healthy areas, and yellow areas show infected banana areas. Figure 4: Weed detection study in spinach field by deep learning [45]. (**a**) With no background information, red dots indicate weeds. (**b**) With background information available, red regions indicate weeds.
# Optimized Design for NB-LDPC-Coded High-Order CPM: Power and Iterative Efficiencies
## 8 Conclusions  
The design of the CPM parameter, decoding delay, and the positive feedback problem in iterative decoding are the main factors that limit the development of the coded CPM system. To address the above problems, NB-LDPC-coded high-order CPM systems were designed and optimized in this paper. A novel design method based on the MSNED and \(B_{99\%}\) was introduced to explore a competitive CPM scheme using particular \(\eta\) and \(R\) under the constraint of implementation complexity. A three-step method based on the EXIT chart and entropy theory was used to design the NB-LDPC code to reduce the convergence threshold, which reduces the convergence threshold by approximately 0.42 and 0.58 dB compared to the candidate schemes. A method of extrinsic information operation was proposed to address the positive feedback phenomenon existing in iterative detection and decoding. The simulation results showed that the proposed method not only effectively inhibits positive feedback phenomenon at low \(\text{E}_{\text{b}}/\text{N}_{0}\) but also accelerate iterative convergence at medium-high \(\text{E}_{\text{b}}/\text{N}_{0}\), and the value of BER can be reduced by approximately \(5\times 10^{-3}\). An improper iteration match between demodulation and decoding was addressed using the EXIT technique and mutual information to improve the iterative efficiency and attain a suitable tradeoff of the communication reliability and the iterative decoding delay. Finally, simulation results show that the resulting NB-LDPC-coded high-order CPM scheme provides an approximately 3.95 dB coding gain compared to the uncoded CPM and achieves approximately 0.5 and 0.7 dB advantages compared with the candidate schemes. The resulting scheme using the proposed method attains the convergence threshold earlier compared with other competitors and further improves power and iterative efficiencies.  
[PERSON] conceived the idea and established the mathematical modeling. [PERSON] did the simulations and wrote the paper. [PERSON] checked the simulation and analyzed the data. [PERSON] contributed to the revisions and the discussion of the results. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 61873070), the Technology Development Project of the China Research Institute of Radiowave Propagation (No. JW2019-114) and the Heilongjiang Province Key Laboratory of High Accuracy Satellite Navigation and Marine Application Laboratory (No. HKL-2020-Y03).  
The authors declare no conflict of interest.
## 6 EMMNet Validation
### Results  
In our original design, there was no limit to the number of times a RM could be sent, even when the collectors had joined the EMMNets. A collector had a great chance of selecting a relay node with a smaller _DtA_ value, when it constantly sent RMs. However, the performance of such relay nodes might be poor. We found that the transmission success rate decreased if a collector chose a parent collector with a very small _DtA_ value. Therefore, we limited the number of broadcast packets when the value of _DtA_ is smaller than _DtA_.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Collector** & **Data Concentrator** \\ \hline ATMEGA128L & ATMEGA128L \\ ADL5320 & ADL5320 \\ nRF905 & nRF905 \\ sp6201 & sp6201 \\ PS2051 & PS2051 \\ – & SIM300C \\ AT45 DB041D-SU & AT45 DB642D \\ \hline \hline \end{tabular}
\end{table}
Table 5: Elements used in EMMNet.  
Figure 6: Two EMMNets deployed in Zhiyang residence community, Yangzhou City, Jiangsu Province, China.  
The distribution of the _DtA_ value is shown in Figure 7, and the transmission success rate is illustrated in Figure 8. From November 3-18, 2009, collectors could broadcast RMs without any limit. As shown in Figure 7, the network structure does not seem very good. All the _DtA_ values of collectors are less than 3, and the average transmission success rate is relatively low. That is because child and parent nodes are too far from each other and the bit error rate is relatively high. Therefore, we reprogrammed the collectors with a hand-held device in the 470 MHz wireless channel. When the value of _DtA_ was smaller than _DtA_Max_ + 1, the collector could only broadcast the RM three times. On Deceber 24, 2009, we reprogrammed the devices and made the EMMNets dynamically choose the limited times that RM packets were broadcasted according to the transmission success rate. At first, the limited-times value _LT_ was set to two; if the performance was poor, the _LT_ was increased by two. Figure 8 shows that average transmission success rate is improved due to the advantage of the network topology. And Figure 9 plots the corresponding network structures of the EMMNets deployed in Zhiyang residence community. With the EMMNets, customers can obtain the energy flow amount at any time through internet and the maintainers can control the parameters of the electricity meters if necessary. The meter monitoring task can be done at the management department by using this system. What should be emphasized is that the operational cost of EMMNet is relatively low, compared with  
Figure 8: Average transmission success rate. System tested for three months; collectors reprogrammed twice during this period.  
Figure 7: Distribution of _DtA_ value in three different cases.  
other existing remote meter monitoring systems based on GPRS. Meanwhile, EMMNets can realize automatic charge if they are connected to bank systems.
## 2 Materials and Methods
### Antioxidant Activity  
#### 2.6.1 DPPH Assay  
DPPH radical scavenging activity was determined according to the method from [PERSON] [31] with slight modifications. A 100 \(\mu\)L aliquot of fresh hydrolysate (or a solution of 1 mg/mL of lyophilized sample), and ascorbic acid as a positive control, were mixed with 2.9 mL of 0.1 mM DPPH in methanol. The mixtures were incubated in the dark for 30 min and the absorbance was measured at 517 nm. For this method, the free radical scavenging activity was calculated by the following equation:  
\[DPPH\ \%\ inhibition\ =\ \left(\frac{Abs\ control-Abs\ sample}{Abs\ control} \right)\times 100\]  
where _Abs control_ is the absorbance of DPPH solution except the sample and _Abs sample_ is the absorbance of the tested sample with DPPH solution. Antioxidant capacity relative to that of ascorbic acid was calculated using the equation generated with an ascorbic acid standard curve as follows:  
\[\textit{Asorbic acid equivalent}\ \left(\frac{mg}{mg}\right)\ =\ \left(\frac{\left(DPPH\ inhibition\ \%-b\right)}{a}\right)\div\textit{sample concentration}.\]  
where \(a\) and \(b\) are the two coefficients of the ascorbic acid standard curve. Results were reported as the mean of three independent reads (_n_ = 3).
# Design, Implementation and Power Analysis of
## 4 Discussion  
### Comparison of Three Scenarios  
Details of the power consumptions seen in three scenarios for different numbers of bulbs are given in Figure 8. Figure 8a,b, show the smart scenario of 60 W incandescent and 8 W LED light bulbs as having the lowest consumption in all the cases. Figure 8c shows the smart scenario of a 0.5 W LED light bulb as having less consumption when compared to the base for all the cases, but it also shows a reduction in the energy consumption in the smart scenario for a 0.5 W LED light bulb in comparison to the manually controlled scenario from eight bulbs. Thus, the authors' proposed model consumes less power for any number of light bulbs for both incandescent and LED light bulbs, but for very low power watt bulbs like 0.5 W, the power is reduced only when it is deployed with eight or more than eight bulbs. For less than eight light bulbs, the consumption of power for smart and manually controlled scenarios was almost the same. As the proposed work can be deployed in any indoor or outdoor lighting application, it is customary to have more than eight LED bulbs if it is only a low wattage for effective use for the low power solution.  
Figure 8: Energy Consumption in KWh vs. number of light bulbs for: (**a**) 60 W incandescent light bulbs in three scenarios, (**b**) 8 W LED light bulbs in three scenarios, (**c**) 0.5 W LED light bulbs in three scenarios.