esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Uncertainty Analysis under Different Noise Conditions
#### 3.2.1 \(\mathrm{FF_{new}}\)  
The ROC curves of \(\mathrm{FF_{new}}\) under different noise levels are shown in Figure 9. The performances of the \(\mathrm{FF_{new}}\) are high when the noise is relatively low (\(-20\) dB), as shown in Figure 9a. The true prediction ratio decreases when the noise is \(-20\) dB. Furthermore, the performances are worse with increasing noise at \(-10\) dB and \(-7\) dB, as shown in Figure 9b,c. Compared with the \(-20\) dB case, the ROC curves shift towards the random guess line. The ROC curve is close to the random line when the noise reaches \(-7\) dB. which indicates that the \(\mathrm{FF_{new}}\) performance in a high-noise situation is poor. The evaluation metrics of the \(\mathrm{FF_{new}}\) are shown in Figure 9d-f. The parameters are above 0.8 when the noise is \(-20\) dB and the water saturation is less than 80%. When the noise is \(-10\) dB, the metrics decrease: the precision is approximately 0.8, whereas the recall, accuracy and F\({}_{1}\) are approximately 0.6 to 0.7. When the noise is \(-7\) dB, the parameters reduce continuously. The precision remains at approximately 0.8, whereas the other three parameters are approximately 0.6. Note that the recall is close to 0.5, which indicates that the performance is near that of the random guess.  
Figure 9: The ROCs of the \(\mathrm{FF_{new}}\) in the different noise scenarios: (**a**) \(-20\) dB, (**b**) \(-10\) dB, (**c**) \(-7\) dB. The metrics of the \(\mathrm{FF_{new}}\) in the different noise scenarios: (**d**) \(-20\) dB, (**e**) \(-10\) dB, (**f**) \(-7\) dB.  
The AUC curves are plotted in Figure 10. The performances in the cases of \(-20\) dB, \(-10\) dB and \(-7\) dB are represented in blue, red and yellow, respectively. The AUC reaches 0.9 when the noise is low. The AUCs decrease with increasing noise. The AUCs are approximately 0.7 and 0.65, respectively, when the noise is \(-10\) dB and \(-7\) dB.
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 2 Related Works  
### CBRSIR Based on Deep Learning  
Deep learning has been extensively applied in CBRSIR and has achieved excellent performance. It has gradually replaced the low- and mid-level feature-based methods. This is due to the ability of deep neural networks to extract high-level semantic features, which can better represent the content of remote sensing images. For instance, [PERSON] et al. [18] trained a mainstream convolutional neural network model on a remote sensing dataset, and the performance of the model was significantly better than that of the low- and mid-level feature-based methods. Other research has attempted to enhance feature extraction by using more complex network structures, such as the contrastive self-supervised learning network [19] and attention mechanisms [20; 21]. On clean datasets, many of the current methods have achieved near-saturation retrieval accuracy. However, when the training data contains noisy labels, the model tends to overfit to noisy samples. This significantly reduces the accuracy of the classification model and, subsequently, the retrieval accuracy. To solve the above problem, [PERSON] et al. [22] proposed a fault-tolerant deep learning method for remote sensing scene classification. The method utilises ensemble learning to enhance the accuracy of the error correction of noisy labels, with data cleaning as the underlying concept. However, the method fuses several large networks and iterates many times in the training process, so the number of parameters and computations is large. [PERSON] et al. [23] proposed a loss with entropic optimal transport (CLEOT), which designed a robust loss by exploring the joint publication of images and labels, and achieved good results, but the method performed poorly at low proportion noise. Overall, there is a need to complement the research on remote sensing image retrieval with noisy labels.
## 5 Optimization Result
### Optimization Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(t_{f}\) (s) & \multicolumn{2}{c}{**Mean (u)**} & \multicolumn{2}{c}{**Standard Deviation (\(\sigma\))**} & \multicolumn{2}{c}{**Coefficient of Variation (=\(\sigma\)/u)**} \\ \hline \multirow{2}{*}{**Case 1**} & \multirow{2}{*}{2.0} & Initial & \(1.693\times 10^{-2}\) & \(1.845\times 10^{-2}\) & \(108.9\%\) \\  & & Optimal & \(4.522\times 10^{-2}\) & \(5.921\times 10^{-5}\) & \(0.1\%\) \\ \hline \multirow{2}{*}{**Case 2**} & \multirow{2}{*}{3.0} & Initial & \(9.163\times 10^{-3}\) & \(7.069\times 10^{-3}\) & \(77.1\%\) \\  & & Optimal & \(2.452\times 10^{-2}\) & \(5.624\times 10^{-6}\) & \(0.0\%\) \\ \hline \multirow{2}{*}{**Case 3**} & \multirow{2}{*}{4.0} & Initial & \(6.678\times 10^{-3}\) & \(4.496\times 10^{-3}\) & \(67.3\%\) \\  & & Optimal & \(1.172\times 10^{-2}\) & \(1.563\times 10^{-5}\) & \(0.1\%\) \\ \hline \multirow{2}{*}{**Case 4**} & \multirow{2}{*}{5.0} & Initial & \(5.162\times 10^{-3}\) & \(3.369\times 10^{-3}\) & \(65.3\%\) \\  & & Optimal & \(6.347\times 10^{-3}\) & \(7.260\times 10^{-6}\) & \(0.1\%\) \\ \hline \multirow{2}{*}{**Case 5**} & \multirow{2}{*}{6.0} & Initial & \(4.056\times 10^{-3}\) & \(2.719\times 10^{-3}\) & \(67.0\%\) \\  & & Optimal & \(3.837\times 10^{-3}\) & \(2.385\times 10^{-6}\) & \(0.1\%\) \\ \hline \multirow{2}{*}{**Case 6**} & \multirow{2}{*}{7.0} & Initial & \(3.357\times 10^{-3}\) & \(2.290\times 10^{-3}\) & \(68.2\%\) \\  & & Optimal & \(2.331\times 10^{-3}\) & \(1.599\times 10^{-6}\) & \(0.1\%\) \\ \hline \multirow{2}{*}{**Case 7**} & \multirow{2}{*}{8.0} & Initial & \(2.872\times 10^{-3}\) & \(1.982\times 10^{-3}\) & \(69.0\%\) \\  & & Optimal & \(1.743\times 10^{-3}\) & \(1.727\times 10^{-6}\) & \(0.1\%\) \\ \hline \multirow{2}{*}{**Case 8**} & \multirow{2}{*}{9.0} & Initial & \(2.514\times 10^{-3}\) & \(1.749\times 10^{-3}\) & \(69.6\%\) \\  & & Optimal & \(1.435\times 10^{-3}\) & \(1.224\times 10^{-6}\) & \(0.1\%\) \\ \hline \multirow{2}{*}{**Case 9**} & \multirow{2}{*}{10.0} & Initial & \(2.238\times 10^{-3}\) & \(1.567\times 10^{-3}\) & \(70.0\%\) \\  & & Optimal & \(1.209\times 10^{-3}\) & \(6.390\times 10^{-7}\) & \(0.1\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mean value, standard deviation, coefficient of variation of the acceleration factors.
# Strength Evaluation and Modification of a 3D Printed Anterior Ankle Foot Orthoses
###### Abstract  
Ankle foot orthosis (AFO) is widely used to prevent foot drop and improve walking ability for individuals with cerebral palsy and stroke. However, traditional anterior AFO (TAAFO) could only last within months because the bilateral neck of TAAFO was easy to break. Currently, a 3D-printing technique is used to develop assistive devices for rehabilitation. The study aimed to implement the finite element (FE) method to revise the 3D printed AAFO (3 DP-AAFO) and evaluate its strength. A 3.2 mm-thickness for the TAAFOs and 3 DP-AAFOs were fabricated, respectively. The stiffness of TAAFO and 3 DP-AAFO were tested by a material machine and compared to the FE model. In the FE analysis, the thickness of AAFO model was increased at the neck to enhance its strength. A plantar flexion and dorsiflexion moment were respectively subjected to 3 DP-AAFO models to undergo stress analysis. Under the mechanical test, the 3 DP-AAFO (K = 1.09 Nm/degree) was 7.8 times stiffer than the traditional AAFO (K = 0.14 Nm/degree). The FE results showed that thickening the 3 DP-AAFO on the neck up to 4.7 mm could moderate stress concentration and increase the stiffness of the 3 DP-AAFO. Therefore, the study concluded that the 3 DP-AAFO was stiffer than the traditional AAFO. Increasing the appropriate thickness around neck of 3 DP-AAFO could avoid neck fracture as much as possible.  
a _Article_  
Strength Evaluation and Modification of a 3D Printed Anterior Ankle Foot Orthoses
## 5 Experimental Simulation Analysis
### Comparison of BER Performance of Different DCSK Systems  
In order to compare the BER performance of different systems, this section compares the BER performance of four modified DCSK systems, including the DCSK system, under different spreading factor conditions. The four systems are DCSK, NR-DCSK, NR-ODBR-DCSK, and RSST-SR-DCSK. The following figure shows the simulation results.  
From the two Figure 12a,b, it can be seen that both the NR-ODBR-DCSK and NR-DCSK systems exhibit better bit error rate (BER) performance than the conventional DCSK and RSST-SR-DCSK systems for different \(\beta\) values (\(\beta=64\) and \(\beta=128\)). As the SNR increases, the BER of all systems decreases, but the NR-ODBR-DCSK system always exhibits the lowest BER, especially at high SNR. Comparing the cases of \(\beta=64\) and \(\beta=128\), it can be seen that the BER performance of the systems improves as the \(\beta\)-value increases; especially, the NR-ODBR-DCSK system has the lowest BER at \(\beta=128\). This indicates that increasing the \(\beta\)-value can further improve the noise immunity of the system, and the NR-ODBR-DCSK system exhibits significant performance advantages at different \(\beta\)-values. Therefore, the NR-ODBR-DCSK system exhibits good BER performance under both low and high \(\beta\) conditions and is especially suitable for applications in communication scenarios with different signal-to-noise ratios and noise environments.
## 3 Results and Discussion
### Effects of Various Factors on the Removal of Et-PABA
The other ions, such as NO3- and SO4\({}^{2-}\), had no significant effect on the removal efficiency of Et-PABA. Although most of the common ions in the water matrix may affect the degradation rate of Et-PABA, the removal efficiency of Et-PABA using the UV/PDS process in the presence of anions was still impressive when the reaction time increased to more than 20 min. Figure 3: Effects of PDS dosage (**A**) and pH (**B**) on the removal efficiency of Et-PABA using UV/PDS. Conditions: [Et-PABA] = 0.05 mM, [PDS] = 2.0 mM, and without pH adjustment. Humic acid (HA) is one of the main natural organic matter (NOM) in natural water. Thus, it's typically used as a representative of NOM to investigate the NOM effect on the UV/PDS oxidation process. Thus, the effects of HA at different concentrations on the removal of Et-PABA were investigated as shown in Figure 4B. The degradation rate of Et-PABA decreased with the increase in the HA concentration. The removal efficiencies at 30 min _were_ 97.4%, 92.3%, and 66.8%, respectively, suggesting that HA had a significant inhibitory effect on the removal of Et-PABA. HA molecules contain a large amount of photosensitive substances such as aromatic carboxyl groups, hydroxyl groups, amine groups, which can directly absorb ultraviolet light, competitively inhibit the absorption of light energy by the PDS [51], and reduce the yield of free radicals in the solution. At the same time, the active group of HA will also compete with Et-PABA for active radicals such as SO4\({}^{\bullet-}\), thus reducing the removal efficiency of Et-PABA [52,53]. In order to study the degradation of Et-PABA in real water bodies, two water samples from different sources (river water and tap water from Jiaxing College) were collected and filtered through a 0.45 \(\upmu\)m filter. In order to compare with the Et-PABA degradation in pure water, the same concentration of Et-PABA (0.05 mM) was added to the two real water samples, respectively. The effect of real water on the degradation of Et-PABA using the UV/PS process is shown in Figure 5. The degradation rate of Et-PABA in the tap water reached as high as 96.5% within 10 min, which was essentially the same as that in pure water. The degradation rate of Et-PABA in surface water decreased, but the removal efficiency also reached 94.3% after a reaction time of 20 min. This result may be attributed to the inhibition of NOM in the real water body and the interaction of other water components, as discussed above. These results suggest that the UV/PDS process has good adaptability regarding the Et-PABA removal of real water samples. [PERSON] et al. [54] also studied the degradation of 2,4-dichlorophenol in the real water using the UV/PDS process. They determined the water quality of several real water bodies. The concentrations of Cl\({}^{-}\), HCO3\({}^{-}\), and NO3\({}^{-}\) in different water samples were 42.5-63.0 mg/L, 128.0-136.6 mg/L, and 0.005-0.070 mg/L, respectively. The results also suggested the UV/PDS process can be further extended to practical applications.
## 3 Methodology
### Distributed MPC
\begin{array}{l}x_{i}(t)=\tilde{x}_{i}(t),\;i\in\mathcal{N},\\ x_{i}(t+k)\in\mathcal{X}_{i},\\ x_{i}(t+N_{\mathrm{p}})\in\Omega_{i},\\ u_{i}(t+k)\in\mathcal{U}_{i},\\ u_{j}(t+k)=u_{j}^{\mathrm{s}}(t+k),\\ u_{l}(t+k)=u_{l}^{\mathrm{s}}(t+k),\\ d_{i}(t+1)=d_{i}(t),\\ d_{i}(0)=\tilde{d}_{i}(t),\end{array} \quad k=0,\;\ldots,N_{\mathrm{p}}-1,\] (14) where set \(\Omega_{i}\) is imposed as the terminal state constraint of agent \(i\). Details regarding the calculation \(\Omega_{i}\) are given in [30]. 4. Agent \(i\) again optimizes its cost \(J_{i}(\cdot)\) maintaining its optimal input sequence \(U_{i}^{*}(t)\) to send to find the input sequence wished for its neighbors \(U_{j}^{w_{i}}(t)\). Here, it is also assumed that subsystems \(l\) follow their current trajectories. To this end, agent \(i\) solves \[U_{j}^{w_{i}}(t)=\arg\min_{U_{j}(t)}J_{i}\big{(}x_{i}(t),U_{i}^{*}(t),U_{j}(t), U_{l}^{\mathrm{s}}(t)\big{)},\] (15) subject to \[ \begin{array}{l}x_{i}(t+k+1)=A_{i}x_{i}(t+k)+B_{ii}u_{i}(t+k)+\\ B_{ij}u_{j}(t+k)+B_{d_{i}}d_{i}(t+k)+\sum\limits_{l\in\mathcal{N}_{i}\setminus \{j\}}B_{il}u_{l}(t+k),\end{array} \] (16) Constraints \[\begin{array}{l}x_{i}(t)=\tilde{x}_{i}(t),\;i\in\mathcal{N},\\ x_{i}(t+k)\in\mathcal{X}_{i},\\ x_{i}(t+N_{\mathrm{p}}  
Hence, the cost function \(J_{i}(\cdot)\) is  
\[ \begin{split} J_{i}\big{(}x_{i}(t),U_{i}(t),U_{j}(t),U_{l}(t)\big{)}= \\ \sum_{k=1}^{H_{p}-1}\Big{(}\left\|x_{i}(t+k)-x_{\tau_{i}}(t+k) \right\|_{Q_{i}}^{2}+\\ \left\|u_{i}(t+k)-u_{\tau_{i}}(t+k)\right\|_{R_{i}}^{2}+\\ \left\|u_{j}(t+k)-u_{\tau_{j}}(t+k)\right\|_{R_{i}}^{2}+\\ \left\|u_{l}(t+k)-u_{\tau_{l}}(t+k)\right\|_{R_{i}}^{2}\Big{)}+\\ \left\|x_{i}(t+H_{p})-x_{\tau_{i}}(t+H_{p})\right\|_{R_{i}}^{2},\end{split}  \tag{19}\]  
with \(Q_{i}\) being a semi-positive definite matrix, \(R_{i}\), \(P_{i}\) being positive-definite matrices, and \(x_{\tau_{i}}\) and \(u_{\tau_{j}}\) being the state and input references calculated by a procedure to remove offset based on [30].
## 1 Introduction
However, the rate-compatible quasi-cyclic LDPC (RC-QC-LDPC) codes are preferred in many practical applications such as data storage systems [24; 25]. An LDPC decoder that fails to support rate compatibility may incur a high complexity for hardware implementations. Although the rate-compatible MIM-FAIDs were investigated in [11; 14], they were only designed based on the flooding schedule [26]. Therefore, how to design the MIM-FAID with a layered/shuffled schedule for LDPC codes with different code rates is still a challenging problem. In this paper, we develop an MIM-QSMS decoder for decoding RC-QC-LDPC codes, which is referred to as the rate-compatible MIM-QSMS (RC-MIM-QSMS) decoder. Compared to other MIM-FAIDs in the literature, our proposed RC-MIM-QSMS decoder integrates the shuffled decoding schedule and the rate compatibility in a single round of the LUT design process. To the best of our knowledge, this is the first design for the FAID to support the rate compatibility with a shuffled schedule. In particular, we modify the DE in [15] and propose the shuffled MIM-DE (SMIM-DE) by considering the weighted expectation of the probability mass functions (pmfs) and the joint degree distributions. Based on the SMI-DE, we are able to construct LUTs that vary with different layers and iterations for decoding RC-QC-LDPC codes. Moreover, an LUT optimization method is further proposed to generate a unique set of LUTs that only vary with decoding iterations. In this way, the memory requirement for storing the LUTs can be significantly reduced. We conduct a comprehensive evaluation on the proposed RC-MIM-QSMS decoder in terms of the error rate performance, the convergence speed, and the memory requirement for decoding. We demonstrate that the proposed RC-MIM-QSMS decoder surpasses the floating-point shuffled belief propagation (SBP) decoder [23] in the high signal-to-noise (SNR) region and has comparable convergence speed to other state-of-the-art MIM-FAIDs. More importantly, the RC-MIM-QSMS decoder can save up to 85.43% memory requirement compared to the benchmark MIM-FAIDs. The rest of this paper is organized as follows. Section 2 provides the preliminaries of this work including the notations, the QC-LDPC codes, the shuffled min-sum (SMS) decoder [27], and the decoding framework of the MIM-QSMS decoder [15]. The proposed SMIM-DE and the LUT optimization method for designing the RC-MIM-QSMS decoder are illustrated in Section 4. In Section 5, we evaluate the proposed RC-MIM-QSMS decoder from the aspects of the error rate performance, the convergence speed, and the memory usage for implementing decoding. Section 6 concludes this paper.
## 1 Introduction
### Paper Organization  
The rest of the paper is organized as follows. Section 2 discusses the related work which mainly comprises the existing benchmark routing protocols. Section 3 explains the system model which includes network model and energy consumption model. Section 4 explains the proposed algorithm, which includes clustering methodology and data flow model. Section 5 describes the nine performance parameters which are used in simulations to measure and compare the performance of the proposed protocol. Results and discussion are presented in Section 6, with simulation environment and network configuration parameters. Finally, the conclusions and future work of this paper are given in Section 7.
# Review of Photogrammetric and Lidar Applications of UAV
## 4 Regulations of UAV Flights
### European Union  
Carrying out a flight with a UAV in the airspace above the territory of the European Union is subject to compliance with the regulations issued and administered by the European Aviation Safety Agency (EASA). To fly in this space, the UAV operator must meet the set conditions in the registration of the pilot and the UAV if it weighs more than 250 g or the device is equipped with a sensor (camera) to capture personal data. Each member state, based on Regulation no. 947, issued by the European Union Commission in 2019, is to compile a register of operators and a register of certified UAVs with their respective identification [29]. To obtain a certificate and permission to fly in the airspace of each country of the European Union, the UAV operator must undergo training provided by the relevant institution in the member country. The regulation also orders that each UAV should contain a member state code and the pilot's unique 12-digit digital registration number. The UAV should be in the pilot's field of vision, called the Visual Line of Sight (VLOS); the flight height should not exceed 120 m from the nearest point on the Earth's surface; and the flight should not occur near other objects. It is forbidden to fly over airports, groups of people, and residential or military zones without permission since privacy or security is violated when pictures or videos are taken. According to Regulation 2019/947, UAVs can be divided into three operational categories (open, specific, and certified) based on flight risk. This risk is determined by the weight of the UAV and the location of the flight. Depending on the flight category, a remote pilot may need specific permits or certificates [30; 31]. The review contains selected European Union member states such as Slovak, Czech Republic, Poland, Hungary, Austria, and Germany.
# A Named Entity and Relationship Extraction Method from Trouble-Shooting Documents in Korean
## 2 Previous Research  
### Named Entity Recognition  
NER is a natural language processing method to identify conceptual objects from terms in text. Conditional Random Field (CRF), Bidirectional Long- and Short-Term Memory (BiLSTM), or Bidirectional Encoder Representations from Transformers (BERT) models could be used to implement the NER approach. NER refers to a technique used in natural language processing that recognizes phrases containing specific meanings within sentences or documents [9]. Most information extraction applications begin with the identification and categorization of named things inside a text. Naming an entity makes it possible to refer to everything by its proper name. This technique of named entity recognition involves identifying and categorizing entities based on their category.  
According to the definition of NER recorded in the Telecommunications Dictionary maintained by the Telecommunications Technology Association [10], NER is the technique of recognizing, extracting, and sorting phrases (named entities) referring to pre-defined people, companies, locations, time, and units from documents. In this paper, the term \"NER\" will denote the process of defining equipment components, failure status, and solutions to failure status as entities and extracting them from documents.  
Multiple methods [11; 12] exist for conducting NER, including but not limited to rule-based methods that utilize rules pre-defined by the user, unsupervised learning methods that apply statistical methods to the input text data without any pre-defined rules or entity names, and feature-based supervised learning that takes in supervised data with labels and applies them to classification or sequence labeling problems.  
This paper utilized feature-based supervised learning to conduct NER. CRF [13] and the Korean BERT-based model KLUE [8] were used to extract equipment, equipment components, failure status, and solutions to failure statuses from equipment maintenance documents.