esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sequence Proxesing and Analyses  
An Illumina MiSeq Reporter v2.5 was used to remove the sequence reads with quality scores below 20. Trimmomatic-0.36 [23] was used to trim poly(N) tails. Each of the resultant paired-end reads was concatenated by QIIME 1.8.0 [24], from which the chimeric sequence was excluded using the \"chimera.vsearch\" command [25] in mothur v.1.39.5 [26] against the database \"uchime_reference_dataset_ITS1_28.06.2017.fasta\" [27]. Reads shorter than 100 bp were removed on the Galaxy web platform [28]. From a total of 13 libraries, 436,925 sequences were obtained, with the number of the reads ranging from 1342 to 51,987 per library. The mean read length of each library ranged from 259 to 309 bp. The sequences were taxonomically assigned by BLASTN2.2.28+ [29] against the UNITE database \"UNITEdatabase\"[11]. To check for potential database biases, the sequences were also searched against the fungal ITS RefSeq database (PRJNA177353) [31], downloaded on 13 March 2019.  
For the diversity analyses, 1000 sequences were sampled from each library using mothur v.1.39.5 [26]. The sampled sequences were assembled into operational taxonomic units (OTUs) at a 97% sequence similarity. For the \(\beta\)-diversity analysis, we compared the fungal assemblages with those obtained during our previous study in South Korea [18]. The results were comparable since the methods used were identical, i.e., the same sampling, analytical, and sequence-processing methods with the same primers, ITS1F and ITS2, were used across these two studies. From each of the Thai and Korean libraries, 1000 sequences were sampled, assembled into 97% OTUs, and mapped onto the same OTU table. The Jaccard indices and Yue and Clayton theta similarity coefficients were calculated to compare fungal assemblage memberships and structures, respectively. P-tests were performed to analyze statistical differences in fungal assemblages between Thailand and South Korea. For the \(\alpha\)-diversity analysis, singleton OTUs were removed by the \"remove.rare\" command, and the observed richness, Chao1 estimators, Shannon indices, and Simpson indices were calculated using mothur v.1.39.5 [26].
# Traffic-Aware Optimization of Task Offloading and Content
## 5 Experimental Evaluation
### Comparative Experiments  
#### 5.2.1 Comparison with Changing of Content Types  
As the content type increases, the average execution time of TOCC is better than that of LOCAL and TFO, as shown in Figure 3(a). The cache of TOCC is sensitive to the content type and is greatly affected by it, which causes the average execution time to continuously increase. In contrast, TFO and LOCAL are less affected by content type changes and the average execution time fluctuates within a certain range of higher levels. The average execution time of TOCC is 46% better than LOCAL and 44% better than TFO. This highlights the effectiveness of MEC and edge content caching in reducing the IoV time latency. TOCC is slightly worse than F_NSGA-III, 12% lower. Figure 3(b) shows that with the increase in content types, TOCC has the lowest total energy consumption compared with LOCAL, TFO, and F_NSGA-III. In terms of total energy consumption, TOCC outperforms LOCAL by 60%, TFO by 85%, and F_NSGA-III by 79%. Through experimental observations, we have made an intriguing discovery. When comparing two experiments, TOCC and F_NSGA-III, we observed that F_NSGA-III slightly outperforms TOCC in terms of average latency. However, when it comes to total energy consumption, TOCC significantly outperforms F_NSGA-III. This intriguing phenomenon was consistently observed in subsequent experiments. Upon analyzing the algorithms used in both experiments, we found that the TOCC algorithm employs a joint optimization strategy that carefully balances the trade-off between minimizing latency and energy consumption, effectively avoiding excessive energy consumption. Of course, this optimization comes at the expense of sacrificing a portion of latency minimization.
# Knowledge Distillation Based on Fitting Ground-Truth Distribution of Images
## 3 Method
As shown in Figure 2. Based on LSH [13], we designed two different knowledge distillation algorithms based on ground-truth distribution fitting of images. The two algorithms are LSH-T and LSH-TL. LSH-T fits the ground-truth distribution of features, while LSH-TL fits the ground-truth distribution of classes. Figure 2a illustrates the method for fitting the ground-truth distribution of features (LSH-T). We address the overly smooth feature distribution of the penultimate layer by introducing a feature temperature \(\tau\) to correct the penultimate layer feature output of the teacher. After the feature temperature treatment, the penultimate layer feature output of the teacher obtains greater discriminability of features and the feature distribution becomes sharper. In the process of distilling the penultimate layer of feature knowledge with \(L_{lsh}\) and \(L_{mse}\), the teacher can provide students with more significant feature relevance information; Figure 2b shows the fitting method of the ground-truth distribution of classes (LSH-TL). The original LSH used dataset labels to supervise student classifier outputs. We believe that there is a discrepancy between the dataset labels and the ground-truth distribution of image classes. Because the teacher has been pre-trained in the relevant task scenarios, the teacher can output category distributions that are similar to the ground-truth distribution. So, we extract the teacher classifier output to supervise the output of the student classifier without using the dataset labels. Such supervision is more in line with the need for the penultimate layer of feature knowledge distillation for fitting the ground-truth distribution.
# Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints
## 4 Results and Analysis
### Comparison with Previous Models  
In predictive modeling studies using machine learning, categorical boosting (CatBoost), random forest (RF), ridge regression (Ridge), and backpropagation neural network (BPNN) are among the most commonly used models. To evaluate the predictive performance of the SMA-MLP model, this study compares its performance with these classical machine learning models, as illustrated in Figure 10. The results indicate that the proposed model achieves the highest trend evaluation scores and the lowest prediction error metrics, demonstrating superior predictive performance. Specifically, in terms of test set accuracy, the SMA-MLP model attains a coefficient of determination of 0.97, significantly surpassing the highest R\({}^{2}\) achieved by other ML models. Additionally, while the RMSE of other models ranges from 0.27 MPa to 0.42 MPa, the SMA-MLP model achieves an RMSE of only 0.1 MPa. This remarkably low RMSE value suggests that the proposed model exhibits higher accuracy in joint shear strength prediction. In summary, due to its enhanced ability to capture the complex nonlinear relationships between input and output variables, the SMA-MLP model outperforms other ML models in terms of prediction accuracy.  
Figure 10: Predictive indicator results for different ML models.  
Figure 9: Feature importance score of inputs.
## 3 Results

Table 3: Diabetes readmission prediction: comparison of accuracy, area under the curve (AUC), recall, precision, F1-score, Kappa, and Matthews correlation coefficient (MCC) for different machine learning classifier models. Cells highlighted in yellow emphasize standout performance metrics (i.e., maximum values). critically important when the consequences of false positives are significant. For instance, in cancer diagnostics, a false positive might lead to unnecessary stress, invasive testing, or treatment, which carries its own risks and costs. Recall, or sensitivity, measures the ability of a model to find all the relevant cases within a dataset. In medicine, high recall is crucial in conditions where failing to identify and treat a disease could lead to severe health consequences or even death. The cost of false negatives is often much higher than that of false positives; missing out on an early diagnosis of a disease such as sepsis or heart attack can be life-threatening. The AUC is an aggregate measure of performance across all possible classification thresholds. It reflects the model's ability to discriminate between patients with and without the condition at various levels of specificity and sensitivity. The AUC is particularly useful in a clinical context because it is not affected by the prevalence of the positive class, hence providing a more-balanced evaluation of the model performance. In clinical practice, these metrics must be balanced and interpreted in light of the disease prevalence, the potential consequences of false positives and false negatives, and the specific clinical application. A model aimed at diagnosing a condition with serious consequences for missed cases might prioritize recall over precision, whereas one used to select candidates for an invasive procedure might prioritize precision to minimize unnecessary interventions.
## 3 Related Work
The third phase consists of constructing a hop-by-hop trust chain to authenticate _Interest_ and _Data_ packets during forwarding. It utilizes highly trusted IPEs to replace highly trusted certificates of the chain. Additionally, it shortens the certificate chain into one certificate. With the objective of reducing the overhead associated with signature verification, the work in [32] presented a lightweight communication strategy for ICN-based UAV networks. The mechanism utilizes the trust score of the node to determine whether data authenticity needs to be checked. In the case that the source UAV is considered trustworthy, the process of _Data_ validation is postponed for a certain period, which is proportional to the UAV's trust score. Blockchain technology is extensively employed in conjunction with the NDN architecture. Some authors have used it to provide solutions against security attacks, such as [33; 34], while others have used it for securing content dissemination [35; 36; 37]. The authors of [38] proposed a blockchain-based mechanism to authenticate mobile producers in ICN. The protocol aims to authenticate producers' prefixes and ensure genuine routing updates. The architecture identifies two domains: the core network and the clusters. The clusters consist of both access gateways and a cluster head. Each cluster uses a private ledger managed by the cluster head. The core network includes a subset of core routers that manage the global blockchain, which stores the transactions generated to and from various clusters. The mechanism involves utilizing the mobile device's SIM card to securely distribute and register key pairs. When a producer connects to the network for the first time, the authorization server verifies the prefix announced by the producer by validating its digital signature. Then, it records the producer's public key and digital signature into the blockchain. In a different context, the work in [39] presented a lightweight consensus mechanism for the IoT. The authors proposed a model named Proof of Evolutionary Model (PoEM) to enhance the consensus efficiency of Blockchain-as-a-Service (BaaS) in the IoT. The model relies on machine learning to achieve consensus. It also includes a mechanism to handle the dynamic nature of IoT environments by managing nodes joining and exiting in real-time.
## 3 Results and Discussion
### Effect of Coating Process on Aging Resistance Properties
The large crack diameter was close to 40-50 \(\upmu\)m, accompanied by small diameter cracks. However, Figures 12D, 13D and 14D show that the paint film with microcapsules in primer had no obvious damage, and only a small number of bubbles with small diameter are observed. Microcapsules made the paint film have good elongation at break. Therefore, the paint film with the microcapsules added after aging test was not easily damaged due to its good toughness. Furthermore, under the action of environmental factors, the microcapsules would crack, and the healing agent could effectively repair the microcracks, while the paint film without microcapsules could not bear the impact of the environment and was prone to cracking [35]. The results showed that the paint film with microcapsules had better stability and aging resistance than those without microcapsules. Figure 11: Effect of aging time on gloss of the paint film in the ZN ultraviolet weather resistance test. Figure 10: Effect of aging time on gloss of the paint film in the 160 \({}^{\circ}\)C oven. Figure 12: SEM of the paint film with and without microcapsules before and after aging in the 120 \({}^{\circ}\)C oven: before aging (**A**) without, (**B**) with microcapsules; after aging (**C**) without, (**D**) with microcapsules. Figure 13: SEM of the paint film with and without microcapsules before and after aging in the 160 \({}^{\circ}\)C oven: before aging (**A**) without, (**B**) with microcapsules; after aging (**C**) without, (**D**) with microcapsules. Figure 15 showed the infrared spectrum of the paint film without microcapsules before and after aging. The peaks at 2929, 2865 and 1447 cm\({}^{-1}\) were the stretching vibration peaks of -CH\({}_{2}\), and 1730 cm\({}^{-1}\) was the vibration absorption peak of C=O group. No peak disappeared or appeared before and after aging, indicating that there was no difference in the composition of the paint film without microcapsules before and after aging. Figure 16 showed the infrared spectrum of the paint film with microcapsules in primer before and after aging. The absorption peak of N-H was at 3360 cm\({}^{-1}\). The stretching vibration peak of C-H was at 2929 and 2865 cm\({}^{-1}\), and the tensile vibration peak of C=O group in the UF resin was at 1639 cm\({}^{-1}\). The C=O characteristic peak appeared at 1730 cm\({}^{-1}\) proved the existence of acrylic resin. No peak disappeared or appeared before and after aging, indicating that the composition of the paint film with microcapsules had no difference before and after aging. The results showed that after aging test, different aging environments had no effect on the composition of the paint film with and without microcapsules. Figure 14: SEM of the paint film with and without microcapsules before and after aging in the ZN ultraviolet weather resistance test: before aging (**A**) without, (**B**) with microcapsules; after aging (**C**) without, (**D**) with microcapsules.
# Impact of Silicon Substrate with Low Resistivity on Vertical Leakage Current in AlGaN/GaN HEMTs
## 4 Conclusions  
In this work, we investigated the impact of different Si substrate types with low resistivity on the VLC in AlGaN/GaN epitaxial layers. The results indicate that a p-Si substrate based structure can form a depletion barrier and hence block the electron injection and finally reduce the VLC at 0-400 V compared to n-type. The temperature-dependent I-V and E\({}_{\text{a}}\)-V results suggest that the sudden increase in the slope of the I-V curve for p-type samples is related to the impact ionization near the interface. Our results show that choice of the substrate greatly affects the VLC at lower voltage for AlGaN/GaN HEMTs.  
Conceptualization, C.S. and X.Y.; methodology, X.Y., M.W., Z.Y., F.X., X.W., W.G. and B.S.; data curation, C.S., P.J. and J.T.; formal analysis, investigation, C.S., S.W. and Y.X.; validation, C.S., X.Y., W.G. and B.S.; writing--original draft preparation, C.S. and X.Y.; writing--review and editing, C.S., A.I. and X.Y.; supervision, X.Y.; project administration, B.S.; All the authors contributed through scientific discussion and reviewed the manuscript.  
This work was supported by the National Key Research and Development Program of China (No. 2017 YFB0402900), the National Natural Science Foundation of China (Nos. 61804004, 61574004, 61521004, 11634002, and U1601210).  
The authors declare no conflict of interest.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 3 System Model
### Energy Model  
Here, we adopt the energy model that is applied in literature [32]. As is shown in Figure 2, energy consumption mainly contains two parts, transmission consumption and reception consumption. The transmission part contains the signal generation and its enhancement. Once the signal is created, it will be enhanced by the amplifier. The amplifier uses two different powers to strengthen the signal according to the transmission distance. Therefore, the energy model for transmission is also divided into a free space model, which is for short-haul communication, and multi-path fading model, which is for long distance communication.  
Figure 1: Network model.  
Transmission consumption represents the energy the transmission circuit and the amplifier use, and the reception consumption represents the energy the reception circuit uses. Each sensor will consume \(E_{Tx}\) energy to transmit an \(l\)-bit data package over the communication distance of \(d\), as is shown in Equation (2):  
\[E_{Tx}(l,d)=\left\{\begin{array}{ll}l\cdot E_{dec}+l\cdot\varepsilon_{fs}\cdot d ^{2}&if~{}~{}d<d_{0}\\ l\cdot E_{dec}+l\cdot\varepsilon_{mp}\cdot d^{4}&if~{}~{}d\geq d_{0}\end{array}\right. \tag{2}\]  
where \(E_{dec}\) denotes the energy consumption to run the transmitter or receiver circuit. \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) denote the amplification coefficient for the free space model and the multi-path fading model, respectively. \(d_{0}\) is a threshold value and it can be calculated using:  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{3}\]  
Reception consumption represents the energy usage of the reception circuit. The energy consumption to receive an \(l\)-bit data package can be calculated using Equation (4):  
\[E_{Rx}(l)=l\cdot E_{elec} \tag{4}\]
## 1 Introduction
However, due to a lack of testing means and test conditions, the study could not define and evaluate the formation and action process of the new free surface by analyzing the dynamic rock-breaking effect in engineering practice. While blasting vibration data is easy to obtain and the analysis and processing methods have been perfected, it is difficult to analyze the formation of a new free surface at the amplitude level because the vibration wave oscillates and decays with time. This paper proposes a method to identify the formation time of a new free surface by comparing the vibration energy for tunnels or roadways excavated using an inclined hole cut pattern. Since the free surface conditions change during the blasting process and the linear superposition method does not consider this change, this leads to a significant separable difference in the vibration energy between the two. This method relies on this difference to carry out the study. The specific approach is to process the field-measured multihole vibration data for delay blasting using the variational mode decomposition (VMD) method [23] combined with the Hilbert transform to obtain the accumulated energy curve of the vibration wave. In addition, the single-shot test provides the single-hole (single-free surface) vibration wave as the base wave. The base wave is superposed to obtain a predicted wave that shares the initiation time sequence [24; 25] in the delay blasting. Finally, we find the formation time of the new free surface by comparing the 'time-accumulated energy' curves of the observed and predicted waves based on the change in the linear correlation of the two curves. This method provides a new idea to study the formation of new free surfaces for blasting. No complex test is needed in reference to solving vibration-reduction problems in engineering. In this paper, Section 2 introduces the basic principle of the linear superposition method and points out why this method does not apply to vibration prediction after forming a new free surface. Then, we provide a vibration energy comparison method to identify the new free surface's formation time. Section 3 presents the results of identifying the new free-surface formation time in the practice during cut blasting through a case study. Section 4 briefly describes the optimal selection of single-hole charges based on identification results and the implementation of blasting operations in the field.
# Quantum LDPC Codes Based on Cocyclic Block Matrices
###### Abstract  
Motivated by a family of binaryocyclic block matrices over GF(2), we proposed a construction method to gain the stabilizer of long-length quantum error-correction codes (QECCs). Stabilizer quantum codes (SQCs) can be obtained by the different rows of the yielded circulant permutation matrices; hence, the quantum codes have the virtue of a fast construction algorithm. The recursive relation of a block matrix is employed in the proposed approach, so that the generator matrix of quantum cocyclic codes with long length can be constructed easily. Furthermore, the obtained quantum codes have the low-density advantage of there being no 4-cycles in the Tanner graph.  
long-length quantum codes; cocyclic block matrices; stabilizer codes; low-density parity check codes +
Footnote †: journal:  
0000-0002-4000-32000-32000
# Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes
## 4 Our Proposal: Difference DV_Distance Using Correction Coefficients of Unknown Nodes  
At present, the correction model based on anchor nodes is often employed by most existing improved DV_Distance localization algorithms, that is, the accumulated hop distances from unknown node to anchor node is corrected using the correction coefficient of the anchor node which is nearest to the unknown node. Actually, the minimum hop numbers from different unknown nodes to anchor nodes are different, and meanwhile the corresponding accumulated hop distances are also different, and thus different unknown nodes should have different correction coefficients. However, the correction model based on anchor nodes cannot reflect the difference of correction coefficients for different unknown node.  
Taking account of the above technical limitation, a difference DV_Distance localization algorithm using correction coefficients of unknown nodes is proposed, and each unknown node is made to compute its own correction coefficient, reflecting the difference of the correction coefficients for all unknown nodes. Because we have no position information about unknown nodes, it is unable to directly compute the difference of accumulated hop distance and actual Euclidean distance from unknown nodes to anchor nodes. In order to indirectly obtain the distance difference from unknown nodes to anchor nodes, we have defined target anchor node and direction anchor node. For the purpose of explanation, as also shown in Figure 2, anchor \(A_{1}\) is considered as target anchor node when the accumulated hop distance is modified from unknown node \(U_{0}\) to anchor \(A_{1}\), simultaneously, the other anchor nodes \(A_{2}\), \(A_{3}\), and \(A_{4}\) are regarded as direction anchor node.  
The improvement of the proposed algorithm mainly includes two aspects: (1) the contributions of direction anchor nodes are made full use of to compute the distance difference from unknown nodes to target anchor node; (2) according to their contribution, some correction coefficients for all direction anchor nodes are weighted to get the final correction coefficient of unknown nodes, which will further improve the correction accuracy of accumulated hop distance.