esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Histology  
Muscle fiber atrophy and focal necrosis could be observed on hematoxylin and eosin staining in the majority (30% T0 vs. 71% T1) of the COVID-19 patients, both at admission and, more prominently, after 7 days of ICU stay (Figure 2A,B). Nearly all patients (80% T0 vs. 100% T1) showed perivascular fibrosis, without signs of inflammation (Figure 2C). No abnormal luminal obliterations nor micro-thrombi could be observed within the arterioles and venules. In some patients (30% T0), interstitial fibrosis could be observed, which seemed to progressively worsen (76% T1) over the 7-day period (Figure 2D).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Fiber Type** & \
\begin{tabular}{c} **T0** \\ **(n = 17)** \\ \end{tabular} &
\begin{tabular}{c} **T1** \\ **(n = 17)** \\ \end{tabular} \\ \hline \multirow{2}{*}{Fiber type distribution (\%)} & I & 43 \(\pm\) 13 & 47 \(\pm\) 13 \\ \cline{2-4}  & II & 57 \(\pm\) 15 & 53 \(\pm\) 15 \\ \hline \multirow{2}{*}{Myonuclear content (number/fiber)} & I & 4.40 \(\pm\) 0.92 & 4.97 \(\pm\) 1.35 \\ \cline{2-4}  & II & 4.14 \(\pm\) 0.88 * & 4.37 \(\pm\) 1.32 * \\ \hline \multirow{2}{*}{Proportion of fibers containing a central nuclei (\%)} & I & 6.4 \(\pm\) 4.9 & 5.8 \(\pm\) 4.5 \(\dagger\) \\ \cline{2-4}  & II & 6.0 \(\pm\) 4.7 & 3.6 \(\pm\) 3.1 \(\dagger\) \\ \hline \multirow{2}{*}{CC} & I & 3.31 \(\pm\) 0.53 & 3.26 \(\pm\) 0.67 \\ \cline{2-4}  & II & 2.85 \(\pm\) 0.41 * & 2.65 \(\pm\) 0.72 * \\ \hline \multirow{2}{*}{C/Fi} & I & 1.15 \(\pm\) 0.29 & 1.16 \(\pm\) 0.43 \\ \cline{2-4}  & II & 0.84 \(\pm\) 0.29 * & 0.76 \(\pm\) 0.26 * \\ \hline \multirow{2}{*}{CFPE index (capillaries-1000 \(\mu\)m\({}^{-1}\))} & I & 5.21 \(\pm\) 0.91 & 4.93 \(\pm\) 0.79 \\ \cline{2-4}  & II & 4.13 \(\pm\) 0.79 * & 3.73 \(\pm\) 0.83 * \\ \hline \multirow{2}{*}{CD (Capillaries per mm\({}^{2}\))} & I & 335 \(\pm\) 97 & 305 \(\pm\) 94 \\ \cline{2-4}  & II & 328 \(\pm\) 105 & 315 \(\pm\) 114 \\ \hline \end{tabular} Data are expressed as mean \(\pm\) SD. I: type I muscle fiber; II: type II muscle fiber. CC: capillary contacts C/Fi capillary to fiber ratio. CFPE index: capillary to fiber perimeter exchange index. CD: capillary density. * significantly different compared with type I muscle fibers (\(p\) < 0.05). \(\dagger\) significant difference compared with T0 (\(p\) < 0.05).  
\end{table}
Table 2: Type I and type II muscle fiber characteristic at day 0 (T0) and day 7 (T1) of intensive care unit admission in COVID-19 patients.
## 4 Experimental Results and Analysis
### Evaluation Indicators  
In order to evaluate the superiority of the algorithm objectively, the evaluation indicators use the precision rate, the recall rate, the mean average precision mAP, and the number of frames per second to detect the pictures to evaluate the performance of the algorithm in this paper. The calculation formula for each indicator is as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{6}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Training Set** & **Test Set** & **Total** \\ \hline Listening & 3105 & 776 & 3881 \\ Looking down & 2742 & 685 & 3427 \\ Lying down & 1843 & 460 & 2303 \\ Standing & 972 & 243 & 1215 \\ Total & 8662 & 2164 & 10,826 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of labels in the student behavior dataset.  
Figure 13: Data enhancement effect.  
\[\text{mAP}=\frac{\sum_{j=0}^{n}AP(j)}{n} \tag{8}\]  
\[\text{FPS}=\frac{\text{Figure Numbers}}{\text{Total Time}} \tag{9}\]  
Among them, TP, FP, and FN represent the number of correct detections, the number of false detections, and the number of missed detections, respectively, \(AP(j)\) is the average precision of the \(j\)th type of defects, Figure Numbers represents the total number of detected pictures, and Total Time represents the total detection time.
## 4 Results
### Evaluation of Winter Wheat Yield at Field Scale  
The winter wheat yield was evaluated at Hengshui City in 2017 and the results are shown in Figures 11 and 12. Similarly, the models that were based on the GPP2 and GPP1 estimation significantly underestimated the yield of the winter wheat (Figures 11 and 12). The evaluated yield based on GPP1 was the lowest, with an error of 92% (Figure 12a,b). The evaluated yield based on GPP2 was also much lower than the actual DAM of the winter wheat, with an error of 80% (Figure 12c,d). By contrast, the model based on ACPM obtained the best accuracy for yield estimation. Based on the ACPM estimation results, the RMSE of the measured yield versus the estimated yield was 0.51 t/ha, which was equivalent to a 8% error in the pure pixels (Figure 12e). Setting the intercept to zero, we observed a linear regression slope of 1.0126 and a regression coefficient of 0.59 in the pure pixels. In addition, the RMSE of the measured yield versus the estimated yield was 0.85 t/ha, which was equivalent  
Figure 10: Validation of the estimated winter wheat DAM based on the GPP1, GPP2, and ACPM models in Hengshui City in 2017, as follows: **(a)** GPP1 model in the pure pixels of the experimental plots in exp2; **(b)** GPP1 model in the pure and mixed pixels of the experimental plots in exp2; **(c)** GPP2 model in the pure pixels of the experimental plots in exp2; **(d)** GPP2 model in the pure and mixed pixels of the experimental plots in exp2; **(e)** ACPM model in the pure pixels of the experimental plots in exp2; and **(f)** ACPM model in the pure and mixed pixels of the experimental plots in exp2; **(e)** is the error.  
to a 12% error in the pure and mixed pixels (Figure 12f). Setting the intercept to zero, we obtained a linear regression slope of 1.0401 and a regression coefficient of 0.46 in the pure and mixed pixels. The mixed pixel reduced the accuracy of the yield estimation, but the results demonstrated that the yield estimation model, based on the ACPM model, was available and practical for Hengshui City. Furthermore, as shown in Figure 10, the Shenzhou County was the highest-yield region, while the Zaoqiang and Wuyi Counties were the lowest-yield regions. These results were very consistent with the actual distribution.  
Figure 11: Distribution of the simulated yield of the winter wheat with a1 km resolution in Hengshui City in 2017. Simulated yield based on the (**a**) GPP1 model, (**b**) GPP2 model, and (**c**) ACPM model.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 2 JSC Network Configurations
### Bistatic Deployment
#### 2.2.2 The Need for Synchronization  
Given that the transmitter and receiver are not co-located, synchronization is essential. In these setups, precise synchronization is particularly critical for time-sensitive safety applications, such as cooperative driving. Potential solutions are being explored, including the implementation of a Cloud Radio Access Network (Cloud-RAN) architecture in mobile networks to ensure effective clock distribution. Additionally, the network infrastructure provides the receiver with information regarding the transmitted signal, e.g., pilots, necessary for signal processing tasks, such as matched or reciprocal filtering. Furthermore, in MIMO systems, achieving less stringent synchronization at the beam level is important to ensure that sensing beams at the transmitter and receiver are aligned, effectively targeting the same region of interest.
## 3 Results and Discussion  
### Characteristics of Tested Sorbents (FTIR, Surface)  
The FTIR spectra of the sorbents from mealworm exoskeletons and mealworm molds are very similar to each other, suggesting their similar chemical composition. Both materials show absorption bands characteristic of chitin, proteins, and lipids (Figure 2).  
The saccharide-specific peaks at 1068 cm\({}^{-1}\) and 1025 cm\({}^{-1}\) indicate the presence of chitin and suggest C-O stretching for the C3 and C6 of the pyranose ring [21]. The peaks at 1155 cm\({}^{-1}\) (symmetrical C-O-C stretching) and 1114 cm\({}^{-1}\) (asymmetrical C-O stretching) are also typical of saccharides [21]. The small peak of 2961 cm\({}^{-1}\) suggests a methylene group in the saccharide ring of chitin [22].  
The distinct peak at 1626 cm\({}^{-1}\) (amide I region) is indicative of a hydrogen bond between a carbonyl and hydroxyl group of the same polysaccharide chitin chain. The peak at 1540 cm\({}^{-1}\) (amide II region) is attributable to N-H bending--a feature of the chitin acetamide group [22; 23] (Figure 2). The presence of acetamide groups in the MES and MMS structure is also indicated by the typical peaks at 3290 cm\({}^{-1}\) and 2870 cm\({}^{-1}\), corresponding to N-H (Amide A) and C=O bonds [24]. The peak at 1233 cm\({}^{-1}\), visible in the amide III region, is also indicative of a C=O bond [25].  
The peaks at 1450 cm\({}^{-1}\) and 1374 cm\({}^{-1}\) correspond to the CH\({}_{2}\) and CH\({}_{3}\) stretching vibrations typical of lipids. Finally, 2917 cm\({}^{-1}\) and 2850 cm\({}^{-1}\) peaks are due to symmetrical and asymmetrical stretching of CH\({}_{2}\)/CH\({}_{3}\), lipid chains, and terminal protein groups [26].  
Using a Gemini VI apparatus, the porous structure and pore distribution were measured for the tested sorbents using the low-temperature nitrogen sorption method. The BET area determined for both MES and MMS is 0.13 \(\pm\) 0.02 m\({}^{2}\)/g and 0.24 \(\pm\) 0.03 m\({}^{2}\)/g, respectively, whereas the total pore volume was 0.00115 cm\({}^{3}\)/g and 0.00166 cm\({}^{3}\)/g, respectively.
## 5 Result Analysis  
This paper explores the idea of an ID \(\rightarrow\) Hash Value \(\rightarrow\) Root Hash one-way transmission from left to right.  
When the devices in the group transmit ID information in the network, this system uses PUF technology for secure transmission; it cannot be deciphered, even if it is intercepted; only the corresponding receiving node can convert the ciphertext into plaintext.  
1. The Root Hash stored in the blockchain cannot be changed nor tampered with.
2. As long as there is an inconsistent leaf node in the MHT algorithm, the resulting Root Hash will be completely different. Thus, it is impossible to obtain the same Root Hash as the original ID to complete the authentication, though an attacker can find an ID from the Root Hash in the blockchain to the group to be verified.  
As implied in Table 5, the authentication success rate for a group of 5000-10,000 members is tested through this experiment. The number of illegal members is 0, 5, and 20. Each group of experiments is performed 100 times. The verification success rate indicates that the group authentication of this scheme has strong security.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of** & **Number of** & **Number of** & **Number of** & **Number of** & **Verification** \\
**Group** & **Illegal** & **Trials** & **Successes** & **Failures** & **Success Rate** \\ \hline
5000 & 5 & 100 & 100 & 0 & 100\% \\
5000 & 20 & 100 & 100 & 0 & 100\% \\
10,000 & 5 & 100 & 100 & 0 & 100\% \\
10,000 & 20 & 100 & 100 & 0 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Group authentication success rate experiment.  
Figure 9: Group authentication structure diagram.
# Check-QZP: A Lightweight Checkpoint Mechanism for Deep Learning Frameworks
## 2 Background and Related Works
### Related Works  
Nowadays, many researchers focus on designing solutions to optimize storage for deep learning workloads as the sizes of models and datasets grow [6; 7; 24; 25; 26; 27; 33; 34]. Some researchers have proposed checkpoint methods that reduce capacity and overhead during deep learning workloads [7; 34]. Check-N-RUN [7] utilizes quantization algorithms and compression mechanisms for large-scale deep learning models. SSDC [34] selectively saves only the differences in important parameters during the training and recovery processes through an adaptive threshold. CheckFreq [6] maintains overall workload performance by adjusting checkpoint frequency and parallel processing, ensuring that checkpoints are performed within user-defined overhead limits. These methods can optimize the overall workload by creating lightweight traditional serialized checkpoints or processing them in parallel during training.  
Training for large-scale deep learning models is performed in data centers [17]. Therefore, many researchers have made efforts to optimize storage capacity and data migration in deep learning workloads besides checkpoints [35; 36; 37]. Smart-Infinity [35] quantitatively measures the data migration cost during the storage offloading process, using computational SSDs to overcome the GPU memory capacity limitations of large-scale language models while reducing costs through gradient compression. [PERSON] et al. proposed a new scheme that benefits from compression to reduce the capacity of high-resolution image data used for deep learning [36]. The authors of ZeRO-Infinity [37] proposed a CPU and storage offloading framework to address GPU memory limitation issues during the training of large-scale models, achieving near-maximum storage bandwidth through a bandwidth-aware mapping strategy and storage read-write overlapping. One group of researchers focused on the non-negative matrix factorization model and proposed a novel approach that adeptly snapshots hierarchical information by integrating a deep autoencoder and symmetry regularization [38].  
Meanwhile, multi-tenant environments still face challenges caused by resource competition [17; 39; 40; 41]. Some researchers have proposed scheduling to reduce training and inference latency in deep learning caused by resource competition in multi-tenant environments [42; 43]. Additionally, UAVs, which are increasingly gaining attention with the advancement of flight technology, typically store vast amounts of data extracted in real time by multiple aircraft in data centers. Deep learning techniques, such as object detection and object tracking, which utilize these data, are also evolving [10; 11; 12; 14; 15; 16; 18; 19].  
Unfortunately, while multi-tenant environments and deep learning are becoming increasingly intertwined with technological advancements, the issue of storage contention caused by deep learning workloads in multi-tenant environments has not been sufficiently addressed. We believe that our efforts contribute to solving this problem.
## 4 Results and Discussion
### Experimental Results of DCGAN Augmentation  
#### 4.4.1 Performance Comparison of Different LCNNs  
The results of the experiments using Dataset C are shown in Table 5. Compared to the accuracy improvement obtained by transferring the LCNNs from Dataset A to Dataset B2, it can be seen that the accuracy improvement obtained by transferring the LCNNs from Dataset B2 to Dataset C decreased. Furthermore, we can see that only MobileNetV1, MobileNetV2 and ShuffleNetV2-1.5 had a slightly smaller correct classification rate (TP) for crack images than for background images (TN), indicating that the DCGAN augmentation made the crack image features more evident in the dataset. Thus, the extraction of crack features was significantly improved for most LCNNs.  
The experimental results show that the migration learning method based on small-scale datasets applies equally to all LCNNs trained on Dataset C. However, the effectiveness of enhancement through transfer learning diminishes as the number of crack samples increases. In addition, the SE module still improves the performance of ShuffleNetV2-1.0. ShuffleNetV2-1.0-SE achieves the highest maximum accuracy of 98.14% on Dataset C. At the same time, its \"Parameters\", \"Complexity\" and \"Weights\" are smaller than those of various LCNNs, which achieve similar accuracy. Its ability to correctly classify cracks is second only to EdificieNet_b2, while its ability to correctly classify backgrounds is the best.  
Figure 11: Validation results of different augmentation methods. (**a**) Experimental results for Dataset B1; (**b**) Experimental results for Dataset B2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Transfer** & **Parameters** & **Complexity** & **Weight** & \multirow{2}{*}{**Accuracy (\%)**} & **TP (\%)** & **TN** \\  & **Learning** & **(Millions)** & **(GFLOPs)** & **(MB)** & & & **(\%)** \\ \hline MobileNetV1 & Yes & 4.25 & 1.16 & 12.50 & 97.66 & 97.45 & 97.88 \\ MobileNetV2 & Yes & 2.26 & 0.59 & 7.47 & 97.44 & 97.31 & 97.56 \\ MobileNetV3\_Small & Yes & 1.53 & 0.12 & 6.32 & 97.44 & 97.50 & 97.38 \\ MobileNetV3\_Large & Yes & 4.23 & 0.45 & 16.70 & 97.47 & 97.50 & 97.44 \\ ShuffleNetV2\_0.5 & Yes & 0.35 & 0.04 & 1.77 & 95.45 & 96.52 & 94.29 \\ ShuffleNetV2\_1.0 & Yes & 1.27 & 0.15 & 5.29 & 96.70 & 97.46 & 95.96 \\  & No & & & & 95.61 & 97.21 & 94.11 \\ ShuffleNetV2\_1.0-SE & Yes & 1.45 & 0.17 & 6.15 & 98.14 & 98.02 & 97.95 \\  & No & & & & 97.24 & 97.49 & 97.00 \\ ShuffleNetV2\_1.5 & Yes & 2.5 & 0.30 & 10.00 & 97.85 & 97.76 & 97.94 \\ ShuffleNetV2\_2.0 & Yes & 5.38 & 0.59 & 20.90 & 97.95 & 98.01 & 97.51 \\ EdificieNet\_b0 & Yes & 4.05 & 0.80 & 15.90 & 96.79 & 97.22 & 96.38 \\ EdificieNet\_b1 & Yes & 6.58 & 1.29 & 25.70 & 97.79 & 97.82 & 97.76 \\ EdificieNet\_b2 & Yes & 7.77 & 1.79 & 30.30 & 97.98 & **98.07** & 97.89 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance validation results of different LCNNs trained on Dataset C.
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 2 Background and Related Works
### Relate Works
#### 2.2.4 Blockchain-Based Searchable Encryption and Access Control Schemes  
This section presents how searchable encryption and access control can work together. To obtain strong conceptions of confidentiality, various technologies must be combined. The integration between PEKS and PRE schemes present in [42] can provide an efficient EHRs sharing protocol based on two types of blockchain: private blockchain for storeddata and public blockchain for storing an EHR index. According to this research [35], the EHR data ciphertext is stored in the cloud, and the EHR data keyword index is stored on the blockchain. Fine-grained access control of cloud data is achieved via the attribute encryption approach, and attribute signature technology is employed to validate the authenticity of the EHR data source. The authors of [43], a new cryptographic primitive called conjunctive keyword search with designated tester and timing, allowed the proxy re-encryption method (Re-dtPECK), which is a time-dependent SE scheme. In this scheme, the duration of time for the delegate to scan and decrypt the delegator's encrypted documents can be regulated. This scheme supports conjunctive keyword search and prevents keyword guessing attacks.  
The authors of [44] introduced a blockchain-based attribute-based searchable encryption scheme with a verifiable ciphertext. The scheme uses the PRE technique to enforce user attribute revocation when the user's attributes or the ciphertext access structure need to be modified, and the authority center is in control of the overall attribute revocation procedure. However, to securely find keywords on the blockchain consortium, the authors of [45] use public encryption with the methods of keyword searchable PEKS. In the decoding phase, proxy re-encryption technology is used to provide secure access to patient data for third-party users. Besides, the authors of [46] proposed a blockchain-based EHR sharing protocol that is both secure and private. To achieve data protection, privacy preservation, and access control are needed. The scheme primarily uses searchable encryption and conditional proxy re-encryption. Finally, to the best of our knowledge, we still need to develop an efficient sharing EHR protocol using PEKS with PRE schemes based on blockchain to solve the KGA problem with low computational and communication costs.
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 5 Challenges and Recent Advances
### Integrating Acoustic Emission with Other Technologies  
Leveraging complementary attributes of multiple techniques presents a pragmatic approach overcoming limitations of individual methods. Hybrid monitoring schemes integrating AE hold immense potential for comprehensive evaluations.  
Combining AE with traditional geodetic methods addresses uncertainties from isolating surface displacements. Cross-correlating underground fracture growth from AE activity with ground-based interferometric SAR deformation patterns augmented slope stability assessments through validated subsurface-surface couplings [15].  
The fusion of AE parameters with continuous hydrological monitoring through in situ pore-pressure sensors quantitatively enabled the discrimination of dilatant failure regions from stable compressive zones, as directly evidenced through hydraulic head changes [137]. Establishing damage-hydraulic couplings optimized predictive modeling by enabling physics-based extrapolations beyond calibration sites.  
Exploiting mutual complementary attributes between techniques like wider spatial/temporal coverage of geodetic/hydrological methods against the subsurface sensitivity of AE presents an effective paradigm for advanced long-term predictive monitoring schemes deployed in operational warning systems. Hybrid schemes promise the overcoming of limitations inherent to the isolation of single modalities optimizing monitoring scopes. Combined with optimized signal processing and machine learning frameworks, future applications utilizing such integrated multi-technology platforms appear most promising.
## 1 Introduction
[13] developed an autonomous driving robot for an agricultural operating environment using GNSS-RTK, a fiber optic gyroscope (FOG) and an IMU. The results showed that the RMS position error of the desired path was less than 3 cm. [PERSON] et al. [14] developed a crawler-type tractor that can be operated autonomously using GNSS-RTK and an IMU. The RMS of lateral error in the navigation tests was shown to be less than 5 cm in straight lines in an open sky field. [PERSON] et al. [15] reported that an automatically guided rice transplanter that uses GNSS-RTK and IMU showed less than 10 cm and 5 degrees for the lateral and heading errors respectively, in straight lines. The summary of previous studies using navigation sensors is summarized in Table 1.  
Recently, advances in micro electromechanical system (MEMS) technology have led to the development of a small and inexpensive MEMS-IMU, and the performance of the MEMS-IMU is being \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference.** & **Sensor** & **Target Vehicle** & **Error** & **Country** \\ \hline [PERSON] et al. [8] & 4 antenna carrier-phase GPS System & tractor & Less than 2.5 cm & United States \\ [PERSON] and [PERSON] [9] & GNSS-RTK & Automatic steering system & 2.5–6.9 cm & Germany \\ [PERSON] et al. [10] & GNSS-RTK & tractor & Centineters & Israel \\ [PERSON] et al. [11] & single-frequency GNSS-RTK & speed sprayer & Centineters & Korea \\ [PERSON] et al. [13] & GNSS-RTK, a fiber optic gyroscope & autonomous driving robot & 3 cm & Japan \\ [PERSON] et al. [14] & GNSS-RTK and an IMU & crawler-type tractor & 5 cm & Japan \\ [PERSON] et al. [15] & GNSS-RTK and IMU & rice transplanter & Less than 10 cm & China \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of previous studies using navigation sensors. improved [16]. In addition, the performance of a low-cost GNSS-RTK module is improving due to advances in the multi-GNSS positioning technique using GPS, a global navigation satellite system (GLONASS), BeiDou, Galileo, and a quasi-zenith satellite system (QZSS). Despite the development of various sensors, a low-cost autonomous driving system is not yet commercially available because most of the previous studies were conducted by using expensive GNSS-RTK and IMU. Therefore, an autonomous driving algorithm using the fusion of a low-cost GNSS-RTK module and a low-cost motion sensor module was proposed in this study to commercialize a low-cost autonomous driving system for crawler-type agricultural vehicles, and its performance was evaluated based on three different trajectories.