esa-sceva/satcom-chunk-collection huggingface dataset:
# AutoFL: Towards AutoML in a Federated Learning Context
## 6 Conclusions
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Pi 4** & **Jetson TX2** & **Laptop** & **Client** & **Server** \\ \hline \hline
**Accuracy** & & & & & \\ \hline Random Forest & 0.850 & 0.850 & 0.849 & 0.850 & 0.849 \\ Logistic Regression & 0.847 & 0.847 & 0.847 & 0.847 & 0.847 \\ Multi-Layer Perceptron & 0.869 & 0.869 & 0.869 & 0.869 \\ TensorFlow-FF & 0.885 & 0.885 & 0.887 & 0.886 & 0.886 \\ TensorFlow-CNN & 0.911 & 0.913 & 0.913 & 0.913 & 0.913 \\ \hline \hline \multicolumn{5}{l}{**F1 score**} \\ \hline Random Forest & 0.847 & 0.846 & 0.845 & 0.847 & 0.846 \\ Logistic Regression & 0.846 & 0.846 & 0.846 & 0.846 & 0.846 \\ Multi-Layer Perceptron & 0.869 & 0.869 & 0.869 & 0.869 \\ TensorFlow-FF & 0.884 & 0.885 & 0.887 & 0.885 & 0.886 \\ TensorFlow-CNN & 0.911 & 0.913 & 0.913 & 0.913 \\ \hline \hline \multicolumn{5}{l}{**CPU time (s)**} \\ \hline Random Forest & 29.410 & 23.557 & 4.757 & 7.779 & 6.979 \\ Logistic Regression & 182.476 & 183.250 & 30.482 & 60.991 & 41.556 \\ Multi-Layer Perceptron & 297.405 & 158.070 & 123.099 & 42.404 & 966.927 \\ TensorFlow-FF & 840.535 & 455.269 & 74.562 & 122.200 & 187.498 \\ TensorFlow-CNN & 3550.677 & 1231.090 & 241.700 & 560.821 & 1466.845 \\ \hline \hline \multicolumn{5}{l}{**Wall clock time (s)**} \\ \hline Random Forest & 29.417 & 23.741 & 4.757 & 7.796 & 6.984 \\ Logistic Regression & 182.495 & 183.790 & 30.530 & 60.997 & 41.567 \\ Multi-Layer Perceptron & 74.555 & 39.965 & 7.791 & 10.675 & 15.146 \\ TensorFlow-FF & 409.170 & 257.432 & 33.184 & 58.599 & 61.631 \\ TensorFlow-CNN & 1031.345 & 390.357 & 38.865 & 186.812 & 133.312 \\ \hline \hline \multicolumn{5}{l}{**Memory (MB)**} \\ \hline Random Forest & 732.901 & 735.800 & 754.356 & 721.377 & 728.786 \\ Logistic Regression & 551.607 & 559.243 & 549.812 & 552.248 & 552.479 \\ Multi-Layer Perceptron & 552.424 & 560.461 & 551.177 & 554.729 & 551.650 \\ TensorFlow-FF & 1095.873 & 1100.167 & 1292.974 & 1220.498 & 1325.968 \\ TensorFlow-CNN & 1132.218 & 1139.928 & 1306.494 & 1256.246 & 1583.449 \\ \hline \hline \end{tabular}
\end{table} 
Table A1: FashionMNIST baseline benchmark for classical ML models and neural networks.
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 1 Introduction
### Positioning Systems of UUVs
#### 2.4.1 Primary Positioning Systems
Combining different navigation technologies ensures that UUVs can effectively adapt to the diverse challenges presented by underwater exploration [92; 93].
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 3 Security Model
### Security Protection Model  
By analyzing the logical loopholes, defense weaknesses and security requirements of data assets in transaction circulation, outsourcing storage, cross-border transmission and so on, this paper aims at covering the protection of rights and interests, circulation control, outsourcing storage, operation trusted management and security return from three dimensions: data asset participation object, whole life cycle stage and key links of operation.  
Figure 3: Security risks of data asset sharing and circulation in an uncontrolled environment.  
The data asset sharing and circulation protection model in an uncontrolled environment is constructed, and the comprehensive security protection ability of data surface, management surface and business surface is formed, as shown in Figure 4.  
On the data side, data protection is an important line of defense of data asset protection system, and it is the basis of the data asset protection model. Combined with the concept of data life cycle, data asset security can be fully guaranteed, and the data asset protection ability can be formed by means of data desensitization, data encryption, data classification, isolation audit, blocking traceability and so on.  
On the management side, in the process of the cross-border circulation of data assets, the protection of the rights and interests of producers, owners, users and managers of data assets is fully considered, their respective powers are clear, and the right of authentication is particularly important. At the same time, it is necessary to carry out safety supervision in the process of operation and provide targeted security control means.  
On the business side, data asset sharing circulation, that is, data cross-subject circulation, is a difficulty and key link of security protection, which is very important. In the whole process of sharing circulation, we must not only abide to the following rules, \"do not leak, no privacy, not exceed limits, comply with the protocol\", but also ensure that upon data leakage, privacy disclosure and other issues, we must have a data traceability mechanism to find the risk point and responsible person to achieve pre-event supervision, mid-event evidence, and post-event retrospection. Specifically, data cleaning, encryption storage, access control, trust evaluation, operation behavior audit, intelligent contract, multi-path secure transmission, secure anonymous authentication and other technical means can be used to realize outsourcing storage security, multi-party sharing security, trusted operation security and cross-domain return security in the whole process of data asset sharing and circulation.
## 6 Conclusions  
This paper proposes a novel diagnosis method for compressor manufacturing defects, including data processing, time-frequency feature extract using CWT and HHT and feature fusion using CNN. The main conclusions are as follows:  
1. The proposed method uses CNN to fuse the features of CWT time-frequency image and HHT time-frequency images to obtain more fault-related features for compressor manufacturing defect diagnosis.
2. The performance of the proposed method is verified by the confusion matrix and t-SNE. The diagnostic accuracy of the proposed method is greater than 88% in each type of manufacturing defect.
3. It is found that the average accuracy of the proposed method can reach 95.9%, which is far better than other methods. The recall, precision and F1-score of the proposed method are significantly improved for each type of manufacturing defect. Although the recall of CWT + ANN is 9.9% higher than that of the proposed method in #0, the precision and F1-score of the proposed method are 31.2% and 13.6% higher than that of CWT + ANN in #0, respectively. Therefore, this further illustrates the effectiveness of the proposed method for compressor manufacturing defect diagnosis.  
The study uses vibration signals to diagnose manufacturing defects of compressors, but the diagnostic performance and the normal compressor's recall of the proposed method need to be further improved. In future work, when the diagnostic rate of the compressor matches the beat of the production line, the addition of different kinds of sensor information, such as current signal and sound signal, can be considered for integrated analysis. Adding different sensor information will improve the diagnostic performance and recall of the algorithm in different working environments.  
Conceptualization, K.L. and Z.S.; Methodology, K.L., H.J. and Y.H.; Software, K.L. and Z.S.; Validation, K.L., Z.S. and Y.X.; Formal analysis, K.L. and J.G.; Investigation, K.L. and H.J.; Writing--Original Draft, K.L.; Writing--Review and Editing, X.S.; Visualization, K.L. and Q.Z.; Funding acquisition, H.J. and X.S.; Supervision, Y.H. and X.S. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Primary Research and Development Plan of Zhejiang Province (Grant No.2020C04010) and Zhejiang Province Public Welfare Technology Application Research Project (Grant No. LGG19E050020).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Discussion  
### Assessing the Results of the Linear Regression Analysis  
This study had two parallel, mutually non-exclusive goals. The first goal was to present multiple statistical methods, ranging from simple to complex, for data analysis and data mining. The second goal aimed to predict ICT implementation intentions in enterprises.  
The results of the descriptive statistics do not provide a deeper understanding of the observed factors. No significant observations or causations can be derived. However, some insights can still be obtained. The majority of respondents (77.6%) stated that they would implement an ICT solution within their enterprise.  
Next, based on the results of the linear regression analysis, the proposed hypotheses can be assessed as follows:  
Figure 5: ROC curve for the validation samples.  
* \(\rm H_{1a}\): Manager/director age positively affects intentions to implement and apply ICTs within an enterprise. Did not gain support.
* \(\rm H_{1b}\): Manager/director gender positively affects intentions to implement and apply ICTs within an enterprise. Did not gain support.
* \(\rm H_{1c}\): Manager/director education positively affects intentions to implement and apply ICTs within an enterprise. Did not gain support.
* \(\rm H_{1d}\): Enterprise size positively affects intentions to implement and apply ICTs within an enterprise. Did not gain support.
* \(\rm H_{2}\): Business performance positively affects intentions to implement and apply ICTs within an enterprise. Did not gain support.
* \(\rm H_{3}\): Awareness of ICT importance positively affects intentions to implement and apply ICTs within an enterprise. Did not gain support.
* \(\rm H_{4}\): Interprise size positively affects intentions to implement and apply ICTs within an enterprise. Did not gain support.
* \(\rm H_{5}\): Market assessment positively affects intentions to implement and apply ICTs within an enterprise. Failed to be rejected.  
Compared to descriptive statistics, linear regression provides a deeper analysis of the relationship between the independent variables and dependent variables.
# Caching-Aware Intelligent Handover Strategy for LEO Satellite Networks
## 4 Results
The simulation results show that the call blocking rate of IMF is the lowest. Especially when the user arrival rate is 10, the call blocking rate of IMF is about 23% lower than that of the RST strategy. Figure 11: Performance comparison of call blocking rate. Figure 10: Performance comparison of handover failure rate.
## 4 Agro-Food Substrates for Anaerobic Digestion
### Livestock Manure
#### 4.1.1. Cattle Manure
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **pH** & **TS\%** & **VS\%** & **C/N** & **BIOGAS YYELD** \\ \hline Cattle manure [47; 50; 51; 52; 53; 54; 55; 56] & 5.33 \(\div\) 8.30 & 9.4 \(\div\) 22.75 & 10.25 \(\div\) 93.11 & 10.5 \(\div\) 26.64 & 169 \(\div\) 270 mL/gVS \\ Pig manure [47; 50; 51; 52; 53; 54; 55; 56] & 6.9 \(\div\) 7.87 & 5.4 \(\div\) 92.10 & 26.93 \(\div\) 80.2 & 6 \(\div\) 18.91 & 318 \(\div\) 409 mL/gVS \\ Goat manure [55; 57] & 8.13 & 81.63 \(\div\) 97.1 & 64.01 & 16 \(\div\) 20 & \\ Sheep manure [54; 58] & 7.8 & 17.4 \(\div\) 37.03 & 16.3 \(\div\) 76.49 & - & 0.572 \(\div\) 1.468 Nm\({}^{3}\)/kg VS \\ Poultry manure [47; 51; 52; 53; 54; 55] & 6.63 \(\div\) 7.95 & 42.9 \(\div\) 90.15 & 47.5 \(\div\) 84.46 & 3.8 \(\div\) 14.44 & 192 \(\div\) 377 mL/gVS \\ Horse manure [55; 59] & 8.25 & 33.49 \(\div\) 53.38 & 30.40 \(\div\) 46.65 & 22.63 \(\div\) 40.12 & 0.164 \(\div\) 0.212 Nm\({}^{3}\)/kg VS \\ Rabbit manure [47] & - & 27.84 & 87.94 & 17.9 & 211 \(\div\) 323 mL/gVS \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Characteristics of manure from different livestock species. As to C/N ratio, Table 4 shows that C/N ratios of cattle manure can vary between 10.5 and 26.64. The suitable range for a regular AD process is between 20 and 35 [69]. However, according to [PERSON] [70], AD could be carried out efficiently even when the C/N ratio is 15 and [PERSON] et al. [12] observed that any C/N ratio ranging from 13.9 to 19.6 might be adequate for digestion.
# A Sparsity-Invariant Model via Unifying Depth Prediction and Completion
## 4 The Proposed Method
### Dual-Branch Encoder-Decoder Architecture  
Dual-branch encoder-decoder architectures have been employed in previous depth completion methods [4; 35; 36], yielding promising results. Additionally, [PERSON] [81] observe that when employing a mixed training strategy, the dual-branch architecture exhibits greater invariance to different sparsity levels compared to a single-branch model regardless  
Figure 2: An overview of our sparse-invariant model. It is a dual-branch encoder–decoder architecture with an integration of multi-scale local planar guidance modules in two decoders.  
of whether the latter incorporates sparse-invariant convolutions or not. Therefore, we adopt this dual-branch architecture to achieve better sparse invariance.  
In the first branch, we take a single color image as input and focus on predicting depth when no input depth information is available. This prediction branch consists of a ResNet101-based encoder [74] and a decoder composed of five up-convolution blocks. Each up-convolution block comprises an upsampling layer, a convolutional layer, a batch normalization (BN) layer, a ReLU activation layer, and a Resblock. These blocks are denoted as the \"F\" or \"U\" blocks in Figure 2. Skip connections are utilized to merge the features from each encoder layer with those of the corresponding decoder blocks. Additionally, between the second and third decoder blocks, a dynamic atrous spatial pyramid pooling (DASPP) module [75] is inserted on the \(\frac{1}{8}\) scale features to capture more contextual information.  
In the second branch, we take a sparse depth map, together with the final feature map generated by the prediction branch \"(f)\", as inputs. The two inputs are fed through a convolution layer individually and then concatenated together, as illustrated by the \"P\" block in Figure 2. In this completion branch, a shallower encoder structure is adopted, which only utilizes two Resblocks at each scale, to handle sparse depth information. At each scale, the features extracted from the first branch are also fused to the second branch through the \"R\" block, which comprises a convolution layer, BN, and ReLU to leverage color information better. The decoder shares the same structure as the one in the first stream.
# Theoretical Study of Large Uncoupling Coefficient Loading for Surface Blasting
## 5 Calculation Examples and Engineering Applications
### Research Significance  
The development of drilling machinery and the requirements of construction efficiency means that large diameter surface blasting holes are gradually more widely used in engineering. The oversized uncoupling coefficient charge can better increase the quasi-static effect of the detonating gas and reduce the damage of the blast shock wave on the borehole, but the traditional theory of glossy blasting can no longer be well solved for the blasting parameters of the oversized uncoupling charge. Therefore, the large diameter surface blasting program can reasonably determine the axial and radial uncoupled charge coefficients and uncoupled charge under the action of the force on the wall of the borehole and the borehole spacing and can effectively improve the utilization of explosive energy and improve the effect of blasting.  
From the bursting theory, one can explore the air spacing uncoupled charge in line with the multi-faceted exponential expansion critical uncoupling coefficient and according to the surface blasting and field charging structure requirements. Derivation of quasi-static pressure on the borehole wall under the action of large uncoupling, uncoupling coefficient and borehole spacing, and other parameters, along with the establishment of an axial uncoupling coefficient and radial uncoupling coefficient equivalence, put forward the uncoupling coefficient and the theoretical relationship between the selection of borehole spacing, revealing that the engineering practice of selecting different parameters of light surface blasting can achieve good results in the mechanism. In addition, a slope in Guizhou is an example of carrying out sample calculations and applications of two different charging structures applied to field loading, which have achieved a good effect from surface blasting. Application case blasting parameters and effects are shown in Table 3.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 2 Background: Drift Phenomena and Explainable/Interpretable AI
### Drift Overview
#### 2.2.1 Data Drift  
As visible from a generated example in Figure 1, the blue histogram time \(t_{0}\) and the orange histogram time \(t_{1}\) portray two snapshots of the same input feature taken at different moments in the data stream. The entire distribution has translated to the right, from a mean around 0 to a mean near 2, while the overall spread (variance) remains comparable. This illustrates data drift:  
\[P_{t_{1}}(X)\ \
eq\ P_{t_{0}}(X),\qquad\text{while}\quad P(Y\mid X)\quad\text{is assumed unchanged}.\]  
Because the learned decision boundary was fitted on the blue distribution, it may extrapolate poorly on the orange one. In practical terms, the model is forced to make predictions in parts of the feature space it has no empirical support for, leading to increases in error, confidence mis-calibration, or even outright failures. Continuous monitoring of the marginal \(\left(P(X)\right)\), for example, with statistical tests or running histograms like the one in the figure, is therefore essential to trigger retraining or adaptation before performance degrades in production.
## 4 Results and Discussion  
### Compression Tests  
The experimental results of compressive tests on the four mixtures at different curing times are reported in Figure 2. All mixtures showed a compressive strength greater than 27 MPa at 28 days. At early ages (3 and 7 days), the different _w/c_ influenced the compressive strength more than the kind of aggregate. The total substitution of natural aggregates with recycled ones, simultaneously with the reduction in _w/c_ and the use of superplasticizer (Rec-0.3), did not substantially modify the compressive strength with respect to the concrete mixture with natural aggregates (Nat-0.6). When cement was partially replaced with FA (Rec + FA-0.6), the mixture showed a lower compressive strength at early ages (3 and 7 days) and a slightly higher compressive strength after 56 days of curing, thanks to the fly ash pozzolanic activity which develops at long ages. The use of lighter aggregate (i.e., recycled aggregate) increasingly influenced the compressive strength value as the cement matrix became stronger, since it represents the \"weak link\" in the chain [15]. The best results have been obtained for the mixture realized with natural aggregates and fly ash (Nat + FA-0.6), which showed a compressive strength of about 44 MPa after 56 days of curing.
# Object and Event Detection Pipeline for Rink Hockey Games
## 3 Automatic Pipeline for Object and Event Detection in Rink Hockey Games
### Object Detection Model  
#### 3.2.1 YOLOv7  
YOLO (You Only Look Once) [24] is a state-of-the-art object detection system that is highly valuable in the domain of computer vision. In contrast to traditional object detection systems that run a classifier on various parts of the image independently, YOLO innovatively applies a single neural network to the entire image at once. The image is divided into regions, and then the system predicts bounding boxes and probabilities for each region. As such, YOLO significantly enhances the efficiency and speed of real-time object detection, making it a pivotal asset in a wide range of applications. Specifically in the context of hockey, YOLO can be effectively utilized to track the movements of players and the puck in the arena. This can furnish extensive insights into the analysis of gameplay, performance review of players, and the development of strategic gameplay. It is noteworthy that YOLO is not a standalone tool, but rather, it refers to a type of neural network architecture and algorithm. It is commonly implemented using comprehensive deep learning libraries, such as TensorFlow or PyTorch.  
The authors in [21] present another version of YOLO, the YOLOv7. Its base architecture and version are significantly better than prior YOLO versions, such as YOLOv4 and YOLOv5, and it has reached state-of-the-art performance on numerous object detection benchmarks, such as the COCO dataset [26]. The use of a \"bag-of-freebies\" is one key feature of YOLOv7. This refers to a set of training strategies that do not require the usage of additional computational resources and may thus be utilized to increase the performance of the model without significantly increasing its complexity. Data augmentation, network architecture changes, and multiscale training are examples of these strategies. By using these techniques, this version is able to detect objects at many scales, resulting in better results on object detection tasks while still running in real time on standard hardware. Overall, apart from its high precision object detection abilities, YOLOv7 is also known for its efficiency and speed, delivering outstanding performance without losing speed or simplicity, making it a valuable tool for a wide range of applications. Typically a visual object detection solution, such as YOLOv7, offers several model options with increasing degrees of complexity, that is, with an increasing number of parameters. In the case of YOLOv7, the models range from YOLOv7-tiny to YOLOv7-E6E. There is a trade-off between speed and accuracy. The simpler, the faster and the more complex, the more accurate. Figure 4 illustrates a comparison between YOLOv7 and other important visual object detectors, showing its better performance. These characteristics make YOLOv7 an appropriate base model for a real-time application, such as the solution proposed in this article, and justify its use in the tests we carried out.  
Figure 4: Comparing YOLOv7 with other object detectors (source: [21]).