esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Results of the Proposed K-BCO Algorithm
### Performance Evaluation of K-BCO Algorithm  
The study evaluated the performance of the proposed K-BCO based on average error rate (AER), average data delivery rate (ADDR), and average energy consumption (AEC) for a data packet to be transmitted from heterogeneous sensors to their respective cluster heads. Also, the K-BCO was assessed in terms of execution time (ET) in Python. K-BCO  
Figure 2: Simulation of sensors, cluster heads, cluster centers and base stations within the 400-m square area.  
Figures 6-8 show a comparison of the proposed K-BCO against H-LEACH, DBCP, and ABC-ACO based on average data delivery rate (ADDR), terms of execution time (ET), and average energy consumption (AEC) or energy cost for a data packet to be transmitted from sensors to their respective cluster heads for ten iterations. The average of the results obtained from the 50 iterations for the various algorithms was evaluated, as shown in Table 3. From the figures, the K-BCO is a better candidate in terms of error resistance, data delivery in WSN, and energy consumption than H-LEACH, DBCP, and ABC-ACO.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithms** & **AER (\%)** & **ADDR (\%)** & **AEC (J)** & **ET (S)** \\ \hline H-LEACH & 24.14 & 75.86 & 0.0237 & 0.03 \\ DBCP & 0.0991 & 72.07 & 1.973833 & 0.011 \\ ABC-ACO & 9.923 & 90.08 & 0.0799 & 0.083 \\ K-BCO & 5.0 & 95.0 & \(1.0688\times 10^{-7}\) & 0.1 \\ \hline \end{tabular}
\end{table}
Table 3: Performance of the K-BCO Compared with Other Similar State-of-the-Art.  
Figure 6: Average data delivery rates of the proposed K-BCO comparison with H-LEACH, DBCP and ABC-ACO.  
Figure 7: Average energy cost of the proposed K-BCO comparison with H-LEACH, DBCP and ABC-ACO.
# Automated Traumatic bleeding Detection in Whole-Body CT Using 3D Object Detection Model
## 2 Materials and Methods
### Detection Method Used in This Study
#### 2.2.1 Preprocessing  
The preprocessing pipeline included body region extraction, slice selection, and isotropic resampling. A detailed description of each processing step is provided in [18]. Body region extraction was performed using a combination of thresholding and morphological operations. To improve the processing efficiency, we parallelized the processing of each axial slice and executed the final connected component analysis on a graphics processing unit (GPU). The slice selection employed a 2D ResNet-18 model [24], which classified each slice as belonging to the head and neck or body trunk. Subsequently, tri-linear interpolation was applied to the cropped CT scans and corresponding body masks to produce isotropic volumes, ensuring that the resulting voxel size matched the in-plane pixel size of the original image.
# Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
## 3 Experiment and Result Analysis
### Experimental Procedure and Result Analysis
The data with reconstruction error exceeding the threshold are marked with red dots, representing abnormal data. The detection results are shown in Figure 7.  
Figure 5: Electricity consumption of 1# workshop. Figure 6: Electricity consumption reconstruction data of 1# workshop. In order to test whether the training set screened by the iForest algorithm had an impact on the autoencoder-based anomaly detection, we trained the autoencoder model with the training set screened by the iForest algorithm and without the algorithm screening. The detection results are shown in Figure 2:0  
In the single autoencoder model, since the training set contains abnormal data, the reconstruction error of the abnormal data also became smaller, which made the model judge part of the abnormal data as normal data and fail to identify all the abnormal data. To further analyze the effectiveness of the proposed method, iForest and SVM algorithms were used to perform experiments on the same data set. The experimental results are listed in Table 4 and illustrated in Figure 2:1  
From Table 4 and Figure 8, it can be seen that the accuracy of this method was 2.359% and 0.604% higher than the SVM and iForest algorithms, indicating that the proposed algorithm had a good recognition effect for both abnormal and normal data. At the same time, the precision of this method also reached 1, illustrating that all the detected abnormal data were real abnormal data. The recall rate of this method reached 0.963, which was higher than the other two algorithms, showing that most abnormal data could be detected. Compared with the two methods, the F1 score was increased by 29.934% and 6.189%, proving that this method had a better comprehensive effect than the other two methods. To sum up, the proposed method had a high accuracy, precision, recall, and F1 score, and it was generally better than other algorithms. Accordingly, it achieved the state-of-the-art performance on metallurgical energy data anomaly detection. \begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline iForest & 0.975 & 0.789 & 0.723 & 0.755 \\ SVM & 0.922 & 1.000 & 0.858 & 0.924 \\ iForest-AE & 0.998 & 1.000 & 0.963 & 0.981 \\ \hline \end{tabular}
\end{table} 
Table 4: Experimental results of the three methods. Figure 7: Anomaly detection results of 1# Workshop. In order to solve the problem that the training data is anomaly-contaminated by autoencoder-based anomaly detection, which makes it difficult to distinguish abnormal data from normal data, this paper proposed an anomaly detection method for metallurgical energy data that combined iForest and autoencoder. The electricity consumption data from 1# workshop of a copper smelting enterprise were used for verification, and the experiment result shows that the F1 score of the proposed method was as high as 0.981, which was 29.934%, 6.189%, and 10.473% higher than the iForest, SVM, and ordinary autoencoder.
## 5 Conclusions & Outlook  
The influence of various external disturbances on an optical measurement method for measuring the acoustic roughness of a rail was demonstrated under laboratory conditions. A setup with four integrated asymmetric chord methods was studied. Water and dust had a degrading effect, especially on wavelengths below 100 mm. It is recommended to carry out measurements with a dry and cleaned rail. The influence of dust and sand on the rail cannot be neglected when installing on a train, as sand is occasionally used to improve adhesion. Displacements which can occur, for example, due to the deflection of the bogie suspension, can be compensated by the chord method, but also cause larger inaccuracy. A relevant deviation was observed at an amplitude of 2 mm, especially in the low wavelength range below 50 mm. Tilting of the structure should also be avoided, as its influence can be considered more damaging to the measurement result than simple displacements. If a comparable measurement setup is mounted on a train, it is important to choose a position that only includes the displacement of the primary suspension. This would be the case at the bogie of the train, in the middle between two wheelsets. The best possible lateral alignment should theoretically be achieved at this position for measuring the running surface. The tests on the test rig were carried out at fixed lateral track settings. The influence of the hunting oscillation should be tested on the real train. Vibrations had no significant influence on the measurement result using the chord method, as long as they occurred with a rather low amplitude of less than 0.1 mm. Nevertheless, the issue of vibration should be taken into account when designing a connection to the train in order not to damage the sensors in the long term. The implementation of four sensors and thus four chord methods showed an improvement of the measurement result compared to the METAS reference. In future, a test should be performed on the train at travelling speed to verify its suitability for use at higher velocities. The results of this optical measurement concept should be compared with a classical, preferably tactiely measured reference measurement.  
Conceptualization, F.M.; methodology, F.M.; software, R.W. and F.M.; validation, R.W. and F.M.; formal analysis, F.M. and R.W.; investigation, F.M. and R.W.; resources, K.W.; data curation, R.W. and F.M.; writing--original draft preparation, F.M.; writing--review and editing, F.M., R.W., T.W., M.K. and K.W.; visualization, F.M.; supervision, M.K. and K.W.; project administration, M.K. and F.M.; and funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Federal Office for the Environment (FOEN) and the Federal Office of Transport (FOT). The APC was funded by ETH Zurich.  
Not applicable.  
Note applicable.  
Some data used during this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 3 Data and Methodology
### Processing Strategy  
Raw recorded field data was downloaded and transformed into usf format to numerically process using TerraTEM equipment TEMPlot V 2.0.0 software (Monex GeoScope Ltd.; The Basin, VIC, Australia), allowing the display of voltage/time curves, automatic and manual filtering to remove wrong time windows and the exporting of initial preprocessed data.  
TDEM was modeled using the commercial IX1D-V3 software (Interpex Limited; Golden, CO, USA), designed for a 1D analysis of electric and electromagnetic data inversion and interpretation. The inversion software obtains 1-D resistivity models, producing geoelectric resistivity versus depth columns for each TDEM.  
Figure 3: Field picture of the (**A**) TerraTEM equipment with the (**B**) Terra TX-50 transmitter.  
The inversion software allows the user to insert an initial geoelectrical model with a preferred number of layers, resistivity values and thickness. For this task, data processing started with a parametric TDEM (TDEM-13) executed close to the Monzon-1 borehole. The available lithological information was used to define the number of layers and its thickness, only allowing the inversion software to adjust the resistivity of the layers in the model, to get the best fit of the model curve convergence with the field data. As a result of this process, an initial model based on the lithological data from the borehole and layer resistivity was obtained. The inversion process was repeated iteratively until a minimum RMS % model was obtained, showing the model adjustment degree to the data.  
The remaining TDEM survey points were then inverted using the parametric model, allowing the inversion software to adjust the model curves to better fit with the field data, adjusting its thickness and contacts.
# Data Acquisition System Based on the Bluesky Suite in the Shanghai Synchrotron Radiation Facility
## 2 Materials and Methods
### Control and Data Acquisition Software
In the experiment of the normal energy scan module and the segmented energy scan module, the SIS3820 should collect data once after each step of the monochromator. These two modules selected the _std_ module at control layer and called _EpicsMotor_ and _blcgscaler_ class in the data acquisition layer. The _EpicsMotor_ class is offered by _ophyd_. By calling _EpicsMotor_, data acquisition layer invocation of the monochromator can be achieved. The _blcgscaler_ is a custom class. The _blcgscaler_ class is inherited from the _Device_ class in _ophyd_. The _blcgscaler_ class added a parameter called _spectrum_ and defined the _spectrum_ value in the _trigger_ function. This _spectrum_ was used to display the absorption spectrum in real time. By calling the customized _blcgscaler_ class, the system can collect the multi-channel data simultaneously and realize the live visualization of the multi-channel data. Compared with the normal energy scan module and the segmented energy scan module, the EPU58 and EPU90 linkage module called two customized class _gap58 Motor_ and _gap90 Motor_. The _gap58 Motor_ and _gap90 Motor_ class added a parameter called _gap_value_ and defined the _gap_value_ value in the trigger function. The _gap_value_ value corresponds to the monochromator value. The _gap_value_ value is assigned to the EPU when the monochromator reaches the target value, and the EPU then moves to the _gap_value_ value. By calling _gap58 Motor_ and _gap90 Motor_, the monochromator and EPU can be interlinked. Moreover, Bluesky suite contains a number of pre-built devices for common hardware as well as the tools to build customized devices. Bluesky suite encapsulates many common functions such as _scan_ and _count_. These ensure that the BL07U data acquisition system is very versatile.
## 2 Concept and Design of C-GFB
\begin{table}
\begin{tabular}{l l l l} \hline
**Part** & **Parameter** & **Value** & **Unit** \\ \hline PZT stack & Thickness, \(t_{p}N_{p}\) & 42.00 & mm \\  & Width(\(w_{p}\)) \(\times\) length(\(l_{p}\)) & \(5\times 5\) & mm \\  & Max. driving voltage range, \(V_{3}\) & 150 & V \\  & Piezoelectric coefficient (manufacturer’s data), \(D_{33}\) & 0.467 & \(\mu\)m/V \\ \hline Foil structure & Number of foil strip & 1 & \\  & Foil Thickness (top, bump/bottom) & 0.147, 0.127 & mm \\  & Bump Height & 0.508 & mm \\  & Bump Pitch & 4.572 & mm \\  & Bump Half length & 1.810 & mm \\  & Foil structure thickness & 0.782 & mm \\  & Foil Young’s modulus & 214 & GPa \\  & Foil Poisson’s ratio & 0.29 & \\  & Foil material & Inconel X750 & \\  & Structural loss factor of foil structure & 0.1 & \\ \hline Bearing & Journal diameter & 40.00 & mm \\  & Bearing Axial length & 35.00 & mm \\  & Nominal radial clearance & 0.10 & mm \\  & Number of preloads & 3 & \\  & Preload angular location & 60, 180, 300 & \\  & Preload arc length & 27.7 & 0 \\ \hline \end{tabular}
\end{table} 
Table 1: Design properties of C-GFB with PZT actuators.
## 4 Experimental Results
### Reinforcement Scenario Experiments  
#### 4.2.1 Experimental Setting  
As mentioned earlier, in the reinforcement scenario, one of the clustering algorithms is improved by augmenting it with information from the remaining algorithms such that only a single clustering algorithm is optimised. In order to be enhanced by different clustering algorithms, this algorithm must satisfy the proposed collaborative method hypothesis, i.e., an objective functional improvement. However, the residual clustering algorithms can optimise different objective functions, which enables the use of various clustering algorithms. In these experiments, the collaborative clustering task framework in this scenario was evaluated to this end. The experiments relevant to this scenario were divided according to the algorithm selected for reinforcement. Algorithms specialised in each data type were therefore chosen and then reinforced with the results of other algorithms. The following experiences were realised:Choosing an algorithm specialised in processing quantitative data and enhancing it through the results of algorithms specialised in processing qualitative data only.
2. Choosing an algorithm specialised in processing quantitative data and enhancing it through the results of algorithms specialised in processing both qualitative and quantitative data.
3. Choosing an algorithm specialised in processing qualitative data and enhancing it through the results of algorithms that only process quantitative data.
4. Choosing an algorithm specialised in processing qualitative data and enhancing it through the results of algorithms specialised in processing both quantitative and qualitative data.  
The proposed model was initially evaluated after the local step. For all the clustering algorithms, the previously described internal and external validation indicators were measured, and their clusters were mapped to accurate class labels and assessed using the classification performance metrics. The meta-classifier (LR model) performance that collects decisions and produces the final decision was subsequently trained and evaluated. The efficacy of the collaborative method was finally compared with the output of the selected clustering algorithm and the meta-classifier both prior to and following the collaborative stage in order to identify any benefit from the collaboration.
## 1 Introduction  
It is a truism that geospatial sciences have crucial roles and effects on the socioeconomic, political and security fortunes of states. Earth observation, remote sensing and geoscientific ground investigation increasingly occupy vantage positions in the legal order of states, particularly in evidential terms and in the verification of facts under international law. How then do these aspects of space law and space sciences affect contemporary Africa and the commercial fortunes as well as international relations among some African states? What impact do they have in relation to: (a) international boundaries disputes and demarcation activities; (b) management and the preservation of the African heritage; (c) disaster and conservation management?  
The paper will test the hypothesis that it is crucial for the development of the continent especially in the areas mentioned above that states should sustain and increase investment in the following areas: archaeological prospection, condition assessment of heritage assets; Geographic Information System (GIS) analysis of spatial settlement patterns in modern landscapes and assessment of natural or human-induced threats to conservation.  
The paper will thus, establish how the geospatial sciences, earth observation, remote sensing and geoscientific ground investigation in their interactions with space law can aid the significant think for development in Africa. The obvious facts are that the space sciences are the progenitors of space activities which are all governed by space law. Thus, space law governs earth observation and remote sensing activities among others. Which specific principles of space law govern these sciencesand towards what developmental ends is an enquiry worth having? It is suggested that certain legal problems may arise in the gathering of geodetic information particularly in the use of remote sensing technology. The paper examines some of these in relation to Africa as a developing region. Our methodology is critical, comparative and socio-legal but includes a focus on the space active African states. In the area of demarcation of boundaries and geodetic ground investigation, we will examine specifically the adoption of space based solutions to the resolution of the Cameroon-Nigeria boundary demarcation process.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 5 Proposed Algorithm  
NOMA technology is considered as one of the key technologies of the 5G mobile communications [52]. It is able to distribute the power and SIC technology allows multiple users to share the same resource, such as time, frequency rate, etc. As a result, it is expected to improve the system performance in terms of throughput and energy efficiency. D2D and cooperative relaying technology are the key technologies in 5G systems. There is also a surge of interest in utilizing NOMA technology combined with D2D and relay technology. The combination of NOMA and D2D gives full play to the advantages of the two hotspot technologies and is very suitable for 5G scenarios. An interesting algorithm  
Figure 6: Principle of transceiver of NOMA system.  
called GADIA is proposed in the literature [53]. The algorithm itself provides a simple fully distributed dynamic frequency allocation strategy, without any information exchange between autonomous systems equipment and no knowledge about the existence of other equipment autonomous entity. Moreover, it can be used as a part in conjunction with any reasonable radio channel model. In the GADIA calculation, each node has the knowledge of possible interference that it may encounter, and then selects the spectrum band with the least interference. In this work, we use the combination of NOMA and D2D to build a system model, and employ the GADIA algorithm to allocate D2D strategies.
## 2 Study Area and Datasets
### Ancillary Data  
We used digital elevation model (DEM) data to develop the upscaling model. The DEM data consist of Global Multiresolution Terrain Elevation Data 2010 with a 30 arcsecond spatial resolution in the GeoTIFF format, which can be obtained from ENVI 5.3. We resampled the elevation data from a 30 arcsecond to 1 km and 5 km spatial resolution using a simple average method.  
We also used the UMD Land Cover Classification (UMD-LCC) dataset developed in 1998 by the Department of Geography at the University of Maryland [63]. The dataset was divided into 14 different land cover types. We used the UMD-LCC dataset with a 1 kmspatial resolution and resampled the dataset of the study area to a 5 km spatial resolution using the nearest neighbor method.
## 4 Results on a Real Case: Proof-of-Concept Validation
### Results of the Path Planner System
#### 4.2.3. Determine Navigation Route from Reference Points  
The results of Algorithm 9 are shown in Figure 9, where the numbered sequence of points is shown for four different initial configurations. In each one, the robot is placed near a different corner, simulating four possible initial places for the robots, so the four possible intermediate point distributions are considered.