esa-sceva/satcom-chunk-collection huggingface dataset:
# Criterion-Related Validity and Reliability of the Front Plank Test in Adults: The ADULT-FIT Project
## 4 Discussion
### Criterion-Related Validity
These results suggest that the lean mass and lean trunk mass did not determine the FPT time, but fat variables did. Moreover, significant differences in body fat and trunk fat mass were found between young adults and adults. The adult group had a higher percentage of body fat than the young adult group, obtaining a lower FTP time. This may indicate that as age increases, body fat percentage and trunk fat mass increase, thus increasing the negative correlation between these variables and FPT time. Furthermore, when the sample was divided by sex, the correlation between these two variables was higher in females, highlighting the role of body fat and trunk fat mass in FPT time. Regarding the correlation between FPT time and muscle activation, only in EO was a low correlation found in the whole sample and in males and a moderate correlation of the ES in females. Non-significant correlation of the other muscles may be due to the fact that the FPT time has been related to %MVIC and not to the total activation, activation time of that muscle, or normalized median frequency slope. However, no significant difference was found in the %MVIC during the test between sexes and age groups. Although an attempt was made to provide criterion-related validity for the FPT with the force in the isokinetic device, the variables of time in the FPT and the isokinetic and isometric force variables obtained a low correlation with the initial peak torque by BW and the total work per BW in the flexors and extensors in the whole sample. This may be because more muscles were involved in the FPT than in the assessment of strength endurance in an isokinetic device, since in the latter, the flexor and extensor muscles of the trunk were analyzed in isolation. Furthermore, as mentioned previously, FPT time is not determined by lean mass or lean trunk mass; instead, fat variables appear to exert a significant influence on this test. It is important to note that previous studies have investigated the reasons why participants terminate the test and have found that they not only terminate the test because of fatigue in the abdominal muscles but also because of fatigue in the legs and/or arms or due to back pain [17; 23].
# Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
## 2 System Architecture
### Architecture of Neural Interface  
Figure 2 shows a photograph of the fabricated wireless neural interface module and flexible microelectrode array with an U.S. dime, and a block diagram of the integrated circuits inside the module. The volume of the neural module is ~1.3 cm\({}^{3}\), and the microelectrode array has 16 recording electrodes and 4 reference electrodes with a large common ground within 18 \(\times\) 18 mm\({}^{2}\) in area. The integrated circuit also has 16 neural recording channels. Each channel contains a power-noise  
Figure 1: System concept for distributed wireless ECoG recordings.  
efficient preamplifier, a programmable gain amplifier, a bandpass filter (with a buffer), and a low power 8-bit SAR ADC as shown in Figure 2 (right). The digitized outputs from each channel are serialized, encoded, and transmitted wirelessly through the skin using an ISCOM driver which generates a charge-balanced (biphasic) output current. To reliably record ECoG signals regardless of their amplitudes, the gain in each channel is independently programmable (40-60 dB). The bandwidth and sampling rate (1-31.25 kS/s) are also adjustable for various sensing modalities to meet the wide range of applications. For system-level power optimization, the interface module utilizes two power supplies: 0.5 and 1.0 V for analog and digital blocks, respectively. These supply voltages are generated by the combination of low dropout regulators (LDOs, not shown here) and an embedded monolithic voltage/current reference generator operating at 1.5-3.0 V, provided by a rechargeable battery (used a coin-shaped Li-Ion rechargeable battery with a nominal output of 3 V). The control signals for SAR ADCs are internally generated and shared among 16 ADCs in the IC. All the system blocks operate with an on-chip clock generator. Also, digital programmability is provided to change the configuration for various users' demands as well as to compensate for possible process variations. The following sections will explain the requirements, function, and design of each circuit block.
# Secure and Efficient Federated Gradient Boosting Decision Trees
###### Abstract  
In recent years, federated GBDTs have gradually replaced traditional GBDTs, and become the focus of academic research. They are used to solve the task of structured data mining. Aiming at the problems of information leakage, insufficient model accuracy and high communication cost in the existing schemes of horizontal federated GBDTs, this paper proposes an algorithm of gradient boosting decision trees based on horizontal federated learning, that is, secure and efficient FL for GBDTs (SeFB). The algorithm uses locality sensitive hashing (LSH) to build a tree by collecting similar information of instances without exposing the original data of participants. In the stage of updating the tree, the algorithm aggregates the local gradients of all data participants and calculates the global leaf weights, so as to improve the accuracy of the model and reduce the communication cost. Finally, the experimental analysis shows that the algorithm can protect the privacy of the original data, and the communication cost is low. At the same time, the performance of the unbalanced binary data set is evaluated. The results show that SeFB algorithm compared with the existing schemes of horizontal federated GBDTs, the accuracy is improved by 2.53% on average.  
horizontal federated learning; data security; gradient boosting decision tree; privacy protection; LSH +
Footnote †: journal: Applied
# A Multi-Agent Optimization Approach for Multimodal Collaboration in Marine Terminals
## 4 Solution Methodology  
Searching for Pareto optimal solutions that improve and satisfy the individual objectives of all agents depends on an efficient search algorithm as previously signaled in Figure 2. Due to the problem's complexity, nonlinearity, and large solution space, meta-heuristic methods are used. We consider multi-objective evolutionary optimization algorithms and a novel technique that combines prioritized planning with neighborhood search for developing the search algorithm. These methods balance conflicting objectives and refine solutions locally based on the requirements and functions defined in Section 3. Specifically, the vessel_process and incur_deviation must be minimized, while the terminal_constraints functions must be satisfied during the solution process.
## 4 Implementation
### Test Setup  
Here, we describe the simulation setup in addition to the state codification and the reward function we used in each test. For both PPO and DDPG, the setup and reward functions were the same in all tests.  
The robot's work-space is limited and a new episode starts when it goes out of limits. Those limits are defined in Equation (13).  
\[x_{r}^{w}=[-1.0,3.0]\ \ \ \ \ y_{r}^{w}=[-1.5,0.0] \tag{13}\]  
In addition, although the robot was penalised by navigating with high speed, the environment bounds the velocities applied to the mobile base. The maximum linear velocity in \(x\) and \(y\) axes was 1 m/s and the maximum angular velocity in \(z\) axis was 1 rad/s. Thus, if the algorithm predicted velocities higher than those, the limit velocities were applied and the agent was penalised proportionally. Besides, high accelerations were also penalised proportionally, but in this case the environment did not bound them.  
The learning process was divided into episodes where the robot had \(T\) time-steps to complete the task. The episode length was \(T=512\) time-steps, which is about 4 s, and a discrete time-step \(t\) was terminal if:  
* The robot collides with the table.
* Robot poses out of limits.
* \(t=T\).  
The tuning hyper-parameters used in each algorithm are described in Table 2 and were not changed across tests.
## 6 Conclusions  
In this paper, the data gathering problem is investigated in lossy WSNs using the simple but efficient proposed CS-DDSG algorithm that combines CS theory and DDS. Compared with other correlative and mainstream strategies, CS-DDSG balances the energy consumption and reconstruction performance effectively. In our proposed algorithm, nodes are selected to be source nodes with the probability \(\mathbf{p_{1}}\) to broadcast their packets. The neighboring nodes around the source nodes receive the broadcasting nodes and update their own packets, which are broadcasted with the probability \(\mathbf{p_{2}}\). Then, all receiving nodes forward their updated packets with the probability \(\mathbf{p_{2}}\). The process will be repeated a few times until there are no receiving nodes. Each receiving node receives only one packet. In this way, the numbers of transmissions and fusions are reduced, and the CS reconstruction accuracy is guaranteed. Moreover, the expression of the total number of transmissions and receptions is formulated via RGG. The simulation results and analysis validate that CS-DDSG outperforms the other algorithms in unreliable links.  
In addition, we investigate how the measurements \(\mathbf{M}\), the packet loss \(\mathbf{p}\) and the probability \(\mathbf{p_{1}}\) influence the performance of CS-DDSG. In future research, we plan to explore the possibility of temporal correlations of node readings. Another potential extension of this work is to more strictly demonstrate that the measurement matrix satisfies the RIP.  
Conceptualization, O.L. and G.L.; Methodology, C.Z. and M.L.; Software, G.L. and M.L.; Writing--Original Draft Preparation, C.Z.; Project Administration, G.L.; and Funding Acquisition, G.L. and O.L.  
This work was supported in part by the National Science and Technology Major Projects of China under grant No. 2016 zx03001010 and National Natural Science Foundation of China No. 61601516.  
The authors declare no conflict of interest.  
Figure 15: Performance of CS-DDSG with different value of \(\mathbf{p_{1}}\) and \(\mathbf{M}\).
## 2 Materials and Methods
### Rotating Polarization-MPSK (RP-MPSK) Modulation
#### 2.1.3 RP-MPSK Demodulation  
Received baseband signals are sampled at frequency \(f_{s}=1/T_{s}=N_{p}f_{r}\ll f_{c},N_{p}\in\mathbb{Z}\). Each sample is then coherently demodulated as below:  
\[\begin{array}{l}h_{p}=h_{r}(pT_{s})\cos\left(\omega_{r}pT_{s}+2p\pi/N_{p} \right)\\ v_{p}=v_{r}(pT_{s})\sin\left(\omega_{r}pT_{s}+2p\pi/N_{p}\right)\end{array} \right\}\quad p\in\left\{0,1,2,\ldots,N_{p}-1\right\} \tag{6}\]
# High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis
## 6 Results and Discussion
### Comparison with Four Popular Methods
#### 6.2.3 Mean Shift  
Mean Shift is a powerful unsupervised clustering algorithm (non-parametric). The Mean Shift algorithm distributes data points to clusters repeatedly by shifting data points toward clusters with the highest data point density. This algorithm has been implemented in this experimental analysis and is being tested with the higher-dimensional dataset. However, in this analysis, Mean Shift is unable to find clusters from the raw higher-dimensional datasets. For both datasets, this algorithm considers the entire dataset as a single cluster. Since this algorithm provides only one cluster with both datasets, it is not possible to calculate the silhouette coefficient.
## 3 The Gain Parameter Adaption and Scheduling Control Method
### The Adaptive Principle of the Influence Coefficient and Gain
#### 3.3.2 The Self-Adaption Methods of the Gain Parameters  
Based on the above analysis, the gain parameters self-adaption and its application steps can be expressed as follows.  
Step 1, the gain scheduling table should be built through a series of offline experiments at a few discrete speeds. The influence coefficients and gain parameters should be included corresponding to each discrete speed in the table.  
Step 2, the unbalancing vibration signals of the spindle system should be measured and separated online in the monitoring period. The correcting vectors of the correcting faces should be calculated according to Equation (14) if the amplitude of the unbalancing vibration signals exceeds the limited value.  
Step 3, the radial positions of the balance disks are adjusted online according to the correcting vectors based on the principle of vector synthesis. The unbalancing vibration signals of the spindle system are measured and separated online again, and then the amplitudes of the unbalancing vibration signals are reassessed. The new influence coefficients and gain paremeters are estimated online, and then the old parameters corresponding to the current balance speed are replaced by the new parameters. The estimated methods of the new parameters are expressed as follows.  
The current influence coefficients corresponding to the balance speed in the scheduling table are taken as the original estimated value of \(\hat{R}_{(k-1)}\), the current testing and caculating values of \(R_{k}\) are obtained according to Equation (20). The new estimated values of \(\hat{R}_{k}\) can be calculated according to Equation (22), and then the gain parameters can be calculated and renewed by replacing the parameter of R by \(\hat{R}_{k}\) in Equation (14).  
Step 4, the step 2 and step 3 are carried out repeatedly until the amplitude of the unbalancing vibration signals is reduced to the allowed range.
# Design of a Computed Tomography Automation Architecture
## 5 Industrial Applications
### Medical Industry  
The medical industry relies on radiographic and CT imaging methods for viewing the internal features of patients. One limiting factor for medical imaging applications that is of much less concern for industrial radiography applications is the need to limit the dose to the patient because humans and animals are much more sensitive to radiation than industrial parts. An interesting CT application is the need to acquire CT images of horses. A solution is the Equimagine System from Universal Medical Systems [37], which is capable of imaging an entire horse in a single series. The two robotic versions are the Equimagine Helios and Equimagine Zeus. The Helios system utilizes two robotic arms for regular 3D CT imaging, densitometry, tomosynthesis, and panoramic imaging. The Zeus system is a four-arm robotic CT unit, which allows for whole-body scanning in minutes, scanning while the horse is in motion and with reduced radiation dosage.  
[PERSON] et al. [38] analyzed a multi-axis intraoperative angiography unit for percutaneous pedicle screw placement in the lumbar spine. This is basically radiographic imaging of the blood vessels for a procedure that stabilizes the vertebrae by placing screws in the spine. A total of 17 patients over about two years were included in this study. Pedicle screw insertion can be conducted with a combination of a navigation system with a CT device or the mobile CT device Airo. The multi-axis angiography unit Artis Zeego was used in this study to capture CT-like images. The focus was on minimizing the error and determination of the screw angle. The use of this multi-axis angiography unit over the conventional method increased the screw angle accuracy, reduced the inspection time, and increased the radiation dose.  
ORBIT [39] was an open X-ray scanner for image-guided surgery that included a robotic arm holding and controlling the position and orientation of an X-ray source, while the patient and detector remained fixed. The first prototype was deployed at the Charite--Universitatsmedizin Berlin in cooperation with Ziehm Imaging GmbH. The ORBIT project follows a similar idea as with Vienne and Costin [9], in that they are limited in their angular range and use an orbital or spherical trajectory instead of a circular trajectory for CT.
# Innovative Data-Driven Machine Learning Approaches for Predicting Sandstone True Triaxial Strength
## 6 Performance Comparison
### Comparisons Using the Collection Dataset
However, it notably outperforms the five strength criteria in terms of R\({}^{2}\) (0.9700), MAE (25.4266), and RMSE (32.7273). Similarly, in the test set performance, the HHO-MLP model exhibits a slight underperformance compared to the MGC in MAPE (HHO-MLP: 0.1587, MGC: 0.14), while still significantly surpassing its five strength criterion counterparts in R\({}^{2}\) (0.967), MAE (26.3836), and RMSE (34.1815). Additionally, the proximity of the evaluation metrics values between the training and test sets for the HHO-MLP model further underscores its remarkable generalization performance. Taylor diagrams, renowned for their comprehensive and intuitive depiction of model performance, have emerged as a favored method for model comparison and have been embraced by numerous researchers [55]. These diagrams enable the presentation of three distinct evaluation metrics (standard deviation, R\({}^{2}\), and RMSE) for models, effectively visualizing performance disparities between different models through the spatial representation of predicted and measurement points. Figure 9 depicts the performance of the six models on both the training and test sets. Upon examination of the figure, it becomes evident that the HHO-MLP model outperforms the five strength criterion models across both the training and test sets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Test**} \\  & **R\({}^{2}\)** & **MAE** & **RMSE** & **MAPE** & **R\({}^{2}\)** & **MAE** & **RMSE** & **MAPE** \\ \hline HHO-MLP & 0.9700 & 25.4266 & 32.7273 & 0.1435 & 0.9615 & 28.5801 & 36.9180 & 0.1587 \\ DP & 0.9564 & 28.6053 & 39.9217 & 0.1681 & 0.9444 & 31.3053 & 44.2664 & 0.2138 \\ HB & 0.9594 & 39.2814 & 51.345 & 0.181
## 2 Materials and Methods
### ISCCP-H Data  
Cloud properties, namely cloud amount, optical depth, and top pressure, used in the RTM were taken from the ISCCP H-series climate data, which contain level 2 and 3 products for monitoring cloud and surface properties. These data are derived from both geostationary and polar-orbiting satellite imaging radiometers with one visible (VIS \(\approx\) 0.65 \(\pm\) 0.05-0.20 \(\upmu\)m) and one infrared (IR \(\approx\) 10.5 \(\pm\) 0.5-0.75 \(\upmu\)m) \"window\" channels. There are several improvements of the ISCCP H-series compared to its predecessors C- and D-series, including quality control measures, use of modified ancillary inputs, higher-spatial-resolution input and output products, and calibration refinements [34]. The database, which covers the period from July 1983 to December 2018, and it is planned to extend to the present, constitutes the longest combined geostationary- and polar-orbitersatellite-based climatology of cloud properties. All ISSCP-H data products are available on [[https://doi.org/10.7289/V5](https://doi.org/10.7289/V5) QZ281S]([https://doi.org/10.7289/V5](https://doi.org/10.7289/V5) QZ281S) (accessed on 1 June 2023). Here, the level-3 gridded ISCCP HGG Basic data, available at a 1\({}^{\circ}\) equal-angle grid spatial and 3-hourly temporal resolution, were used. The original ISCCP data were regridded at the MERRA-2 spatial resolution (0.500\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\)) using the first-order conservative remapping scheme [61] and the operator \"remapcon\" from the Climate Data Operators (CDOs).
## 3 Methodology
### Model Architecture  
In the study, three different model architectures were created. The first is Model1, which is the Bi-LSTM model, the second is Model2, which has the LSTM architecture, and the last is Model3, in which the custom attention layer is added to the LSTM architecture.  
Model1 has a Bi-LSTM layer and a dense layer following it. In addition, a dropout layer was added between each Bi-LSTM layer to reduce overfitting. The number of neurons and epochs in the Bi-LSTM layers are 512 and 60, respectively. The batch size is 72, the learning rate is 0.0001, the dropout is 0.2, and the optimizer is Adam. The activation function is linear activation and the loss function is mean absolute error (MAE). The mean squared error (MSE) was used as the validation metric of the model. Seventy-five (75)% of the dataset was reserved for training and 25% for testing. Model2 is the LSTM applied version of Model1. In Model3, the custom attention layer from the Keras library was added after the LSTM layer. The attention layer consists of two dense layers. The hyperparameter settings of the three separate models are shown in Table 2.
# End-to-End Delay Model for Train Messaging over Public Land Mobile Networks
## 2 Scenario and Test Trials
Similarly, the receive time is evaluated at application level after having received the entire message. The common GPS time base has been adopted for timestamps generation. Since timestamps are provided at the application level, this prevents us from the possibility of separating the delay components of the E2E delay in Figure 2. The availability of \(T_{N\_NR\_prop}\) in Figure 2 would have allowed us to achieve a delay model independent on the characteristics of the transmission/receiving chain. However, the mobile network operator has been unable to provide us with this information. Finally, we consider the message loss probability as obtained by the ratio between the number of lost messages and the total number of transmitted messages.