esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Bench Press and Squat Velocity Performance  
Table 2 shows the bench press velocity performance after the BP + S and S + BP protocols. There were significant differences between protocols only in the MPV after the first and second training sets and in the PV after the second set. These results indicate that performing the squat first decreased the subsequent bench press velocity performance. This is particularly notable when the MPV is analyzed in each repetition and compared between  
conditions. In Figure 3, it is evident that the bench press was faster when performed before the squat (BP + S), especially in the first set. In this set, five out of six repetitions showed higher values, while in the second and third sets, three repetitions were found to be faster. There was a tendency to achieve these higher values in the first repetitions. Table 3 shows the squat velocity performance after the BP + S and S + BP protocols. Although the S + BP protocol exhibited higher squat velocity values in all variables compared to the BP + S protocol, there were no significant differences between protocols in terms of squat velocity performance. These results can be confirmed in Figure 3, where it can be verified that the MPV value in each repetition of the squat exercise was not different between the conditions assessed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **BP + S** & **S + BP** & **Difference (95\% CI)** & \(p\)** & \(g\)**-Value** \\ \hline MPV-S1 (m\(\cdot\)s\({}^{-1}\)) & 0.50 \(\pm\) 0.08 & 0.42 \(\pm\) 0.08 & 0.07 (0.01 to 0.14) & **0.03 \({}^{*}\)** & 0.81 \\ MPV-S2 (m\(\cdot\)s\({}^{-1}\)) & 0.50 \(\pm\) 0.07 & 0.42 \(\pm\) 0.08 & 0.08 (0.01 to 0.14) & **0.03 \({}^{*}\)** & 0.94 \\ MPV-S3 (m\(\cdot\)s\({}^{-1}\)) & 0.47 \(\pm\) 0.07 & 0.42 \(\pm\) 0.06 & 0.05 (\(-\)0.01 to 0.11) & 0.07 & 0.73 \\ PV-S1 (m\(\cdot\)s\({}^{-1}\)) & 0.75 \(\pm\) 0.12 & 0.65 \(\pm\) 0.12 & 0.09 (\(-\)0.01 to 0.20) & 0.08 & 0.72 \\ PV-S2 (m\(\cdot\)s\({}^{-1}\)) & 0.74 \(\pm\) 0.11 & 0.62 \(\pm\) 0.10 & 0.11 (0.03 to 0.20) & **0.01 \({}^{*}\)** & 1.03 \\ PV-S3 (m\(\cdot\)s\({}^{-1}\)) & 0.68 \(\pm\) 0.12 & 0.61 \(\pm\) 0.08 & 0.07 (\(-\)0.01 to 0.15) & 0.09 & 0.59 \\ TPV-S1 (m\(\cdot\)s\({}^{-1}\)) & 1040.30 \(\pm\) 258.57 & 1162.40 \(\pm\) 352.79 & \(-\)122.10 (\(-\)343.08 to 98.88) & 0.24 & \(-\)0.36 \\ TPV-S2 (m\(\cdot\)s\({}^{-1}\)) & 1075.10 \(\pm\) 202.98 & 1136.00 \(\pm\) 267.14 & \(-\)60.90 (\(-\)254.25 to 132.45) & 0.49 & \(-\)0.23 \\ TPV-S3 (m\(\cdot\)s\({}^{-1}\)) & 1043.20 \(\pm\) 199.92 & 1109.80 \(\pm\) 209.62 & \(-\)66.60 (\(-\)270.58 to 137.38) & 0.48 & \(-\)0.30 \\ \hline \hline \end{tabular} \({}^{*}\)_p \(<\) 0.05; \(p\)_ in bold denotes statistical significance; data are presented as mean \(\pm\) standard deviation unless otherwise stated. BP + S, bench press followed by squat; S + BP, squat followed by bench press; CI: confidence interval; g: Hedge\({}^{*}\)s g effect size; MPV: mean propulsive velocity; PV: peak velocity; TPV: time to peak velocity; S, set.  
\end{table} 
Table 2: Comparison between training protocols on bench press performance after each set.
## 5 Integration of Reputation on Meshtastic
### The Reputation Model  
Let us assume that we have two nodes, \(A\) and \(B\), in the Meshtastic network, that have interacted for a certain resource \(r\); then, each node will give feedback to all the other nodes of the network. Then, let \(F_{(a,b)}\) and \(F_{(b,a)}\) be the feedback generated by \(A\) against \(B\) and vice versa. First of all, the two feedbacks will be flooded to all the nodes of the network, so they will use this information to update the scores of nodes \(A\) and \(B\).  
The parameter \(\vartheta\) (interaction), in our implementation, takes into account the feedback \(F\), the economic relevance \(\delta\) of the resource, the frequency \(\phi\) of interaction between the two nodes, and a parameter \(\xi\) describing the truster's reputation, as in the original definition described in Section 4:  
\[\theta_{a,b}=f(\delta_{a,b},\phi_{a,b})\cdot\xi_{b}\cdot F_{b,a} \tag{8}\]  
and the economic relevance (\(\delta\)), in this specific context of Meshtastic, is defined by  
\[\delta_{a,b}=\begin{cases}0,&\text{if}\quad Hops_{(a,b)}=0\\ \dfrac{Hops_{(a,b)}}{Hops_{\text{Max}}},&\text{if}\quad 0<Hops_{(b,a)}\leq Hops _{\text{Max}}\end{cases} \tag{9}\]  
where \(Hops_{\text{Max}}\) is represented by the maximum number of jumps allowed in the Meshtastic network in which the two nodes \(A\) and \(B\) participate. The protocol provides three-layer modes to exchange packages, with the first two including zero-hop messaging and the third one flooding multi-hop messaging. The latter does not aim to optimise flooding (in fact, in a network of N nodes, N could be the re-transmission of the same message in the network) and a header is inserted in each package called \(Hops_{\text{Limit}}\) that is decremented with each arrival in an intermediate node. When the number reaches the value 0, then the package is discarded. As a consequence, our choice was to define the price as dependent on the Meshtastic layer mode, as defined by Equation (9).  
We left the frequency of interactions \(\phi_{a,b}\) the same as defined in the original model and as defined by Equation(3), unlike the \(\mu\) parameter, where the Threshold \(T\) parameter is not defined at the application level but by the node itself during the _resource search_ activity.  
Finally, we left the function \(f(\delta,\phi)\) as described in Equation (5); the parameter \(\xi\), that is, the truster's relevance as described in Equation (6); and the final computed reputation \(R^{new}\) as in the original model described in Section 7.  
The parameters \((\alpha,\sigma)\in[0,1]\subset\mathbb{R}\) are generally defined during tests and experiments. Typically, the \(\alpha\) is defined as the difference between the resource value received and the thresholds expected.
## 5 Discussion
### Cross-Examination of Transponder Cal/Val Results  
The S3 MPC publishes the Sentinel-3 altimeter Cyclic Performance Reports. These reports assess the performance of the scientific instruments on-board Sentinel-3A and Sentinel-3B. They are available online [33]. The S3 MPC report for the Cycle No. 56 of the SRAL altimeter of Sentinel-3A contains Sentinel-3A and Sentinel-3B range bias results using the CDN1 Cal/Val transponder. These results, as published by the S3 MPC, have been determined independently to the PFAC methodology presented in Section 2. Figure 29 illustrates the range bias results as calculated by the S3 MPC and the PFAC for Sentinel-3A Cycles 3-56 and Sentinel-3B Cycles 11-35 as of their launch dates: 16-Feb-2016 and 25-April-2018, respectively.  
Figure 29: Absolute range calibration results for Sentinel-3A Pass No. 14 as determined by the Sentinel-3 Mission Performance Center and the PFAC based upon the transponder at the CDN1 Cal/Val site.  
The range bias of Sentinel-3A and Sentinel-3B has been determined by the S3 MPC to be +6.79 mm and \(-\)6.59 mm, respectively [29]. The same biases as presented under FRM methodology in this investigation (Table 7) are, respectively, +4.90 mm and \(-\)3.46 mm. Thus, there is an agreement of the two transponder processing techniques at the \(\pm\)2 mm level. This is within the expected uncertainty of the different and independent methods of data analysis used.
# High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method
## 4 Experimental Design
### Benchmark Models  
To evaluate the forecasting and trading performance of our proposed approach on rebar futures, we compared its experimental results with other well-known machine-learning-based approaches and famous passive trading strategies, including XGBoost, Random Forest (RF), Support Vector Machines (SVM), Artificial Neural Network (ANN), Buy-and-Hold (BAH), and Short-and-Hold (SAH). Additionally, an integration method called XGBoost-SMOTE is also constructed to compare with the proposed approach on the rebar futures price prediction. A list including the proposed approach and benchmark approaches in the experiments with their brief descriptions is displayed in Table 3. Method 1 is our proposed approach, which is designed as a multiclassification method based on the XGBoost algorithm. Before direction prediction, as described in Section 4.2, the SMOTE technique is employed for solving the imbalanced data problem. Next, NSGA-II is used for optimizing the parameters of the trading rule as introduced in Section 4.3. In order to validate the direction prediction accuracy and profitability of the proposed approach with the application of the multiobjective optimization method, XGBoost and SMOTE are integrated in Method 2 to forecast the movement direction and to execute simulation trading. In particular, since there is an obvious category imbalance in the training data if the SMOTE is not employed, Methods 3-6 are designed as binary classification models to forecast the price-movement direction of rebar futures. If the predicted result is positive, those methods will execute a long transaction at the next opening price. Otherwise, they would execute a short-selling transaction if the classification result belongs to the negative class. Method 3 is designed based on a binary XGBoost model to identify whether the integration of SMOTE could successfully enhance the classification performance of the classifier. Method 4 adopts a tree-based algorithm RF to construct the forecasting model. Moreover, Methods 5 and 6 develop an SVM-based model and an ANN-based model, respectively, which are utilized to compare the efficiency of XGBoost with those two classic and commonly used machine learning methods for the financial time series prediction. Note that the model hyperparameters of all methods (Methods 1-6) are optimized by the grid search algorithm. Moreover, for the purpose of investigating whether the proposed approach is superior to the classic passive trading strategies, BAH and SAH are also involved (Methods 7 and 8). Furthermore, as recommended by [PERSON] [48], a forecasting period of five periods could be a suitable time horizon. Thus, it is chosen in the simulation trading for our proposed method and all benchmarks.
# An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition
## 4 Experimental Results  
In order to evaluate the performance of MS-LZM, we performed experiments on three different face datasets which are frequently used in the literature. The first of these was FERET [54], and using this, the identification performance of MS-LZM was evaluated. In addition to this, since there is only one image for each person in the gallery of this dataset, single sample per person success was also tested. LFW [55] was another dataset used in the experiments and is a dataset collected for the verification problem. The evaluation of the verification performance of MS-LZM was measured by performing experiments on this dataset. The last dataset used in this study was SCface [56], which has very-low-resolution probe images. This dataset was used to observe the performance of MS-LZM for video surveillance.  
Some preprocessing operations were done on the images in the datasets mentioned above before the feature extraction. First, the images were aligned using a similarity transform so that the horizontal coordinates of the eyes were the same. Then, an image pyramid was generated because a multi-scale feature extraction approach was adopted in this study. The number of scales used differed according to the dataset since each image was presented in different sizes and resolutions in each dataset. While more scales and higher image sizes were used for datasets with high-resolution images, less scales and smaller image sizes were used for datasets with low-resolution images. To remove the effect of illumination on the images in the image pyramid, images were normalized to have unit variance and zero mean, and the resulting images were used in the feature extraction process. This process was performed as described in Section 3. On the aligned and normalized face images, the cascaded LZM transformation was applied and then PMHs were calculated using the complex images obtained by the second transformation. Before each LZM transformation, we separately re-normalized the \(k\times k\) local regions to be encoded to have zero mean and unit variance. In order to calculate PMHs, as a result of the experiments, we decided to use 18 bins for each histogram and \(40\times 40\) grids with \(4\times 4\)cells which were located at each facial point. After normalizing PMHs using the z-score, all histograms were concatenated and thus a feature vector for each facial point was constructed. In the last stage, WPCA was applied on the feature vectors, and for each facial point, descriptors with a length of 1000 were obtained.
# Estimation of Direct Normal Irradiance at Antarctica for Concentrated Solar Technology
## 2 Materials and Methods
### Data Analysis and Model Development
The validation of the acquired dataset therefore has the purpose of identifying those values that are incorrect and excluding them from subsequent processing. The validation tests that are used to check the accuracy of the measurement data in particular identifies inaccurate data, which do not satisfy the validation tests (Figure 5), and which are declared incorrect and excluded from processing. Nevertheless, exact data may be inaccurate where the measured data still can be affected by a lack of precision the magnitude of which depends on the (in)sensitivity of the instrument, random disturbances, and so on. In theory, indicated with \(\Delta\), the allowable inaccuracy interval, a measure \(m\) is acceptable if the value falls in the amplitude interval \(\Delta\) centered on the true value \(\
u\):  
\[m\in\
u\,\pm\,\frac{\Delta}{2} \tag{4}\]  
Figure 4: Dispersion graph of the hourly observed values (DNI, obs) vs the calculated DNI (DNI, calc) with bisector reference line (in green). However, since the true value is unknown, it will be a question of transforming the previous criteria, from time to time, in a suitable manner. Figure 5 graphically illustrates the criteria described above. The intervals of imprecision for the horizontal global and diffuse irradiance are: \(I\pm\Delta I/2\) and \(I_{\mathrm{d}}\pm\Delta I_{\mathrm{d}}/2\) respectively. With reference to the possible reciprocal positions, there are therefore four cases, of which only the first is incompatible with the criteria \(I_{\mathrm{d}}\leq I\); the other three are collectively translated into the condition \(I_{\mathrm{d}}-\frac{\Delta I_{\mathrm{d}}}{2}\leq I+\frac{\Delta I}{2}\). In the night-time irradiance measurements, which theoretically should have zero values, they were found to have values almost always between the range \(\pm 10\) W\(\cdot\)m\({}^{-2}\). Therefore, it is reasonable to follow the inaccuracy interval as:  
\[\Delta I=\Delta I_{\mathrm{bn}}=\Delta I_{\mathrm{d}}=20\ \mathrm{W}/\mathrm{m}^{2} \tag{5}\]  
The same inaccuracy interval is followed for all three radiations components. Consequently, the summary of the validated data following the above criteria is (from now on) shown in Table 1:
# Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage
## 2 Materials and Methods
### Monitoring System  
The proposed monitoring system is a type of contact method for the detection of conveyor belt damage. It is based on strain gauges, exploiting the principle described in [25]. However, for the current research, the system was modified and patented [26]. In this solution, the long, flexible RP-L-170-type thin-film sensor [27] produced by DFRobot (Chengdu, China) was applied. Figure 1a illustrates the strain gauge, and Figure 1b shows its position on the tail pulley surface.  
Figure 1: The strain gauges used in the conveyor belt monitoring system: (**a**) view and dimensions; (**b**) the positioning of three strain gauges on the tail pulley surface.  
The resistance \(R_{T}\) of a strain gauge is directly dependent on the pressing force applied by the belt \(R_{T}=(F_{\text{n}})\). To register the signal from the strain gauges, a special electronic unit was projected and built into the hollow tail pulley. Figure 2a shows the holder of the printed circuit board (PCB) fitted to the inside of the pulley. Figure 2b shows the PCB designed to collect, transmit, and process the signals initially so that the results can be presented in a remote laptop in the form of graphs and data tables for further analysis.  
In principle, the electronic system was similar to the one described in [25]. Its tasks were to collect signals generated by the force \(F\) between the conveyor belt and the strain gauges placed on the pulley surface, to transmit the signal through the Bluetooth port, and to process collected data with a dedicated LabView program. The entire system underwent calibration as described in [28], and the Type A expanded uncertainty was below 1% of the measured value for the 99% gradient boosting decision tree, while the approximation error was ca. 8%. These results cover all of the uncertainty sources, including the electronic unit. The monitoring system was expected to support a predictive maintenance strategy able to safeguard the continuous operation of the conveyor and to minimize supply chain disruptions in agreement with the Industry 4.0 concept [29].
## 3 Cooperative Perception Information-Sharing
### The Effect of Network Performance on Cooperative Perception
#### 3.3.1 Latency  
Latency in V2X cooperative perception includes task offloading latency, data fusion calculation latency, and result feedback latency. The size of the computing task, the bandwidth of the wireless channel, and the computing power of the edge server or mobile device all have a certain impact on latency.
## 2 Materials and Methods
### Measurement Conditions  
As shown in Table 1, measurements were performed in the frequency bands used for FM/TV broadcasting, mobile phone base stations including 5G systems, broadband wireless access (BWA) base stations, and wireless LANs. Note that some of the frequency bands are allocated for time division duplex (TDD), which includes not only downlinks but also uplinks in the same frequency band.  
The low-frequency probe was used to measure the E-field at frequencies from 76 to 95 MHz and from 700 MHz to 2.7 GHz, and the high-frequency probe was used at frequencies from 470 to 710 MHz and from 3.4 to 5.8 GHz. The time required for measurement and recording for each probe was about 6 s. From the viewpoint of radio wave protection, it is necessary to measure for 6 min and calculate the time average [23]. However, since the measurement was carried out while driving, the time average was not used in this study. The square root of the sum of the squares of the E-field (\(E_{\text{int}}\)) measured in each band was calculated using Equation (1) [1]. In this measurement, the resolution bandwidth (RBW) was set to 1 MHz (200 kHz for FM and TV). \(\Delta f\) is a frequency resolution (Hz), and \(f_{1}\) and \(f_{1}\) + (\(n-1\)) \(\times\)\(\Delta f\) are the lowest and highest frequencies in a desired frequency bandwidth, respectively. The E-field strength below the predetermined threshold (noise floor) is excluded when integrating within the band.  
\[E_{int}=\sqrt{\frac{\Delta f}{1.0552 RBW}\sum_{k=0}^{n-1}(E(f_{1}+k\Delta f))^{2}} \tag{1}\]
## 3 Test Results and Analysis
### Failure Characteristics of Sandstone Specimens  
The fundamental mechanisms behind the deformation and fracturing of rock materials are primarily governed by the emergence, growth, and merging of micro-cracks within the natural rock matrix. Figure 6 illustrates the ultimate failure patterns of sandstone samples under varying confining pressures, clearly indicating that macroscopic fracturing of the rock specimens typically presents as shear failure during standard triaxial compression tests. This phenomenon is similar to the results of studies by [PERSON] and [PERSON] et al. [41; 42]. For confining pressures ranging from 1 to 20 MPa, the breakage planes within the specimens remain relatively straightforward, with minimal secondary cracking and the primary fissure not spanning the entirety of the specimen. At a confining pressure of 25 MPa, the fractures evolve into tensile-shear types, fully traversing the specimen's surface and forming an X-shaped fracture plane. In CLU experiments, the specimens suffer extensive damage, predominantly exhibiting tensile-shear failure along with some shear fracturing. Contrary to outcomes from standard triaxial compression tests, subjected to varying levels of confining pressures (1, 5, 10, and 15 MPa), a complex network of primarily shear or tensile fractures complemented by secondary cracks develops, whereas at 20 and 25 MPa, shear failure extending across the specimen's surface is observed. This analysis underscores the following: (1) under lower confining pressures, as the axial load increases, slippage of oblique micro-cracks--which are more prevalent than transverse cracks--leads to their propagation and eventual coalescence into a macroscopic fracture plane, resulting in shear failure of the specimen. (2) When subjected to increased confining pressures, the external confinement inhibits the slippage of oblique cracks, causing them to extend along their initial paths and merge into a comprehensive shear fracture from top to bottom of the specimen. Overall, the prevailing mode of failure in sandstone, across a range of confining pressures, primarily involves shear deformation. This insight is crucial for designing support strategies aimed at mitigating shear failure, especially during the construction and excavation of deep underground caverns subject to high geostress, to prevent extensive damage to the intact rock surrounding the structure.  
Figure 6: _Cont._