esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedures
A standardized warm-up was performed before each testing session using the RAMP (Rise--Activate and Mobilize--Potentiate) [33] method, consisting of 5 min of jogging, 5 min of dynamic stretching, mobility, and core activation, ending with submaximal 1 \(\times\) 5 CMJ and 1 \(\times\) 5 rebound CMJ. A total of 2 min of passive rest was provided between each warmup block. The warm-up gradually increased in intensity to prepare participants for maximal performance during jump testing. To ensure proper weighing of the subjects (and thus a proper forward dynamic process) the force platforms were zeroed. Immediately after, the subjects were placed on the force platforms and held as still as possible for at least 1 s to ensure proper weighing [21]. The center-of-mass (COM) velocity was calculated by dividing the vertical ground reaction force (minus body weight) by body mass and then integrating the product using the trapezoid rule [34]. Instantaneous power was calculated by multiplying the vertical force by the COM velocity. COM displacement was determined by double integrating the vertical force data. To consider a jump successful, participants had to perform it with their arms akimbo and remain completely still for at least one second during the weighing phase. The onset of movement was determined when a drop of 20 N from the baseline force (recorded during the weighing phase) was observed. Several variables derived from the force-time data were included in the reliability analysis because they may be of interest to strength and conditioning coaches for different reasons [7; 13]. For intersession reliability the mean of the 3 jumps was used for analysis while intersession reliability was calculated with the 3 jumps of the first testing day. The drop jump test was performed by dropping from a 30 cm box, which has proven to be one of the optimal heights for this test [35], following previous guidelines using just one force platform [5]. Before stepping onto the 30 cm box, the participants weighed themselves on the strength platforms. The weight recorded during the weighing phase was used throughout the drop jump test. For this purpose, the mean forces during at least 5 s were recorded until the body weight fluctuates by no more than +/ - 0.1 kg. At this moment, the Forcedecks(r) software (v2.0.7782; Vald Performance, Brisbane, QLD, Australia) accepted this weight as the weight of the subject. After weighing, participants remain for one second on top of the box and then drop onto the force platforms after a 3-2-1 countdown. The moment when the force plates recorded the landing was determined by detecting the initial force that exceeded a threshold of 20 N. The landing velocity was estimated from the height of the box using the conservation of mechanical energy principle, as the square root of 2 \(\times\) 9.81 \(\times\) box height (in m). Similar to the countermovement jump (CM), various kinetics, kinematics, and strategy variables of different phases of the jump were incorporated into the reliability analysis (Tables 2 and 3).
## 3 Materials and Methods  
### Data Acquisition  
A field dataset from a real pre-salt operation was used in this analysis. The dataset represents a carbonate hard rock reservoir (pre-salt) interval drilled in the South Atlantic Ocean, and contains values of several parameters such as the depth, rate of penetration (ROP), drill string rotational speed (RPM), weight-on-bit (WOB), torque-on-bit (TOB), downhole annulus pressure (DHAP), downhole annulus temperature (DHAT), equivalent circulating density (ECD), standpipe pressure (SPP), and flow rate (FLOW), among others. The analysis was performed considering a measured depth (MD) interval from 17,069.50 to 17,344.50 [ft], which was drilled using a polycrystalline diamond compact (PDC) drill bit with a diameter of 12.25\({}^{\prime\prime}\).
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 3 Materials and Methods
### Stage 1: Identification  
#### 3.1.1 Study Selection  
Study selection was based on a systematic review following the Prisma Guidelines [21]. It was executed using the following databases: Springer, Scopus, IEEE, Association for Computing Machinery (ACM), Web of Science, and Science Direct. These databases were chosen since they are the most relevant sources of information corresponding to Internet of Thing. All publications from 2015 to 2019 were included in the search, and the used the keyword string was \"(IoT OR Internet of thing)\" AND \"(LoRa OR LoRAWAN OR LPWAN)\".
# Utility Analysis about Log Data Anomaly Detection Based on Federated Learning
## 4 Performance Evaluation
### Dataset  
This paper employs two public datasets for the experiments, the Hadoop Distributed File System (HDFS) [22] and Blue Gene/L (BGL) [12] log datasets outlined in Table 3. The HDFS dataset consists of a total of 11,175,629 log messages generated over 200 days of experiments on Amazon EC2, of which an anomaly log is contained at a rate of about 2.9%. The dataset was recorded according to the identifier (\(block\_id\)); thus, the experiment was conducted using the session window method. The BGL dataset consists of 4,747,963 log messages recorded by the Lawrence Livermore National Labs BGL supercomputer system,  
Figure 5: CNN1D-LSTM-based log anomaly detection model architecture.  
with approximately 0.07% containing anomalous logs. In the case of the dataset, unlike HDFS, there is no identifier record, so the experiment was conducted through the sliding window method.  
Each dataset was partitioned for learning on five local servers configured for federated learning, and the overview is presented in Tables 4 and 5.
## 4 Experimental Results and Analysis
### Cross-Species Prediction  
In this section, we explore the cross-species performance of our method experimentally. We trained a model on the training dataset of one species and then evaluated it on the independent test datasets of all three species. The results are shown in Figure 8. It can be found that the models trained on _E. coli_ and Salmonella datasets achieved MCC values of 0.50 and 0.52, respectively, when testing on the human test dataset, which shows a much worse performance than that trained on the human dataset with an MCC value of 0.67. Moreover, when testing on _E. coli_ and Salmonella testing datasets, the model trained on the human dataset performed poorly with MCC values of 0.38 and 0.45, respectively. However, the models trained on the _E. coli_ and Salmonella datasets had similar performances on all testing datasets of the three species. The AUCs of cross-species prediction also show that the models trained on the _E. coli_ and Salmonella datasets had closer AUCs. It is worth noting that the model trained on _E. coli_ had a slightly higher MCC on Salmonella than _E. coli_. However, the model trained on _E. coli_ still had a significantly higher AUC on _E. coli_ than Salmonella. These results demonstrate that the proposed CnnRBP method has a robust performance for cross-species prediction between close species, i.e., _E. coli_ and Salmonella. When two species are quite different, the performance of the model decrease significantly, which suggests the necessity of a species-specific prediction model.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 3 Measurement Techniques
### Range Free Technique
#### 3.2.5 Approximate Point-in-Triangulation Test  
The approximate point-in-triangulation test (APIT) is a region-based localization technique. In this approach, the network is divided into triangular areas among the anchor nodes. The unknown sensor node selects three anchor nodes who have sent the message to it, and then it examines whether it lies inside the triangle generated by the three anchor nodes or not, as shown in Figure 8. The selection process continues with distinct sets of three anchor nodes until all probable anchor triangles are generated or the expected precision is attained [43]. APIT algorithm works very well for the IoT networks with irregular sensing regions with random node locations and expects lower communication costs.  
A comparison of range-based and range-free localization techniques based on evaluation metrics is given in Table 2.  
Figure 8: Approximate point-in-triangulation test.
# Power Control Optimization of an Underwater Piezoelectric Energy Harvester
## 5 Harvester Control Modeling
### Hydro-Mechanical Model  
The hydro-mechanical model proposed in the current work is defined by the Equation (8), where the hydro-mechanical torque \(T_{hydro}\) is determined by Equation (9) and the moment \(T_{m}\) generated by the piezoelectric force \(F_{m}\) can be found in Equation (10).  
\[J_{wt}\frac{d^{2}\theta}{dt^{2}}=\ T_{hydro}\ -\ K_{spring}\theta\ -\ f\frac{d\theta}{dt}-T_{m} \tag{8}\]  
\[T_{hydro}\ \approx\ \mathrm{Asin}(wt) \tag{9}\]  
\[T_{m}=\ F_{m}\cdot a \tag{10}\]  
The system has been designed with a torsion spring with the corresponding spring constant \(K_{spring}\) in order to keep the harvester device in a vertical equilibrium position without piezoelectric force. This spring lets the system keep energy from the fluid as a harmonic resonator. Thus, the spring is an element that stores the mechanical energy while the piezoelectric is taking the energy from it in a harmonic known state.
## 4 Discussion  
Studies that have assessed the effects of natural products in association with WBV exercise in animals [14; 15; 32] stimulated the motivation for this current investigation. In this study, biological responses of treatment with guarana extract alone or combined with WBV exercise have been verified based on the biodistribution of the Na\({}^{99m}\)TcO\({}_{4}\), on the concentration of selected blood biomarkers, body mass, food intake, and stool consistency, in Wistar rats. In general, the distribution pattern of a radiopharmaceutical in the body depends on the physiological conditions of an organ or tissue [32]. Previously, [PERSON] et al. [37] demonstrated, in rats, that exposure to 20 Hz of WBV could alter the uptake of a radiopharmaceutical. [PERSON] et al. [32] showed that the association of coriander (natural product) and WBV (12 Hz) increased the uptake of Na\({}^{99m}\)TcO\({}_{4}\) in the spleen. The determination of the Na\({}^{99m}\)TcO\({}_{4}\) uptake in organs (Table 1) made it possible to check the effects of different treatments in organs. Guarana treatment increased the uptake of Na\({}^{99m}\)TcO\({}_{4}\) in muscle. This increase could possibly be due to the caffeine, a component of guarana [4]. According to [PERSON] et al. [38], the ingestion of caffeine induced increases in the voluntary activation and strength of the quadriceps muscle during isometric, concentric, and eccentric contractions. Furthermore, in general, the interventions did not promote modifications at different levels (molecular or cellular) in the several organs that were capable of interfering in the uptake of the radiopharmaceutical Na\({}^{99m}\)TcO\({}_{4}\). The addition of WBV-E to the GUA treatment did not alter the uptake of Na\({}^{99m}\)TcO\({}_{4}\) as compared with the CON. In the current study, the intervention with WBV-E only or associated with guarana did not modify the uptake of the radiopharmaceutical, \begin{table}
\begin{tabular}{c c c c c c c} \hline
**WEEK** & **CON** & **GUA** & **WBV-E** & **GUA+WBV-E** & \(p\) & \(\varepsilon^{2}\) \\ \hline
0 & 100.00 \(\pm\) 8.89 & 100.00 \(\pm\) 8.21 & 100.00 \(\pm\) 11.7 & 100.00 \(\pm\) 2.10 & 0.4828 & 0.1294 \\
1 & 103.90 \(\pm\) 7.20 & 103.27 \(\pm\) 7.67 & 105.29 \(\pm\) 9.54 & 105.12 \(\pm\) 3.80 & 0.3452 & 0.1746 \\
2 & 109.00 \(\pm\) 5.29 & 107.00 \(\pm\) 7.24 & 113.40 \(\pm\) 6.38 & 108.92 \(\pm\) 3.42 & 0.0839 & 0.3501 \\
3 & 112.31 \(\pm\) 5.78 & 111.99 \(\pm\) 7.04 & 116.77 \(\pm\) 5.33 & 113.55 \(\pm\) 3.63 & 0.1014 & 0.3273 \\
4 & 115.31 \(\pm\) 6.34 & 115.42 \(\pm\) 7.82 & 120.38 \(\pm\) 4.41 & 116.94 \(\pm\) 4.29 & 0.1101 & 0.3174 \\
5 & 118.61 \(\pm\) 6.04 & 119.15 \(\pm\) 4.94 & 123.27 \(\pm\) 4.45 & 121.57 \(\pm\) 3.88 & 0.0736 & 0.3657 \\ \hline \end{tabular}  
* CON, control; GUA, group treated with guarana; WBV-E, group exposed to mechanical vibration generated in platform; GUA + WBV-E, group treated with guarana and exposed to mechanical vibration. Values are presented in percentage (%) as the means \(\pm\) SD and \(\varepsilon^{2}\), epsilon squared.  
\end{table} 
Table 4: Body mass (%) of the animals submitted to some interventions.
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 4 Problem Formulation
### Tradeoff between Data Freshness and Fidelity
#### 4.3.2 Monotonicity of \(\overline{D}(N)\)  
It is not difficult to conclude from Equation (13) that \(\overline{D}(N)\) is a monotone decreasing function w.r.t the number of sensor nodes \(N\) by using Mathematical induction:  
\[\overline{D}(2)-\overline{D}(1) =\Gamma(0,1)e-2\Gamma(0,2)e^{2}<0;\] \[\overline{D}(3)-\overline{D}(2) =\Gamma(0,1)e-4\Gamma(0,2)e^{2}+3\Gamma(0,3)e^{3}<0;\]  
by applying the same method, it yields:  
\[\overline{D}(N)-\overline{D}(N-1)<0.\]
# Experimental Research into the Ultrasonic P-Wave Velocity of Coal Slime Based Backfill Material
###### Abstract  
To study the influences of different mix proportions of materials on the ultrasonic p-wave velocity of coal slime based backfill materials, influences of four factors on the p-wave velocity of specimens were investigated by designing orthogonal tests and taking coal slime as the principal raw material. The four factors included the following: (A) the mass concentration of coal slime, (B) the content of high-water-content material, (C) cement content, (D) and content of fly ash. Test results revealed the following: (1) the ultrasonic p-wave velocity is in the range of 3.916 to 8.319 km/s and various factors are listed in a descending order as A, D, B, and C according to their influences on the ultrasonic p-wave velocity; (2) the ultrasonic p-wave velocity is positively correlated with the compressive strength and shear strength, with correlation coefficients separately of 0.87 and 0.65; (3) the equations for variations in the ultrasonic p-wave velocity under influences of different factors are fitted. The ultrasonic p-wave velocity has a quadratic polynomial relationship with factor A, while following exponential relationships with factors B, C, and D. A predictive model for characteristic parameters of the ultrasonic p-wave velocity of coal slime based backfill materials jointly influenced by the four factors was established based on the fitting equation for variations of single factors and ultrasonic p-wave velocity. The predictive model was then verified.  
coal slime; backfill material; correlation coefficient; ultrasonic wave speed; formula fitting
## 2 Materials and Methods  
### Case Study  
The Jinggualiao stream in the Feitsui Reservoir (24.909097, 121.579736) was taken as a case study to present the idea of this study. Most domestic sewage within the watershed is well collected and treated. Therefore, nonpoint source pollution, especially tea farms, has become the largest potential pollution source of the reservoir. The total area of the reservoir watershed is 303 km\({}^{2}\). The land use in the watershed is 96.6% forestland, 3% agricultural land, 1.2% artificial land, 4.1% water area and 1.1% other types. No industrial land uses, large commercial buildings or mining areas occur within the watershed. Three major inflow streams flow to the Feitsui Reservoir, and Jinggualiao stream is the target stream because of its relatively low water quality. The total area of the Jinggualiao watershed is 2350.96 ha, of which 93.9% is forest, 3.4% is agricultural land (mostly tea farms) and approximately 1% is for residential use. The land use of the Feitsui Reservoir and its inflow streams are shown in Figure 1. The average annual rainfall is 3500 mm, and the rainfall is concentrated from May to October, and affected by the early summer rainy and typhoon seasons.  
The Feitsui Reservoir has very good water quality and from 1987 to 2018, the reservoir reached a eutrophic state only 3% of the time according to official data [25]. In recent years, the trophic level has been mesotrophic to oligotrophic. However, the detailed inflow streams sometimes exceed the required water quality standard in terms of total phosphorous (TP), especially the Jinggualiao stream. The TP concentration in the Jinggualiao stream from 2015 to 2018 is shown in Figure 2. The TP water quality standard is 20 \(\mu\)g/L for this reservoir and its streams. While the average TP concentration from 2015 to 2018 was 19.69 \(\mu\)g/L, which was less than the standard concentration, some of the observations did not meet the requirement.  
Figure 1: The land use and main streams of the Feitsui Reservoir watershed.
## 5 Results and Discussions
### Cointegration Test  
A cointegration test was used when the F or W-statistic applies an upper bound of the selected significant level. It is worth observing that the F test assumes that there is no cointegration null hypothesis between variables. Cointegration results are illustrated in the Table 3.  
The bounds tests shown in the table summarizes the existence of a cointegration connection among dependent and independent variables at the 1%, 5%, and 10% significance levels. Furthermore, we also employed the [PERSON], (1990) [71] cointegration test, and the results are interpreted in Table 4 with trace statistics and maximum eigenvalues.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Augmented Dickey–Fuller Unit Root Test**} & \multicolumn{2}{c}{**Phillips–Perron Unit Root Test**} \\ \hline
**Variables** & **At Level** & **First Difference** & **At Level** & **First Difference** \\ \hline LnEAP & \(-1.272365\) & \(-13.17405\)* & \(-2.945287\) & \(-16.37533\)* \\ LnEARP & \(-1.664904\) & \(-11.97595\)* & \(-2.602248\) & \(-13.37829\)* \\ LnEAUP & \(-4.350711\) & \(-5.926737\)* & \(-4.972231\) & \(-21.59359\)* \\ LnEN & \(-0.673060\) & \(-4.635827\)* & \(-0.744445\) & \(-4.634863\)* \\ LnGDPPC & \(-1.575934\) & \(-4.340708\)* & \(-1.575934\) & \(-4.318162\)* \\ LnPG & \(-1.607271\) & \(-3.544057\)*** & \(-1.703555\) & \(-3.745637\)** \\ LnRPG & \(-1.232135\) & \(-4.406125\)* & \(-1.851566\) & \(-3.908624\)** \\ LnUPG & \(-2.216098\) & \(-4.502952\)* & \(-2.422004\) & \(-4.281174\)** \\ \hline \hline \end{tabular}
* *, ***, *** show the rejection of null hypothesis at 1%, 5% and 10% level of significance.  
\end{table}
Table 2: Augmented Dickey–Fuller and Phillips–Perron Unit Root Test Results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**ARDL Bounds Test for Cointegration Results**} \\ \hline
**F-Statistic** & **Significance Level** & **Lower Bound** & **Upper Bound** & **Decision** \\ \hline \multirow{3}{*}{5.355108} & \(10\%\) & \(2.03\) & \(3.13\) & \multirow{3}{*}{Cointegrated} \\  & \(5\%\) & \(2.32\) & \(3.5\) & \\ \cline{1-1}  & \(1\%\) & \(2.96\) & \(4.26\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Autoregressive Distributed Lag (ARDL) Bounds Test for Cointegration Results.
## 3 Results
### Results and Analysis
#### 3.2.3 RPC Positioning Accuracy Evaluation Without Control Points Considering Atmospheric Delay  
To assess the effectiveness of the RPC model with atmospheric correction, several checkpoints were selected from the SAR imagery. Using the updated RPC parameters, an  
Figure 8: Atmospheric delay.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Group** & **Image ID** & \(\Delta R_{atm\_ref}\)**/m** & \(\Delta R_{atm\_vec}\)**/m** \\ \hline \multirow{4}{*}{A} & gf1 & 3.0596 & 3.0596 \\  & gf2 & 3.0602 & 3.0601 \\  & gf3 & 3.0605 & 3.0605 \\ \hline \multirow{4}{*}{B} & gf4 & 3.2641 & 3.2641 \\  & gf5 & 3.2668 & 3.2668 \\  & gf6 & 3.2674 & 3.2674 \\ \hline \multirow{2}{*}{C} & gf7 & 2.5860 & 2.5860 \\  & gf8 & 2.5843 & 2.5843 \\ \hline \multirow{2}{*}{D} & gf9 & 3.3463 & 3.3463 \\  & gf10 & 3.3433 & 3.3433 \\ \hline \end{tabular}
\end{table}
Table 5: Atmospheric delay.  
uncontrolled positioning experiment was conducted to compare the positioning accuracy of the checkpoints before and after atmospheric correction, as shown in Table 6 and Figure 9.  
Table 6 provides a detailed quantitative analysis of the variations in the positioning accuracy of the SAR images before and after the implementation of atmospheric corrections. Following these corrections, there is a notable enhancement in the accuracy of image positioning across all datasets. As depicted in Figure 9a, the planar error within the image space is substantially reduced after the application of atmospheric corrections, with improvements achieving an accuracy level exceeding three pixels. It is particularly noteworthy that the errors measured column-wise show a marked decrease, whereas the errors measured row-wise are minimally altered, maintaining a nearly constant value. The image-space accuracy improved by 18% to 44%. In Figure 9b, the object-space planar error also decreases after atmospheric correction, with the object-space positioning accuracy reaching 12 m. Specifically, the range-direction error is significantly reduced, whereas the azimuth-direction error fluctuates within a narrow range. The object-space accuracy improved by 19% to 32%. Additionally, the image positioning accuracy in study area 2 is lower than that in study area 1. Study area 1, located in Beijing and its surrounding regions, is characterized by low elevation and relatively flat terrain, whereas study area 2, situated in Sichuan, has high elevation and mountainous terrain. Therefore, this discrepancy is likely due to the influence of topographical factors.
## 2 Background
### O-RAN Architecture: Components and Interfaces  
The Open Radio Access Network (O-RAN) architecture represents a significant paradigm shift from traditional RAN systems, aiming to build flexible, intelligent, and cost-effective 6G networks [5]. It achieves this by disaggregating network functions and promoting interoperability through open interfaces and virtualization [20].  
At the heart of O-RAN are the RAN Intelligent Controllers (RICs) [21], which come in two types: the Near-Real-Time (nRT-RIC), responsible for rapid decision-making tasks (e.g., scheduling, handovers) [22], and the Non-Real-Time RIC (non-RT RIC), which handles slower tasks such as policy optimization [23]. These RICs host modular applications--xApps in the nRT-RIC and rApps in the non-RT RIC--which enable dynamic control and optimization of the RAN [21].  
The Service Management and Orchestration (SMO) framework oversees the lifecycle of these functions and facilitates integration across multi-vendor components [24]. The architecture uses standard interfaces such as the E2 interface (connecting nRT-RICs to Distributed Units) and the A1 interface (enabling policy exchange between the SMO and RICs), ensuring smooth coordination across functional layers [21].  
While this modular, software-centric design fosters innovation and flexibility, it also increases system complexity and introduces new vectors for misconfiguration, misbehavior, and malicious exploitation [25, 26].