esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Methods and Results  
### Experimental Method  
In this experiment, a laser with a pulse width of 300 fs and a wavelength of 1030 nm was used. The laser beam has a gaussian spatial distribution and was focused on the upper surface of the sample to be processed by a focusing mirror with a beam radius of 5 \(\upmu\)m at the focal point. The laser beam in this system was moved in a specific area by adjusting the galvanometers in the \(x\) and \(y\) directions, as shown in Figure 1a. The number of pulsesreceived at each point on the sample surface is determined by the following relationship between the laser repetition frequency, scanning speed, and beam radius:  
\[n=\frac{2\cdot\omega_{0}\cdot f}{v} \tag{1}\]  
where \(\omega_{0}\) denotes the beam radius,\(f\) denotes the repetition frequency, and \(v\) denotes the laser beam scanning speed.  
The nickel-based single crystal high-temperature alloy DD15 was employed in this study. The experimental sample is 3 mm thick to ensure that an observable micro-structure is obtained on the material's surface. The laser was moved with velocity \(v\) along the \(x\)-direction and was set at \(\Delta\)1 = 40 \(\upmu\)m along the \(y\)-direction. A series of experimental explorations revealed that the evolutionary pattern of the microstructure is most evident at 40 \(\upmu\)m. Therefore, a 40 \(\upmu\)m spacing was chosen to illustrate the connection more clearly between experiments and simulations. The scanning area is 1 mm\({}^{2}\). One scan is defined as the completion of the motion of the beam within this area, and the process is then repeated continuously within this area, as shown in Figure 1b. All samples were surface polished to achieve an acceptable roughness. Following that, the samples were ultrasonically cleaned in acetone to remove any potential surface adhesion. Scanning electron microscopy (SEM) was used to observe micro-structural organisational features, while a Laser scanning confocal microscope (LSCM) was used to observe micro-structural morphological features, with the number of scans m being recorded by the equipment.  
LSCM was used in this study to measure the distance in the depth direction of the micro-structure. The depth value is calculated by taking the difference between the highest and lowest points of any five lines in the measurement area. The device determines the number of scans and stops automatically when the value is reached. During the process, an auxiliary blowing system is used to remove the material dynamically.
## 1 Introduction  
In the last fifty years, selected low-frequency time-varying magnetic fields have been studied as therapeutic tools for several musculoskeletal diseases such as bone non-unions, failed arthropods, osteonecrosis, chronic refractory tendinitis. The pulsed electromagnetic fields (PEMFs), within the wide electromagnetic spectrum, resulted in clinically effective exploiting of the piezoelectrical properties of the bone [1], modulating of the membrane signal transduction processes [2], and promoting differentiation [3] and proliferation of osteoblastic cells [4]. These positive effects have been largely used for therapeutic purposes when osteogenic stimulation is requested for bone tissue repair [5; 6]. Although a remarkable number of studies have been conducted on low-intensity (LI) low-frequency (LF) PEMFs, very little is known about the effects of high-Intensity (HI) LF-PEMFs on vital musculoskeletal tissues. HI LF-PEMFs, with intensity in the range of 0.3-16.4 Tesla (T) have been studied for their ability to induce electroporation (i.e., the capacity to increase transmembrane molecular transport) [7; 8; 9; 10; 11] and, with adequate levels of energy, a water repulsive and molecular diamagnetic effect [12]. This repulsive effect (diamagnetic repulsion or diamagnetism) refers to the magnetic property of some materials which, subjected to a high intensity magnetic field, receive the pushing effect as already experienced in the field of biology by the magnetic levitation [13]. At the extracellular matrix (ECM) level, diamagnetism moves water and other diamagnetic substances such us proteins and ions [12]. This occurs also at the cellular level in terms of movement of ions and the activation of transmembrane proteins, while the variability of the MF and of the magnetic field gradient influence the fundamental ion-channel on/off switching events by membrane magnetomechanical stressing [14,15] mainly for Ca\({}^{2+}\), Na\({}^{+}\), K\({}^{+}\), Li\({}^{+}\), Mg\({}^{2+}\) ions or other cellular activities. We, therefore, deemed interesting to ascertain the effects of HI LF-PEMF on bone regeneration. In this regard, we chose an experimental model based on the zebrafish (_Danio rerio_). This fish has several attractive features for research (amongst others, bone developmental and repair mechanisms very similar to those of vertebrates) [16]. Thus, zebrafish seems to be a good target for experimental studies about the effects of physical stimulation on bone tissue, especially, using caudal fin regeneration as a model [17]. Despite these interesting features, very little is known about the results of PEMFs on zebrafish. The only scientific report that can be found in medical literature is about the stimulatory effect on the pigmentation of zebrafish embryo in vivo [18]. The purpose of this experimental study is to evaluate the effects of HI LF-PEMF on caudal fin adult zebrafish regeneration.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 3 The Architecture Proposal
### Architecture Components
#### 3.2.3 Path Computer  
The _Path Computer_ component computes the paths to be installed into the OpenFlow network of the domain that uses the proposed architecture. It sends instructions to the _OpenFlow Speaker_ component to install the OpenFlow rules in the OpenFlow switches. It also receives information about the states of the OpenFlow table that are used to inform the _Network Administrator_ component about the bandwidth utilization of each prefix.
## 4 Discussions, Conclusions, and Future Directions
### Discussions
#### 4.1.4 Discussion: Deep Learning Approach  
The BER versus SNR curves demonstrate similar trends, yet the DNN consistently exhibits lower BER values compared to the traditional approach across a range of SNR values. This consistent outperformance of the DNN indicates its superiority in terms of BER under the simulated conditions. The remarkable performance of the DNN can be attributed to its proficiency in learning intricate patterns and representations from the provided data. Unlike the traditional methods that may rely on predetermined algorithms or manually engineered features, DNNs possess the capability to autonomously discern and adapt to complex patterns in the dataset.  
DNNs excel particularly in tasks that involve vast datasets and intricate relationships owing to their ability to uncover hierarchical features and dependencies through their layered architecture. Furthermore, during the training phase, DNNs iteratively adjust numerous parameters to minimize errors, enabling them to finely tune their performance to the specific characteristics of the given problem.  
Considering the BER versus SNR curves, DNNs' aptitude for capturing subtle relationships within the data and their adaptability to varying SNR conditions are likely contributors to their consistently superior performance, as evidenced by the consistently lower BER values compared to the traditional approach.
## 3 Results  
### Alert Distribution  
We successfully developed a dominant window detector using the Golang programming language, and retrieved alert dwell times in the homegrown CPOE system of an academic medical center in Taiwan. The alert log collector was implemented on 11 November 2017, in WFH: in total, 3,737,697 triggered alerts were collected in the 14-month study period (from 25 December 2019, to 20 February 2021). The numbers of alerts were highly concentrated to the top 100 most frequent categories, consistent with the results of our previous study [16]. First, in the data combination process, 462,463 alerts were excluded due to missing alert dwell time values because some clinicians would terminate the dominant window detector before starting the visits. The missingness is 12% for all data, so we believed that removing these data with missing values would not affect our results. A further 28,763 alerts were excluded for being outside the top 100 most frequent categories.  
Figure 3: A schematic diagram of the time gap calculation. Rectangles with different colors, red circles, and blue dashed arrows represent dominant window records, alert log records, and time gaps, respectively. In this example, the time gaps were \(-118\) s, \(-62\) s, \(-27\) s, \(+0.2\) s, etc. In this case, the minimal time gap between the ALC and DWD records of the relevant alert is 0.2 s.  
Ultimately, 3,246,471 alerts relating to the top 100 most frequent categories were included in our study (Figure 4).
## 5 Simulation Scenarios
This model can be used to represent the movements of search-and-rescue teams during disaster relief efforts, where different teams perform the same operations (_i.e._, search and rescue survivors) in different sectors of the affected area [49]. In this study we considered a HWSN based on WiFi (IEEE 802.11 standards). Fifteen simulations were done for each scenario and nine of the most representative simulations where used in this study in order to avoid the influence of the special cases. The results shown in this section are the average values of these nine selected simulations, thus we have tried to normalize the network behavior. All simulations were done using OLSR (the standard protocol), and then repeated using OLSRp (the protocol with the predictor). We have assumed that all nodes are similar, and with technical features equivalent to a smartphone. Particularly, we have considered the features of the iPhone 4 for stationary and mobile nodes. These devices have an effective WiFi communication range of 80 m approximately in open areas. In such range we can expect that the ad hoc communication among devices is stable and the bandwidth of at least 50 Kbytes, which is appropriate to support reliable synchronous interactions among mobile nodes. Figure 3: Potential interaction scenario in a pedestrian promenade. The routing protocols were set to deliver HELLO messages every two seconds and TC messages every three seconds. We also generated data traffic (_i.e._, messages with content generated by the mobile application) that consists of several UDP messages transmitted every second. Table 1 summarizes the simulation parameters, and Table 2 indicates the parameters used for each node's mobility model. The next sections present the results obtained in the square and the narrow interaction area; _i.e._, interaction scenarios that are representative of a park and a promenade respectively. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Simulation time & 1,800 s \\ Simulation areas & 300 \(\times\)300 m \\  & 30 \(\times\)3,000 m \\ Number of nodes & 10, 20, 30 or 40 \\ MAC Protocol & IEEE 802.11 \\ Propagation Model & TwoRayGround \\ Transmission power & 0.66 W \\ Transmission range & 80 m \\ \hline OLSR & UM-OLSR \\ Implementation & \\ Hello Interval & 2 s \\ TC Interval & 3 s \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Mobility model & Random Walk \\ Max. speed & 1, 2, 4 or 6 mps \\ Max. pause & 60 s \\ Mobility model & SLAW \\ Cluster ratio & 25 m \\ Max. pause & 60 s \\ \hline Mobility model & RPGM \\ Avg. nodes per group & 4 \\ Group size deviation & 2 \\ Max. distance & 15 m \\ Max. speed & 1, 2, 4 or 6 mps \\ Max. pause & 60 s \\ \hline Mobility model & Nomadic \\ Avg. nodes per group & 4 \\ Group size deviation & 2 \\ Max. distance & 15 m \\ Max. speed & 1, 2, 4 or 6 mps \\ Max. pause & 60 s \\ \hline \end{tabular}
\end{table} 
Table 2: Parameters of the mobility models.
## 3 Forecasting Models and Evaluation Metrics
### Metrics to Evaluate the Forecasting Models  
In this work, three kinds of error metrics are examined for the hybrid forecasting models, as follows:  
* The coefficient of determination (\(R^{2}\)): \[R^{2}=1-\frac{RSS}{TSS}\] (3) where \(RSS\) is the sum of squares of residuals, and \(TSS\) is the total sum of squares.  
Mean Absolute Error (MAE): \[MAE=\frac{1}{n}\sum_{j=1}^{n}|y_{j}-\hat{y}_{j}|\] (4)
* Root Mean Square Error (RMSE): \[RMSE=\sqrt{\frac{1}{n}\sum_{j=1}^{n}(y_{j}-\hat{y}_{j})^{2}}\] (5) where \(n\) is the number of samples, and \(y\) and \(\hat{y}\) are the predicted value and actual value, respectively.  
A key feature of the error metrics employed for model evaluations is their ability to differentiate among model results. The metric that identifies higher variations in regression models' performance is often the more desirable. In this instance, MAE could be impacted by many average error values without sufficiently showing some of these large errors. The RMSE could be better at identifying model performance differences by providing higher weighting to unfavorable conditions.  
It is well known that the sensitivity of the RMSE to outliers is one of the key concerns when using this metric. In practical situations, it is common for modelers to omit the outliers that are several orders greater than the other samples when computing the RMSE, especially if the sample size is small. If the model biases are significant, the systematic errors may need to be removed before computing the RMSEs.  
One key merit of RMSEs over MAEs is that RMSEs do not need to compute the absolute value, which is highly undesirable in many mathematical calculations. For example, it may be challenging to compute the sensitivity or gradient of the MAEs with respect to model parameters. In addition, in data assimilation, the sum of squared errors is usually defined as the cost function to be minimised by adjusting model parameters [26]. In this case, penalising large errors through the defined least-square terms is demonstrated to be effective in enhancing model performance. In the scenarios of computing model error sensitivities or data as simulation applications, MAEs are not favored over RMSEs.
## 3 Results and Discussion
### Wear Resistance  
The grinding ratio is an important indicator to evaluate the wear resistance and tribological performance of diamond-impregnated composites [28,29]. The bar chart in Figure 7 summarizes the results for the grinding ratio of impregnated diamond samples with different deep cryogenic treatment times. The grinding ratio increased remarkably after deep cryogenic treatment, proving that deep cryogenic treatment improved the wear resistance of the impregnated diamond composites. With increasing deep cryogenic treatment time, the grinding ratio first increased and then decreased. The grinding ratio of sample SD1 showed a 32.9% increase in comparison with SD0.  
The grinding ratio of impregnated samples depends largely on the bending strength. Some related studies analyzed the theoretical relationship between bending strength and wear resistance [5,30]. A lower bending strength means that the metal matrix supports the diamond grits weakly, leading to the pull-out of diamond grits from the matrix under complex stress conditions during working. Hence, the grinding ratio of SD1 subjected to 1 h of deep cryogenic treatment was higher than the reference one (SD0) without deep cryogenic treatment.  
The grinding ratios of SD2 and SD3 were larger than that of SD0. However, the bending strengths of SD2 and SD3 were similar to that of SD0, indicating that there are other factors affecting the wear resistance. The mismatch in the coefficient of thermal expansion between diamond (\(\approx\)1 \(\times\) 10\({}^{-6}\) K\({}^{-1}\)) and matrix material (\(\geq\)13 \(\times\) 10\({}^{-6}\) K\({}^{-1}\)) needs to be given further consideration. During the deep cryogenic treatment process, the metal-matrix-impregnated diamond composites were subjected to a thermal shock, with a temperature drop of 215 \({}^{\circ}\)C, which increased the residual compressive stress on diamond grits. This change of the stress state increased the diamond retention capacity of the matrix. Furthermore, the matrix materials penetrated into the microcracks on the surface of diamond grits, which could also improve the diamond retention capacity of the matrix. Facing the action of complex alternating cutting force, diamond grits usually exhibit a tendency to rotate first and then pull out of the matrix easily. The improvement of the matrix's diamond retention capacity can reduce the rotating tendency, which is conducive to improving the wear resistance of diamond composites.
# Battery Management System Hardware Concepts: An Overview
## 2 Battery Management System Requirements  
Designing a BMS is a complex task that requires considering the application's specific needs, the system context, as well as the characteristics of the adopted battery cells. From these considerations, a list of system requirements can be derived. This section aims to list aspects that usually need to be considered and to provide short explanations for each of them. Implementation considerations and examples are given in later parts of the paper.  
In general, the following BMS component and functional requirements will usually be of relevance:  
* Acquisition of temperature,
* Acquisition of voltage (individual cells, stack or whole pack and Direct Current (DC)-link voltage as well),
* Acquisition of current (stack or whole pack),  
As well as data communication between:  
* BMS master module and BMS slave modules (that acquire above mentioned values),
* Battery pack and surrounding application (e.g., car, aeroplane).  
And finally the requirements on:  
* Robustness against Electromagnetic interference (EMI).
* Contactors and
* Redundancy of the system in terms of functional safety.
* Galvanic isolation of functional systems.
* Balancing
* Power consumption, size, weight, etc.
# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique
### The Application of Remote Sensing and Machine Learning Technique into Weed Detection
#### 5.6.2 Deep Learning (DL)
Nevertheless, based on the previous study, it is not impossible to produce an accurate map that will highly benefit weed management in the paddy field, especially when dealing with herbicide consumption.
# Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV
## Methods
### Locally Stationary Chirp Processes  
A zero mean real valued stochastic process \(X(t)\), \(t\in[T_{0},T_{f}]\subseteq\mathbb{R}\) has covariance \(C(t_{1},t_{2})=\mathbb{E}[X(t_{1})X(t_{2})]\), where \(\mathbb{E}\) denotes the expected value and \(t_{1},t_{2}\) time points in a time interval \([T_{0},T_{f}]\subseteq\mathbb{R}\). A LSCP covariance is defined as  
\[C(t_{1},t_{2})=q\left(\frac{t_{1}+t_{2}}{2}\right)\cdot r\left(t_{1}-t_{2} \right)\cdot f_{I}\left(t_{1},t_{2}\right), \tag{1}\]  
where \(q\) is a non-negative function, also called \"power schedule\", \(r\) a stationary covariance function and \(f_{I}\) a chirp covariance function defined by  
\[f_{I}\left(t_{1},t_{2}\right)=e^{im(t_{1}-t_{2})(\frac{t_{1}+t_{2}}{2}-d)}, \tag{2}\]  
with \(m,d\in\mathbb{R}\). This means that the covariance \(C(t_{1},t_{2})\) is modulated by the chirp \(f_{I}\left(t_{1},t_{2}\right)\), whose frequency varies linearly with a delayed time axis \(\frac{t_{1}+t_{2}}{2}-d\). If \(f_{I}\left(t_{1},t_{2}\right)\equiv 1\), then Equation (1) reduces to a LSP covariance [19].  
To unequivocally identify an LSCP, the required functions and their parameters must be specified, and the resulting function \(C\) must be positive semidefinite. The proposed LSCP model for the task related HRV is identified by  
\[C(t,\tau)=q(t)\cdot r(\tau)\cdot e^{im\tau(t-d)}, \tag{3}\]  
where \(t=\frac{t_{1}+t_{2}}{2}\), \(\tau=t_{1}-t_{2}\) and  
\[q(t) =a\cdot\exp\left(-b\cdot t\right), \tag{4}\] \[r(\tau) =\exp\left(-\frac{c}{8}\cdot\tau^{2}\right), \tag{5}\]  
with \(a,b,c>0\), \(t\in[0,210]\) s. The instantaneous power of a process \(X(t)\), defined as  
\[P(t)=\mathbb{E}[X(t)^{2}]=q(t), \tag{6}\]  
motivates the choice of the function \(q\), see Figure 2. The value of the chirp parameters \(m,d\) and the parameters \(a,b,c\) must be estimated from the data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Women**} & \multicolumn{3}{c}{**Men**} \\  & \multicolumn{3}{c}{**(n = 43)**} & \multicolumn{3}{c}{**(n = 49)**} \\ \cline{2-7}  & **Median** & **Mean** & **sd** & **Median** & **Mean** & **sd** \\ \hline Age & 25 & 28.05 & 7.86 & 27 & 30.84 & 10.42 \\ Weight & 62 & 63.56 & 9.70 & 79 & 79.14 & 10.39 \\ BMI & 21.77 & 21.97 & 2.70 & 23.45 & 24.23 & 3.05 \\ State Anxiety & 33 & 34.30 & 7.02 & 30 & 31.73 & 7.76 \\ Trait Anxiety & 39 & 40.25 & 10.33 & 33 & 35.79 & 9.55 \\ SMBQ & 3.05 & 3.40 & 1.20 & 2.59 & 2.95 & 1.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Median, mean and standard deviation of the variables among the participants according to gender.
# On the Optimal Calculation of the Rice Coding Parameter
## 6 Performance on Different Datasets Using Multi-Parameter Rice Coding
### Cases Where Multi-Parameter Rice Coding Is Beneficial  
First, we calculate the optimal partitioning of every sequence previously described, and checked whether such a solution produces shorter codes than the solution when a single Rice coding parameter is used. In nearly 10% of all batches used for validation in this article, a shorter Rice coding could be found by multi-parameter Rice coding.  
In Figure 10, we can observe the percentage of the total number of batches for each dataset and batch size for which a shorter code can be found, if sequence partitioning is considered. As expected, the datasets interval rain and rain intensity can substantially be better compressed when a large batch size is considered.
# Ultrasound-Based Smart Corrosion Monitoring System for Offshore Wind Turbines
## 3 Corrosion Monitoring System Concept  
Our corrosion monitoring system design concept is focused on a novel and smart low-power and low-cost corrosion monitoring system for offshore wind turbines. The zones of interest are the splash zone and the atmospheric zone. The splash zone is the area that is intermittently exposed to seawater due to the action of tide or waves or both. This intermittence produces the highest level of corrosion. The atmospheric zone is the zone that is permanently exposed to marine air conditions. Therefore, the level of corrosion is also very high. Our approach includes two types of deployment, namely (i) a fixed and (ii) a mobile solution. In the case of the splash zone, the fixed sensors are attached to the structure due to the difficulties to access by a mobile platform. However, the atmospheric zone is covered by the mobile solution based on a drone flying inside the tower. Therefore, the main requirements to be achieved by our corrosion monitoring solution can be highlighted as follows.  
* The monitoring solution shall be operational under the harsh conditions of offshore platforms.
* The corrosion sensor of the mobile and fixed solutions shall perform one measurement every month. However, this rate must be parameterizable with the aim of being increased when necessary.
* The corrosion sensor must be able to measure the ToF related to the thickness loss of the selected steel with a thickness between 40 mm and 70 mm but also with a thickness of 5 mm or 6 mm, which shall be the thickness of the produced samples to characterize the ultrasound solution.
* Be able to operate unattended for long periods of time.
* Both fixed and mobile solutions shall be able to perform corrosion measurements in the field and send those measurements wirelessly.
* The size of the overall final monitoring solution shall be up to 6 \(\times\) 11 cm with a weight of up to 100 g. The sensor head shall be connected to the processing platform through wires.