esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In sub-surface drilling rigs, one key critical crisis is unwanted influx into the borehole as a result of increasing the influx rate while drilling deeper into a high-pressure gas formation. Although established risk assessments in drilling rigs provide a high degree of protection, uncertainty arises due to the behavior of the formation being drilled into, which may cause crucial situations at the rig. To overcome such uncertainties, real-time sensor measurements are used to predict, and thus prevent, such crises. In addition, new understandings of the effective events were derived from raw data. In order to avoid the computational overhead of input feature analysis that hinders time-critical prediction, EventTracker sensitivity analysis, an incremental method that can support dimensionality reduction, was applied to real-world data from 1600 features per each of the 4 wells as input and 6 time series per each of the 4 wells as output. The resulting significant input series were then introduced to two classification methods: Random Forest Classifier and Neural Networks. Performance of the EventTracker method was understood correlated with a conventional manual method that incorporated expert knowledge. More importantly, the outcome of a Neural Network Classifier was improved by reducing the number of inputs according to the results of the EventTracker feature selection. Most important of all, the generation of results of the EventTracker method took fractions of milliseconds that left plenty of time before the next bunch of data samples.  
drilling disaster; feature selection; forward selection; sensitivity analysis; random forest classifier; neural networks +
Footnote †: journal: Sensors  
2023  
23
# A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform
## 3 Materials and Methods
### Model Optimizer  
Optimization methods are essential to machine and deep learning model training for enhancing the model's predictions. Various techniques have been created to tackle specific optimization problems, including handling sparse gradients or quickening convergence [64]. In this study, we utilized different optimizers to ascertain the most effective one. SGD, Adam, Adagrad, Adamax, and RMSProp were the five optimizers employed.  
One of the most straightforward yet most powerful optimization methods is SGD. It uses a small portion of training data to update the model's weights instead of the entire dataset, speeding up computation significantly.  
Figure 5: The architecture of the proposed model.  
By scaling each parameter inversely proportional to the square root of the total of all its previous squared values, Adagrad modifies the learning rates of all parameters. It works exceptionally well with sparse data.  
RMSprop uses a moving average of squared gradients to solve the Adagrad problem of declining learning rates. This technique dynamically modifies the learning rate for each parameter, increasing it for steps with small gradients to expedite convergence and decreasing it for steps with significant gradients to avoid divergence.  
By modifying the learning rate according to a moving average of the gradients and their squares, Adam combines the benefits of Adagrad and RMSprop. Because of its ability to traverse the optimization landscape efficiently, Adam is one of the most often used optimizers for deep learning model training.  
An Adam variant based on the infinite norm is called Adamax. Despite being less popular than Adam, Adamax can help in some situations when its normalizing technique has benefits.  
The particulars of the training job and the type of data can influence the choice of the optimizer, as each optimizer has advantages and uses [65]. For example, Adagrad or RMSprop may be favored for jobs with sparse data, although Adam is frequently used due to its broad usefulness across various situations.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 2 Theoretical Background of TD-NIRS
### Time-Resolved (TR) Pathlength  
Time-Resolved pathlength, expressed as \(l(t,\rho)\), is defined as the pathlength of the trajectory that light takes from the impulse source position at time zero to the position where light exists at time \(t\) on the way to a detector with the SD distance of \(\rho\), and is equal to the TR mean TOF, \(<\)\(t\)\(>\)\((t,\rho)\), multiplied by the speed of light, \(c\). The TR mean TOF, \(<\)\(t\)\(>\)\((t,\rho)\), is calculated for the measured TOF distribution, expressed by \(F(t,\rho)\), as the following,  
\[l(t,\rho)=c<t>(t,\rho)=\frac{c\int_{0}^{t}trF(t,\rho)dt}{\int_{0}^{t}F(t,\rho) dt}. \tag{22}\]  
If the medium is multi-layered, the TR partial pathlength of the \(i\)-th layer is defined as,  
\[l_{i}(t,\rho)=c<t_{i}>(t,\rho)=\frac{c\int_{0}^{t}t_{i}F(t_{i}{{}^{\prime}}, \rho)dt_{i}{{}^{\prime}}}{\int_{0}^{t}F(t_{i}{{}^{\prime}},\rho)dt_{i}{{}^{ \prime}}}, \tag{23}\]  
where \(t_{i}\) is time when the light is propagating inside the \(i\)-th layer. For CW light, the (partial) pathlengths are obtained by evolving the upper limit of time \(t\) to infinity, \(\infty\).  
In the \(i\)-th layer of a multi-layered medium or in a homogeneous medium, the optical properties are assumed to be constant. Then \(F(t,\rho)\) can be generally expressed by \(f(t_{i},\rho)\)exp\((-\mu_{ai}ct)\) as seen from the TR reflectance of Equation (15), and Equation (23) is modified as follows,  
\[l_{i}(t,\rho)=c<t_{i}>(t,\rho)=\frac{c\int_{0}^{t}t_{i}f(t_{i}{{}^{\prime}}, \rho)\exp(-\mu_{ai}ct_{i})dt_{i}{{}^{\prime}}}{\int_{0}^{t}f(t_{i}{{}^{\prime} },\rho)\exp(-\mu_{ai}ct_{i})dt_{i}{{}^{\prime}}}=-\frac{1}{F(t,\rho)}\frac{ \partial F(t,\rho)}{\partial\mu_{ai}}=-\frac{\partial\ln[F(t,\rho)]}{\partial \mu_{ai}} \tag{24}\]  
This equation holds not only for the DE but also for the RTE [32] (p.37), and is very useful for estimating total and partial pathlengths in various media.  
Differential pathlength factor (DPF) is often referred to as, which is defined as the ratio of the CW pathlength, \(l=c\)\(<\)\(t\)\(>\), to the SD distance, \(\rho\), i.e., \(DPF=l/\rho\), and is approximately expressed as \(DPF(\rho)=[\mu_{\textit{eff}}/(2\mu_{a})][1+1/(\rho_{\textit{eff}})]^{-1}\) for your reference [31].
# A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance
## 4 Method  
### System Model  
Face de-identification in the context of image utility maintenance is complicated work. In the process of pure privacy information removal, the possible loss of image utility is ignored, so the service quality will also suffer as a result. To solve this problem, we present the QM-VAE framework, a unique generative model of image utility maintenance, and establish a loss function including the loss of service quality to guide the model's training. Figure 2 depicts the general structure of the de-identification module and QM-VAE. Firstly, we applied four protection methods (blindfold, cartoon, Laplace, and mosaic) for face images to construct de-identified datasets. Following that, we input these datasets into QM-VAE, a generative model guided by service quality evaluation. During the training phase, we calculated whether the utility of the output images in the service scenario was consistent with the original data and took this calculation result as a part of the loss function. Backpropagation plays a significant role in updating the output continuously in order to maintain service quality.
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 4 Statistical Analysis of Channel Propagation Characteristics
### Time-Related Characteristics
#### 4.1.3 RMS Delay Spread

Table 5: RMS Delay spread at 3.5 GHz band from the literature. supported by the observation that in all the three locations with a high number of samples, which is location 1 for Op1 and location 1 and 6 for Op2, a normal distribution is the best approximation, as expected. The probability density function (pdf) of the RMS delay spread values in Figure 10 to show the actual data points of the RMS delay spread values.
## 2 Attenuation and Scattering Characteristics of MMW Signals under Rainfall Conditions
Conversely, for larger raindrop diameters, the amplitude at 0 rad is significantly higher than at other angles, suggesting that the scattering energy of MMW signals from large raindrops is primarily concentrated in forward scattering, with only a small portion scattered backward.
## 4 Discussion
[39] employed MRI-based TA for the quantitative evaluation of the mandibular condyle in juvenile idiopathic arthritis, demonstrating the technique's utility in assessing bone changes in inflammatory conditions. Furthermore, [PERSON] et al. [40] extended the application of TA to cone beam computed tomography (CBCT) images of the mandibular condyle, correlating texture features with gender and age. These studies collectively underscore the growing importance of TA in TMJ research and its potential to enhance diagnostic accuracy across various imaging modalities. Our work contributes to this emerging field by focusing specifically on TMJ degeneration, thereby expanding the application of TA in TMJ disorders and reinforcing its value as a quantitative tool in radiological assessment. Despite the promising results, this study has several limitations that need to be addressed. Firstly, the relatively small sample size limits the robustness and generalizability of our findings; a larger cohort would provide more comprehensive data. Secondly, the retrospective study design may introduce selection bias. Thirdly, while experienced radiologists performed image analysis, there is a potential annotation bias due to the inherent subjectivity in interpreting imaging findings, which could have impacted our results. This limitation underscores the need for future studies to implement measures for quantifying inter-rater reliability. Additionally, the study has primarily focused on imaging findings, and clinical correlation with symptoms, functional outcomes, and patient-reported measures is crucial to validate the clinical use of TA in the evaluation of TMJ degeneration. Future research should address these limitations to further strengthen the applicability of texture analysis in assessing TMJ degeneration.
# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 6 Conclusions and Perspectives  
In this study, we proposed a method for:  
* Sizing the PV plant required to properly charge a certain number of EVs, given a targeted Mean Power price;
* Estimating the Mean Power price, given a PV plant size and the number of EVs to be charged;
* Estimating the number of chargeable EVs for a particular PV installation and charging price.  
We have applied this method to a car park located in a research center in southern France. The main input of this study is a massive empirical dataset collected over more than 6 years with 350 EV users and 80 charging points. To generate the EV charging power profiles, simulations were conducted with different charging strategies based on historical real data. This allowed us to obtain simulated EV profiles for the individual power demand and for different sizes of the PV plant. The effect of implementing rule-based charging strategies (Mean Power and Solar Smart Charging) has been proven to be significantly beneficial as compared to the simple Plug and Charge mode. In one of the showcases, the PV peak power required for 1000 vehicles to attain a charging cost of 265 \(\upcelsius\)/MWh for Smart Charging, Mean Power and Plug and Charge strategies are 1.28, 2.14 and 3.42 MWh, respectively.  
The main advantage of our methodology is its modularity, i.e., each key parameter (PV production, EV charging demand, business models and EV charging strategies) may be analyzed independently from each other. Thus, when the method is fully applied once on a given case, some computations may be re-used for other \"similar\" cases. In some cases,
## 4 Discussion
### Practical Implications  
The findings of this study have several practical implications for sports scientists, coaches, and clinicians. The use of non-invasive tools such as the MyotonPRO to assess the viscoelastic properties--particularly of the rectus femoris--can serve as a strategic pre-assessment measure to determine an athlete's readiness for exhaustive exercise. By identifying baseline tissue characteristics that correlate with performance metrics like VO2 max, practitioners may tailor warm-up protocols and training regimens to enhance neuromuscular efficiency and reduce the risk of overuse injuries. Furthermore, regular monitoring of muscle and tendon stiffness could inform the design of recovery strategies, ensuring that athletes maintain optimal tissue properties for peak performance. Ultimately, integrating viscoelastic assessments into routine performance evaluations could provide actionable feedback, allowing for timely adjustments to training loads and preventive measures against potential injuries.
# Design of Parabolic Off-Axis Reflector Optical System for Large Aperture Single Star Simulators
## 4 Parabolic Off-Axis Reflective Optical System Design  
### Optical System Design Results  
The design of the optical system is optimized using Zemax software, and the structure of the optimized parabolic off-axis reflective optical system is shown in Figure 3.
## 7 Conclusions and Future Scope  
In this research, we propose a priority-based resource allocation for 5G C-RAN with multi-traffic slices categorized based on the slice's traffic priority and QoS. It includes two queues for lower-priority traffic, eMBB and mMTC, to increase their chances of completing their services. Hence, this will increase the utilization of C-RAN resources without degrading the QoS of the highest priority class. An analytical model using CMTC with multidimensional states is developed to derive the system performance measures. The analytical results of the proposed system are conducted and validated with simulation-based results. In order to investigate and compare our proposed system, it was studied using varied methodologies. In Methodology I, the system operates without using queues for lower-priority slice types, similar to the previous related models, and is used as a reference methodology. In methodology II, we use two queues for both eMBB and mMTC without allowing the interrupted mMTC to rejoin its queue for a possible service reattempt. Finally, Methodology III is similar to Methodology II in that it allows the interrupted mMTC to rejoin its queue for a possible service reconnect.  
Based on the conducted results, Methodology II and Methodology III show differences in behavior when an mMTC is interrupted. With Methodology II, when there are few requests of type uRLLC and many requests of type eMBB and mMTC, the model is very efficient in terms of eMBB and mMTC SCR and forced probabilities. Methodology III allows the interrupted mMTC to rejoin its queue and hence reduces its forced termination probability, while Methodology II leads to its increase. Therefore, Methodology III will work well when we have high arrival rates of uRLLC, while Methodology II is better when we have low loads of uRLLC and high loads of mMTC. Nevertheless, Methodology III is very active in the sense that uRLLC is very active.  
In the future, a mixing of these methodologies could be very effective if they are made hybrid by adding multiple constraints and utilizing multi-objective functions that may enhance the effectiveness of the future schemes proposed.  
The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, for funding this research (IFKSURC-1-7103).  
Not applicable.  
The author declares no conflict of interest.
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 3 Problem Statement  
### Residual Doppler Shift  
In LEO satellite communications, the problem of residual Doppler shift compensation must be addressed. In 3 GPP, residual Doppler shift values after pre-compensation are tabulated. The parameters required for this study are given in Table 1, and we try to compensate for the residual Doppler shift value of 1.05 ppm [21]. Currently, the maximum Doppler shift an NB-IoT receiver can compensate for is around 950 Hz [22; 23]. In this section, we formulate the residual Doppler shift compensation problem of NB-IoT NTN by calculating the Doppler shift compensation range through the formula.  
The range of the Doppler shift compensation depends on the frequency offset, which is calculated as follows [13]:  
\[\hat{e}=\frac{N_{FFT}\times\hat{\theta}}{2\pi\times L}, \tag{7}\]  
where \(N_{FFT}\) is the fast Fourier transform (FFT) size (128) and \(\hat{\theta}\) is the phase difference between channel estimation symbols. \(L\) is the number of time samples between channel estimation symbols. Here, the channel estimation symbol is expressed as a _DMRS_ symbol in the NB-IoT. \(L\) is calculated as follows [13]:  
\[L=\sum_{i=1}^{n}\left(L_{cp}(i)+N_{FFT}\right), \tag{8}\]  
where \(n\) is the number of symbols between the _DMRS_ symbols and \(L_{cp}(i)\) is the number of cyclic prefix (CP) time samples of the \(i\)-th symbol. CP is a guard interval that removes interference between previous symbols [37]. In the NB-IoT, there are 10 CP time samples in the first symbol and 9 in the rest. As a result, \(L\) for the NB-IoT is 960-time samples.  
The NB-IoT uplink modulation technique is quadrature phase shift keying (QPSK). Therefore, the maximum phase difference between _DMRS_ symbols is \(\pi\). Using Equation (7), \(\hat{e}\) is 1/15. The maximum Doppler shift for which the NB-IoT receiver can compensate is calculated as follows [13]:  
\[f_{d,max}^{(2)}=SCS\times\hat{e}, \tag{9}\]  
where _SCS_ is 15 kHz. The value of \(f_{d,max}^{(2)}\) is 1 kHz based on Equation (9). This value is the theoretical maximum possible Doppler shift compensation. Considering this, 3 GPP uses  
Figure 8: Doppler shift between the reference position (latitude 36.35080\({}^{\circ}\) N, longitude 127.30122\({}^{\circ}\) E) and a satellite receiver over a simulation time based on the actual satellite orbit data on 22 February 2022.  
Equation (4) to say that compensation is possible up to about 950 Hz when \(v\) is 500 km/h. The residual Doppler shift to be compensated for is about 2100 Hz using Equation (6). As a result, current NB-IoT receivers cannot compensate for the residual Doppler shift of 2100 Hz. Therefore, additional solutions are needed to compensate for the residual Doppler shift.
# An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells
## 4 Design and Acceleration of Instruction Set
### Efficiency and Performance Comparison
16:Interp dst [G11-G14:X0-X3], src1[G7-G10:X0-X4] src0[G6:X0,X1];//subcarriers parallelism
17:Interp dst [G11-G14:X4-X7], src1[G7-G10:X4-X8] src0[G6:X0,X1];}
```  
**Algorithm 4** Assembly instruction code for Wiener interpolation in frequency domain. The instruction set proposed in this paper performs well in LS estimation and the Wiener interpolation algorithm in the frequency domain, because one pilot subcarrier needs to be interpolated between every two pilot subcarriers in the frequency domain, and the inverse and interpolation are calculated on the basis of four pilot subcarriers, which makes the data arrangement more complex and gives more opportunities for acceleration. Because of the accelerated instructions in Table 3, compared with the existing general vector processing instruction set, the instruction improvement of the accelerated instruction set proposed in this paper in the LS estimation module is 50%, and that of the Wiener interpolation module in the frequency domain is 37.5%. For each RE, Wiener filter interpolation requires 3/2 more instructions in the frequency domain and 4/3 more instructions in the time domain than linear interpolation, but each gives a BER optimization of 2 db, which is worth the effort. Second-order interpolation not only has the maximum number of instructions, but also shows no improvement in BER performance, so it is not used in this study.
## 4 Case Study
### Tolerance Analysis Modeling  
The third step of the methodology is exemplified by the analysis of the proposed Functional Condition that is depicted in Figure 13. To analyze this Functional Condition, a chain is built using elements from the TTRS_based representation of the PunchTool and the DieTool. In this particular case, the six MGREs (M1, M2, M3... ) are the nodes (Node1, Node2, None3... ), and the four GTs between MGREs are the links (Link1, Link2, Link3, Link4), as shown in Figure 20a. Figure 20b depicts the chain elements and their dependencies with the elements of the TTRS-based representation. Moreover, one additional inter-part constraint (ConfigCond) is specifically added to define the assembly configuration, characterized by the contact between surfaces Node3 and Node4. Finally, the chain is closed by the Functional Condition (Figure 20c).  
The BDD of Figure 21 represents the structure of the CoinageSystemAnalysis Block, which includes an AnalysisFC_1 Block for the analysis of the proposed Functional Condition. This AnalysisFC_1 Block is composed of a <<ChainConstruction>> Block (Chain_1) and a <<AnalysisSpecification>> Block (AnalysisSpecif_1).  
Figure 19: IBD to represent the TTRS-based internal structure of DieTool Block.  
Figure 20: Graphical representation of the tolerance analysis developed: (**a**) elements involved in the analysis, (**b**) additional constraint and Functional Condition definition, (**c**) Functional Chain to be analyzed.  
On the other hand, the AnalysisSpecif_1 Block includes (a) the previous Chain_1 Block (shared relationship), (b) the necessary <<AnalysisParameter>> properties (InputPar_Link1, for example) to define the input analysis Parameters, and (c) the ParAssignment_Chain1 ConstraintBlock to assign input Parameters to Properties of the TTRS_Links. This assignment is represented in the PAR diagram in Figure 23.  
The previously described model, defined using SysML4 TA, supports the definition of all the necessary elements for the analysis of the identified Functional Condition, including the detailed definition of all the geometries involved in the Functional Chain and the mathematical relations that relate them. Thus, from this SysML model, model transformations can be automated to obtain the simulation model in a textual language (such as Modelica)  
Figure 21: BDD of the general tolerance analysis model structure (“1.”” is a “one to many” multiplicity).  
Figure 22: IBD of the Chain_1 Block.  
that allows the resolution of equations system. In these automatic transformations, the SysML profiles also play an important role in facilitating the mapping between languages, but these issues are outside the scope of this work and are topics for future research.