esa-sceva/satcom-chunk-collection huggingface dataset:
# Distribution and Deposition of Cylindrical Nanoparticles in a Turbulent Pipe Flow
## 5 Results and Discussion
### Distribution of Particle Number Concentration and Orientation at Outlet
#### 5.2.3 Effect of the Reynolds Number  
The distributions of the particle volume concentration on the cross-section for different Reynolds numbers are shown in Figure 8 where the concentration distributions become more uniform with increasing the Reynolds number, i.e., the larger the Reynolds number is, the smaller the difference in particle volume concentration between the near-wall region and near-center region is. The turbulent flow is characterized by some parameters among which the most important is the Reynolds number. As the Reynolds number increased, even though the mean velocity profile on the whole cross-section and the turbulent kinetic energy in the near-center region were almost unchanged [16], the turbulent kinetic energy and induced turbulent diffusion (\(\
u_{t}=0.09\,k^{2}/\varepsilon\)) in the region around \(r/R\approx 0.95\) increased obviously [16], resulting in a stronger particle diffusion and making the concentration distributions become more uniform.  
Figure 9 shows the distributions of mean particle orientation in region 1 and 5 at the outlet for different Reynolds numbers. As the Reynolds number decreased, the distributions of mean particle orientation become more non-uniform, i.e., more particles aligned with the flow direction. As we know, the larger the Reynolds number, the smaller the scale of minimum vortex, the wider the distribution of energy spectrum contained in the vortices of different scales, and less the energy contained in the large vortex related to the mean shear rate of the fluid are. Therefore, the shear-induced torque exerted on the particle by the mean shear rate of the fluid was smaller for the case with larger Reynolds number, resulting in not so many particles aligning with the flow direction.
# Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments
## 2 System Description and Implementation Aspects
### Implementation Aspects  
#### 2.2.1 Tag Requirements and Data Codification  
Firstly, tags must fulfill several requirements, in particular, low weight and size, suitable performance on metallic surfaces, a ruggedized package, and sufficient storage capacity for encoding different fields--such as the type of sign, road identifier, and kilometric location. These features will be detailed in Section 4.1.1, which describes the selected commercial equipment.  
The implemented codification for a 96-bit EPC is described in Table 2. This encoding takes as reference the nomenclature of the Spanish roads, which are divided in stretches of 5 Km. By using this division, it is possible to implement an organized and easily upgradeable inventory.  
Note that 6 bits of the 96-bit EPC are left unused, so the proposed codification would enable future extensions, or even migrations to other-countries road networks. Moreover, though this codification is oriented to road inventory applications, a similar frame could be used, previous reconfiguration of the EPC, for example, to control the inventory and the location of street facilities which can be displaced, such as dumpsters, thus opening up new opportunities to develop the paradigm of smart cities [33; 34]. These capabilities for updating, extensions and migrations purposes will be ease by using passive RFID technologies which employ open communication standards and, usually, longer EPC identifiers than the active RFID case, such as described in Section 4.1.1.
## 3 A Case Study for Validating the Proposed Model and Process
### Calculating the Stress Intensity Factor Range and Load Adjustment  
After calculating the J-integral value, it could then be used to calculate the stress intensity factor range. A relation between the J-integral and the stress intensity factor for all three fracture modes was by [PERSON] (1976) for linear elastic solids as shown in Equation (19).  
\[J=\frac{1-\
u^{2}}{E}(K_{I}^{2}+K_{II}^{2})+\frac{1+\
u}{E}(K_{III}^{2}) \tag{19}\]  
where \(K\) denotes the stress intensity factor (SIF) and the subscripts \(I\), \(II\), and \(III\) denote the particular fracture modes. For plane strain conditions \(K_{III}\) is equal to zero and Equation (19) can be simplified to  
\[J=\frac{1-\
u^{2}}{E}(K_{I}^{2}+K_{II}^{2}) \tag{20}\]  
For the CT specimen the loading is mode I dominated with small mode II contributions and the J-integral can be approximated as  
\[J\approx\frac{1-\
u^{2}}{E}(K_{I}^{2}) \tag{21}\]  
To establish the relation between the SIF and the SIF range, the definition of the load ratio is revisited. Previously, \(R\) was defined in Equation (9) as the ratio of minimum and maximum loading. By noting that the SIF is proportional to load for materials with linear response, \(R\) can be redefined as  
\[R=\frac{K_{MIN}}{K_{MAX}} \tag{22}\]  
\(K_{MAX}\) is the mode I contribution of the SIF and is obtained from the FE solution of the J-integral and Equation (21). For a specific load ratio \(R\), the SIF range can be calculated without directly simulating the minimum load condition as done in the study of [PERSON] et al. [PERSON] et al. (2018)  
\[\Delta K_{I}=K_{MAX}-K_{MIN}=K_{MAX}-RK_{MAX}=K_{MAX}(1-R) \tag{23}\]  
In order to maintain a constant \(\Delta K_{I}\) = 20 MPa (m)\({}^{1/2}\), same as in the experiment, the stress intensity factor range is computed according to Equation (24) provided by [PERSON] et al. [PERSON] et al. (2018) and the applied load is adjusted.  
\[\Delta K_{I}\approx\sqrt{\frac{JE}{1-\
u^{2}}}(1-R) \tag{24}\]  
The load is adjusted according to Equation (25). During the first cycle an initial load, \(P_{0}\), is applied to produce \(\Delta K_{I}\) = 20 MPa (m)\({}^{1/2}\). In subsequent steps the initial load is reapplied and the J-integral obtained and used to calculate the SIF range. By taking the product of the initial load \(P_{0}\) and the ratio of SIF ranges at the initial time step and at the \(n\)th time step, the updated load \(P_{n}\) can be calculated.  
\[P_{n}=P_{0}\frac{\Delta K_{0}(P_{0})}{\Delta K_{n}(P_{0})} \tag{25}\]
###### Abstract  
Stereotactic body radiation therapy (SBRT) allows for the high-precision delivery of a large dose of radiation to a small area. SBRT has shown overall survival benefits in a variety of oligometastatic cancers in recent trials. However, prospective data in recurrent or oligometastatic gynecologic cancer are limited. The current literature shows that SBRT in gynecologic oligometastases is safe and effective, resulting in 2-year local control rates exceeding 80%. However, progression outside of the radiated field remains a significant issue despite local control. This review discusses the advantages, limitations, and future directions of SBRT in gynecologic oligometastases including a discussion on the synergistic effects of combination with immunotherapy.  
Sterotactic body radiation therapy; oligometastases; gynecologic cancer; ovarian cancer; cervical cancer; uterine cancer 2023153526  
14 April 202314 April 202314 April 202314 April 202314 July 202314 April 20231  
therapy to oligometastatic sites offers a potential for systemic control; and the number of lesions treated are largely dependent on the safe administration of large doses of radiation.  
The SABR-COMET phase II clinical trial randomized 99 patients with oligometastatic cancer and multiple tumor types (lung, breast, prostate, etc.) in a 1:2 ratio to palliative standard of care treatment versus Stereotactic Ablative Radiotherapy (SABR) to all metastases plus standard of care. Patients must have had a controlled primary tumor and 1 to 5 metastases amenable to SABR. SABR improved median overall survival from 28 months to 41 months and exhibited an overall favorable toxicity profile [1]. However, only 2 out of the 99 total patients were reported to have a gynecologic primary. Several studies were published over the last decade evaluating the role of SBRT in recurrent or oligometastatic gynecologic malignancies. To our knowledge, no prospective randomized study specifically evaluated the impact of SBRT in recurrent or oligometastatic gynecologic cancer (ROMGC). Most studies were retrospective, single institutional, and did not differentiate between primary malignancies. This review focuses on reporting the role of SBRT in ROMGC, including the advantages and drawbacks from this technique, by examining the current literature to guide radiation oncologists in patient-centered decision making.
# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
## 4 Sensing Algorithms Generation: Target Detection and Localization
### Distance and Angular Direction Estimation Algorithm  
In this section, a localization algorithm based on the estimation of the sensing channel is presented. To extract the embedded information of \(\hat{\mathrm{H}}_{\mathrm{eff}}(k)\), we can first acquire the taps of the channel using an Inverse Discrete Fourier Transform (IDFT), which allows us to identify the temporal sample where the target is located. This can be expressed as  
\[\hat{\mathrm{h}}_{\mathrm{eff}}(n)=\mathrm{IDFT}\{\hat{\mathrm{H}}_{\mathrm{ eff}}(n)\}(t), \tag{8}\]  
where \(\hat{\mathrm{h}}_{\mathrm{eff}}\in\mathbb{C}^{M\times M}\) represents the impulse response of the channel \(\hat{\mathrm{H}}_{\mathrm{eff}}(k)\) at the \(n\)-th temporal sample. Given the expression in (8), we can evaluate the channel response when the BS emits power towards a specific Angle of Departure (AoD). This analytical process involves the utilization of steering vectors, \(\mathrm{a}(\theta)\), which denotes the antenna array's configuration, where \(\theta\) is the AoD. Then, the estimated energy of \(\hat{\mathrm{h}}_{\mathrm{eff}}(n)\) in a certain \(\theta\) and at a certain temporal sample \(n\) is  
\[P(\theta,n)=||\hat{\mathrm{h}}_{\mathrm{eff}}(n)\mathrm{a}(\theta)||^{2}= \mathrm{a}^{*}(\theta)\hat{\mathrm{h}}_{\mathrm{eff}}^{*}(n)\hat{\mathrm{h}}_{ \mathrm{eff}}(n)\mathbf{a  
where \(c\) is the speed of light and \(f_{\text{s}}\) is the sampling frequency. The next subsection will use the estimated angle \(\hat{\theta}\).  
A summary of the above steps is provided in Algorithm 1 to benefit the reader's understanding.  
```
Input:\(\text{H}_{\text{pre}}(k)\), \(\text{s}\), \(\text{y}\), \(L\) Output:\(\hat{\theta}\), \(\hat{d}\), \(\hat{\text{h}}_{\text{eff}}(n)\) \(1\). for\(k=1:K\)do (a) \(\hat{\text{H}}_{\text{eff}}(k)=\frac{1}{L}\sum_{l=1}^{L}\left(\text{y}_{l,k}- \text{H}_{\text{pre}}(k)\text{s}_{l,k}\right)\text{s}_{l,k}^{*}\) (b) Compute \(\hat{\text{h}}_{\text{eff}}(n)=\text{IDFT}\{\hat{\text{H}}_{\text{eff}}(k)\}(n)\) endfor
```  
**Algorithm 1** Distance and angle estimation algorithm
# Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review
## 3 Experimental Techniques
### On-Line Chatter Classification, Detection, and Monitoring
[PERSON] [100] proposed a neural network analysis for detecting chatter vibration in turning. It was demonstrated that this approach was more efficient (fast) and reliable than the frequency domain method.
# Stochastic Analysis of a Priority Standby System under Preventive Maintenance
[PERSON] and [PERSON] [15] studied the reliability and availability analysis of a repairable retrial system with mixed standbys, a single repair facility subjected to preventive maintenance or active breakdown. They obtained the steady-state availability by solving the steady-state probability in matrix form. [PERSON] et al. [16] developed a reliability model for a non-identical cold standby system for the evaluation of system reliability, mean time to system failure, steady-state availability, the busy period of server, expected number of repairs, expected number of visits by the server, and the profit function of the system by considering all time-random variables to be Weibull distributed. [PERSON] and [PERSON] [17] discussed a two-unit cold standby system by considering the concepts of degradation, inspection, preventive maintenance (PM), and priority. They considered the unit to work at a reduced capacity after its repair and thus called it a degraded unit. Furthermore, they discussed various reliability characteristics, such as mean time to system failure (MTSF), availability, busy period of the repairman, expected number of visits by the repairman, and profits of the system. [PERSON] et al. [18] addressed a derivation procedure for the reliability function of the two-component priority standby redundant system based on the maximum entropy principle. The procedure based on the maximum entropy principle in information theory is widely known as an elegant approach to deriving a probability density function by utilizing the given information about the stochastic property of random variables. [PERSON] [19] analyzed the cost-benefit analysis of a cold standby repairable system under varying environmental conditions: normal and abnormal. [PERSON] and [PERSON] [20] investigated the stochastic analysis of an identical two-unit standby system model with the concept of preventive maintenance of the operative unit after some significant time, which is considered to be a random variable with some probability distribution. In this paper, we propose a system of two dissimilar units: one prioritizes operation, while the other can be kept as a standby unit. In addition, we assume that there are two types of PM for the priority unit, namely, the costlier type with probability \(p\) and the cheaper type with probability \((1-p)\). All of the failures, repairs, and PM times are arbitrary distributed, except for the repair time of the ordinary unit, which is exponentially distributed. In addition, we assume that the priority unit has normal, partial failure or total failure modes. Next, we use the regenerative point technique to investigate the performance of the proposed system in the steady-state case in terms of some reliability measures: (i) the mean time to system failure (MTSF); (ii) availability; (iii) busy periods with repair due to repair and PM; (iv) the profit gain of the system. Finally, in order to show the effect of PM and system parameters on the reliability measures of the proposed system, we provide some numerical illustrations and graphs.
# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 4 Experiments and Results
### Results of Simulated Data
There are noticeable differences between crown localization results, with locations by the highest point being higher. This is due to the fact that simulated tree models are perfectly upright, so the highest point is where the true tree top is. The variance of the RMSE of tree locations (RMSE_xy) is rather small, but the RMSE of tree height (RMSE_h), when located by the fitted crown center, is greater compared to that by the highest point, whereas the RMSEs of crown spreads (RMSE_1 and RMSE_1) are smaller. It means those extra matched trees have precise tree height estimations, but less precise crown spreads. The compared region growing method shows the best precision and recall (match), hence the highest overall F1-score. The differences in location and height are of a similar magnitude, but the crown spread is much worse, as it only approximates the average crown diameter, which is different from the longest crown spread.
## 6 Sensitive Analysis  
Sensitivity analysis plays a crucial role in ML modelling by providing insights into the behavior and performance of a model. A sensitivity analysis is required to determine the effect of each input variable on the output, and hence the corresponding importance of one variable over the other. Here are some key reasons why sensitivity analysis is important:  
* Understanding variable importance: Sensitivity analysis helps in identifying the input variables that have the most significant influence on the output. By quantifying the sensitivity of the model to different inputs, it becomes possible to prioritize variables based on their importance. This information is valuable for feature selection, where less-relevant variables can be eliminated to improve efficiency and interpretability.
* Model robustness assessment: Sensitivity analysis helps to evaluate the robustness of the ML model by measuring its sensitivity to changes in input values. By systematically varying the inputs within specified ranges, analysts can determine the stability of the model's predictions. This assessment is particularly useful in scenarios where the accuracy and reliability of the model are critical, such as in financial or medical applications.
* Data quality assessment: Sensitivity analysis can highlight the importance of data quality. If certain input variables have a disproportionately large impact on the model, it underscores the need for accurate and reliable data for those variables.
* Model validation: It can be a crucial part of model validation, providing a means to check whether the model behaves as expected and aligns with domain knowledge. Deviations in sensitivity analysis results from what is expected can signal issues with the model.
# On the Need for a General REST-Security Framework
## 5 Related Work Analysis
### HTTP Scram  
Salted challenge response HTTP authentication mechanism (HTTP SCRAM) [37] is another experimental RFC. The authentication process is structured in two steps. In the first one, the clientsends a request containing an hash-based message authentication code (HMAC) signature algorithm name, the username and a self-generated nonce. The server replies with the client-generated nonce concatenated with a server-generated nonce, a salt and a sequence number. Based on this information, the client performs the second authentication step. It computes an HMAC signature composed of the password, the salt and the sequence number. To access the HTTP SCRAM protected resource, the calculated signature value is embedded in the authorization header including the client-generated nonce concatenated with the server-generated nonce and an HTTP SCRAM-specific description. Once the server receives this request, it verifies the signature and the concatenated nonces. If both values pass the verification process, the server returns the desired response to the client. The response contains a signature value as well which is created by means of the client's password, the salt and the sequence number, so that the client can proof the authenticity of the responding server.  
Unlike HOBA, API-keys as well as HTTP basic and digest, HTTP SCRAM provides the option to authenticate requests as well as responses. However, this approach does not guarantee the integrity of the whole message, as the signature does not cover the body and most of the headers.
## 4 Prediction Process
### Parameters of LSTMs and SVRs  
Keras framework [44] was used to establish LSTM models, while TensorFlow was used as the back end, and Python language was adopted to write the codes. As a time series forecasting experiment, hyperparameters of LSTM models were tuned with the Grid Search (GS) method [44]. Its process can be described as follows: plausible values for each parameter are combined, and then all the combination values are used in the training process of the model. The performance metric of each combination is typically measured by validation on the validation dataset. When all the parameter combinations have been tried, an optimal parameter combination with the best performances is returned automatically.  
For LSTM models establishment, batch-size, and numbers of layers of LSTM models were determined with the GS method successively. In GS processing, the gird range of batch-size is [1; 20] with a grid step of 1, the gird range of layers is [2; 8] with a grid step of 1. The optimal epochs were obtained with an early-stopping set during the algorithmic process, and the patience of early-stopping was set as 20 which means the algorithmic process will stop when the result has no improvement within 20 steps.  
The SVR models were also operated in the Python language environment. The GS method was used to search for the optimal parameters of the SVR model. As stated in Section 2.4, the RBF was adopted as kernel function for the SVR model. The values of gamma (the kernel coefficient for RBF) and the values of \(C\) (the penalty parameter) were determined with GS. In GS processing, the search range of gamma is [0.0075, 1] with a grid step of 0.0075, the search range of C is [1; 20] with a grid step of 1. The best parameters are shown in Table 3.  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**Candidate Factors** & **Description** & **ZK2-1** & **ZK2-3** \\ \hline \(f_{1}\) & the precipitation during the current month & 0.65 & 0.66 \\ \hline \(f_{2}\) & the precipitation during the past two months & 0.67 & 0.63 \\ \hline \(f_{3}\) & the average reservoir level & 0.63 & 0.60 \\ \hline \(f_{4}\) & the change of the reservoir level during the current month & 0.72 & 0.66 \\ \hline \(f_{5}\) & the change of the reservoir level during the past two months & 0.71 & 0.69 \\ \hline \(f_{6}\) & the change of the displacement that during the last month & 0.68 & 0.76 \\ \hline \(f_{7}\) & the change of the displacement that during the last two months & 0.71 & 0.75 \\ \hline \(f_{8}\) & the change of the displacement
## 3 Simulation Results
### Effect of Polytropic Index on Transient Cavitation Threshold  
The polytropic index controls the thermal dynamic behavior of the gas in the bubble. The polytropic index \(k=1.4\) corresponds to the adiabatic process of the bubble, while \(k=1.0\) corresponds to the isothermal process. The relevant research shows that the adiabatic process underestimates the maximum radius of the bubble, while the isothermal process overestimates the maximum radius of the bubble [26; 45]. Figure 7 takes the liver as an example to analyze the influence of the polytropic index on the transient cavitation threshold under different frequency combinations by changing the value of the polytropic index. Figure 7 shows the relationship between the transient cavitation threshold and the initial bubble radius under the combinations of single-frequency (2.6 MHz), dual-frequency (2.6 + 2.0 MHz), and triple-frequency (2.6 + 2.0 + 1.4 MHz).  
It can be found from Figure 7 that under the same irradiation conditions, the change of polytropic index has little effect on the transient cavitation threshold of multifrequency combinations in the medium, whether it is a single-frequency ultrasound, dual-frequency combination, or triple-frequency combination.  
Figure 7: The influence of polytropic index on transient cavitation threshold under three different frequency combinations: (a) 2.6 MHz; (b) 2.6 + 2.0 MHz; (c) 2.6 + 2.0 + 1.4 MHz.