esa-sceva/satcom-chunk-collection huggingface dataset:
# G-Networks with Adders
## 1 Introduction  
Queueing models with \"product form\" solutions and their efficient computational algorithms [1; 2] are useful in many engineering fields including computer systems and networks [3; 4], machine learning [5; 6; 7], transportation systems [8; 9], job-shop and manufacturing systems [10], and emergency evacuation [11; 12]. G-networks [13; 14; 15] are a significant extension of earlier queueing models [16]. They include \"triggers\" which re-route traffic [17] for load balancing among multiple servers, \"negative customers\", \"batch removal\" [18] of customers, and \"string transitions\" [19] which extend trigger multiple steps of customer movements.  
Starting from the G-network model, this paper discusses a new type of \"customer\" we call an \"Adder\". When a service ends at some node, the departing customer can be transformed into an Adder which replaces the number of customers it finds, at the queue where it arrives, with a random number of customers which depends on two probability distributions: (a) the probability that queue to which it arrives was previously empty, and (b) an externally defined arbitrary probability distribution on the non-negative integers, which depends on where the Adder comes from and which queue it targets. We show that the resulting system obeys a system of non-linear traffic equations, and has a steady-state joint probability distribution for the network with \"product form\" solution.
### Estimating Weather  
Directly leveraging weather information would require a vision system to directly infer weather conditions from the captured data [8; 9]. By treating it as a classification problem, deep learning methods have shown great promise at classifying categories of weather [8; 9; 10]. This shows that a weather signal can be somewhat extracted from single images and categorized into distinct classes. Most weather classification approaches focus on the binary classification of distinct weather conditions (i.e., cloudy, sunny, raining, etc.), and they lack the granularity observed during long-term deployment. To address this, datasets like Rain Fog Snow (RFS) [11] and Multi-class Weather Dataset (MWD) [12] propose treating it as a multi-label classification problem in order to capture the ambiguity between different weather phenomena and transitive weather conditions [11; 12]. When estimating weather conditions from a single image, all regions are not created equal [12; 13]: thus, some methods isolate predetermined regions, such as the sky [9; 14], or leverage region-proposal networks [12; 13] to extract region-specific features.
## 3 Results
### HE Classification Directly Based on Automated Segmentation Volumes  
Using the hematoma volumes from automated ICH segmentation on baseline and follow-up scans, we achieved an ROC area under curve (AUC) of 0.86 to 0.92 for different HE classifications in the internal test (ATACH-2) cohort and 0.82 to 0.87 in external test (Charite) cohort (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Vale (\(n\) = 684)** & **ATACH-2 * (\(n\) = 890)** & **Charite * (\(n\) = 687)** & \(p\) **Value** \\ \hline Male & 358 (54.8\%) & 543 (60.80\%) & 530 (56,36\%) & 0.345 \\ Age [years] & 69.7 \(\pm\) 14.3 & 62.2 \(\pm\) 13.1 & 69.4 \(\pm\) 14.2 & \(<\)0.001 \\ Baseline NIH Stroke Scale & 8 (2 – 17) & 11 (6–16) & 6 (1–12) & \(<\)0.001 \\ Baseline ICH volume [mL] & 18.7 \(\pm\) 20.6 & 12.9 \(\pm\) 12.5 & 30.2 \(\pm\) 30.7 & \(<\)0.001 \\ Follow-up ICH volume [mL] & 22.9 \(\pm\) 25.8 & 15.6 \(\pm\) 16.6 & 31.9 \(\pm\) 34.5 & \(<\)0.001 \\ \(\geq\)3 mL HE & 171 (25\%) & 226 (25.4\%) & 180 (26.2\%) & 0.872 \\ \(\geq\)6 mL HE & 129 (18.9\%) & 148 (16.6\%) & 128 (18,6\%) & 0437 \\ \(\geq\)9 mL HE & 102 (14.9\%) & 110 (12.4\%) & 104 (15.1\%) & 0.201 \\ \(\geq\)12.5 mL HE & 84 (12.3\%) & 69 (7.7\%) & 83 (12.1\%) & 0.006 \\ \hline \multicolumn{4}{c}{CT scan characteristics} \\ In-plane pixel spacing [mm] & [0.46 \(\times\) 0.46] & [0.46 \(\times\) 0.46] & [0.46 \(\times\) 0.46] & \\ Slice thickness [mm] & 4.81 \(\pm\) 0.70 & 5.29 \(\pm\) 1.81 & 4.54 \(\pm\) 0.66 & \(<\)0.001 \\ Min axial image matrix [\(n\)\(\times\)\(n\)] & [472 \(\times\) 472] & [512 \(\times\) 512] & [434 \(\times\) 434] & \\ Max axial matrix [\(n\)\(\times\)\(n\)] & [1024 \(\times\) 1024] & [512 \(\times\) 734] & [512 \(\times\) 671] & \\ Number of slices & 35.9 \(\pm\) 11.6 & 30.9 \(\pm\) 17.6 & 30.26 \(\pm\) 7.8 & \(<\)0.001 \\ \hline \hline \end{tabular}  
* Yale dataset was used for training/cross-validation of hematoma segmentation and HE prediction models; ATACH-2 dataset was used as an internal test to determine thresholds for different sensitivity and specificity predictions; and Charite dataset was used as an external test cohort.  
\end{table}
Table 1: Comparison of patients’ characteristics, hematoma volumes, and CT scan variables in different study cohorts.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Internal Independent Test (ATACH-2, \(n\) = 890)** & \multicolumn{4}{c}{**External Independent Test (Charite, \(n\) = 687)**} \\ \hline  & **AUC** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline \(\geq\)**3 mL** & 0.86 & 0.90 & 0.76 & 0.95 & 0.82 & 0.87 & 0.71 & 0.93 \\ HE & (0.84–0.88) & (0.88–0.92) & (0.70–0.81) & (0.93–0.97) & (0.79–0.85) & (0.84–0.90) & (0.64–0.78) & (0.91–0.95) \\ \(\geq\)**6 mL** & 0.89 & 0.95 & 0
# Optical Element Surface Defect Size Recognition Based on Decision Regression Tree
## 1 Introduction
Since the diffraction effect exists in the MSDI system due to the limit of the aperture to the scattered light, the defect simulation light intensity distribution will be inconsistent with the defect actual gray distribution. This diffraction effect can be described by point spread function (PSF) in theory [19]. Hence, we measure the gray distribution of a step source in our MSDI system and get PSF by taking the derivative of the step source gray distribution. Then, we convolve the simulation light intensity distribution with the PSF as the revision of the simulation light intensity distribution. The mean value of the distance between three groups of feature points, whose intensity is 0.75, 0.5, and 0.25 of the revised simulation light intensity distribution peak value, is taken as the feature parameter. Finally, we use DRT to gain the relationship between the feature parameter of the revised light intensity distribution and the scratch width. To verify the effectiveness of DRT in scratch width recognition, the standard glass plates with the process of electron beam exposure and the ion beam etching are taken as the tested scratch samples, and the scanning electron microscope (SEM) is applied to measure the real width of the scratch. Moreover, the Otsu threshold segmentation method is also employed to recognize the scratch width on the standard glass plate in comparison to DRT, and the experiment result indicates that DRT has higher accuracy than the Otsu method, especially in the situation where the width of the scratch approaches to the diffraction limit of the MSDI system. The remaining part of this paper is organized as follows. The layout of our MSDI system is presented, and the principle of dark-field imaging is illustrated in Section 2. In Section 3, the electromagnetic simulation model of MSDI is proposed, and the principle of the DRT for defect size recognition is described. Section 4 shows simulation and experiment results, and some conclusion is given in Section 5.
## 6 Purpose-Based Encryption
### The Life Cycle of PII in Federated Security Domains  
In the following part, we revisit the scenario where a user [PERSON] from Telekom consumes SPs in Salesforce. Telekom authenticates [PERSON] and issues a _Time Access Token_ (TAT). Salesforce is in charge of the purpose authorisation request and issues a _Purpose Access Token_ (PAT) for SPs in its domain. An SP can decrypt the PII if it possesses both the time and purpose token. This results in the following request flow as in Figure 13. Briefly, step 2, 4, and 5 represent the standard request flow of Attribute-based Access Control [70]. We only extend the request flow in steps 1 and 3.  
_(1) Encryption:_ [PERSON] encrypts his data with the algorithm in Section 6.1. In particular, he uses the public key of Telekom to encrypt the data with the policies \"access time\", \"domain\", and \"country\". He uses the public key of Salesforce to encrypt the data with the policies \"purpose\" and \"ppurpose\". The algorithm outputs one ciphertext for each user attribute and disseminates it to Telekom.  
_(2) Authentication:_ [PERSON] accesses SP1 and is redirected to Telekom for authentication. Telekom authenticates [PERSON] and issues a SAML response (step 1a). Telekom uses the key generation algorithm to issue a TAT. This token consists of three key attributes: \"access time\", \"domain\", and \"country\". The key generation also uses the user identifier and the session identifier (in the SAML response) as a \"linchpin\" for tying all key attributes together. Telekom includes the TAT in a SAML response and returns to the user. In addition, the IdP uses the public key of SP1 to encrypt the TAT so that only SP1 can decrypt it.  
_(3) Authorisation:_ [PERSON] gives the SAML response to access SP1 at Salesforce (step 1b). The PEP validates the SAML for authentication (not shown in the figure) and forwards the SAML to the Policy Decision Point (PDP) for authorisation (step 2). The PDP again submits the SAML to the Salesforce's IdP to exchange for a PAT (step 3). Salesforce uses the key generation algorithm to issue a PAT with two key attributes \"purpose\" and \"ppurpose\". It also uses the user identifier and the session identifier delivered in the SAML as a \"linchpin\" for tying all key attributes together.  
_(4) Decryption:_ Now the PDP has enough key attributes. It combines the TAT with the PAT. If all key attributes satisfy the disclosure policy and the \"linchpin\" is the same, the PDP can decrypt the ciphertext. Otherwise, the decryption fails. After the PDP decrypts the PII, it returns the user attributes to the PEP and to the application in the backend (steps 4 and 5).  
Figure 12: An example of PBE.  
Figure 13: Request flow.
## 5 Conclusions  
In this paper, a WLS-SVM predicting model for the MSSCD in UVAG of optical glass is presented. A focus is on the systematic understanding of the influence of multiple process parameters on the proportion of subsurface crack forms from carefully designed experiments. The following conclusions can be drawn from this study:  
1. The experimental results for UVAG of optical glass show that the subsurface crack forms are dominated by arc-shaped, bifurcated, and straight median cracks. The crack with the maximum propagation depth had the probability of 72.5% (29/40) in this work to be an arc-shaped or bifurcation crack.
2. The relational analysis results demonstrate that the proportions of arc-shaped and bifurcated cracks are most sensitive to the ultrasonic vibration amplitude, for which the intrinsic reason is the impulse on the cracks exerted by the downward vibration of abrasive grains. The proportions of straight median and other cracks are most sensitive to the average diameter of abrasive grains, whereas the other grinding process parameters are relatively noninfluential to the crack proportion.
3. The comparison of prediction and experimental results shows that the maximum error of the developed WLS-SVM prediction model is 10.54%, which is smaller than that of the other two widely used methods (LS-SVM and GPR), while the relative error is 5.59%, which demonstrates its high prediction accuracy.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11167553/s1](https://www.mdpi.com/article/10.3390/app11167553/s1)]([https://www.mdpi.com/article/10.3390/app11167553/s1](https://www.mdpi.com/article/10.3390/app11167553/s1)), Table S1: Experimental grinding and ultrasonic vibration parameters.  
Writing-original draft preparation, L.Z.; writing-review and editing, P.Z.; Conceptualization, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51875145.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Maximum Relative** & **Minimum Relative** & **Average Relative** \\  & **Error** & **Error** & **Error** \\ \hline GPR method & 14.56\% & 0.8\% & 7.61\% \\ LS-SVM method & 21.72\% & 0.7\% & 11.07\% \\ WLS-SVM method & 10.54\% & 1.8\% & 5.59\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Errors between predicted and experimental results.
## 2 Analysis Method
### Balanced Armature Receiver Model  
The BA receiver integrates an electromagnetic system, a mechanical vibration system, and an acoustic system. A three dimensional (3D) cross-sectional view of the BA receiver model, with details provided in the exploded view, is shown in Figure 2. The electromagnetic system has an armature, a voice coil, two permanent magnets (PMs), and a magnet housing. The mechanical vibration system includes a drive pin, the center diaphragm (CDP), and the side diaphragm (SDP). Meanwhile, the acoustic system has a front cover, a back cover, and a spout to release the pressure. The armature is the shared part of the electromagnetic system and the mechanical vibration system, which is the core of the BA receiver and retains balance when no forces are acting upon it.  
The material properties of the BA receiver include the mechanical and electromagnetic properties, but only the mechanical properties are necessary for this article, as shown in Table 1. As the material of armature is permalloy, which is a nickel-iron magnetic alloy that has excellent magnetic properties, such as high magnetic permeability, but its mechanical properties are enough for subsequent analysis,  
Figure 1: Cross-sectional view of the balanced armature (BA) receiver earphone.  
Figure 2: BA receiver structure: (**a**) Three dimensional (3D) cross-sectional view; (**b**) exploded view.  
such as density, Young's modulus, and Poisson's ratio. The item labeled \"the others\" in the table include the drive pin, the spout, and the magnet housing.
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 5 Experiments
### Setting
#### 5.1.3 Evaluation Metrics  
To evaluate the effectiveness of our POI extraction algorithm, we constructed a ground truth dataset using the GPS data collected from volunteers who shared their locations. This dataset was manually annotated with known points of interest, including their locations. If  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Time Threshold & 5 min \\ Noise Reduction Window & 10 \\ Metadata API & Google Places API, OpenStreetMap \\ Unimportant Location & Road (e.g., highways, minor streets) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Data preprocessing parameters for evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Userid** & **N. of Track Points** & **Significant Location** & **Location Category** & **Distance Collect** \\ \hline
001 & 83,491 & 90 & 15 & 2440.21 km \\
002 & 1205 & 12 & 4 & 74.68 km \\
003 & 37,011 & 56 & 33 & 2989.02 km \\
004 & 34,249 & 134 & 32 & 876.31 km \\
005 & 43,292 & 25 & 6 & 1412.42 km \\
006 & 13,298 & 112 & 9 & 1076.82 km \\
007 & 24,324 & 34 & 12 & 1989.23 km \\
008 & 32,424 & 102 & 21 & 489.38 km \\
009 & 42,032 & 56 & 11 & 2231.12 km \\
010 & 3923 & 20 & 7 & 54.28 km \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics for dataset for evaluation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Day Index & 1 = Weekday, 2 = Weekend, 3 = Holiday \\ Time Index & 1 = Morning, 2 = Afternoon, 3 = Night \\ Categorization for Stay Duration & Short, Medium, Long \\ Categorization for Visit Frequency & Low, Medium, High \\ Utility Threshold & \(-0.5\), \(-0.75\), \(-1\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: POI extraction parameters.  
Figure 7: Example of data points on the map.  
an extracted POI matched a true POI, we determined whether the predicted POI agreed with the true POI assignment. The prediction was categorized as a true positive (\(TP_{POI}\)) if it agreed with the ground truth label and as a false positive (\(FP_{POI}\)) otherwise. We classified any selected POI that did not match a true significant POI as a false positive. Additionally, we considered a true POI that was not matched by any selected POI as a false negative (\(FN_{POI}\)).  
With \(TP_{POI}\), \(FP_{POI}\), and \(FN_{POI}\), we commonly calculated the precision (\(P_{POI}\)) and recall (\(R_{POI}\)) scores as follows:  
\[P_{POI}=\frac{TP_{POI}}{TP_{POI}+FP_{POI}},\quad R_{POI}=\frac{TP_{POI}}{TP_{ POI}+FN_{POI}}. \tag{4}\]  
Then, the F1 POI score was obtained by its harmonic mean:  
\[F1_{POI}=\frac{2\times P_{POI}R_{POI}}{P_{POI}+R_{POI}}=\frac{2\times TP_{ POI}}{2\times TP_{POI}+FN_{POI}+FP_{POI}}. \tag{5}\]
## 4 Numerical Illustration
Then, the unconditional distribution of \(k_{i}\) becomes an NBI distribution, with pmf given by \[P(k_{i})=\frac{\Gamma\Bigl{(}k_{i}+\frac{1}{\phi_{i}}\Bigr{)}}{k_{i}!\Gamma \Bigl{(}\frac{1}{\phi_{i}}\Bigr{)}}\Bigl{(}\frac{\phi_{i}\mu_{i}}{1+\phi_{i} \mu_{i}}\Bigr{)}^{k_{i}}\Bigl{(}\frac{1}{1+\phi_{i}\mu_{i}}\Bigr{)}^{\frac{1}{ \phi_{i}}}.\] (30) The mean and the variance of the NBI distribution are given by \[\mathrm{E}(k_{i})=\mu_{i}\] (31) and \[\mathrm{Var}(k_{i})=\mu_{i}+\mu_{i}^{2}\phi_{i}.\] (32)
* The mean and dispersion parameters of the NBI distribution are modelled in terms of covariates \[\mu_{i} = \exp\Bigl{(}\mathbf{x}_{1,i}^{\top}\mathbf{\beta}_{1}\Bigr{)}\text{ and}\] (33) \[\phi_{i} = \exp\Bigl{(}\mathbf{x}_{2,i}^{\top}\mathbf{\beta}_{2}\Bigr{)},\] (34) where \(\mathbf{x}_{1,i}\) and \(\mathbf{x}_{2,i}\) are covariate vectors with dimensions \(p_{1}\times 1\) and \(p_{2}\times 1\), respectively, with \(\mathbf{\beta}_{1}=\bigl{(}\beta_{1,1},\ldots,\beta_{1,p_{1}}\bigr{)}^{\top}\) and \(\mathbf{\beta}_{2}=\bigl{(}\beta_{2,1},\ldots,\beta_{2,p_{2}}\bigr{)}^{\top}\) the corresponding parameter vectors, and where it is assumed that the matrices \(\mathbf{X}_{1}\) and \(\mathbf{X}_{2}\), with rows given by \(\mathbf{x}_{1,i}\) and \(\mathbf{x}_{2,i}\), respectively, are of full rank. * The pmf of the ZIP regression model is given by \[P(k_{i})= \begin{cases}\pi+(1-\pi)exp(-\mu_{i}),&\text{if }k_{i}=0\\ (1-\pi)\frac{e^{-z_{i}y_{i}}(z_{i}y_{i})^{k_{i}}}{k_{i}!},&\text{if }k_{i}=1,2,3 \ldots\end{cases} \] (35) The mean and the variance of the ZIP distribution are given by \[\mathrm{E}(k_{i})=\mu_{i}(1-\pi)\] (36) and \[\mathrm{Var}(k_{i})=\mu_{i}(1-\pi)[1+\mu_{i}\pi],\] (37)where \(\mu_{i}=exp(x_{1,i}^{\top}\mathbf{\beta}_{1})\), and where \(x_{1,i}\) is a covariate vector with dimension \(p_{1}\times 1\), with \(\mathbf{\beta}_{1}=\left(\beta_{1,1},\ldots,\beta_{1,p_{1}}\right)^{\top}\) the corresponding parameter vector, and where it is assumed that the matrix \(\mathbf{X}_{1}\) with rows given by \(x_{1,i}\), respectively, are of full rank (note that \(\pi\) can also be modelled in terms of covariates using the logit link function. However, we refrain from doing this in this paper since this approach did not lead to better fitting performances for the ZIP model for the MTPL data).
## 2 II-36, IL-37, and IL-38: A Complex Group of Pro- and Anti-Inflammatory Cytokines
### Maturation
#### 2.1.2 II-37  
The six exons of the _IL-37_ gene encode five isoforms (IL-37a, IL-37b, IL-37c, IL-37d, and IL-37e), of which IL-37b is the best characterized so far. Similar to other members of the IL-1 family, the N-terminus of IL-37 does not contain a signal peptide but encloses a caspase-1 cleavage site, which only partially mediates the maturation and anti-inflammatory activity of IL-37 [17,18]. However, in the presence of caspase-1 mutations, the IL-37 ability to translocate into the nucleus and form a molecular complex with Smad3 to downregulate the transcription of specific key genes [19] is impaired [17]. Additional in silico-predicted IL-37 cleavage sites were described by [PERSON] and colleagues in 2017 and included Cathepsin K, elastase-2, and matrix metalloproteinase (MMP)-9 cleavage sites [20]. Further studies will help to understand the maturation processes of all the IL-37 isoforms fully.
## 1 Introduction
The optimal twist angle for a tested turbine was found to be 60 degrees, as it provides the best aerodynamic performance [24]. In addition to the shape and twist angle of the blades, the number of rotor blades also has a significant impact on the aerodynamic efficiency of a turbine. Another geometric parameter that has a significant impact on turbine performance is the number of rotor blades. In a computational investigation of an H-shaped [PERSON] wind turbine, it was shown that a turbine with a higher number of blades performs better at low TSR values. However, as the TSR increased, a turbine with fewer blades achieved a higher power factor [25]. Another study found that a three-bladed turbine produced the highest torque, while a four-bladed turbine produced the least destructive vibrations in the gearbox [26]. The aspect ratio, which is the ratio between the radius and height of the rotor, has a significant effect on the turbine efficiency. The results of another study showed that as the aspect ratio decreases, the Reynolds number increases, which in turn leads to an increase in VAWT efficiency [27]. The blade pitch angle, which determines the position of the blade's leading edge and directly affects the attack angle, is considered to be an important design parameter. Based on the results of a previous study, the best performance was achieved with a negative blade pitch angle, and as it increased, the Cp power factor values decreased [28]. Based on the analysis of the above studies, to address the issue of the self-starting and efficiency of the Darrieus turbine, we have proposed an original asymmetric blade profile design [29]. The NACA0021 profile was chosen as a basis, and a part of the same blade of a smaller scale was cut out from it. Figure 1 shows our blade with the distribution of aerodynamic forces acting on the blade. The presented profile is optimized for operation at different rotation-speed-to-wind-velocity (TSR) ratios, which makes it universal for a wide range of wind conditions. The wind turbine blade has an aerodynamic profile with a rounded nose and an extended trailing edge, which increases lift and reduces drag. The upper surface is convex, enhancing the flow, and the lower one reduces induced drag. Smooth transitions of the profile coordinates minimize swirls and turbulence. Thus, the developed blade profile combines optimized aerodynamic characteristics that can contribute to increasing the efficiency of the Darrieus rotor. However, despite the confirmed theoretical advantages, experimental and numerical studies are needed to evaluate the impact of asymmetric blades on the operation of wind turbines. This paper examines the aerodynamic characteristics of a Darrieus rotor with the proposed asymmetric blades in two design configurations--with and without horizontal parallel plates. To obtain reliable data, experimental studies were conducted in a wind tunnel, as well as numerical CFD modeling in ANSYS Fluent.
## 5 The Concept of IoV
### Security Threats and Attacks in IoV Environments
#### 5.6.7 Man-in-the-Middle Attack (MITM)  
In this case, the attacker acts as a middleman, but they impersonate both the sender and the receiver. As such, the attacker intercepts messages from both sides and sends wrong information to unsuspecting nodes. This type of attack can be either active or passive. This is one of the most dangerous types of attack because the attacker can cause massive damage, even with minimal information about the network. This type of attacker often targets roadside unit nodes because they are responsible for providing services by sending messages, updates, and resources to handle all communication requests.