esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Anifrolumab Mechanism of Action and Clinical Trials
### Clinical Trial Data
#### 3.2.2 Phase III Trials--TULIP-1 and TULIP-2  
In the phase III trial TULIP-1, ANI 150 mg or 300 mg were compared to PBO. TULIP-1 randomized 457 patients; the primary endpoint systemic lupus erythematosus responder index-4 (SRI-4) was assessed at 52 weeks. There were no statistically significant differences in patients receiving 300 mg of ANI compared to PBO regarding this outcome measure (36% vs. 40%, respectively). Since the primary endpoint was not met, no statistical testing was performed as per the prespecified study analysis plan. However, the British Isles Lupus Assessment Group-based composite lupus assessment (BICLA), another robust outcome measure used for SLE, was numerically different (37% vs. 27% responders for ANI 300 mg vs. PBO) [2].  
Therefore, the TULIP-2 clinical trial used the BICLA as the primary outcome measure [1]. TULIP-2 randomized 365 patients to ANI 300 mg or PBO. At 52 weeks, there was a statistically significant difference in the BICLA response in favor of ANI 300 mg (47.8% vs. 31.5%). These results finally led to ANI's approval by the Federal Drug Administration  
Figure 4: Timeline of major clinical trials, primary outcome measures, and authorization of airflow-umab [1; 2; 3; 4; 5; 6; 7; 8]. BICLA, British Isles Lupus Assessment Group-based composite lupus assessment; EMA, European Medicines Agency; FDA, Federal Drug Agency; SC, subcutaneous; SRI-4, systemic lupus erythematosus responder index-4; UPCR, urine protein–creatinine ratio; w, weeks. Numbers in circles denote the number of participants. Created with bioreactor.com.  
(FDA) in 2021 and the European Medicines Agency (EMA) in early 2022. The different results of TULIP-1 and TULIP-2 regarding their primary efficacy measures have been discussed widely [18; 19; 20]. Table 1 gives an overview of the main published clinical trials and the primary outcomes.
# Mechanical Analysis of DS in Horizontal Directional Drilling
## 6 Conclusions  
1. Taking the directional crossing engineering, for example, the dynamic model of the DS and the dynamic characteristic of the DS system are simulated and calculated to obtain the real-time variation law of the deformation and drag and torque of the DS, and successfully guide the yangba mountain crossing project.  
Figure 15: The contact force distribution and torque distribution of DS. (a) The first stage reaming. (b) The second stage reaming. (c) The third stage reaming.  
2. In the operation of the guide hole, when the entrance thrust is too large (greater than 30T), the DS occurred spiral buckling, which greatly increased the contact force between the DS and the hole wall. The DS friction increased sharply, and the drill pressure transfer is difficult. The results show that the smaller inlet thrust, or the preselected larger DS could prevent the spiral buckling of the DS.  
3. Through calculation, it founds that the third-stage reaming operation and the pilot hole operation with the thrust of 20T, when the DS without buckling or buckling deformation is relatively small, the contact between the DS and the borehole wall is the most serious at the bending section (the contact force at the bending section was more than 1/3 higher than the contact force at the horizontal section). At this point, the drag and torque of DS were the fastest. When the buckling deformation of the DS is relatively small, the friction and drag reduction of the DS at the bend should be ensured first.  
4. The relationship between the buckling deformation and the contact force of the DS is studied by the calculation of the DS dynamics:  
(1) After sinusoidal buckling occurred to the DS, the contact force between the DS and the borehole wall increased obviously and the contact position and contact force size distribution is no longer uniform. This phenomenon was more obvious when the DS occurred spiral buckling.  
(2) Severe spiral buckling led to self-locking of DS, then the transfer efficiency of WOB is reduced. Continue to increase the thrust force, the WOB would not increase.  
Conceptualization, Y.S.; Data curation, Y.S.; Methodology, H.T.; Validation, H.T.; Writing--review & editing, H.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Spectral/hp Element Methods
### High-Order Splitting Scheme  
Consider the incompressible N-S equations for viscous Newtonian fluids governed by the following:  
\[\frac{\partial\mathbf{u}}{\partial t}+\mathbf{u}\cdot\
abla\mathbf{u}=-\
abla p+\mu\ \
abla^{2}\mathbf{u}+\mathbf{f}, \tag{4}\]  
\[\
abla\cdot\mathbf{u}=0, \tag{5}\]  
where \(\mathbf{u}\) denotes the velocity vector, \(p\) is the pressure, \(\mu\) is the kinematic viscosity of fluid, and \(\mathbf{f}\) is the body force.  
From [27; 28], the high-order splitting scheme can be divided into two steps:  
1. Firstly, we derived the weak pressure approximation as follows: \[\int_{\Omega}\
abla q\cdot\
abla p^{n+1}=\int_{\Omega}q\
abla\cdot\Big{(}- \frac{\hat{\mathbf{u}}}{\Delta t}+N(\mathbf{u})^{\alpha+1}\Big{)}-\int_{\Omega\Omega }q\Big{(}\frac{\partial\mathbf{u}^{n+1}}{\partial t}+N(\mathbf{u})^{\alpha+1}+\mu( \
abla\times\
abla\times\mathbf{u})^{\alpha+1}\Big{)}\cdot\mathbf{u}.\] (6)  
This equation can be written as an equivalent strong form of the pressure Poisson equation:  
\[\
abla^{2}p^{n+1}=\
abla\cdot\left(\frac{\hat{\mathbf{u}}}{\Delta t}-N^{\ast,n+1 }\right) \tag{7}\]with consistent Neumann boundary conditions \[\frac{\partial p^{n+1}}{\partial n}=-\bigg{(}\frac{\partial\mathbf{u}^{n+1}}{\partial t }+\mu(\
abla\times\
abla\times\mathbf{u})^{*,n+1}+N^{*,n+1}\bigg{)}\cdot\mathbf{n}.\] (8)
2. Secondly, we discretize the Equation (4) at time step \(n+1\) and solve the velocity \(\mathbf{u}^{n+1}\) by using the pressure at step \(n+1\) from step 1. Now the approximation of the time derivative is: \[\frac{\partial\mathbf{u}^{n+1}}{\partial t}\approx\frac{\gamma_{0}\mathbf{u}^{n+1}- \hat{\mathbf{u}}}{\Delta t}.\] (9) Then, the Helmholtz problem is derived as follows: \[\bigg{(}\Delta-\frac{\gamma_{0}}{\mu\Delta t}\bigg{)}\mathbf{u}^{n+1}=-\bigg{(} \frac{\gamma_{0}}{\mu\Delta t}\bigg{)}\hat{\mathbf{u}}+\frac{1}{\mu}\
abla p^{n+1}.\] (10)  
The Helmholtz problem can be solved. A detailed discussion can be found in [25; 27; 28].  
In this paper, the governing equations are discretized by using the spectral/hp element methods. The spatial discretization is performed by using orthogonal polynomials, for example, Legendre or Chebyshev polynomials. The time discretization is performed by using the high-order splitting scheme. A detailed analysis of the high-order splitting scheme can refer to [27; 28].
## 3 Materials and Methods
### Position Algorithm Calculation
#### 3.1.3 Step 3: Error Correction  
The random error in the experiment can be converged by the wavelet theory. The present article uses the wavelet threshold denoising method, sets the wavelet coefficients outside the range \(\delta\) and \(\delta\) as noise, compresses the noise, and re-obtains the value. The thresholding can delete small amplitude noises or undesired signal data and obtain the required data through inverse wavelet transform. In the threshold denoising process, the \(n\)-layer wavelet decomposition is processed in the first step, and the decomposed scale coefficients and wavelet coefficients form a coefficient vector, \(W\); \(\delta\) is used to transform vector \(W\) to obtain a new vector, \(W_{\delta}\); and wavelet reconstruction is executed on vector \(W_{\delta}\) to obtain the denoising signal.  
The most typical wavelet in the wavelet denoising method is the Haar wavelet. Set \(\varphi(x)\) and \(\psi(x)\) as the Haar scale function and the wavelet function; then, scale space \(V_{j}\) and wavelet function \(W_{j}\) are respectively generated by Formulas (14) and (15).  
\[V_{j}=\left\{2^{\frac{j}{2}}\varphi\Big{(}2^{j}x-k\Big{)}\right\}k\in Z,j=N-1, \cdots,N-M \tag{14}\]  
\[W_{j}=\left\{2^{\frac{j}{2}}\psi\Big{(}2^{j}x-k\Big{)}\right\}k\in Z,j=N-1, \cdots,N-M \tag{15}\]  
The wavelet decomposition refers to the stepwise decomposition from \(f_{N}\) to \(f_{N-M}\) and \(g_{i}\) is as in Formula (16).  
\[f_{n}=g_{N-1}+g_{N-2}+\cdots+g_{n-m}+f_{N-M} \tag{16}\]  
where \(g_{i}\in\left\{V_{j}\right\};f_{N-M}\in\left\{W_{j}\right\}\); and \(j\in Z\).  
Traditional threshold functions mainly include a hard threshold function and a soft threshold function. The selection rule of threshold adopts unbiased likelihood estimation. The soft threshold method is easy to handle, the result obtained is smoother, and the denoising effect is better. However, the hard threshold method can better retain the edge information and be closer to the actual situation. The two methods have their characteristics. In contrast, the soft threshold method has a better denoising effect. Therefore, the soft threshold method is selected in the present article to denoise the data. The soft threshold is shown in Formula (17).  
\[\omega_{\delta}=\left\{\begin{array}{c}\text{sgn}(W)(|W|-\delta),|W|\geq \delta\\ 0,|W|<\delta\end{array}\right. \tag{17}\]  
Correcting the positioning error, the error distance comparison chart can be obtained, as shown in the figure below.  
It can be seen from Figure 1 that the distance error of the positioning data corrected by wavelet denoising has changed significantly. For the coordinate points with an error of more than 100 m, the error distance has obvious convergence, and for the coordinate points with an error distance of less than 100 m, the fluctuation of the error change is reduced and the change tends to become smooth. After wavelet denoising processing, the random error of the distance between nodes effectively converges according to the threshold standard, indicating that the error correction method is effective.
## 4 Conclusions  
In this study, the radiation shielding ability of six alloys coded as MAR-302, MAR-247, Inconel-625, Inconel-718, Nimocast-75, and WI-52 were evaluated. The studied alloys had a different chemical composition and their densities varied in the range of 8.22-9.21 g cm\({}^{-3}\). The gamma radiation shielding features were simulated using the MCNP-5 code in the energy range between 0.015 and 15 MeV. The XCOM program was used to validate the simulated MAC values for the selected alloys. Additionally, the SRIM program was used to calculate the stopping power and projected range of protons and alpha particles. The current study recorded the following outcomes:  
1. The highest MAC was achieved for thee eMAR-302 and MAR-247 alloys and varied between 0.035-72.938 cm\({}^{2}\cdot\)g\({}^{-1}\) and 0.035-71.98 cm\({}^{2}\cdot\)g\({}^{-1}\), respectively, while the lowest MAC was achieved for Inconel-718 and Nimocast-75 and varied in the range of 0.033-59.25 cm\({}^{2}\cdot\)g\({}^{-1}\) and 0.032-59.30 cm\({}^{2}\cdot\)g\({}^{-1}\), respectively.
2. The obtained results between the simulated MAC using the eMCNP-5 code and theoretical MAC using the XCOM database were proven to greatly agree with each other.
3. The thickest HVL was achieved for the alloy encoded as Inconel-625 and varied between 0.001 and 2.604 cm, while MAR-247 had the thinnest HVL, which varied between 0.001 and 2.159 cm.
4. The highest values for the \(Z_{\rm eff}\) and \(Z_{\rm eq}\) were achieved for the alloys encoded as MAR-302 and MAR-247.
5. The lowest fast neutron effective removal cross-section was obtained for MAR-302 (\(\sum_{\rm R}=0.01841\) cm\({}^{2}\cdot\)g\({}^{-1}\)), while Inconel-718 (\(\sum_{\rm R}=0.01945\) cm\({}^{2}\cdot\)g\({}^{-1}\)) and Nimocost-75 (\(\sum_{\rm R}=0.01940\) cm\({}^{2}\cdot\)g\({}^{-1}\)) had the greatest values.  
Figure 10: Variation of the mass stopping power (MSP) and the projected range versus the kinetic energy.for the projected particles: (**a**) Protons, (**b**) Alpha particles versus.  
According to the aforementioned results, the studied alloys (especially MAR-302 and MAR-247) were proven to be highly effective against charged-particles and fast neutrons. Thus, MAR-302 and MAR-247 are desirable shielding materials for gamma-ray applications, despite their low fast neutron effective removal cross-section.  
Conceptualization, M.I.S.; Methodology, M.I.S.; Software, E.L.; Validation, E.I. and K.M.K.; Formal analysis, F.Q.M.; Investigation, M.I.S.; Resources, M.I.S.; Data curation, K.A.M. and M.I.S.; Writing--original draft preparation, M.I.S.; Writing--review and editing, M.U.K.; Visualization, E.L.; Supervision, K.M.K.; Project administration, M.R.I.F.; Funding acquisition, M.R.I.F. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Research Universiti Grant, Universiti Kebangsaan Malaysia, Gearan Universiti Penyelidikan (GUP), code: 2018-134.  
The authors declare no conflict of interest.
## 2 IoTMADLab and Methodology
### IoT Network Reference Architecture
#### 2.3.2 Connectivity Layer Technologies  
After analysing the current IoT connectivity technologies, the IoTMADLab has selected those that are (or may be in the future) compatible with the uCIFI data model and that, due to their characteristics, cover all the requirements of the possible use cases from smart city services: Wi-SUN, NB-IoT, and LoRaWAN. For the first phase of the implementation of SUSs, Wi-SUN has been chosen as its compatibility with the data model has already been validated.  
The Wi-SUN (Wireless Smart Ubiquitous Network) Alliance defines the Wi-SUN FAN (Field Area Network) specification, which has resulted in the IEEE 2857-2021 standard, a mesh network protocol, which works over 6 LoWPAN and is based on several IETF, IEEE, and ANSI/TIA standards. 6 LoWPAN (IPv6 over Low Power Wireless Personal Area Network) is a communication standard designed to enable connectivity of low-power and resource-limited devices over wireless personal area networks (WPAN) based on IEEE 802.15.4. As it is based on Internet Protocol Version 6 (IPv6), it allows assigning unique IP addresses to each connected device, facilitating its identification and communication in the network.  
Hence, communications at the lowest layer of the stack are carried out by means of 6 LoWPAN-based technology up to the IoT gateway, where the next step until the server-side platform coordinator is usually conducted via standard Ethernet connectivity, falling back to Wi-Fi or LTE when the lack of wiring becomes a factor. Regardless, this piece within the full-stack communications path is always tunnelled inside a VPN connection so that the actual connectivity infrastructure can be transparent in the deployment process while at the same time ensuring security.  
Future IoTMADLab deployments will also be targeting NB-IoT and LoRaWAN at the lowest layer of the stack. This means that the network might even drop the gateways or coordinators when NB-IoT is used. For LoRaWAN use cases, a proper gateway is usually required, so that the stack and logical behaviour of the network shapes that of Wi-SUN's use cases. Nonetheless, that one hop is avoided in the case of NB-IoT, where network gateways become nil, at least functionally and in terms of provisioning.  
Wi-SUN implements a meshed network topology, which enables direct communication between devices (expanding coverage) and uses a hierarchical, self-organizing routing approach, where nodes act as routers to relay packets between devices. On the security side, encryption and authentication mechanisms (128-bit AES) are used to protect the communication between the devices and the network. The following communication parameters within the Wi-SUN FAN specification have been defined, ensuring compatibility with uCIFI reference mesh implementation:  
* Frequency bands: EU1 (863-870 MHz) and EU2 (870-876 MHz).
* PHY mode of operation: 2a (100 kbps).
## 4 Evaluation
### Discussion
#### 4.3.4 Practical Considerations for FL  
The proposed framework ensures privacy, efficiency, and security in FL by addressing critical aspects of encryption, computation, and communication. HE enables secure computations on encrypted gradients, preserving data confidentiality during local training on edge devices. This ensures that sensitive information is never exposed, even in a distributed environment. While HE introduces computational overhead, the use of a Tesla T4 GPU with optimized FP32 and FP16 performance mitigates this impact, maintaining a balance between privacy and training efficiency.  
To optimize communication, federated averaging with weighted contributions reduces the frequency and size of encrypted model updates transmitted across the network. Blockchain technology complements this process by securely logging aggregated updates, ensuring tamper-proof validation without increasing communication overhead. Together, these mechanisms demonstrate how the proposed framework achieves robust, privacy-preserving training suitable for dynamic IIoT environments.
###### Abstract
Finally, a simple equation was developed that can predict the deflections caused by deep excavations. [PERSON] et al. [13] used a PLAXIS-based finite element method to reveal the crucial mechanical behaviors of the retaining structure and soil layers as a result of different types of anchorage failures. According to the simulation results, the responses to anchorage failures were analyzed, and the safety coefficient for the retaining structure was presented. The analysis for this numerical work indicates that dangerous failure positions appear at the top of the pile wall or in the region near the excavation bottom. From the above research, the study of pile support systems and the cast-in-place pile and anchor cable support system for deep foundation pits are very abundant. Scholars focus on the pile and soil interaction mechanism, pile and anchor interaction mechanism and the influence of support parameters on the stability of foundation pits. For the ultra-deep foundation pit, whether the cast-in-place piles and anchor cable support system can meet the deformation control requirements remains to be discussed. Based on a foundation pit with depth of 35 m of Changsha international financial center, this article analyzes the shortcomings of the pile-anchor-beam (PAB) support system and then proposes a new pile-anchor-ribbed-beam (PARB) support system. This study establishes a numerical simulation model to calculate the control effects of the two support systems. Finally, the influence of pile embedded depth, ribbed beam size and waist beam size on the support effect of the PARB support system is analyzed. The research results can provide reference for the design of a pile and anchor retaining structure for deep foundation pits.
## 3 DDoS Attack Detection Methods
### DDoS Attack Detection Studies Using Anomaly-Based Methods
#### 3.3.3 Statistical-Based Detection Methods
The use of feature-feature score (FFSc) for DDoS attack detection was suggested by [PERSON] et al. [110]. In this method, the behaviour of network traffic was examined using three fundamental network traffic parameters: packet rate, entropy of source IPs, and changes in source IPs. Next, a similarity value is calculated for each network traffic sample using the FFSc. The FFSc is contrasted against a pre-defined threshold, and, if it is above the threshold, an attack alarm is raised. The viability of the approach was verified using CAIDA and DARPA datasets. It was noticed that the pre-defined threshold greatly affects the detection accuracy. The accuracy of the method substantially declines as the detection threshold increases. Thus, the choice of the detection threshold is a major concern. Continuous rand probability score (CRPS) has also been used in recent years to distinguish between legitimate and attack traffic. The CRPS is primarily employed to assess the correctness of a statistical forecasting methodology [111]. It is presently utilized for anomaly detection since CRPS can compare a whole distribution with an observation [112; 113; 114]. To use the CRPS for DDoS attack detection, the CRPS is generated for every traffic measurement. Each incoming traffic network measurement is then contrasted with the traffic distribution under no attack. It is assumed that, in an attack-free network, the traffic distribution is Gaussian [112] with mean \(\mu\) and variance \(\sigma^{2}\). The CRPS is computed using (23) [112; 113]:  
\[CRPS\Big{(}N(\mu,\sigma^{2}),x\Big{)}=\sigma\bigg{[}\frac{x-\sigma}{\sigma} \bigg{(}2\Phi\bigg{(}\frac{x-\sigma}{\sigma}\bigg{)}-1\bigg{)}+2\varphi\bigg{(} \frac{x-\sigma}{\sigma}\bigg{)}-\frac{1}{\sqrt{\pi}}\bigg{]} \tag{23}\]  
where \(\Phi\) and \(\varphi\) are the Gaussian probability and cumulative density functions, respectively. The monitored traffic is considered normal when the CPRS exhibits values that are very small and close to zero. Higher CRPS values, however, point to the existence of malicious traffic in the network traffic being monitored. With these metrics, it is feasible to know which traffic is legitimate or an attack. The CRPS results are subjected to an exponential smoothing approach by [PERSON] et al. [112] to set a decision threshold and increase the existence of attack traffic. The proposed approach performed well when demonstrated on ICMPv6 and DARPA datasets, with a 100% detection rate. The authors argued that the proposed attack detection method is for a single timescale and may be inappropriate to identify malicious activities at different scales. Motivated by [112], [PERSON] et al. [114] used a similar procedure to detect attacks in fog-enabled IoT, with better detection accuracy when validated using the DARPA'99 dataset.
###### Abstract  
This study evaluated Maltodextrin (MD), Gum Arabic (GA), and Carboxymethylcellulose (CMC) in different ratios as coating materials to encapsulate citrus pomace phenolic compounds. Citrus encapsulates were obtained by ultrasound-assisted extraction followed by the freeze-drying process and were characterized regarding the microencapsulation efficiency, physical, and chemical properties. Carrier material choice reflected a significant effect on encapsulation efficiency, phenolic compounds retention, and reconstitution properties of encapsulated extract. The encapsulation efficiency of prepared encapsulates ranked from 50.909% to 84.000%, and it was strongly dependent upon CMC addition. A wide range of reconstitution parameters (water absorption index-WAI and water solubility index-WSI) suggested possible release mechanism modifications. HPLC analysis revealed the presence of three main phenolic compounds, namely Hesperidin, Naringin, and Rutin. A wall material mixture of MD, GA, and CMC in the same proportions was optimal for freeze-drying. This combination resulted in encapsulates with a low moisture content (1.936 \(\pm\) 0.012%) and a low water activity (0.110 \(\pm\) 0.001), indicating prolonged stability. Based on the obtained results, freeze-drying as an encapsulation technique should be considered as a promising solution to recover compounds from industry byproducts and protect them from environmental and gastrointestinal circumstances.  
citrus byproducts; juice processing; encapsulation; phenolic compounds; freeze-drying +
Footnote †: journal: Applied sciences  
Article
## 3 Parallel Programming and Implementations of GRICS
### Communication Complexity Analysis  
In order to understand the performance of the MPI, we suggest a process communication analysis. First, each process receives its share of the data for different motion states. The size of data communicated here depends on the number of motion states \(N_{t}\) and the size of the data \(N_{\rho}\). The initial distribution of \(x\) (line 4, Algorithm 2) to all processes requires sending \(N_{\rho}\times P\) elements. Hence, the communication complexity of the data distribution is \(\mathcal{O}(N_{\rho}P)\) per process. Then, after computing partial results, each process will send its local \(y\) (line 9 in Algorithm 2) back to the root process. The communication complexity per process for