esa-sceva/satcom-chunk-collection huggingface dataset:
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 4 State of the Art Detection and Mitigation Strategies
### Rate-Limiting Strategies  
Rate-limiting approaches restrict the rate of incoming interest packets deemed malicious to protect router resources. The simplest form is to throttle an interface or sender if it issues too many unsatisfied interests. Routers may monitor the Interest Satisfaction Ratio (ISR) per interface (the fraction of interests that successfully return data), and if the ISR drops below a threshold, the router slows or stops forwarding from that interface [37]. Other schemes apply rate limits on a name prefix basis, detecting a prefix under attack and throttling all interests for that content prefix. Additionally, a router may limit an interface to only certain name prefixes if that combination is deemed malicious. Rate limiting is straightforward to implement and can immediately reduce load on the Pending Interest Table (PIT) and downstream links. It requires only local monitoring and simple threshold-based decisions, making it lightweight.
## 3 Validation of MANTiS-32 Sensing Performance
### Integrated Measurement Performance Evaluation of the MANTiS-6050 Sensor and MANTiS-32 System
#### 3.4.2 FE-Based Modal Characterization of the Cantilever Beam for MANTiS-32 Performance Verification  
To evaluate the sensing performance of the MANTiS-32 wireless measurement module, an FE analysis was conducted to characterize the dynamic behavior of the cantilever beam. Modal analysis was carried out using SIEMENS NX integrated with the NASTRAN solver, utilizing CQUAD4 quadrilateral shell elements, as illustrated in Figure 13. The beam model was discretized into 180 elements and 217 nodes, with eight nodes strategically selected to correspond exactly to the physical sensor locations on the test specimen. This spatial alignment facilitated a direct one-to-one comparison between the experimental measurements and the numerical simulation results, thereby enabling a rigorous validation of the system's dynamic response accuracy.  
The cantilever beam was modeled with its left end fully constrained in the x-y plane, representing the fixed support, while the right end remained free, allowing unrestricted vertical vibrations along the \(z\)-axis. Given the lightweight nature of the beam, the influence of the relatively heavier MANTiS-605 sensors was non-negligible. To accurately replicate the physical testing conditions, lumped masses of 4 g corresponding to the mass of each sensor were applied at eight discrete locations (S1 through S8), as indicated in Figure 13. Incorporating these mass effects, the finite element modal analysis yielded a first natural  
Figure 12: Test structure specifications and sensor configuration for MANTiS-32.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & Steel & **Unit Weight** & 72.59 kN/m\({}^{3}\) \\ \hline
**Length** & 770 mm & **Modulus of Elasticity** & 170 GPa \\ \hline
**Width** & 60 mm & **Thickness** & 5 mm \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specification of cantilever beam.  
frequency of 8.445 Hz and a second natural frequency of 53.024 Hz, providing a reliable dynamic baseline for comparison with the experimental measurements.
# Design of Parabolic Off-Axis Reflector Optical System for Large Aperture Single Star Simulators
## 3 Initial Structural Parameter Calculation and Design Optimization Process
### Calculation of System Structure Parameters
\begin{array}{l}S_{1}=\left[\frac{\alpha(\beta-1)^{2}(\beta+1)}{4}- \frac{\alpha(\beta+1)^{3}}{4}e_{2}{}^{2}\right]-\frac{\beta^{3}}{4}\big{(}1- e_{1}{}^{2}\big{)}\\ S_{2}=\frac{1-\alpha}{\alpha}\left[\frac{\alpha(\beta+1)^{3}}{4\beta}e_{2}^{2}- \frac{\alpha(\beta-1)^{2}(\beta+1)}{4\beta}e_{2}^{2}\right]-\frac{1}{2}\end{array} \right. \tag{7}\]  
Due to the co-axial system, it is necessary to satisfy the condition of the spherical aberration \(S_{1}\) and the wisp aberration \(S_{2}\). This requires \(S_{1}=0\) and \(S_{2}=0\) in the reflection optical aberration to find the primary mirror face type coefficient \(e_{1}{}^{2}\) and secondary mirror face type coefficient \(e_{2}{}^{2}\), as shown in Equation (8). \[\left\{ \begin{array}{l}e_{1}^{2}=1+\frac{2\alpha}{(1-\alpha)\beta^{2}}\\ e_{2}^{2}=\frac{\frac{2\beta}{1-\alpha}+(1+\beta)(1-\beta)^{2}}{(1+\beta)^{3}} \end{array} \right. \tag{8}\]  
According to the structural parameters of the co-axial system can be calculated the distance \(d\) between the primary and secondary mirrors, known as primary mirror through the light aperture \(D_{1}\), can be derived from the approximate range of values of the axial quantity \(S_{\rm x}\), as shown in Equation (9). \[\left\{ \begin{array}{l}d=f_{1}{}^{\prime}-l_{2}=f_{1}{}^{\prime}(1-\alpha) \\ S_{x}\geq\frac{D_{1}(R_{1}-d)}{2d}\end{array} \right. \tag{9}\]The primary mirror of this optical system is a parabolic reflector, and the secondary mirror is a planar reflector. Combining the design index of the optical system and the above formula to solve the initial structural parameters of the optical system, we get the initial structural parameters of the optical system: \(R_{1}\) = 10,000 mm, \(R_{2}\) = 0, \(e_{1}{}^{2}\) = 1, \(e_{2}{}^{2}\) = 0, \(d\) = \(-\)3200 mm, and initially select the system off-axis amount \(S_{\rm x}\) = 300 mm.
## 2 Review of the Multiple Cracks Coalescence Criteria in Codes  
In this study, to propose a coalescence criterion for adjacent multiple surface cracks on the same plane, the coalescence criteria of various codes were investigated. The results of analyzing codes such as ASME Section XI, API 579, BS 7910, and British Energy R6 are summarized in Table 1. The investigation revealed that, although various codes initially proposed different coalescence criteria, continuous development has led to the suggestion of the same critical distance (\(S_{m}\)) for cases where the crack depth is less than or equal to the crack length. However, for cases where the crack depth exceeds the crack length, only BS 7910 and R6 provide separate coalescence criteria. Figure 1 illustrates the concept of coalescence for multiple cracks. In Figure 1, \(a_{1}\) and \(a_{2}\) represent the depths of each crack, while \(c_{1}\) and \(c_{1}\) represent half of the crack lengths. The distance between the two adjacent cracks is denoted as \(S\). Additionally, \(a^{\prime}\) and \(c^{\prime}\) represent the depth and half of the crack length, respectively, when the two surface cracks are considered as a single semi-elliptical crack according to the coalescence methodologies presented in the codes. If \(S\) is less than or equal to \(S_{m}\) (\(S\leq S_{m}\)), the two adjacent surface cracks are coalesced into a single semi-elliptical surface crack, where the crack depth is defined by Equation (1) and the crack length by Equation (2), as presented in Table 1.  
\[a^{\prime}=\max(a_{1},a_{2}) \tag{1}\]  
\[2c^{\prime}=2(c_{1}+c_{2})+S \tag{2}\]  
Figure 1: Geometry of dual adjacent coplanar surface cracks.  
The depth of the coalesced single semi-elliptical crack is defined as the depth of the deeper of the two surface cracks, while the length is defined as the sum of the lengths of the two surface cracks and the distance between them.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Control Models
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Architecture** & **Name** & **Northbound IF** & **Developer** & **Found Year** & **Open-Source** & **Consistency** & **Scalability** & **Prog. Lang.** \\ \hline \multirow{7}{*}{Logically and physically centralized control} & NOX [273] & Ad Hoc-API & Nicra networks & 2008 & Yes & Strong & Low & C++ \\ \cline{2-8}  & NOX-MT [312] & Ad Hoc-API & Nicra networks & 2012 & Yes & Strong & Low & C++ \\ \cline{2-8}  & POX [337] & Ad Hoc-API & Nicra networks & 2013 & Yes & Strong & Low & Python \\ \cline{2-8}  & Beacon [313] & Ad Hoc-API & Stanford University & 2013 & Yes & Strong & Low & Java \\ \cline{2-8}  & Floodlight [278] & RESTful-API & Big-switch networks & 2012 & Yes & Strong & Low & Java \\ \cline{2-8}  & SF-Foodlight [314] & RESTful-API & Big-switch networks & 2013 & Yes & Strong & Low & Java \\ \cline{2-8}  & Trena [215] & Ad Hoc-API & NEC corporation & 2012 & Yes & Strong & Low & C, Ruby \\ \cline{2-8}  & Ryu [36] & Ad Hoc-API & NIT labs & 2013 & Yes & Strong & Low & Python \\ \cline{2-8}  & Meridian [317] & Extensble-API & ONF & 2013 & Yes & Strong & Low & Java \\ \cline{2-8}  & Rosemary [275] & Ad Hoc-API & ON Lab & 2014 & Yes & Strong & Low & C \\ \cline{2-8}  & Masetto [318] & Ad Hoc-API & Rice University & 2011 & Yes & Strong & Low & Java \\ \cline{2-8}  & ParaFlow [274] & Abstract-API & Behiang University & 2017 & Yes & Strong & Low & C++ \\ \cline{2-8}  & StaRLight [319] & RESTful-API & University of Lisbon & 2014 & No & Strong & High & Java \\ \cline{2-8}  & Hyperplane [269] & N/S & University of Toronto & 2010 & No & Weak & High & C++ \\ \cline{2-8}  & ONOS [28] & Intent, REST API & Open Networking lab & 2014 & Yes & Weak, Strong & High & Java \\ \cline{2-8}  & OpenDaylight [277] & Intent, REST API & Linux foundation & 2013 & Yes & Weak, Strong & High & Java \\ \cline{2-8}  & Onix [300] & NVP NBAPI & Nicra Networks & 2010 & Yes & Weak, Strong & High & Python, C \\ \cline{2-8}  & Ravana [320] & N/A & Princeton University & 2015 & No & Strong & Low & Python \\ \cline{2-8}  & Logically centralized, physically distributed distributed distributed distributed distributed control control & Kandoo [308] & Java RPC & University of Toronto & 2012 & Yes & Strong & High & C, C++, Python \\ \cline{2-8}  & Orion [322] & N/A & Tsinghua University & 2014 & Yes & Weak & High & Java \\ \cline{2-8}  & Bat [234] & RESTful-API & Google & 2013 & No & Strong & High & Python, C \\ \cline{2-8}  & Expresso [325] & RESTful-API & Google & 2017 & No & Strong & High & Python, C \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of details of different controller frameworks.
## 5 Experiment
### Experimental Results  
To prove the effectiveness of the identification method put forward in this article, the experimental study was carried out. The experimental process is that: firstly, starting the system, and the gas temperature and fuel flow-rate of the HHHTS are controlled at a steady state value. Then, changing the value of fuel flow-rate to realize the value change of gas temperature, and the value of fuel flow-rate and gas temperature are collected in this process, so a set of input and output data of the system are obtained. Finally, an identification model of the gas temperature can be obtained by using the method designed in this paper. In addition, in the experiment, in order to reduce the measurement error, we chose the K-type thermocouple that conforms to the national Class I accuracy standard, and its error was \(\pm\) 0.4%. The identification model of the gas temperature can be expressed by Equation (29).  
\[G_{3}(s)=\frac{5368}{3.19s+8.56}e^{-4s} \tag{29}\]  
It can be seen from Equation (29) that there was a big error between the mathematical model of gas temperature identified by sample data and the mathematical model  
Figure 6: Physical picture of the HHHTS.  
Figure 7: Experimental equipment.  
established by first principle modeling method. The error of \(K_{p}\) was 4.99%, the error of \(T_{p}\) was 18.41%, the error of \(a\) was 29.14%, and \(\tau\) was within the intended range. This indicates that it is difficult to obtain a precise model of the HHHTS by using first principle modeling method.  
To prove the accuracy of the model described by Equation (29), and the output of the identification model was compared with that of the actual system under the same input, so the step response data of the actual system needs to be collected. Since the system will have a large temperature impact when it is just started, the step response data of the system should be collected after the system enters the steady state working condition. In the experiment, firstly, the input fuel flow rate was set to 1 L/min, and then it was adjusted to 1.5 L/min after the gas temperature of the system was stable; finally, the step response data of gas temperature were obtained. To facilitate comparison, the step response curves of the experimental model and the identification model were plotted on a figure, as shown in Figure 8. It can be seen from the figure that the step response curves of the two models were close, and the error of the two models after stabilization was about 2.5%. Therefore, the mathematical model of gas temperature established by the modeling method proposed in this paper had high accuracy, and the mathematical model established can be used for the control of gas temperature.
# Performance Analysis of the Korean Positioning System Using Observation Simulation
## 4 Performance Analysis for KPS
### Signal-In-Space Ranging Error  
To comprehensively assess the accuracy of the simulated KPS and GPS precise orbits, SISRE values for KPS and GPS are analyzed. SISRE is one of the key indicators for evaluating the performance of a GNSS system [41; 42]. The SISRE can be expressed as Equation (7).  
\[\text{SISRE}=\sqrt{w_{R}^{2}.R^{2}+w_{A,C}^{2}.(A^{2}+C^{2})} \tag{7}\]  
where \(w_{R}^{2}\) and \(w_{A,C}^{2}\) are the weight factors of orbit errors for radial (\(R\)), along-track (\(A\)), and cross-track (\(C\)), respectively. \(w_{R}^{2}\) and \(w_{A,C}^{2}\) are 0.99 and \(\frac{1}{127}\) for KPS IGSO and GEO satellites, 0.98 and \(\frac{1}{54}\) for GPS satellites [43]. The SISRE is computed as the RMS value of the orbital accuracy. It is also determined as the relative weight of each orbital error. The smaller the SISRE, the better the performance of the system.  
Figure 6 presents the orbit-only SISRE for KPS satellites with an additional division to GEO and IGSO satellites. The blue and orange vertical bars represent SISRE values for KPS GEO and KPS IGSO satellites, respectively. The RMS values of SISRE range from 2 to 6 cm. The SISRE values for KPS IGSO are slightly smaller than those for KPS GEO. The averaged RMS values of SISRE are approximately 4.3 and 3.9 cm for KPS GEO and KPS IGSO, respectively. An SISRE value from real-time GNSS data via Internet streams was reported by [PERSON] et al. [44]. They showed that the averaged RMS values of SISRE are approximately 12.3 and 11.1 cm for BeiDou GEO and BeiDou IGSO, respectively. The SISRE of KPS is about three times smaller than that of BeiDou. However, it is still worse compared to GPS (\(\sim\)1.2 cm). Therefore, it can be attributed to the difference in orbital errors between KPS and BeiDou. Since the SISRE for KPS is computed by observation simulation, it may differ from real values. In addition, the SISRE values for some KPS satellites significantly differ from the averaged value, as shown in Figure 6.  
Figure 7 presents the orbit-only SISRE for each GPS satellite. The RMS values of the SISRE for GPS are at the level of a few centimeters. The averaged RMS value of the SISRE is approximately 1.2 cm for GPS. As shown in Figure 4, GPS orbit accuracy calculated by the orbit simulation was at the level of 2-5 cm. Therefore, SISRE values for GPS reflect the corresponding orbit accuracy. [PERSON] et al. [45] showed that the orbit-only SISRE for each GPS satellite is achieved at a level of 5 cm using the MADOCA  
Figure 6: Signal-in-space ranging errors (SISRE) of KPS precise orbit. Blue and orange vertical bars indicate SISRE values for KPS GEO and KPS IGSO, respectively. Blue and orange solid lines denote the averaged RMS values of SISRE.  
real-time products. Therefore, it can be seen that the SISRE calculated by simulated orbits has little difference compared to the real orbit products. Moreover, the orbit-only SISRE can be influenced by different distributions of ground reference stations.
# Approximation by Bicomplex Favard-Szasz-Mirakjan Operators
## 1 Introduction
In this paper, our primary contribution is to present novel results regarding the bicomplex Favard-Szasz-Mirakjan operators within the frame of approximation theory. We have established fundamental inequalities satisfied by these operators, along with a Voronovskaja-type theorem for analytic functions derived from them. Our work bridges the gap between classical approximation theory and bicomplex analysis. More interestingly, it provides a motivational foundation for further investigation of linear positive operators in hypercomplex domains. This paper includes four sections. In Section 1, we provide an introduction and literature survey regarding the brief history of the development of approximation theory, as well as Bernstein and Bernstein-type polynomials such as Favard-Szasz-Mirakjan operators. An elegant motivation regarding bicomplex numbers and their relationships with other similar types of numbers in the corpus of numerical data is also presented. In Section 2, an extensive use of the principles of the concept of a bicomplex number and numerical operations acting on the space of the bicomplex numbers, as well as their important properties, is made. Section 3 is a cornerstone of the current work. In this context, we first define a bicomplex extended version of the Favard-Szasz-Mirakjan operators and calculate an upper estimate for them, as well as their derivatives. Also, a Voronovskaja-type approximation result is investigated. In Section 4, we finalize the paper, summarizing our main contributions. Additionally, we mention that our findings have potential to open up new avenues in the intersection of approximation theory and bicomplex analysis.
# Diagnosis of the Multiepitope Protein rMLEEISH3 for Canine Visceral Leishmaniasis
## 2 Materials and Methods
### Assessment of the Accuracy of rMELEISH3 Antigen in ELISA  
Preliminary ELISA experiments were performed to establish the most effective concentration of rMELEISH3 antigen to differentiate samples derived from _L. infantum-infected_ (n = 16) and uninfected (n = 8) dogs. The assays were performed with rMELEISH3 concentrations of 100, 200, 300, and 400 ng/well, and the accuracies were evaluated by constructing ROC (Receiver Operating Characteristic) curves.  
After selecting the best antigen concentration, additional ELISA experiments were performed to evaluate the accuracy of rMELEISH3 antigen as a diagnostic tool for CVL. The assays were performed with 23 _L. infantum-positive serum samples from dogs with_ different parasite loads and comprised nine samples with low load, eight samples with medium load, and six samples with high load. Additionally, five positive and eight negative controlsamples, as determined by PCR, were used in the experiments [17]. The diagnostic accuracy of rMELEISH3 in detecting antibodies against _L. infantrum_ was evaluated using ROC curves.
# Practical Enhancement of User Experience in NVMe SSDs
## 2 Related Works  
Several studies on improving the multi-queue block I/O layer for NVMe SSDs have been reported in the literature. [PERSON] et al. [17] implemented a mechanism that supports four I/O priorities using two features. One feature is the I/O scheduling classes of Linux that consist of real-time, best-effort, none, and idle; the other feature is the WRR of the NVMe SSDs, a method for NVMe SSDs to retrieve more I/O requests from SQs with higher priorities. The authors increased the number of SQs in a  
Figure 1: Type of block I/O layer: (**a**) Single-queue block I/O layer and (**b**) multi-queue block I/O layer.  
single set allocated to each CPU core from 1 to 4, and each SQ belonging to the set had one of the following SQ priorities; urgent, high, medium, and low. By mapping the I/O scheduling classes to the SQ priorities, differentiated I/O services were provided according to the I/O classes.  
[PERSON] et al. [21] solved a write interference problem, a situation in which the small number of write requests in a read-intensive workload negatively affects the performance of the workload. The problem was solved by splitting and inserting I/O requests into different SWQ according to the I/O type; that is, read or write. The I/O requests isolated in the different SWQ are also sent to different HWQ and SQ. This suggestion alleviates the write interference and consequently increases read performance by 33%.  
[PERSON] et al. [12] analyzed runtime behaviors in nonuniform memory access (NUMA) architecture consisting of multiple CPUs and NVMe SSDs in terms of I/O performance and energy efficiency. Based on this, the authors proposed an energy efficient I/O scheduler that manages I/O threads accessing NVMe SSDs, not only to reduce energy consumption and CPU usage, but also to guarantee I/O throughput and latency. [PERSON] et al. [26] also studied systems based on NUMA. The authors proposed an I/O resource management technique--weight-based dynamic throttling--to facilitate an efficient sharing of I/O resources in Linux cgroup on NUMA multi-core systems that use high-performance NVMe SSDs.  
[PERSON] et al. [27] solved a problem of the multi-queue block layer of the current Linux kernel being unable to reflect process priority when the process requests I/O operations sent to NVMe SSDs. The authors added additional queues between existing SWQs and HWQs to hold I/O requests that are issued by processes and lack opportunities--called to a token in their paper--to send the I/O requests to the NVMe SSDs at that point. Considering these works, it is clear that several studies have solved various problems, especially those caused by the structure of the current multi-queue block layer. However, no study has proposed an appropriate solution at the level of the Linux kernel for I/O-intensive and user-centric tasks to achieve more services, whether or not SSDs already process a large number of I/O requests issued by non-user-centric tasks.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 3 Results--Radiometric Comparison
### Sensor Linearity
Here, we now propose to apply the correction originally defined in [10] to both SLSTR-A and B data, \(\Delta L\), as  
\[\Delta L=X\Delta L_{\text{BB1}}-(1-X)\Delta L_{\text{BB2}}\;, \tag{9}\]  
where \(X\) is  
\[X=\frac{C_{\text{E}}-C_{\text{BB2}}}{C_{\text{BB1}}-C_{\text{BB2}}}\;, \tag{10}\]  
with \(C_{\text{E}}\), \(C_{\text{BB1}}\), and \(C_{\text{BB2}}\) the observed counts from the Earth and black body calibration targets, respectively. Finally,  
\[\Delta L_{\text{BB1/BB2}}=g\left[L(T_{\text{Stray}})-L(T_{\text{BB1/BB2}}) \right]\;, \tag{11}\]  
where \(g\) and \(L(T_{\text{Stray}})\) are the model parameters to be optimised. The result of fitting and applying this correction to both SLSTR-A and B based on the tandem dataset are shown in Figure 5. A significant overall improvement in the agreement between SLSTR-A and B is found, removing the strong trending bias in the uncorrected data. However, the performance is still worse than the equivalent channels in the nadir view, particularly in the S9 channel. Further investigation is required before any recommendation could be made for implementation, to test the performance of the correction.