esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.12. Multicast Code-redistribution Protocol (MCP)  
MCP [38] is envisaged to provide support for a Multi-Application WSN (MA-WSN) where nodes switch between different application images as required. Nodes periodically broadcast advertisement messages for known applications, using an _application information table_ built from incoming advertisements. To perform an application switch, a dissemination command is flooded from the sink, containing the information of which active application is to be switched to which new one (probabilities can be used to switch a random subset). If a receiving node has the new application it becomes a _source_; if it needs this new application, it becomes a _requester_; otherwise it becomes a _forwarder_. A requester periodically multicasts request messages to its nearest source, until it has acquired all of the new application. Non-responsive sources are timed out. Sources respond with data, and relays forward both request and data messages. Sensors advertise different applications in a round-robin manner. Requesters and forwarders cache a small number of packets, and respond immediately for any requests to cached content. An associated size reduction scheme, using an _update conscious compiler_ is described in [39].  
The reduction in traffic and latency reported for MCP are valuable features, and the support for switching selected subsets of nodes with a random component, is a potentially valuable feature for autonomous operation.
## Materials and Methods
### Minimal Inhibitory Concentration (MIC)  
The effect of QAC and PAA on the growth of _Asaia_ spp. was investigated in liquid minimal medium (0.3% (\(w/v\)) (NH4)\({}_{2}\)PO4, 0.3% (\(w/v\)) KH\({}_{2}\)PO4, 0.3% (\(w/v\)) MgSO\({}_{4}\cdot\)7H\({}_{2}\)O, and 0.05% (\(w/v\)) yeast extract) with 2% (\(w/v\)) glucose placed in 96-well plates. For this purpose, 150 \(\upmu\)L of 4% (\(w/v\)) for QAC or 4% (\(v/v\)) for PAA working solutions of each disinfectant was added to 150 \(\upmu\)L of minimal medium and mixed; 150 \(\upmu\)L was then transferred to the next cell with 150 \(\upmu\)L of minimal medium. After preparing this series of dilutions, each microplate cell was inoculated with 150 \(\upmu\)L of the standardized bacterial suspension. The final concentration of bacterial cells in the standardized suspension was approximately 10\({}^{5}\)-10\({}^{6}\) cells per mL. The control sample was inoculated minimal medium without disinfectant. Multiplates were incubated using a UV-vis MULTISKAN GO spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) at 25 \({}^{\circ}\)C for 24 h. Measurements of absorbance were carried out automatically every 1 h at a wavelength of 540 nm. The MICs were defined as the lowest concentrations of disinfectants at which there was no growth of _Asaia_ spp.
###### Abstract  
This paper presents a novel algorithm for structural reliability analysis based on the finite step length and Armijo line search to remove the drawbacks of the Hasofer-Lind and Rakwitz-Fiessler (HL-RF) algorithm that may be subjected to non-convergence in the first-order reliability method (FORM). Initially, the sensitivity factor with finite step length is introduced for preventing the iterative process of the algorithm from entering a periodic loop. Subsequently, an optimization method based on the sufficient descent condition with the Armijo line search technique is proposed. With that, the initial step length and adjusting coefficient are optimized to enhance the applicability of the algorithm emphatically for highly nonlinear functions. A comparison analysis is carried out between the proposed algorithm and existing FORM-based algorithms to validate the robustness and efficiency of the proposed algorithm. The results of this demonstrate that the proposed algorithm is superior to the HL-RF algorithm in terms of robustness and surpass the other existing FORM-based algorithms in connection to efficiency.  
reliability analysis; first-order reliability method; HL-RF algorithm; finite step length; Armijo line search +
Footnote †: journal: Applied Sciences
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 5 Experiment Setup and Results  
### Experiment Setup  
Contiki OS was used to create an adaptive intrusion detection system that is distributed. This operating system supports the IP for sensor networks and was initially built on the foundation of IPv4. Later, when the 6 LoWPAN specification was established, IPv6 functionality was introduced. The RPL now supports IPv6 implementation, which was previously lacking [29]. Contiki was chosen because it supports IPv6 and RPL. To test and simulate the system, the Cooja simulator was employed, which is a Java-based simulator that supports Contiki OS on multiple levels. It has different interfaces that are used to collect various types of information. The RPL protocol implementation is available in the Contiki source code, and default parameters have been used, including a wake-up interval of 125 ms. The more output window in Figure 3 records packet information, such as packet length and type of information being exchanged. Power Tracker, available in Cooja, is used to record power usage in the motes and display radio communications between nodes, including RX and TX signals.  
The power utilization in motes is shown in Figure 4 above, and once the simulation has run for 120 s, it can be observed that it is eminently reasonable. Every more uses less than 5% of the available power. The fundamental cause of the low power consumption is that, because fewer DIS packets are being transmitted across the network, the motes do not need to be turned on all the time. The nodes use less energy as a result.  
The following diagram shows the network and the mote power usage with the malicious mote in the network.  
Figure 5 to the right of the screen displays a network that includes the malicious node \(11\) as well as other nodes. Figure 5 on the right shows the network's power utilization. On the left side of Figure 5, it can be observed that the network's power consumption is significantly more than 15%. RX is high for typical sensor nodes. This is because a DIO packet from within the malicious mote is continuously received by the nodes. TX and RX are both high in the malicious mote. This is because they are transmitting and receiving high multicast packets simultaneously.  
Figure 4: Cooja simulation network snap (**right**) and power usage (**Left**) in the sensors without malicious mote. Sink (green) and sources (yellow).  
Figure 3: Cooja network simulator used to compute the performance metrics.
## 2 Experimental Methodology
### Characterization  
The morphology, elemental composition, and distribution of the prepared SiPMANs were analyzed via scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS, Tesscan Mira Lms, Brno, Czech Republic). Fourier-transform infrared (FTIR) spectroscopy (IRTracer-100, Shimadzu Corp., Kyoto, Japan) was used to examine the structural information and functional groups of the resin and the changes in chemical bonding at characteristic peaks before and after adsorption. The mass loss of the resin's organic components was determined using a simultaneous thermal analyzer (TGA-DSC, SD7650, TA Instruments, Newcastle, DE, USA). The pore size distribution, pore volume, and specific surface area of the resin were determined using a Brunauer-Emmett-Teller specific surface area and porosity analyzer (BET, ASAP 2460, Micrometrics, Norcross, GA, USA). The elemental composition was determined using an organic elemental analyzer (Elementar Unicube, Hanau, Germany). Changes in characteristic bonds before and after uranium adsorption on the resin were analyzed via X-ray photoelectron spectroscopy (XPS, Thermo Scientific K-Alpha, Waltham, MA, USA).
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 2 Materials and Methods
### Methods  
The overall network architecture of UniInterNet is shown in Figure 1. The input is the real-valued noisy spectrum \(X\) derived from the noisy speech \(x\) by STDCT. UniInterNet consists of parallel speech and noise branches, which are responsible for clean speech and pure noise modeling, respectively. Both branches employ the CRN structure. To better capture the sequential correlations along both the time and frequency dimensions, we stack multiple time-frequency sequence modeling (TFSM) blocks [22] to construct the recurrent module. A skip connection between the encoder and decoder is used in each branch, enabling a more efficient information flow. The proposed unidirectional interaction modules are inserted between the encoders, recurrent modules, and decoders of speech and noise branches. This realizes unidirectional information communication from the speech branch to the noise branch. Finally, the output of the speech branch is the estimated DCTIRM \(\hat{M}_{s}\) for target speech recovery:  
\[\hat{s}=\text{ISTDCT}(\hat{M}_{s}\otimes X) \tag{8}\]  
where \(\otimes\) denotes the element-wise multiplication operator.  
Similarly, the output \(\hat{M}_{z}\) of the noise branch is used to reconstruct the noise signal as follows:  
\[\hat{z}=\text{ISTDCT}(\hat{M}_{z}\otimes X) \tag{9}\]  
Additionally, in order to reduce the algorithm delay during practical deployment, we ensure the causality of the CRN structure in the speech branch. However, since the noise branch does not need to be deployed after model training stage, the CRN structure in the noise branch adopts a non-causal version, which is more complicated but stronger than the causal one.  
The details of the encoder, decoder, TFSM block, unidirectional interaction module, and loss function are described as follows.
# Variation of Fracture Toughness with Biaxial Load and T-Stress under Mode I Condition
## 3 Results and Data of Biaxial Fracture Test
### Critical Distance According to Test Results  
Our test results show that \(K_{c}\) will change with the change of \(A_{c}\) or \(T_{c}\), which is consistent with the theoretical analysis results. Figure 4 shows a scatter plot of the critical stress intensity factor \(K_{c}\) versus \(A_{c}\) and \(T_{c}\).  
Equations (9) and (12) are derived in the case where the crack is split along the crack tip extension line, therefore, this equation cannot be established when the crack initiation angle is not \(0^{\circ}\). In the experimental data in Table 1, when \(F_{x}\) = 2.01 kN and 2.54 kN (NO. 7 and 8), the crack starts to curve from the crack initiation point, so it does not meet the condition of Equation (9) and should be eliminated when fitting the curve. The specimens of these two tests are shown in Figure 5. The photo shows that the crack begins to bend only at the crack initiation point on one side, while the other crack begins to bend after extending a certain distance in the horizontal direction. Theoretically, the crack should expand in both directions in the same way if the specimen and load are completely symmetrical. However, such a situation is too idealistic, and the difference in load and crack length causes the crack on one side to expand first, which causes the load to drop at this moment, and the crack on the other side to expand along a straight line. In this paper, we ignore this effect and assume that if the crack on either side begins to bend at the crack initiation point, it means that the cracks on both sides are in a stress state where bending will occur.  
Figure 4: Scatter plot of \(K_{c}\): (a) with \(A_{c}\) with (b) \(T_{c}\).  
Thus, the test data of the specimens No. 7 and 8 apparently do not conform to the assumptions assumed in Equation (9), and therefore need to be eliminated when the straight line is fitted. The fitting results have been shown in Figure 5. If we consider that the crack propagation is controlled by the stress at the critical distance, according to the conclusion of Equation (11), the experimental data can be obtained that \(r_{\rm c}=2.29\) mm. If we consider that the crack propagation is controlled by the strain at the critical distance, according to the conclusion of Equation (14), the experimental data can be obtained that \(r_{\rm c}=0.04618\) mm.
## 1 Introduction
Similarly, even though there are a few studies that simultaneously compared the GPP estimates obtained from SIF-based methods with those from LUE-based or process-based modeling approaches, their comparisons both focused on the single timescale and did not address the aspect of parameter stabilities. For example, [PERSON] et al. [4] and [PERSON] et al. [44] evaluated the performance in GPP simulations of the LUE-based model, the process-based model, and the SIF-based model at a maize site. Both results showed that the accuracies in GPP estimates of the SIF-based model and the LUE-based model were similar (R\({}^{2}\) = 0.67-0.88), but they were lower than that of the process-based model (R\({}^{2}\) = 0.87-0.94). Thus, in this study, we compared the effects of multiple temporal aggregation levels (mid-day, daily, and monthly scales) and different integrated meteorological conditions (absorbed photosynthetic active radiation (APAR), air temperature, vapor pressure deficit (VPD)) on the robustness of parameters for SIF-based and LUE-based models. The major objectives of this study are threefold: (i) Compare the parameter stability between the SIF-based model and LUE-based models across multiple temporal aggregation levels, (ii) analyze the effects of integrated meteorological conditions on the parameter robustness of SIF-based and LUE models for each timescale, and (iii) evaluate the estimation performances of the SIF-based and LUE-based GPP models from mid-day to monthly timescales using an independent dataset. This study provides the first assessment of the effects of viewing the zenith angle in OCO-2 Glint mode on the magnitude of SIF values and the GPP-SIF relationship, and for the first time compares and addresses the influence of multi-aspect on the SIF-based and LUE-based models (both one-leaf (SL)-LUE and two-leaf (TL)-LUE models). We aim at providing a better understanding of the different capabilities or limitations of the SIF-based and LUE-based models for the estimation of GPP.
## 2 Numerical Methods
### Validation of the Simulation Results  
To further validate the CFD tool and the computational method, comparisons between the CFD result and the experimental results at a 0\({}^{\circ}\) incidence angle were performed. Figure 3 shows the computational results and the corresponding experimental data. Although some small deviations exist between the CFD and experimental results, reasonable agreement can be seen from Figure 3, which indicates that the size of 3D separation region in the blade passage is realistically predicted.  
The total pressure loss coefficient is defined as:  
\[Y_{p}\ =\ \frac{P_{02}-P_{01}}{P_{01}-P_{1}} \tag{1}\]  
where \(P_{02}\) is the local total pressure, \(P_{01}\) is the inlet total pressure, and \(P_{1}\) is the inlet static pressure.  
The relative displacement thickness can be expressed as:  
\[\delta_{rel}\ =\ \{\delta^{*}(r)-\delta^{*}_{mildspan}\}/c \tag{2}\]  
The displacement thickness is given as:  
\[\delta^{*}(r)\ =\ \int_{0}^{5}\Bigg{[}1-\frac{\rho v(r,s)}{\rho_{fs}V_{fs}} \Bigg{]}ds \tag{3}\]  
Figure 3: Spanwise distribution of pitch-averaged flow parameters at the EXP test plane: (**a**) pitch-averaged total pressure loss coefficient; (**b**) relative displacement thickness; and (**c**) exit flow angle.  
Based on above analyses, it can be concluded that the numerical techniques and the commercial software FLUENT is reliable and can be used for the prediction of the 3D separation in this study.  
The main mechanism for controlling of corner separation by the slot configuration is the flow induced from the pressure surface to the suction surface by the pressure difference can accelerate flow in the slot, which forms the jet flow from the slot, removing the low energy flow in the corner separation. Figure 4 shows the volume streamlines in the flow passage for the original cascade with and without the slot configuration. As shown in Figure 4, the adverse flow and blockage in the passage caused by the corner separation has been eliminated by the jet flow from the slot. The most important criteria for the slot is the outlet position of the slot which is correlated with the separation point of the corner separation. For the increasing of the incidence angle, separation point moves towards to the leading edge which also indicates the development of the corner separation. Based on the analysis of the outlet position of the slot, slot outlet position is set at 50% of the axial chord (which is the position of point B). In order to further accelerate the flow in the slot, a tapered shape is chosen for the slot. Thus, the inlet length and outlet length of the slot are set the 25% and 10% of the axial chord, respectively.
# Practical Submodule Capacitor Sizing for Modular Multilevel Converter Considering Grid Faults
## 4 Design Consideration of SM Capacitors
### ESR, Thermal Resistance, and Lifetime Assessment  
To ensure a long-term reliable operations of the MMC, the thermal stresses of SM capacitors must be evaluated. According to [35], a simple lifetime model of SM capacitors can be given as  
\[L_{life}=L_{0}\times(\frac{V_{dc}}{NV_{rated}})^{-\delta}\times 2^{\frac{T_{0} -T_{h}}{10}} \tag{38}\]  
where \(L_{0}\) is the lifetime of the capacitor under rated voltage \(V_{rated}\) and testing temperature \(T_{0}\); the exponent \(\delta\) is normally selected from 7 to 9.4 for metallized polypropylene film (MPPF) capacitor; \(T_{h}\) is the operating hot-spot temperature of the capacitors.  
In order to study the thermal behavior of the SM capacitors, a lumped thermal model of film capacitors are given in Figure 6[36], where the steady-state hot-spot temperature of capacitors can be expressed as  
\[T_{h}=R_{th}P_{loss}+T_{a} \tag{39}\]  
The thermal resistance of the capacitor \(R_{th}\) relates to the material of the capacitor. \(T_{a}\) is the ambient temperature, \(P_{loss}\) is the power losses of the SM capacitor.  
By neglecting the harmonics on the circulating current, the SM capacitor charging current can be decomposed by a fundamental line frequency component and a double-line frequency component, which can be expressed as [37]  
\[i_{smj}=\beta_{\omega 1}\hat{I}_{s}+\beta_{\omega 2}\hat{I}_{s} \tag{40}\]  
\[\beta_{\omega_{1}}=\frac{1}{8}[2\cos(\omega_{1}t-\varphi_{j})-m^{2}\cos(\omega _{1}t)\cos(\varphi_{j})] \tag{41}\]  
\[\beta_{\omega_{2}}=-\frac{m}{8}[\cos(2\omega_{1}t-\varphi_{j})] \tag{42}\]With different modulation index and phase angle, the RMS value of \(\beta_{\omega_{1}}\) is shown in Figure 7, while the RMS value of \(\beta_{\omega_{2}}\) equals \(m/(8\sqrt{2})\). Thus, the power losses for SM capacitors can be calculated as  
\[P_{loss}=(R_{\mathit{csr\_\omega 1}}\times\beta_{\omega 1\_\mathit{rms}}^{2}+R_{ \mathit{csr\_\omega 2}}\times\beta_{\omega 2\_\mathit{rms}}^{2})I_{s}^{2} \tag{43}\]  
where \(R_{\mathit{csr\_\omega 1}}\) is the equivalent-series-resistance (ESR) of the SM capacitor under fundamental line frequency, \(R_{\mathit{csr\_\omega 2}}\) is the ESR under double line frequency.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 3 Overview of Telemedicine
### Components of Telemedicine
#### 3.1.3 Remote Medical Server Unit  
This unit is where the expert physician or caregiver accesses the desired services remotely for the patient. The patient's information from the Patient WBAN will have already been transmitted through the communication unit and made accessible to the caregiver. All the transmitted data or information must be received using the same transmission protocol. The information is received and stored in a database, and with the right authentication the physician can access the information for diagnosis. Videoconferencing is widely used in online consultations. Compatible cameras, microphones and other electronic devices and computers are used to assess patient EHRs.
## 5 Conclusions  
This paper proposes a new pseudolite-based indoor positioning system. The proposed system includes a Receiver-and-Transmitter (Rx/Tx), server and user terminal. As the kernel of the system, Rx/Tx demodulates the received real-world GPS signal (which includes several satellites' signals), separates them and repeats them, respectively. This process has two advantages. One is that all Tx can be synchronized with each other since one single clock is used in the Rx/Tx system. The other advantage is the GPS-like signal can be processed (signal acquisition and tracking, navigation data decoding) by the general receiver and hence there is no need for hardware-level modification on the receiver. The proposed system is simulated using a software defined GPS receiver. The simulation results show the indoor positioning system is able to provide high accurate horizontal positioning in both static and dynamic situations. In the future, the study will focus on the realization problem, such as indoor transmitting antennas distribution, multi-path effects and so on.  
Figure 11: Dynamic simulation results: (**a**) positioning results and (**b**) positioning error.  
Figure 12: Positioning error variation with velocity.
## 2 Basic Theory
### Experimental Modal Analysis  
Modal analysis is a crucial aspect of vibration theory and serves as a widely employed method for studying the dynamic characteristics of structural components in modern engineering. It involves the identification of system dynamic characteristics and finds applications in the field of engineering vibrations. Modal analysis can be broadly categorized into two types: theoretical modal analysis and experimental modal analysis.
# Emerging Trends and Management for Sjogren Syndrome-Related Dry Eye Corneal Alterations
## 3. **Glandular Ocular Involvement: Dry Eye Disease (DED) or Keratoconjunctivitis Sicca (KCS)**
### Histology, Biochemistry, Anatomy, and Physiology
#### 3.1.3. Meibomian Glands  
Slit lamp evaluation or new non-invasive instruments, such as keratography [29], have shown that approximately 20% of SS patients have associated anterior plepharitis, and 45-60% are affected by a Meibomian gland dysfunction (MGD), i.e., a chronic alteration of the specialized sebaceous glands producing the lipid layer of the tears.