esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Monthly Variation Characteristics
As a result, the average detection range in DBS mode increased, and the variation in detection range decreased significantly, indicating a reduced impact of low clouds on the detection range. A comparison also shows that the upper quartile value for September was similar to that in August, suggesting that, under conditions of fewer clouds, the vertical detection range in both months was comparable, likely reflecting similar boundary layer heights. In summary, although the low boundary layer height from August to September hindered the diffusion of aerosol particles, precipitation and its washing effect on particle concentration were the main reasons for the reduced detection efficiency of the DWL in August. From October to January, temperature, humidity, and wind speed reached their lowest levels, with relative humidity dropping below 50% (Figure 3c), and the number of days with extensive low-cloud cover significantly decreased. As shown in Figure 3d, the average detection range and the upper quartile values for the DBS mode of the DWL both increased significantly, and the fluctuations in detection range decreased. This suggests that the boundary layer height above Gonggar Airport likely increased during this period. At the same time, as the dry season set in, the water levels of the Yarlung Zangbo River dropped, exposing the sandy riverbeds, which led to an increase in dust concentration in the air. Although atmospheric diffusion conditions improved, the rise in particle concentration offset some benefits, resulting in an overall improvement in DWL detection efficiency. From February to April, low-cloud cover gradually increased as temperature and relative humidity rose. However, the low-cloud cover in April was still lower than in August. By April, scattered precipitation had started to occur, with wind speed increasing, particularly noticeable (Figure 3c). This suggests that with the strengthening of low-level atmospheric thermodynamic and dynamic processes, the boundary layer height may have increased further, allowing particles to be dispersed to higher altitudes. As a result, the average detection range in DBS mode continued to increase each month from February to April, with the upper quartile values reaching the DWL's maximum detection range. However, with the renewed activity of low-cloud cover, the maximum fluctuations in the detection range also increased again. In addition, the detection efficiency in PPI mode also significantly improved, indicating that the concentration of low-level aerosols had increased and become more uniform.
# Unity Power Factor Operation in Microgrid Applications Using Fuzzy Type 2 Nested Controllers
## 5 Simulations and Experimental Results
### Simulation Results
#### 5.1.2 Case (2): Step Change in the Load  
Figure 9 illustrates a step change in the MG's AC load side. Load 1 with \(p\) = 2.5 kW and Q = 1.2 kVAR (S = 2.7 kVA = 58%) was stepped up to Load 2 with \(p\) = 4.2 kW and Q = 2 kVAR (S = 4.65 kVA = 100%) and then stepped back down to Load 1. The DC link voltage, source current, and one cycle of the source voltage and source current for the three control techniques are shown in Figure 9a-d, respectively. Figure 10 depicts one source's current cycle and its spectrum for the three control approaches when Load 2 was applied. The conventional technique is depicted in Figure 10a,d, the Fuzzy type 1 method is depicted in Figure 10b,e, and the proposed control method is depicted in Figure 10b,e,c,f. Table 4 shows the THD, displacement factor, power factor, and whether it agrees or disagrees with the IEEE 1547 standards. According to Table 4, the proposed controller had the closest to unity power factor and adhered to the IEEE 1547 standards.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \begin{tabular}{c} **Total Harmonic** \\ **Distortion (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Displacement Factor** \\ **(cos \#)** \\ \end{tabular} & **Power Factor** &
\begin{tabular}{c} **Agree or Disagree with** \\ **IEEE 1547 Standards?** \\ \end{tabular} \\ \hline Conventional & & & & \\ (auto-tune) & 7.16 & 0.949 & 0.9466 & Disagree \\ Fuzzy type 1 & 5.35 & 0.967 & 0.9654 & Disagree \\ Proposed & 2.09 & 0.991 & 0.9905 & Agree \\ \hline \end{tabular}
\end{table}
Table 4: Summary of the simulation results.  
Figure 8: Source of the grid current and its spectrum for the three control techniques. (**a**,**d**,**g**) conventional method, (**b**,**e**,**h**) Fuzzy type 1 method, and (**c**,**f**,**i**) the proposed method.
# Graphol: A Graphical Language for Ontology Modeling Equivalent to OWL 2
## 3 Preliminaries  
Description logics (DLs) [11] are formalisms used to model a domain of interest in a formal way. They are portions of first-order logic that allow for decidable reasoning. In DLs, the domain of interest is represented in terms of _objects_ (i.e., individuals), _concepts_ (i.e., abstractions for sets of objects), and _roles_ (i.e., binary relationships between concepts). Moreover, some DLs consider also _value-domains_, denoting sets of values, and _attributes_, denoting binary relationships between concepts and value-domains. We assume to have an alphabet \(\Gamma\) partitioned into \(\Gamma_{P}\) and \(\Gamma_{C}\). The former is in turn partitioned into sets of symbols for atomic concepts, atomic roles, atomic attributes, and atomic value-domains. \(\Gamma_{C}\) instead contains symbols for constants, and is further partitioned into the sets \(\Gamma_{O}\), which is the set of constants denoting objects, and \(\Gamma_{V}\), which is the set of values. We now provide syntax and semantics of the DL language of interest in this work. We notice that the DL we provide in this section captures the DL \(\mathcal{SROIQ}(D)\)[66; 67], i.e., the DL underlying OWL 2. In Section 4.4, we will discuss which syntactic restrictions to impose to make this language equivalent to \(\mathcal{SROIQ}(D)\) (and so to OWL 2). Concepts, roles, attributes, and value-domains expressions in such DL are defined by the following rules:  
\[\begin{array}{lcl}C&\longrightarrow&A\ \ |\ \
eg C\ |\ \ C\ \cap\cdots\cap C\ |\ \ C\ \sqcup\cdots\sqcup C\ |\ \exists R\ |\ \exists R.C\ |\ \forall R.C\ |\\ &&\ \ \ \ \ \ \ \ \  
The semantics of a DL KB is given in terms of interpretations. An _interpretation_\(\mathcal{I}=(\Delta^{I},\cdot^{I})\) consists of:  
* A nonempty interpretation domain \(\Delta^{I}=\Delta^{I}_{\mathcal{O}}\cup\Gamma_{V}\), where \(\Delta^{I}_{\mathcal{O}}\) is the _domain of objects_, and \(\Gamma_{V}\) is the set of values previously introduced (indeed, in every interpretation each value is intepreted by itself);
* An _interpretation function_ \(\cdot^{I}\) that assigns an element of \(\Delta^{I}_{\mathcal{O}}\) to each constant in \(\Gamma_{O}\), and interprets each DL expression as shown in Table 1.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 4 Practical Implementation of PQC-Based PKE Protocols
### Post-Quantum Cryptography (PQC) Uses in Applications  
To demonstrate the impact of the characteristics of PQC algorithms, PQC-based protocols need to be utilized in real-world applications. However, current implementations of PQC algorithms using PKE are limited to encrypting messages of a fixed size. For instance, CRYSTALS-Kyber, which is based on an IND-CPA-secure PKE scheme, and BIKE, which utilizes PKE from Quasi-Cyclic Moderate Density Parity-Check (QC-MDPC) codes, can only encrypt messages of a fixed length of 32 bytes. HQC, also based on IND-CPA-secure properties, can accept messages of a fixed length of 16 bytes before encoding.  
Conversely, when a hybrid approach is employed, the KDF generates symmetric keys of a consistent length for all PQC algorithms. While AES cipher modes typically use key sizes of 128, 192, or 256 bits (16, 24, or 32 bytes), it does not support key sizes larger than 256 bits. Therefore, to use the PQC standalone PKE directly for the encryption and decryption of data that exceeds these fixed sizes without the hybrid implementation, it is necessary to segment the message and reassemble the decrypted segments to reconstruct the original data. The process for this splitting is shown in Figure 1. In this instance, first, the input data is split into blocks of an acceptable size, as shown in Figure 1a. Each block is then encrypted sequentially using the receiver's public key. The encrypted blocks are sent to the intended party. As shown in Figure 1b, on the receiver side, after the data is received, the encrypted data is processed as a stream. Each block is decrypted using the receiver's private key and then aggregated to reconstruct the original data.  
In our proposed PQC standalone protocol, after establishing a secure communication (i.e., session key) using the standardized PQC KEMs and digital signatures (e.g., CRYSTALS-Kyber, BIKE, and HQC), each segmented message is encrypted within that session using the established encryption and decryption keys. This approach aligns with standard cryptographic practice, where a session key securely negotiated using an IND-CCA2 secure KEM is reused for multiple encryptions within the same session. The security of each encrypted segment is ensured by the PQC algorithm and the sequence number mechanism introduced to counter replay attacks. Moreover, the proposed PQC standalone protocol leverages periodic re-keying when a session is too long. For this, a new set of PQC KEM and signature algorithm keys is generated. The re-keying process is currently not implemented in our evaluation and will be considered as part of future work.  
Figure 1: Encryption and decryption processes of PQC protocols. (**a**) Encryption process. (**b**) Decryption process.
# Methods for Detecting Picric Acid--A Review of Recent Progress
## 3 Sensors for Detecting and Measuring PA
### Metal-Organic Frameworks Sensor
#### 3.1.1 d-Block MOFs  
Of the MOFs, whose luminescence properties are being studied for PA detection, structures based on d- and f-block metals have become particularly popular. MOFs containing Zn atoms have attracted particular interest [58; 59; 60; 61] due to their properties such as high sensitivity in detecting PA, stability and low detection limit, which are associated with the possibility of obtaining strong emission properties and, above all, low toxicity [59]. The selectivity of PA detection was also confirmed for some MOFs containing Cd [59; 62], Cu [63] or Zr atoms [64] (Figure 2).  
It should be noted that most works on the use of MOFs for detecting PA focus on the role of the organic ligands. Consequently, apart from the purely empirical observation that Zn-based MOFs afford good sensing performance, little theoretical foundation is available to justify using Zn-based MOFs over MOFs containing other d- and f-block metal atoms (Table 2).  
Figure 2: Structures of selected Zn-MOF compounds: **(1)** [Zn\({}_{4}\)(DMF)(Ur)\({}_{2}\)(NDC)\({}_{4}\)] (a) Perspective view of the three-dimensional porous framework along the crystallographic c axis; (b) Perspective view of each of the single one-dimensional channel decorated with fluorescent \(\pi\)-electron rich naphthalene. Reprinted with permission from [60]. Copyright 2015, American Chemical Society; **(4)** 3D network with rectangular tunnels of [Cu\({}_{2}\)(tpt)\({}_{2}\)(tda)\({}_{2}\)]\(\cdot\)H\({}_{2}\)O. Reprinted with permission of Elsevier from [63].
# Minimal Collagen-Binding Epitope of Glycoprotein VI in Human and Mouse Platelets
## 3 Results
### Effect of EA-20 Antibody on GPVI-Clustering and Shedding in Human Platelets  
Previously, Losartan has been used to inhibit collagen-induced GPVI activation by reducing the clustering of GPVI on the platelet surface [39]. To study whether EA-20 may inhibit GPVI clustering as well, human platelets were activated by collagen in the presence or absence of Losartan and the EA-20 antibody (Figure 5A,B). We found a strong inhibition of aggregation response to collagen when human platelets were treated with both Losartan and EA-20 (Figure 5A,B), indicating that EA-20 antibody does not inhibit GPVI clustering, and only affects collagen binding. To investigate whether the EA-20 antibody may induce GPVI shedding from the surface of human platelets, platelet-rich plasma samples were collected from healthy donors and stimulated with collagen (Col-I), thrombin (Thr), and the thiol-modifying agent N-ethylmaleimide (NEM) as a positive  
Figure 4: EA-20 antibody impairs aggregation of mouse and human platelets in response to Col-I and CRP. Washed mouse and human platelets were stimulated with Col-I (**A,C,E** Col-I 2.5 \(\upmu\)g/mL) and CRP (**B,D** CRP 0.2 \(\upmu\)g/mL and F CRP 0.4 \(\upmu\)g/mL) in the presence of control IgG (Ctrl) or EA-20 antibody. Experiments were performed using (**A,B**) _WT_ mouse, (**C,D**) humanized GPVI (_hGP6_) mouse, and (**E,F**) human platelets; sb: seroblock. Platelet aggregation was measured using light transmission aggregometry and recorded for 10 min. Representative aggregation curves are shown. Statistical analysis is shown in Supplementary Figure S4.  
control (Figure 5C). All these treatments have previously been shown to induce GPVI shedding from the human platelet surface, thereby increasing soluble GPVI levels (sGPVI) in the plasma [40]. Using a standardized GPVI ELISA, we found that collagen, thrombin, and NEM treatment increased the level of sGPVI, while co-incubation of platelets with the EA-20 antibody did not further increase the amount of sGPVI in the plasma (Figure 5C), indicating that EA-20 antibody-mediated inhibition of platelet aggregation is not mediated by GPVI shedding.
# Urbanization and Grain Production Pattern of China: Dynamic Effect and Mediating Mechanism
## 3 Materials and Methods
### Methodology
#### 3.1.4 Spatial Mediating Effect Model  
Drawing on the study of [PERSON] et al. [44], and considering the time inertia effect and spatial dependence effect of the change in GPP, the time lag term and spatial spillover term of the explained variable were introduced into each path established to construct a spatial mediating effect model to examine the transmission mechanism of urbanization affecting GPP [45]:  
\[\ln GPP_{it}=\alpha_{0}+\theta_{0}\ln GPP_{it-1}+\rho_{0}W_{ji}\ln GPP_{it}+ \beta^{\prime}URBAN_{it}+\sigma_{0}\sum control+\mu_{i}^{\prime}+\omega_{t}^{ \prime}+\varepsilon_{it}^{\prime} \tag{11}\]  
\[M_{it}=\alpha_{1}+\theta_{1}M_{it-1}+\rho_{1}W_{ij}M_{it}+\beta^{\prime}URBAN _{it}+\sigma_{1}\sum control+\mu_{i}^{\prime\prime}+\omega_{t}^{\prime\prime}+ \varepsilon_{it}^{\prime\prime} \tag{12}\]  
\[\ln GPP_{it}=\alpha_{2}+\theta_{2}\ln GPP_{it-1}+\rho_{2}W_{ij}\ln GPP_{it}+ \eta M_{it}+\beta^{\prime\prime}URBAN_{it}+\sigma_{2}\sum control+\mu_{i}^{ \prime\prime\prime}+\omega_{t}^{\prime\prime\prime}+\varepsilon_{it}^{\prime\prime} \tag{13}\]  
A common method for determining a mediating effect is the stepwise test proposed by [PERSON] and [PERSON] [46], with the following test steps.  
Firstly (Equation (11)), we test the direct effect of urbanization on the GPP, and if the coefficient of urbanization is significant, it indicates that urbanization affect the GPP and can be followed by the presence of a mediating effect.  
Secondly (Equation (12)), we use the mediating variable (\(M_{it}\)) as the explanatory variable to test the effect of urbanization on each mediating variable, and if the coefficients of urbanization are all significant, then urbanization can have an impact on the mediating variables. The mediating variables (\(M_{it}\)) include cropland utilization (MCI, multiple-crop index), planting structure (PS), and agricultural technology progress (ATECH).  
Thirdly (Equation (13)), we test the effects of urbanization and each mediating variable (\(M_{it}\)) on the GPP: if the coefficients of both urbanization and \(M_{it}\) are significant, it indicates that the existence of partial mediating effect--i.e., both direct and indirect effects--while if the coefficient of urbanization is not significant, it indicates the existence of only an indirect effect, at which point it is a full mediating effect.
# A Robust Localization System Fusion Vision-CNN
## 5 Experiments
### _Relocalization Experiment_  
#### 5.2.1 Test of Visual Relocalization Accuracy  
The occupancy grid map of the indoor environment with a grid resolution of 0.05 m was established by using a SLAM algorithm Cartographer [19] as the laser-based positioning accuracy on the map was one order of magnitude higher than the visual relocation accuracy. So, after establishing the indoor environment grid map, we let the robot run in the environment while recording the image information released by the camera and the measurement information released by the laser scanner. Then, we used laser-based pose estimation as the true value of the pose during the visual neural network training process.  
After completing the training, the following will analyze the accuracy of vision relocation and the precise relocation of the fusion of vision and laser information. We let the robot run along the green trajectory shown in Figure 15d. The laser-based positioning was used as a reference for analyzing the relocation accuracy of the visual neural network. The absolute pose errors based on posenetLSTM and the neural network proposed in this paper are shown in Figure 15a,b, respectively. The statistical results of the absolute pose error are shown in Figure 15c. Taking RMSE statistics as an example, the absolute positioning accuracy of the proposed neural network improved by 27.84% compared with the original postenetLSTM.  
Figure 14: Experiment setup. (**a**) The experimental platform; (**b**) The measuring equipment.  
The pose estimations in the \(x\), \(y\), and \(yaw\) directions are shown in Figure 16a,c,e, respectively, and the corresponding errors are shown in Figure 16b,d,f. The error statistics results are shown in Table 4. The positioning accuracy in the actual environment was higher than that in the simulation environment. The main reason was that the visual features in the actual environment were obviously richer than those in the simulation environment.  
Figure 15: (**a**) Absolute pose error of posenetLSTM; (**b**) Absolute pose error of the proposed neural network; (**c**) Statistics of absolute pose error; (**d**) The robot’s trajectory.  
Figure 16: (a,c,e) The comparison of pose estimation in the \(x\), \(y\), and \(yaw\) directions, respectively; (b,d,f) The comparison of pose estimation errors in the \(x\), \(y\), and \(yaw\) directions, respectively.
## 1 Introduction
Additionally, the need for duty control to manage gain variations due to load changes or wind speed variations can lead to hard switching at minimum duty during light or no-load conditions [8]. Figure 1: (**a**) Schematic of the small-to-medium-sized wind turbine, (**b**) IPT-based small-to-medium-sized wind power system. Furthermore, in IPT-based small WPGSs, it is essential to maintain a constant output voltage (V\({}_{\rm o}\)) at the secondary-side rectifier output, which serves as the input supply voltage for the grid-tied inverter. This typically requires controlling the output voltage (V\({}_{\rm o}\)) via wireless communication such as Bluetooth or Wi-Fi. However, failure in wireless communication control can result in unstable output voltage (V\({}_{\rm o}\)), potentially causing issues in the overall wind power system. To address this issue, a DC-DC converter for the stabilized power supply must be applied at the secondary rectifier output. However, this approach can lead to increased power conversion losses, reducing efficiency. This paper proposes an IPT-based three-phase single-stage AC-DC converter that integrates a PFC stage for power factor correction and a wireless power transfer DC-DC stage into a single power conversion unit. This design aims to improve the power factor and ensure electrical safety by addressing brush wear in the nacelle of small wind power systems. In the proposed IPT-based three-phase single-stage AC-DC converter, the gain characteristics of the link voltage (V\({}_{\rm Link}\)) according to the value of the boost inductor (L\({}_{\rm B}\)) in the PFC stage for three-phase power factor correction were analyzed and presented. The DC-DC stage for wireless power transfer applies an S-S (series-series) compensation circuit, which shows no gain variation at the resonant frequency due to the air gap of less than 10 mm between the primary and secondary sides of the contactless coupler. Through a 1 kW prototype experiment, it was demonstrated that, in the small wind turbine generation voltage range (112 V\({}_{\rm rms}\) to 250 V\({}_{\rm rms}\)), according to wind speed, the secondary output voltage (V\({}_{\rm o}\)) could operate within a 3% variation range solely by controlling the link voltage (V\({}_{\rm Link}\)) in the PFC stage, without the need for wireless communication control. Furthermore, comparative experiments were conducted to control the secondary output voltage (V\({}_{\rm o}\)) in real time via wireless communication, demonstrating the feasibility of applying this system to small wind power systems.
# Acoustic Emission Analysis of the Cracking Behavior in ECC-LWSCC Composites
## 3 Experimental Program
### Tested Beams  
To evaluate their structural performance, a total of six beams were cast and subjected to four-point loading tests. Figure 1 illustrates the dimensions and reinforcement details of the tested beams. All the beams had a total length of 2160 mm, with a square cross-section of 250 \(\times\) 250 mm and a concrete cover of 30 mm. The tensile reinforcement used consisted of three deformed steel bars with a diameter of 25 mm, while the compression reinforcement used consisted of two deformed steel bars with a diameter of 10 mm. Since all the beams were designed to fail in flexure, 10 mm stirrups were spaced at 100 mm intervals to prevent shear failure. The six tested beams are described as follows:  
* The first two beams were fully cast with ECC containing either PVA fibers or SFs. These beams are referred to as ECCP and ECCS, respectively, and were used as control beams for comparison;  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Mixture** & **BC** & **C/BC** & \begin{tabular}{c} **SCM** \\ **Type** \\ \end{tabular} & **FA/BC** & **MK/BC** & **S/BC** & **CA/BC** & **W/BC** & \begin{tabular}{c} **Fibers** \\ **\%** \\ \end{tabular} & \begin{tabular}{c} **Fibers** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Dry** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} & \
\begin{tabular}{c} **fc/** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **STS** \\ **(MPa)** \\ \end{tabular} \\ \hline LWSCC & 1 & 0.40 & FA+MK & 0.40 & 0.20 & 0.94 & 0.8 & 0.40 & - & - & 1727 & 42.5 & 2.6 \\ ECCP & 1 & 0.45 & FA+MK & 0.35 & 0.20 & 0.36 & - & 0.27 & 2 & PVA & 2091 & 77.5 & 8.8 \\ ECCS & 1 & 0.45 & FA+MK & 0.35 & 0.20 & 0.36 & - & 0.27 & 2 & SF & 2222 & 82.5 & 10.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mixture designs and proportions for all tested beams [9].  
* The third and fourth beams were repaired in compression. The top layer was either ECCP or ECCS and was 85 mm in depth, while the bottom layer was LWSCC and was 165 mm in depth. These beams are referred to as ECCP-C and ECCS-C, respectively;
* The fifth and sixth beams were repaired in tension. The top layer was LWSCC and was 165 mm in depth, while the bottom layer was either ECCP or ECCS and was 85 mm in depth. These beams were designated as ECCP-T and ECCS-T, respectively.  
The repaired depth for all the beams was selected to be 85 mm, which provided adequate space for the concrete around the longitudinal bars in the tension or compression zone. Specifically, the depth of the repaired layer was chosen to maintain a depth equal to the covering (30 mm) above and below the longitudinal bars. The bond between the ECC layer and the LWSCC layer was achieved through the placement of shear stirrups and the roughening of the surface of the interface between the two layers [9].