esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Characterization of Synthesized CuO Nanostructures  
The crystal structures of power samples obtained after the synthesizing process were analyzed with the results showing on the XRD patterns (Figure 3). Three pH-controlled samples at values of 4, 7, and 12 showed characteristic diffraction peaks of monoclinic \(CuO\) based on JCPDS card File No. 48-1548 including the peaks corresponding to the Bragg angles of 32.6\({}^{\circ}\) (110), 35.7\({}^{\circ}\) (002), 38.8\({}^{\circ}\) (111), 49\({}^{\circ}\) (112), 53.6\({}^{\circ}\) (020), 58.3\({}^{\circ}\) (202), 61.8\({}^{\circ}\) (113), 66.2\({}^{\circ}\) (310), 68.4\({}^{\circ}\) (220), 72.8\({}^{\circ}\) (311), and 75.4\({}^{\circ}\) (004). The XRD analysis revealed no detectable impurities in the samples. The peaks correspond exactly with the reference \(CuO\) pattern obtained under pH-controlled conditions of 4 and 12. Interestingly, in the case of the pH-controlled condition of 7, the sample lacked the characteristic peaks at the (020) and (011) faces, but other signature peaks of \(CuO\) were still present.  
Figure 3: XRD patterns of the synthesized \(CuO\) materials.  
Figure 2: The PEC sensor system for glucose detection.  
As shown in Figure 4, SEM images show \(CuO\) synthesized under controlled pH conditions. The pairs (a, b), (c, d), and (e, f) of Figure 4 correspond to pH-controlled values of 4, 7, and 12, respectively. The analysis reveals a clear influence of the controlled pH value before the hydrothermal process on the morphology of the \(CuO\) samples. \(CuO\) exhibits NPs morphology at pH 4, transitioning to NRs morphology at pH 7, and finally forming nanosheet NSs morphology at pH 12.  
Figure 5a presents the UV-VIS absorption spectra of \(CuO\) nanostructures with different morphologies (NPs, NRs, and NSs). All three materials exhibit light absorption in the ultraviolet and visible regions. The \(CuO\) NPs and NSs demonstrate a broad absorption band that varies from approximately 350 to 750 nm. Notably, the NPs exhibit a peak absorption around 700 nm, while the NSs show higher absorption below 650 nm. In contrast, the \(CuO\) NRs display a narrower absorption region, primarily below 500 nm, compared to the other morphologies. As shown in Figure 5b, the bandgap values calculated using the Tauc plot for \(CuO\) NPs, NRs, and NSs were 1.33 eV, 1.59 eV, and 1.32 eV, respectively. These values are consistent with previous studies reporting a range of bandgap values (1.2 to 2.0 eV) for \(CuO\) with different morphologies [43].  
Figure 4: SEM images of \(CuO\) synthesized at pH values of 4, 7, and 12 corresponding to (**a**,**b**) particles, (**c**,**d**) rods, and (**e**,**f**) sheets morphologies.  
Figure 5: (**a**) UV\(-\)VIS absorbance spectra and appearance of \(CuO\) NPs, \(CuO\) NRs, and \(CuO\) NSs. (**b**) Tauc plots of \(CuO\) NPs, \(CuO\) NRs, and \(CuO\) NSs.
# Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications
## 3 Routing Protocols for Low-Power and Lossy Networks
### LOADng
Both messages also can transport another routing metric that should be computed and updated according to its specifications. As mentioned above, LOADng is based on the well-known AODV. However, to permit the protocol to be executed on devices with severe hardware restrictions, some simplifications were made. In the LOADng, the routing table does not store the whole path to reach a destination. Besides, the control messages do not carry the address of each node traveled. Furthermore, only the destination of an RREQ message can reply to it using an RREP. Thus, the use of \"intermediate RREP\" mechanisms is not allowed. Finally, LOADng permits the use of a wide variety of addressing schemes (e.g., IPv6, IPv4, and Rime), and allows the use of different routing metrics as an alternative to the hop count.
## 1 Introduction
Since the IMM algorithm is incorporated in both the kinematic and dynamic model of the vehicle, the positioning performance is improved demonstrating high accuracy under the various driving conditions. Thanks to the advantages described above, we propose an intersection-target-intent estimation algorithm based on IMM [27]. In this research, we focus on the target intent estimation algorithm at urban intersections, and the paper is structured as follows. The next section demonstrates use of the intersection driver behavior model to represent the intent of the driver's maneuvering at the intersection. In Section 3, an IMM-based target intent classification algorithm is demonstrated by reflecting continuity of driver behavior while improving the accuracy of state prediction. The target intent estimation algorithm for urban intersections is verified via simulation studies in Section 4. Finally, conclusions are provided in Section 5.
## 2 Analysis of the State of the Art
### Context
#### 2.2.1 A Chronological Review of the Evolution of Context Management  
The following figure shows the evolution for context modeling by chronological order for the last 15 years.  
[PERSON] [42] also defines _context-aware systems_ as those systems which, using context data, provide significant information and/or services to the user where the relevancy of the given information depends on the user task. This domain dependency implies that each solution or context model is too concrete or specific and poorly extensible to other environments. [PERSON] _et al_. [43] consider several issues about context modeling emphasizing the excess of abstraction about context-aware systems and environments which causes a lack of models to be compared. Therefore, a categorized model for context-aware systems is presented. In 2001 [PERSON] [44] studies how context-aware computing represents a challenging frontier for researchers. In this work information about the environment, the user's current state, longer term user properties and the user behavior are compared in order to take the correct adaptation decision. Several research investigations focused on user interface adaptation area base their processes on context changes as triggers. However, they lack of a common model of context in their platforms [45,46]. This fact emphasizes the argument established by [PERSON] _et al_. [43].  
In this section, a review of the most popular context-aware systems is presented, mainly focused on the modeled context parameters, techniques, domains and dependencies. Besides, several authors define context and context-awareness through their own experience.  
2000, [PERSON] and [PERSON]  
[PERSON] and [PERSON] [47] define context as follows: _Context is the set of environmental states and settings that either determines an application's behavior or in which an application event occurs and is interesting to the user_. Besides, an active and passive definitions of context-aware computing are also given:  
* Active context awareness: _\"an application automatically adapts to discovered context, by the changing in the application's behavior\"_.
* Passive context awareness: _\"an application presents the new or updated context to an interested user or makes the context persistent for the user to retrieve later\"_.  
Based on the work by [PERSON] _et al_. [48], [PERSON] and [PERSON] consider time as an important and natural context feature for many applications. Besides, they introduce the term _\"context history\"_, which is an extension of the time feature recorded across a time span.  
A significant problem remarked in this work is the impossibility to exchange context information between the studied context-aware systems due to the way they model the environment. Furthermore, as location is one of the most modeled context features, [PERSON] and [PERSON] provide a study of several aspects that should be taken into account when researchers face the problem of modeling it.
# Evaluating Cluster-Based Synthetic Data Generation for Blood-Transfusion Analysis
## 1 Introduction
Similar to anonymisation techniques such as \(k\)-anonymity [24] or \(l\)-diversity [25], this causes individuals to become indistinguishable from one another within a group of peers. At the same time, relations between variables can be preserved with the number, size and location of clusters, e.g., a cluster with females taller than 2 m will typically be smaller than a cluster with males over 2 m.  
CBSDG has been subject to extensive experiments with simulated data, which demonstrated that the algorithm can generate high quality, privacy-preserving data [23]. In this controlled setting, several aspects of the data could be varied (types of data and sample size), but complex data features found in real data were not investigated. Contrary to other synthetic data generators, CBSDG has not been tested on real data, and further analysis is required to assess its practical potential. Considering that CBSDG is unique in generating synthetic data with tractable privacy preservation, it is meaningful to research whether the performance on simulated data is comparable for real data. This current work investigates this by replicating a published blood-transfusion analysis by [PERSON] et al. (2022) [26]. The data used reflect a wider variety of challenges, such as heavily skewed binary variables and multimodal continuous distributions. Furthermore, we take a deeper look into the quality of synthetic data than in [23], by assessing the preservation of intricate relations between variables. The objective of this study is to evaluate the practical potential of CBSDG by assessing whether a prediction model trained with synthetic data has the same predictive power as the model trained with real, personal donor data, and to what extent privacy is preserved.
## 4 Experimental Results
The integration of ANN based PSO + PF shows the lowest error and has a median value that is close to 0. The scatter plot presents the correlation of in situ wind speed with wind speed estimation. The first case, in Figure 10, shows the training sample at 30%. The R-square of the regression technique, the ANN based BP, the ANN based PSO, and the ANN based PSO + PF are 0.297, 0.689, 0.823, and 0.874, respectively. Figure 11 shows the second case: that with the training sample at 70%. The R-square of regression technique, the ANN based BP, the ANN based PSO, and the ANN based PSO + PF are 0.284, 0.842, 0.882, and 0.913, respectively. The covariance matrices of the particle filter were set by a trial and error technique. A comparison of the statistical results is illustrated in Table 1.  
Figure 10: Comparison of wind speed estimation results using different techniques with 30% of the training sample. Figure 7.0 An example of the reflected signal at lag 26: (**a**) 70% training sample; (**b**) relationship between the training sample and the wind speed.
## 3 Results
### Spatiotemporal Variation Characteristics of Drought in Xinjiang
#### 3.1.2 Overall Spatial Distribution of Drought in the Study Region  
Based on the drought indices of four different timescales, SPEI-03, SPEI-06, SPEI-12, and SPEI-24, the spatial distribution of drought in Xinjiang from 2000 to 2020 was analyzed across short-term (SPEI-03), medium-term (SPEI-06), and long-term (SPEI-12 and SPEI-24)  
Figure 3: Temporal variation of SPEI in Xinjiang from 2000 to 2020 (**a**) Temporal variation of SPEI at a 3-month timescale (SPEI-03). **(b**) Temporal variation of SPEI at a 6-month timescale (SPEI-06). **(c)** Temporal variation of SPEI at a 12-month timescale (SPEI-12). **(d)** Temporal variation of SPEI at a 24-month timescale (SPEI-24). Each panel shows the SPEI data series (blue) and trend line (red). Statistical values, including Z-score, \(p\)-value, and slope, are provided for each timescale to indicate the trend significance and direction.  
scales (Figure 4). Overall, the distribution of drought severity was consistent across all four timescales. The most severe drought areas were primarily concentrated in the R4 ecological region, and the minimum value reached \(-1\) for moderate drought. The severity of drought increased with an increase in scale, with the minimum value for SPEI-24 reaching \(-1.5\), indicating severe drought, and the affected area expanded gradually to include the R2 ecological region. On the SPEI-03 scale, the non-drought areas were primarily located in regions R1, R3, and R5. However, as drought severity and spatial extent increased, the non-drought area significantly decreased to approximately half of its original size. Meanwhile, the maximum SPEI value increased from 0.5 at SPEI-03 to 1.0 at SPEI-24, indicating that, although the non-drought area decreased, the level of wetness in these regions increased over longer timescales.
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 5 Performance Evaluation  
We present the performance evaluation of our proposed Q-RPL in this section. We compare its effectiveness against a standard RPL implementation (MRHOF) as well as three other routing solutions previously discussed in the related work section: RPL+ [34], ML-RPL [18], and RI-RPL [40]. To ensure a comprehensive analysis, we utilize two distinct scenarios based on actual deployments in the cities of Montreal and Barcelona. These scenarios vary in the number of nodes and the network topology.  
The next subsection outlines the specifics of our simulation settings, followed by subsections presenting the results obtained from each scenario and another subsection with a general discussion of the results.
## 1 Introduction
The results demonstrated that an increase in confining pressure notably enhances both compressive strength and deviatoric stress; additionally, under small displacement increments, the rock more readily reaches its ultimate strength, thereby verifying the feasibility of substituting experimental tests with numerical simulations. However, the aforementioned studies have primarily focused on the shear characteristics of granite under natural cooling or ambient conditions, and have not thoroughly investigated the compressive-shear failure mechanisms induced by high-temperature/liquid nitrogen thermal shock and water thermal shock. The considerable difference in thermal stress gradients generated by liquid nitrogen thermal shock (-196 \({}^{\circ}\)C) versus water thermal shock (26 \({}^{\circ}\)C) could significantly alter the shear failure mode through crack anisotropy, yet no study has quantitatively characterized this phenomenon to date. Despite the significant body of research conducted on the subject of liquid nitrogen cold shock and its effects on dry hot rock, the majority of studies have focused primarily on its impact on the physical properties and compressive and tensile strengths of high-temperature granite. Research on shear properties remains limited, and furthermore, the grain size of granite also influences its shear properties after liquid nitrogen cold shock. Therefore, in this study, variable-angle shear tests were carried out on high-temperature granite samples of different grain sizes subjected to liquid nitrogen and water-cooling shocks. This study analyzed the effects of treatment temperature, cooling method, and grain size on the shear strength, cohesion, and angle of internal friction of granite. In addition, nuclear magnetic resonance (NMR) and scanning electron microscopy (SEM) tests were used to investigate the internal pore evolution. The results of this study are expected to assist in the fracturing and modification of dry hot rock reservoirs.
# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 5 Discussion
Errors might therefore be introduced by AMS points in very small rice paddies, which cannot be detected with MODIS data, as well as mixed pixels when the station was located at a field's edge.
# A Simple Approach to Dynamic Optimisation of Flexible Optical Networks with Practical Application
## 2 Related Problems  
Some aspects of the efficient management [8] and optimisation [9] of FONs have been recently documented as the technology and its benefits have become more widely known. However, until now, the approach has been to define a multi-objective problem, involving several aspects of traffic management, in turn requiring expensive optimisation [10]. In [3], for example, a complex off-line linear programming formulation is given, which requires considerable computing power to solve. We propose two significant variations to this general approach in this paper:  
1. We do not consider complex traffic management, such as dimensioning [11], routing [12], or shaping [13], in our formulation, focusing solely on _relay use_.
2. Instead, we require a dynamic, computationally-inexpensive solution to the problem, which can be implemented in real-time in response to changing traffic patterns.  
Although (1) simplifies the problem, (2) significantly increases the challenge.  
For completeness, relevant optimisation problems and solutions from other fields (in networking and graph theory) include the Minimum Dominating Set (MDS) [14] and the Minimum Connected Dominating Set (MCDS) [15], Minimum Spanning Tree (MST) [16], Maximum Leaf Spanning Tree (MLSP) [17], and Shortest Path (SPP) [18] problems. We draw on components of all of these in this paper, described, where appropriate, in what follows.
# Research Advances and Application Prospect of Low-Temperature Plasma in Tumor Immunotherapy
## 5 Anti-Tumor Immune Mechanism of LTP
### LTP Regulates the Tumor Microenvironment  
Immune escape is one of the important mechanisms of malignant tumors. The tumor microenvironment (TME) is considered the main reason for immune suppression and evasion of immune surveillance. The tumor microenvironment plays an important role in the survival, growth, invasion, and metastasis of tumor cells. The immunosuppressive molecules and inhibitory molecules in the TME can affect the function of immune cells [50]. Due to the tumor microenvironment, many targeted therapies have failed to achieve desired results. LTP not only affects the tumor cells themselves, but also regulates the TME. It has been observed that long-term LTP treatment inhibited cell viability and collagen production of murine fibroblasts [51]. LTP could lead to increased cytotoxicity and a sharp decrease in the release of VEGF. [PERSON] et al. [31] found plasma exposure not only decreased M2 macrophages, but also remarkably increased the expressions of IL-1\(\alpha\), IL-1\(\beta\), IL-6 and TNF-\(\alpha\). The microenvironment is changed by LTP to prevent the growth and metastasis of tumors, and to inhibit immune cells, achieving a lasting therapeutic effect.
## 3 Results and Discussion
### Method Validation  
#### 3.4.1 Specificity  
Respective volumes of the four fat-soluble vitamin standards (2 uL for vitamin \(\rm D_{2}\), \(\rm D_{3}\), and E; 4 uL for vitamin \(\rm K_{1}\)) were applied and the HPTLC plates developed with the optimized mobile phase chloroform: cyclohexane (55:45, \(v/v\)). The specificity of the developed method was confirmed by distinctly different \(\rm R_{F}\) values for the four vitamins (Figure 2), 0.20 for vitamins \(\rm D_{2}\) and \(\rm D_{3}\), 0.46 for vitamin \(\rm E\), and 0.68 for vitamin \(\rm K_{1}\).
# Plasmonic Nanomaterials for Micro- and Nanoplastics Detection
## 3 Plasmonic Nanomaterials for MNPs Detection  
Metal nanoparticles have a wide application in modern sensing and analytical and bioanalytical techniques, mainly due to their peculiar optical properties. In this section, we describe all of the analytical approaches relying on localized surface plasmon resonance (LSPR).  
We recall that localized plasmonic resonance is the result of the confinement of a sur-face plasmon in a nanoparticle of a size comparable to or smaller than the wavelength of light used to excite the plasmon [34]. When a small spherical metallic nanoparticle is irradiated by light, the oscillating electric field causes the conduction electrons to collectively oscillate. This collective oscillation is called a localized surface plasmon. This physical entity can be used to exploit different analytical approaches for sensing. They can be generally divided into colorimetric methods (i.e., relying only on color change), SPR reflectance methods (based on the use of a SPR sensor chip), and plasmon enhanced fluorescence methods (based on the enhancement of the emission properties of the material).
# A Fiber-Optic Six-Axis Force Sensor Based on a 3-UPU-Compliant Parallel Mechanism
## 6 Conclusions  
This study presents a six-dimensional force sensor based on bend-sensitive optical fiber. A theoretical mapping matrix was established through static analysis and stiffness analysis. Finite element simulation was carried out to obtain the chord length changes of the FEMs. The loading forces were further calculated by using the established mapping matrix. The results show that the maximum error of the simulation is 0.98%, which verifies the correctness of the established mapping matrix. The measurement experiments were carried out, and the experimental results reveal that the type-I errors are 4.52%FS, 3.65%FS, 2.63%FS, 2.53%FS, 2.64%FS and 1.83%FS in the \(F_{x}\), \(F_{y}\), \(F_{z}\), \(T_{x}\), \(T_{y}\), and \(T_{z}\) principal measurement directions, respectively. The maximum type-II error is 3.26%FS. The relative experiments are carried out to investigate the repeatability and linear range of sensor. The maximum repeatability error is 3.27% when the force is applied in the Z-direction. The linear ranges of force loading \(F_{x}\), \(F_{y}\) and \(F_{z}\) are \(\pm\)60 N, \(\pm\)72 N and \(\pm\)90 N, and the linear ranges of torque loading \(T_{x}\), \(T_{y}\) and \(T_{z}\) are \(\pm\)2.5 N-m, \(\pm\)3.25 N-m and \(\pm\)3.75 N-m, respectively.  
Due to the many advantages of optical fibers, the proposed fiber-based six-axis force sensor has the characteristics of resistance to electromagnetic interference and adaptability to high-temperature and humid environments. This provides a novel approach for measuring force information in harsh environments.  
In the experiment, the temperature variations can lead to the instability of LED light sources, which cannot be ignored. In the future, we will introduce reference fiber in the sensing system to reduce the influence of the temperature. Improving the performance of the sensor is what we need to further consider. This may involve consideration of doping elements [23] and optimizing structural parameters.  
Conceptualization, H.D. and K.L.; methodology, J.M.; validation, S.C. and J.M.; formal analysis, J.M.; investigation, S.C.; resources, K.L.; data curation, K.L. and J.M.; writing--original draft preparation, K.L. and J.M.; writing--review and editing, S.C. and H.D.; visualization, K.L.; supervision, H.D.; project administration, H.D.; funding acquisition, H.D. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Heilongjiang Provincial Natural Science Foundation of China (LH2019F003).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.