esa-sceva/satcom-chunk-collection huggingface dataset:
In recent years, the application of direct injection mass spectrometry methods in VOC analysis has been presented as a good alternative in this field [14]. One of them is proton transfer reaction mass spectrometry (PTR-MS), which is gaining popularity. Although this technique was developed almost exclusively for the detection of gaseous organic compounds in air, it has become a remarkably versatile tool with applications in many areas of science and technology. Most published papers are based on the study of VOCs in the environment, particularly for atmospheric science, but PTR-MS application in food science/technology and health science has increased in recent decades. One of the main advantages of PTR-MS is that PTR-MS fingerprints can be obtained very quickly, which significantly accelerates the speed of testing because there is no need to separate individual sample components [15]. In addition, this technique allows real-time quantification while maintaining good sensitivity (even in ppq\({}_{\text{v}}\) levels), a wide dynamic range, and broad applicability (most VOCs, except, e.g., short-chain alkanes) [16; 17]. In this paper, the analysis of kaffir lime juice stored under different conditions, namely, 20 \({}^{\circ}\)C (room temperature), 4 \({}^{\circ}\)C (refrigerator conditions), and 35 \({}^{\circ}\)C (average daily air temperature of places where kaffir lime is grown during the kaffir lime harvest) was performed. The aim of this study was to assess the decomposition/degradation products of selected terpen in the kaffir lime juice. Therefore, the determination of the terpenes and their degradation products was performed using solid phase microextraction (SPME) and GC-MS. Data were processed using chemometrics, resulting in the selection of the major terpene degradation products. In the next step, high-throughput headspace analysis of juice was performed using PTR-MS. To the best of our knowledge, this was the first attempt to determine the terpene degradation pathway using GC-MS and PTR-MS. Providing research results in the field of food chemistry may contribute to the increasing interest in real-time food monitoring, especially using the PTR-MS technique. Because of the health-promoting effects of terpenes contained in kaffir lime juice, the obtained results create new opportunities for the potential use of kaffir lime juice extracts in the pharmaceutical industry, which will contribute to better disposal of waste after leaf picking.
# Self-Sustainable Biomedical Devices Powered by RF Energy: A Review
## 3 RF Energy Harvesting Application in Medical Devices  
The widespread availability and the diversity of RF energy sources (external and ambient) and the exponential growth in Internet of Things (IoT) and wireless sensor nodes has encouraged recent reported research work to employ RFEH techniques to energize low-power wearable and implantable medical devices. Biomedical devices gained widespread application in healthcare sectors due to the favorable feature of continuous monitoring of vital signals from the human body or apparent spurs from the internal body organs. RF energy is a prominent integral block for wearable and IMDs in sustaining the lifetime of the battery in use for an extended period, relaxing the need to change or recharge the battery frequently. This is enabled through the development and integration of ultra-low-power electronics, which decreases the power consumption of the primary sensor components to less than a milliwatt [108]. The current power consumption range of the wearable and IMDs is (nW to mW), as shown in Table 4. Therefore, RFEH technology is an appropriate solution to power these devices. In this section, we will discuss the implementation of the RF energy harvester in wearable and IMDs.  
Figure 5: Various rectifier topologies: (**a**) circuit diagram of half-wave rectifier; (**b**) circuit diagram of full-wave rectifier; (**c**) circuit diagram of bridge rectifier; (**d**) circuit diagram of three-stages Cockcroft-Walton voltage multiplier; (**e**) circuit diagram of four-stages Dickson voltage multiplier; (**f**) circuit diagram of four-stages Dickson voltage multiplier utilizing CMOS technology.
## 1 Introduction
These methods include support vector machines [40; 41], random forests [42; 43], Bayesian networks [44; 45; 46], and deep autoencoder networks [47]. Some small ore deposits or occurrences are overlooked in actual mineral prospecting if the association rules among elements are not considered [19]. How to efficiently delineate the metallogenic target area has become one of the main objectives of geochemical exploration. [PERSON] et al. (2014) found that local Moran's I could better detect the spatial clustering of elements in stream sediments on a small spatial scale than classical statistics, and local G* is suitable for detecting high clusters on a large scale [48]. [PERSON] et al. (2015) used geostatistics, as well as fractal and spatial autocorrelation methods, to study the spatial characteristics of geochemical data for stream sediments in southwest Fujian and concluded that the spatial autocorrelation method delineates the geochemical anomaly [49]. [PERSON] et al. (2017) used local Moran's I to analyze the spatial clustering and outliers of elemental concentrations and extracted geochemical anomalies [50]. [PERSON] et al. (2021) proposed a local correlation coefficient based on spatial neighborhoods to characterize the global distribution of elements [16]. The mutual influence and interaction among different elements produce a spatial pattern [51; 52]. The effects of regional geological and geochemical processes can be inferred from the spatial patterns in the concentration field. Therefore, exploring the association rules among different elements is of great significance for understanding geological processes. Association rule mining is one of the branch fields of data mining. The Apriori algorithm can uncover Boolean association rules between itemsets and has been widely used in spatial data mining [53; 54; 55; 56]. The Apriori algorithm was proposed by [PERSON] et al. (1993), who used it to mine association rules of sales data obtained from a large retailingcompany [57]. [PERSON] and [PERSON] (2019) used the Apriori algorithm to derive the anomalies of elements for metallogenic prediction [58]. In this paper, we propose an association rule mining method to study the cross correlation of concentration fields based on clustered events of spatial autocorrelation. This method can be used to comprehensively understand the spatial distribution of geochemical concentrations and co-existing of elements. Moreover, we compared the advantages and limitations of bivariate spatial autocorrelation and association rule mining results and finally explored the relationship of specific geological features with the results of association rule mining.
# Smart Grid Testing Management Platform (SGTMP) +
## 5 Conclusions  
In this paper, we introduced a smart grid testing management platform (herein referred to as SGTMP) for the execution of real-time hardware-in-the-loop SG tests and simulations that can help to improve the overall reliability, resilience, availability, integration, and security of the SG infrastructure. SGTMP is composed of a Mosaik interface written in Python and a Java-based testing platform that includes test definition, management, scheduling, execution, visualization, and other aspects supported by a web-based interface. We presented the central architecture, the testing management components, and co-simulation interfacing, together with the usage of the platform through an SG component stress testing scenario. Furthermore, we discussed briefly further scenarios (e.g., data model verification/optimization) that can extend the benefits of real/virtualized devices testing provided by SGTMP.  
There are several enhancements for SGTMP that are under evaluation, such as the addition of a domain-specific language (DSL) for the configuration (similarly to [PERSON] and [PERSON] (2010), [PERSON] et al. (2010)), allowing more natural creation and understanding of the models, resulting in faster development of simulation/testing scenarios. Another direction is to evaluate other co-simulation frameworks, looking at the implications for the interaction with power and communication simulators. Many co-simulation frameworks can be considered ([PERSON] et al., 2013), however, they differ by several criteria, like the type of synchronization offered (discrete events, time stepped, etc...), or like the degree of real-time and HIL support--among other characteristics. Giving support to other co-simulation frameworks can make SGTMP more flexible, at the expense of increased complexity.  
Figure 16: The development of the cost function in SGTMP environment across multiple model generations (lower values are better).  
**Author Contributions:** Conceptualization, M.S. and B.R.; Methodology, M.S. and K.H.; Software, M.S.; Supervision, B.R.; Visualization, M.S.; Writing--original draft, M.S., K.H., and B.R.; Writing--review & editing, B.R. and T.P.  
**Funding:** The research was supported by ERDF/ESF \"CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence\" (No. CZ.02.1.01/0.0/0.0/16_019/0000822).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Analysis  
Kinematics and kinetics were processed with a cut-off frequency using a low-pass Butterworth digital filter of 4 th order applied in a zero phase. A cutoff frequency of 6 Hz was used after a residual analysis for the kinematics and at 10 Hz for the force plate data. Euler XYZ body-fixed rotation angles are used to express the orientation using OpenSim [33] after an inverse kinematics process with body frames defined according to ISB recommendations [27; 28]. The COM position of the whole body was also extracted after the inverse kinematics process. In the coordinate system used, X is the anteroposterior axis, Y is vertical, and Z is the mediolateral axis.  
The stepping motion was divided into five phases [24]: anticipatory postural adjustments (APAs) and first double support phase (P1), swing phase of the dominant leg (P2), second double support phase (P3), swing phase of the second step (P4) and third double support phase until the end of the movement (P5), also named the restabilization phase. The APAs were considered to begin when one of the anteroposterior and mediolateral accelerations of the COM deviated 2.5 standard deviations from its baseline value until the toe-off of the dominant foot [25; 34]. The end of the motion was defined as the time-point at which the mediolateral COM velocity remained within 2 standard deviations of the mean calculated during the terminal-stance quiet standing after the end of the stepping [25; 35]. Each phase was normalized from 0 to 100%.  
The resulting subject-specific kinematics data, i.e., positions, orientations and velocities of body segments, and the inertial parameters were exported to a custom-made Matlab program for the angular momentum computation. The whole-body angular momentum at the COM (\(\mathbf{H^{COM}}\)) was calculated in three dimensions, as described by [PERSON] et al. [24]. Then, the derivative of the angular momentum was extracted using classical methodology via finite difference (\(\mathbf{K^{COM}}=\mathrm{d}\mathbf{H^{COM}}/\mathrm{d}t\)). Finally, the distance between the MMA and the COM was computed as explained below.
# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 4 Performance Comparison of Deep Learning-Based TKGC Methods  
In this section, we summarize the experimental results of existing deep learning-based TKGC methods on the public dataset ICEWS05-15, which contains the most temporal information, and analyze their performance, aiming to help researchers understand the current status of deep learning-based TKGC methods in more detail and provide references for subsequent research. It should be pointed out that the methods in Table 3 were implemented under different evaluation settings, i.e., raw setting, static filtered setting, and time-wise filtered setting. The same method may produce different experimental results under different settings. Therefore, it is necessary to explain the settings first. The raw setting and the static filtered setting are a pair of opposing settings, originally designed for static KGC [17]. The time-wise filtered setting [58] is a modified version of the static filter setting for TKGC. Specifically, in the raw setting, for each test triple, its corrupted triples are constructedby randomly replacing its subject entity or object entity with every entity in the entity set, while the static filtered setting constructs corrupted triples by removing all corrupted triples that are repeated with triples in the train, validation, and test sets to reduce the impact of these repeated corrupted triples. Since directly applying the static filtered setting to TKGC may wrongly remove corrupted quadruplets, [PERSON] et al. (2018) introduced temporal information into the static filtered setting and proposed the time-wise filtered setting. More specifically, for each test quadruple \((s,r,o,t)\in test\), it constructs the corrupted quadruples as \((s^{{}^{\prime}},r,o,t),(s,r,o^{{}^{\prime}},t)\
otin\mathcal{G}\), where \(s^{{}^{\prime}},o^{{}^{\prime}}\in\mathcal{E}\). From Table 3, we can observe the following:  
(1) Among the methods under the static filtered setting, the GNN-based method STRGNN achieves the highest Hits@1 value of 66.7%, which shows the effectiveness of GNN in TKGC. Similarly, among the methods under the time-wise filtered setting, the GNN-based method HyGNet achieves the highest Hits@1 value of 61.2%, which also shows the effectiveness of GNN in TKGC. Both of these indicate that GNN is worthy of further exploration for TKGC. (2) By comparing GNN-based, GCN-based, and GAN-based methods under the static and time-wise filtered settings, we find that GNN-based method achieved the best experimental results, which demonstrates that using methods with stronger feature characterization capabilities or more parameters does not necessarily improve experimental performance. Therefore, it is necessary to consider the characteristics of the TKGC task itself and design a model that is as simple as possible.
## 5 V. Test Framework  
To evaluate the performance of this framework in terms of data delivery and delay time, we proposed a test framework, as shown in Figure 3. The purpose is to measure the latency of the algorithm for both GOOSE publishing and subscribing in the data link layer and the DDS time delay over the network layer. This framework contains four Linux-based embedded agents shown by numbers (1) to (4). Agent (1), receives the analog input signal, converts it to digital mode (0/1), sets the GOOSE DataField according to this value, and publishes it to the network by the APPID # 1000. Agent (2) has an active GOOSE subscriber with the same APPID (#1000) and subscribes to this message. The GOOSE DataField along with StNum and SqNum are extracted and are used by DDS data writer to publish these data over the DDS topic. In Agent (3), the DDS message is subscribed and being published by the GOOSE publisher with another APPID (# 2000). Agent (4) receives this message, converts its digital DataField value to an analog signal, and delivers it to output ports of this agent, where this signal could be compared with input to determine the time delay due to communication platform.
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 2 Related Work
### ResNet Network Architecture  
The deeper the network, the richer the features that can be extracted from different levels. However, in deep CNN networks, exploding gradients can result in an unstable network. The gradient explosion problem can be solved through standardized initialization and standardized intermediate layers, but at the same time, it will also bring degradation problems. With the network depth increasing, accuracy gets saturated and then degrades rapidly.  
Due to the degradation problem, the residual neural networks use shortcuts to skip connections or skip certain layers. The typical ResNet model is implemented by two- or three-layer skips that include nonlinearity (ReLU) and batch normalization in between [45]. The shortcut connections simply perform identity mapping, and their outputs are added to the outputs of the stacked layers. Thus, identity shortcut connections add neither extra parameters nor computational complexity. Formally, we denote the desired underlying mapping as \(H(x)\) if we let the stacked nonlinear layers fit another mapping of \(F(x):=H(x)-x\). Then, the original mapping is recast into \(F(x)+x\). The formulation of \(F(x)+x\) can be realized by feedforward neural networks with'shortcut connections' [45].  
If the dimensions of \(x\) and \(F\) are equal, the output vectors of the layers can be defined:  
\[y=F(x,\{W_{i}\})+x \tag{21}\]  
The function \(F(x,\{W_{i}\})\) represents the residual mapping to be learned, and \(x\) is the input vector. For example, Figure 1 has two layers, and the relationship between \(W_{1}\), \(W_{2}\), ReLU, and function F can be represented as:  
\[F=W_{2}\sigma(W_{1}x) \tag{22}\]  
If the dimensions of \(x\) and \(F\) are not equal, a linear projection \(W_{s}\) will be performed by the shortcut connections to match the dimensions.  
\[y=F(x,\{W_{i}\})+W_{s}x \tag{23}\]  
For each residual function \(F\), a bottleneck was designed by using a stack of three layers. To reduce the input/output dimensions, the connected \(1\times 1\), \(3\times 3\), and \(1\times 1\) layers are responsible for reducing, increasing, and restoring the dimensions, respectively.
## 2 Measurement Principle and Causes of Dynamic Error  
### Measurement Principle  
Specifically, the measurement model of wMPS can be abstracted as two rotating laser planes. The normal vectors and z-axis intercepts of two laser planes at the reference point are described as:  
\[\left[\begin{array}{cccc}a_{ij}&b_{ij}&c_{ij}&d_{ij}\end{array}\right]^{T} \tag{1}\]  
where \(i\) and \(j\) indicate the \(i\)'th transmitter and \(j\)'th laser module. They are constant after the transmitter is assembled.  
When the two lasers scan across the receiver counterclockwise, the normal vectors can be expressed as:  
\[\left[\begin{array}{c}a_{ij\theta}\\ b_{ij\theta}\\ c_{ij\theta}\\ d_{ij\theta}\end{array}\right]=\left[\begin{array}{cccc}\cos(\omega_{i}t_{ij} )&-\sin(\omega_{i}t_{ij})&0&0\\ \sin(\omega_{i}t_{ij})&\cos(\omega_{i}t_{ij})&0&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]\left[\begin{array}{c}a_{ij}\\ b_{ij}\\ c_{ij}\\ d_{ij}\end{array}\right] \tag{2}\]  
\(\omega_{i}\) refers to the rotation speed of the \(i\)'th transmitter, and \(t_{ij}\) refers to the time difference between the synchronization signal and \(j\)'th scanning signal.  
When a laser plane scans over a receiver, the receiver will locate on the plane and the constraint formula between the laser plane and receiver can be constructed:  
\[\left[\begin{array}{cccc}a_{ij\theta}&b_{ij\theta}&c_{ij\theta}&d_{ij\theta }\end{array}\right]\left[\begin{array}{cccc}R_{11}&R_{12}&R_{13}&T_{x}\\ R_{21}&R_{22}&R_{23}&T_{y}\\ R_{31}&R_{32}&R_{33}&T_{z}\\ 0&0&0&1\end{array}\right]\left[\begin{array}{cccc}x_{g}&y_{g}&z_{g}&1\end{array} \right]^{T}=0 \tag{3}\]  
\(R\) and \(T\) represent the rotation and translation components from the global coordinate system to the local coordinate system respectively. They are derived from measurement network calibration [17]. \(\left[\begin{array}{cccc}x_{g}&y_{g}&z_{g}&1\end{array}\right]\) represents the homogeneous coordinate of the receiver in the global coordinate system. There are three unknowns in the coordinate of each receiver and they will be calculable as long as more than three formulas are available. Consequently, signals from more than two transmitterswith different rotation speeds will be capable for coordinate calculation. The accuracy is positively related to the number of transmitters scanning across the receiver.
## 3 System Model  
In this section, we discuss the considered system model, emphasizing the role of the controller in resource reallocation. Then, we describe an algorithm for resource reallocation oriented towards maximum resource utilization. This algorithm serves as a baseline \"fixed\" algorithm, meaning that it is a function assigning a new resource partitioning to each system state. Additionally, we introduce two other principles that will be taken into account in the model for the construction of a \"flexible\" algorithm, considering not only resource utilization.
# Chaves Thermal Spring Water Impact on Skin Health: Potential Cosmetic Application
###### Abstract  
Since ancient times, thermal spring water has been proven to be beneficial to the skin and to improving dermatologic disorders, explaining its incorporation into cosmetic formulations as an active ingredient. Chaves thermal spring water, from northern Portugal, has been used as a local spa since Roman times, and its customers are satisfied with its medicinal quality. Despite the lack of published evidence on its specific effects on the skin, this study evaluates the potential of using Chaves thermal water as a cosmetic ingredient. The physiochemical composition demonstrated that Chaves thermal spring water is low-mineralized water, and its major components are sodium, potassium, silicon, and calcium. In vitro experiments demonstrated that this low mineralization might explain the absence of antioxidant and antiaging potential, and the maintenance of collagen and fibronectin levels. The quantification of the IL-6 levels showed that Chaves thermal spring water could be used as an anti-inflammatory product, suggesting its use by individuals with skin diseases. In agreement with this result, in vivo experiments revealed that Chaves thermal spring water improved the integrity of the skin barrier and preserved the skin microbial community. Overall, the present work suggests that Chaves thermal spring water might be used as a cosmetic product.  
thermal spring water; cosmetic application; anti-inflammatory properties Article