esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Vegetation Filter Strip on Slope Runoff, Sediment Yield and Nutrient Loss
## 3 Results
### The Effects of VFS on Runoff Production and Sediment Yield
#### 3.1.2 The Effects of the VFS on Sediment Yield  
When the rainfall intensity was 30 mm/h, as shown in Figure 5a, under three slope conditions, the difference of sediment reduction effect of the two VFSs was consistent with that of runoff reduction effect. However, when the rainfall intensity increased to 60 mm/h, the sediment interception effect of VFS1 and VFS2 showed a completely opposite performance to the interception effect according to Figure 5b. In addition, for three slopes with a rainfall intensity of 90 mm/h (Figure 5c), the effect of the two VFSs in reducing sediment yield can be concluded to be consistent with the rainfall intensity of 60 mm/h. This indicated that the rainfall intensity had a great influence on the sediment interception effect of VFS under the combination of two setting modes and three slope gradients in this study.  
According to the results of sediment yield on the slope, the sediment yield on the slope increased with rainfall intensity and slope gradient. Compared with the bare land control, regardless of the rain intensity and slope conditions, VFS can effectively reduce the sediment yield on the slope. VFS can increase surface resistance and promote rainfall infiltration and sediment deposition so as to reduce slope soil loss simply and effectively [39; 40]. Similar to the runoff data, there was no linear correlation between the sediment interception effects of the two VFSs. When the slope remained unchanged, the average reduction rate of VFS1 for slope sediment yield was 40%, 33% and 52%, respectively, as the rainfall intensity increased from 30 mm/h to 60 and 90 mm/h. When the rainfall intensity remained unchanged and the slope increased from 5\({}^{\circ}\) to 20\({}^{\circ}\) and 35\({}^{\circ}\), the average reduction rate of VFS1 for slope sediment yield was 38%, 45% and 42%, respectively. In terms of the overall sediment interception effect of VFS1 and VFS2, the sediment reduction effect was negatively correlated with the slope under the condition of light rainfall intensity, but the steeper the slope was, the smaller the impact of the slope on the sediment reduction rate was. This was consistent with the simulation study [41]. However, with the increase of rainfall intensity, the sediment interception effect of VFS1 increased with the increase of slope. After the rain intensity continued to increase, the effect of VFS1 changed inconsistently at about 20\({}^{\circ}\), so it was speculated that the impact of critical slope might be the same as the runoff yield. This was also reflected in the effects of VFS2. A study showed that when the average stalk spacing of vegetation was greater than 3 cm, soil erosion was likely to occur within the filter strip, leading to an increase in slope sediment yield. In this experiment, the spacing of VFS according to their actual growth distribution may also affect the sediment interception effect of VFS.
## 2 Ensemble Neural Network and Signal Graph-Based SEI
### The Proposed Ensemble Neural Network  
In this study, an ensemble neural network (ENN) (Figure 2) was designed to make full use of sequence features and graph features, which are in two different spaces. It integrates sequence feature extractor with graph feature extractor and has a sequence classifier, a graph classifier, and an ensemble classifier.  
Based on the two feature extractors, the ENN can extract sequence features in Euclidean space and graph features in non-Euclidean space from a signal sequence and a signal graph simultaneously and then fuse the two features together, obtaining the ensemble feature. As the ensemble feature contains the features in both Euclidean space and non-Euclidean space, it has more sufficient feature information for SEI than a single sequence feature or a graph feature.  
The sequence classifier can identify a specific emitter based on the sequence feature extracted by the sequence feature extractor, and the graph classifier can identify a specific emitter based on the graph feature extracted by the graph feature extractor. However, the ensemble classifier can use the combination of sequence features and graph features to identify a specific emitter. The ENN treats the result obtained by the ensemble classifier as the final result. The sequence classifier and graph classifier support the training of ENN. Section 2.2.3 will show how to help the ENN obtain better SEI performance.  
Figure 2: Structure of the ensemble neural network proposed in this paper.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 1 Introduction
Thereafter, the isolation forest (iForest)-based error evaluation and anomaly detection model is trained using the four-dimensional features extracted from the training data. Thus, in the detection stage, MTAD\({}_{\text{mt}}\) first extracts the features of the spacecraft data via the four proxy tasks and then inputs those features into the MTAD\({}_{\text{ad}}\) to realize unsupervised and robust anomaly detection. The main contributions of this study are as follows:  
1. Multi-task learning is introduced to spacecraft anomaly detection, fusing original data and its joint latent representation to facilitate simultaneous reconstruction and prediction, to model the complex spatial and temporal patterns of the spacecraft data. 2. Proxy tasks that utilize LSTM networks to capture complex temporal dependencies are set (Proxy Tasks 1 and 4, as detailed in Section 3). 3. Proxy tasks to capture the complex spatial dependencies between different parameters are set (Proxy Tasks 2 and 3); these tasks use both a temporal convolutional AE and LSTM VAE to generate two latent representations. 4. An iForest-based error evaluation and anomaly detection model is constructed, which integrates features extracted by the above four tasks for anomaly detection. 5. Experiments performed on real spacecraft data demonstrate that the proposed MTAD exhibits superior performance to comparative methods. Moreover, ablation experiments performed for each task demonstrate the effectiveness of the employed combination of the four proxy tasks. The rest of the paper is organized as follows. Section 2 revisits the related work. Section 3 presents the data reprocessing process and our multi-task model, i.e., MTAD. Our approach is evaluated in Section 4. At last, we conclude the paper and discuss about the future work in Section 5.
Nevertheless, one of the main challenges is to assess food features by detecting variations in the metabolic composition of the product under investigation. The \"traditional\" approach relies on a targeted analytical method, by which a few a priori known compounds are detected and, eventually, quantified. However, this approach may fail when unknown substances are indicative of either accidental or intentional alteration of the food product. In this context, the non-targeted analytical method is becoming a more valuable tool for quality control and authenticity assessment [3]. Over the last decades, the use of an NMR-based analytical approach for food quality and authentication has significantly increased [4; 5; 6]. The distinctive ability of NMR spectroscopy compared to other analytical techniques is that of providing reproducible spectral data in terms of relative signal intensity, regardless of the spectrometer features [7; 8]. Recently, our research group contributed to the harmonization of NMR data production by differently configured spectrometers [9; 10]. A further attractive feature of the NMR-based analytical approach relies on the possibility of collecting a large amount of data related to the same sample. Such an NMR feature allows the entire metabolic profile (fingerprint) of a food product to be obtained rapidly and reproducibly [11; 12; 13; 14; 15]. Thus, variations of food fingerprints derived from possible alterations can be easily unveiled by NMR analysis at any stage of the control process in the supply chain. As a consequence, NMR data can be exploited, through Distributed Ledger Technologies such as blockchain, to trace food products and certify their quality, origin, and authenticity. In this work, we report on the application of the non-targeted NMR method toward the detection of adulterated saffron, which represents one of the most expensive spices on the market (up to 20.000 \(\mathrm{\upmu}\)\(\mathrm{\upmu}\)\(\mathrm{\  
conducted on saffron samples extracted both in water and in DMSO-\(d_{6}\). As a result, a pool of metabolites indicative of the contamination of the saffron samples was identified. In addition, during the present study, the developed non-targeted method was exploited to obtain useful insights into the agronomical practice adopted during the cultivation of the saffron under investigation, allowing discrimination between organic saffron and the conventional type.
## Methodology
### Generation of Meta-Models Using DOE  
DOE is a traditional and effective methodology based on statistical techniques that supports the analysis of complex processes and systems. DOE allows one to establish in a systematic way how changes in the parameters of a system or function affect their outcome, minimizing the uncertainty and the number of required experiments to complete such characterization. DOE covers the whole spectrum, from the planing of the experiments to the statistical analysis of the results [28; 29]. We used DOE techniques to develop meta-models to estimate the von Mises stress in Schwarz primitive lattice structures.  
The von Mises stress \(\sigma_{VM}\) is a material failure criterion used in the design and analysis of lattice structures in various works (e.g., [30; 31; 32]). The von Mises stress is defined per Equation (5):  
\[\sigma_{VM}=\sqrt{\sigma_{1}^{2}+\sigma_{2}^{2}+\sigma_{3}^{2}-(\sigma_{1} \sigma_{2}+\sigma_{1}\sigma_{3}+\sigma_{2}\sigma_{3})}, \tag{5}\]  
where \(\sigma_{1},\sigma_{2},\sigma_{3}\) are the principal stresses. The criterion states that, for preventing failure, the von Mises stress must be below the tensile strength of the material. However, failure in lattice materials is also governed by buckling instabilities that occur before material failure [13]. For the sake of demonstration of the methodology, this work is limited to the estimation of the von Mises stress, although a more complete failure criterion for lattice structures should consider buckling phenomena.  
The procedure to devise meta-models using DOE is summarized in three phases: (1) identification of potential features (also called factors) that may affect the variable of interest, (2) selection of the most influential (main) factors and (3) development of simple mathematical expressions (meta-models) that relate the main factors and the response variable.  
We applied DOE to develop meta-models for the von Mises stress in Schwarz primitive lattice structures of different relative densities. In an ideal case, we should have attained a meta-model for each relative density \(\rho\in(0,1)\). This computational demand makes this option unfeasible. Since we were only seeking approximations of the von Mises stress, we found meta-models for the relative densities \(\rho=0.25,0.50,0.75,1.0\). To find the four meta-models, we used lattice domains formed by a single unit cell of the mentioned relative densities. Below, we describe in detail every stage of the procedure.
# Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring
## 6 Discussion
### Connectivity  
Regarding connectivity, it is worth mentioning that our previous approach [16] and the approach presented in this work are the only ones from the table designed to be deployed in indoor environments. The rest of the approaches were designed for outdoor WASNs.  
As it can be observed in the table, MESSAGE [8] is the only one that uses ZigBee to connect the nodes. Such connectivity protocol can also be used for indoor spaces. However, adding such a protocol involves using an external antenna, which would increase the cost of the proposed nodes. Moreover, ZigBee is a widely used protocol in indoor spaces (mainly in home automation), but each maker can use it in different ways, causing incompatibilities among them. Furthermore, ZigBee has a 256 Kbps throughput, compared to the 600/54 Mbps throughput that can be achieved using WiFi [48]. For these reasons, authors consider WiFi a better option, as it is integrated into the Raspberry Pi computing unit. This way, no additional costs per node are required. Considering that there is a linear relation between the cost of the nodes and the final cost of the WASN, reducing costs per node is of great importance in terms of scalability. Additionally, the selected connectivity technology (WiFi) enables achieving faster throughput, which is beneficial for real-time applications.  
Regarding the comparison of direct WiFi connections to meshed connections, Table 4 has demonstrated that there is a better performance when using a mesh network, compared to a direct WiFi connection. Therefore, the presented meshed approach is more suitable for the proposed application than the approaches designed for the SONYC project [23] and our previous approach [16].  
The work presented by [PERSON] et al. [9] uses a mesh connection as well. In this case, they use the Optimized Link State Routing (OLSR) protocol. In the presented approach, we chose to use the Hybrid Wireless Mesh Protocol (HWMP). Both approaches are standard and widely used mesh protocols [49]. As it can be seen in Table 4, the HWMP protocol has enabled us to obtain good results as all the nodes of the network were able to send data frames with more than 99% of success. As the HWMP protocol was working correctly, no other mesh protocols were experimentally evaluated.
# Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice
## 2 Data  
### Study Area  
The study area in this paper encompasses the ocean surrounding Antarctica, specifically at latitudes above 60\({}^{\circ}\)S. Due to the regional influences, sea ice and its surface snow exhibit significantly different characteristics around Antarctica [24; 25]. To better analyze the spatial distribution and the underlying processes, the Antarctic Ocean is commonly divided into five sectors: Weddell Sea (60\({}^{\circ}\)W-20\({}^{\circ}\)E), Bellingshausen and Amundsen seas (130\({}^{\circ}\)-60\({}^{\circ}\)W), Ross Sea (160\({}^{\circ}\)E-130\({}^{\circ}\)W), Southern Indian Ocean (90\({}^{\circ}\)-160\({}^{\circ}\)E), and the Southern Pacific Ocean (20\({}^{\circ}\)-90\({}^{\circ}\)E) (Figure 1a). In general, Antarctic sea ice has rapidly decreased in recent years. After breaking low Antarctic sea ice extent records in 2016 and 2022, the extent reached its lowest historical level in the past 40 years in 2023 [26; 27]. In the Antarctic Ocean, sea ice levels fluctuate seasonally; only the coastal regions of the Antarctic continent retain ice during the melting season, whereas the ice cover extends further from the shore during the ice growth season [28].
## 1 Introduction  
Surface solar irradiance (SSI) is a key variable in the dynamic process that occurs in the boundary layer. SSI plays an important role in different vital processes such as: photosynthesis, energy generation, agriculture and water resources management. For energy generation, the direct use of SSI requires solar energy technologies such as photovoltaic (PV) and Concentrated Solar Power technologies (CSP). PV technology and CSP are clean, sustainable, and renewable energy conversion technology that can contribute in meeting the energy demands of the world's growing population, whilst reducing the adverse anthropogenic impacts of fossil fuel use. Ref. [1] examined the current and future potential of solar and wind energy over Africa using the RegCM4 within the framework of the CORDEX-CORE ([2]-Coordinated Output for Regional Evaluations; CORE) ensemble. They found that the RegCM4 model has a good capability in reproducing the spatial pattern of the SSI against satellite products (SARAH-2).  
Ref. [3] showed that runoff parameterization plays an important role in simulating the surface climate, particularly in the June-July-August (JJA) season. However, this role was not examined for the SSI. In addition, the vegetation status was static (i.e., vegetation parameters were retrieved from the MODIS satellite product). Therefore, this study aims to explore the potential role of the vegetation-runoff coupled system (in simulating the SSI) within the framework of the regional climate model (RegCM4). Section 2 describesthe study area, data and methodology, while Section 3 presents the study results and are discussed in Section 4.
# GNSS RTK Positioning Augmented with Large LEO Constellation
###### Abstract  
It is widely known that in real-time kinematic (RTK) solution, the convergence and ambiguity-fixed speeds are critical requirements to achieve centimeter-level positioning, especially in medium-to-long baselines. Recently, the current status of the global navigation satellite systems (GNSS) can be improved by employing low earth orbit (LEO) satellites. In this study, an initial assessment is applied for LEO constellations augmented GNSS RTK positioning, where four designed LEO constellations with different satellite numbers, as well as the nominal GPS constellation, are simulated and adopted for analysis. In terms of aforementioned constellations solutions, the statistical results of a 68.7-km baseline show that when introducing 60, 96, 192, and 288 polar-orbiting LEO constellations, the RTK convergence time can be shortened from 4.94 to 2.73, 1.47, 0.92, and 0.73 min, respectively. In addition, the average time to first fix (TTFF) can be decreased from 7.28 to 3.33, 2.38, 1.22, and 0.87 min, respectively. Meanwhile, further improvements could be satisfied in several elements such as corresponding fixing ratio, number of visible satellites, position dilution of precision (PDOP) and baseline solution precision. Furthermore, the performance of the combined GPS/LEO RTK is evaluated over various-length baselines, based on convergence time and TTFF. The research findings show that the medium-to-long baseline schemes confirm that LEO satellites do helpfully obtain faster convergence and fixing, especially in the case of long baselines, using large LEO constellations, subsequently, the average TTFF for long baselines has a substantial shortened about 90%, in other words from 12 to 2 min approximately by combining with the larger LEO constellation of 192 or 288 satellites. It is interesting to denote that similar improvements can be observed from the convergence time.  
Keywords:real-time kinematic (RTK); LEO constellation augmented GNSS; medium-to-long baseline; convergence time; time to first fix (TTFF) +
Footnote †: journal: remote sensing
# Design Techniques for Low-Voltage RF/mm-Wave Circuits in Nanometer CMOS Technologies
## 5 Conclusions  
This paper has provided the reader with an overview of state-of-the-art low-voltage design techniques for RF/mm-wave ICs, from body biasing for low-\(V_{\text{T}}\) operations of nanometer CMOS transistors to reactive resonant coupling for sub 1-V circuits. A special insight was dedicated to transformer-based power-combining approaches to boost the PA output power, especially at mm-wave frequencies. Thanks to proper combinations of the described techniques, several high-frequency applications can be addressed by using sub 1-V CMOS ICs. This process started with RF wireless communications and now involves mm-wave applications, such as \(W\)-band automotive radar and beyond. Nanometer FD-SOI CMOS technologies are the natural candidates for future low-voltage RF/mm-wave ICs, especially with thick BEOL for better passive components.  
This work was supported in part by the University of Catania through the Project \"Programa Ricerca di Ateneo UNICT 2020-22 linea 2\".  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The author declares no conflict of interest.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 8 Vulnerabilities in HE
### Administrative and Cultural Vulnerabilities in HE
#### 8.1.2 Insufficient Information Security Management  
[PERSON] [1] argues that security management is a highly under-investigated topic. UNIT [6] found that several of the surveyed institutions had implemented or where in the process of implementing infosec management systems. However, these were mainly not operationalized due to lack of personnel, resources and limited knowledge of practical infosec and privacy work. We can assume that institutions with smaller security budgets do not invest in security management systems. Additionally, few institutions had contingency plans to restore operations of systems or IT-infrastructure; neither did they know which systems were critical for operations. The six surveyed Turkish universities also scored poorly on aspects within security management, for example, five of them did not have sufficient data classification policies which is a cornerstone of security management. Additionally, all were missing appropriate disposal routines [4]. The case study of the Jordanian University concluded that _\"information systems are facing real possible dangerous security breaches due to the presence of a huge number of different kinds of vulnerabilities in their information systems\"_[52]. Moreover, the authors write that they assume _\"most of Jordanian universities have similar IT setup,\"_ and they conclude missing ISMS as one of two primary vulnerabilities.  
Poor information security management can make the organization vulnerable within several areas: Industrial espionage and illegal data extraction is documented as reoccurring events within the Norwegian university [58]. Not having defined appropriate security policies to address these issues will leave the organization uncertain regarding what is allowed and not. Furthermore, both [PERSON] [8] and [PERSON] [9] document copyright and policy violations as significant contributors to incidents. Clarity of both the security policy and the possibility for sanctioning violations are key elements of this vulnerability. Additionally, when polled about security leaks and routines, 11% of the employees at a Norwegian University said that knew about occurrences of data leaks of PII within the five previous years [58]. The same report also documents weak data handling routines. Security management in terms of policy, guidelines, and routines are vital to prevent sloppy data handling. All must be tailored to support the organization's mission.