esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential Metal Contamination in Foods of Animal Origin--Food Safety Aspects
## 4 Exposure Calculation for Risk Management
### Tolerable Intake  
In order to protect the health of consumers, the maximum daily, weekly, or monthly (the latter in the case of cumulative substances) intakes of major pollutants are determined, which do not cause health damage even in the case of continuous, lifelong intake. This amount is called tolerable daily, weekly, or monthly intake (TDI/TWI/TMI) for pollutants.  
The international organization (the relevant joint expert committee of Food and Agriculture Organization [FAO] and Word Health Organization [WHO]; Joint FAO/WHO Expert Committee on Food Additives [JECFA]) dealing with risk assessment and the determination of tolerable intake values based on this can often only provide provisional (provisional = P) TDI, TWI, and TMI values (PTDI/PTWI, PTMI) based on the available data, which are reviewed from time to time based on new toxicological and epidemiological data and, if necessary, changed.  
The previous PTWI value of arsenical (15 ug/kg b.w.) has been withdrawn by EFSA and a new one has not yet been estimated [33]. Similarly, the EFSA decided that the PTWI of 25 ug/kg b.w. for lead is no longer appropriate [111]. Regarding mercury, the following two PTWI values are set: 4 ug/kg b.w. for inorganic mercury and 1.3 ug/kg b.w. for methylmercury [133]. The CONTAM Panel of EFSA established a tolerable weekly intake (TWI) for cadmium of 2.5 ug/kg b.w. [63].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Type of Food** & **Maximum Limit, to** \\  & **Total Weight (mg/kg)** \\ \hline \hline  & Copper \\ \hline Meat products (raw-smoked, fermented, stuffed, cooked, & 5.0 \\ smoked meat products, etc.) & 10.0 \\ Preserved meat products in can (excluding liver pate) & 10.0 \\ Liver pate in tube or can & 20.0 \\ Game meat, meat product produced from them & 5.0 \\ \hline  & Lead \\ \hline Special formulas for infants and young children & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum limit of copper and lead in foods of animal origin (49/2014. (IV.29.) Regulation of Ministry of Rural Development) [149].  
During the calculation of intake level, the following equation is used:  
\[\text{Tolerable Intake = (Conc \times Cons)/BW} \tag{1}\]  
where Conc is the concentration of the metal (mg/kg) in the investigated food; Cons means the daily average consumption of the examined food (g/day/person); and BW is an average human body weight (kg).  
Then, this amount must be multiplied by seven for the calculation of weekly intake or by twenty-eight for the establishment of monthly consumption.  
The calculated intake must be compared to the existing officially recommended PTWI or PTMI value mentioned above to decide whether the investigated food is safe for human consumers or if it is objectionable.  
Due to the withdrawal of some of them, newer terms/parameters, such as EDI (estimated daily intake), THQ (target hazard quotient), and HI (Hazard Index) were introduced to assess the potential exposure of contaminants.
# Analysis and Applications of GlobeLand30: A Review
## 5 Discussion and Outlook
### Monitoring Progress towards Sustainable Development Goals (SDGs)  
The GlobeLand30 dataset has shown its potential in the status and change analysis, cause and consequence analysis, as well as the environmental parameterization of earth system models. The dataset has been listed as one of major GLC datasets to support monitoring progress towards the UN 2030 SDGs through its 230 indicators [88]. As a global and international data product, GlobeLand30 can be used first as a supplement or a potential alternative to national data when the reliable land cover data of one country is unavailable or lacks the capacity to generate them. Second, natural disasters, displaced populations, environmental change, water shortages, pandemics, and widespread malnutrition do not stop at national borders or the water's edge [104]. GlobeLand30 may have higher consistency across space, whereas the integration and harmonization of datasets from neighboring countries might be extremely difficult because of their differences in reference frames, spatial resolution, thematic types as well as periodicity. Third, global datasets can serve as a sound basis for preparing global reporting of certain SDG indicators and for visualizing or communicating their global status and trends to policy-makers and end users.  
Methodological development and overall data availability are two challenges that face monitoring progress towards SDGs with GLC and other geospatial datasets. From the methodological point of view, analytical models, metrics, and tools need to be developed to compute or derive SDG indicators/indices from GLC or geospatial data. Sensitivity and uncertainty analysis need also be considered to test the efficacy and robustness of the computing approaches. As far as the overall data availability is concerned, a number of technical issues need to be solved to integrate GLC, statistical data and other geospatial data. For instance, disaggregation approaches will be developed and used for generating a dataset with a more refined thematic content by combining global data and ancillary data sources. Aggregation may be used to downscale high-resolution datasets into the desirable scale.  
This work has been funded by Special Fund for Surveying, Mapping, and Geoinformation Scientific Research in the Public Welfare (grant no. 201512028), the National Science Foundation of China (project #41231172), and the 863 project of China (grant no. 2013 AA122802).  
[PERSON] conceived and designed this review article; [PERSON], [PERSON] and [PERSON] contributed the literature and materials; [PERSON] and [PERSON] wrote and revised the paper.  
The authors declare no conflict of interest.
## 3 Experimental Setup
### Raw Data Segmentation  
The acquired three-phase current signals were converted into the D-axis component using Equations (1) and (2). From the 10,000 samples per second, the first 1000 samples were discarded to account for signal loss due to motor startup and filtering. Therefore, 9000 samples were used for the actual image conversion.  
As shown in Figure 6, the 9000 samples were segmented into chunks of 1024 data points to generate 32 \(\times\) 32 pixel images. Consequently, 8 image samples were obtained per second.  
Figure 4: Data measurement setup.  
Figure 5: stator winding short-circuit configuration.  
Figure 6: Raw data segmentation.
# Improving the Impact Resistance of Anti-Ram Bollards Using Auxetic and Honeycomb Cellular Cores
## 1 Introduction
For example, when the material is stretched, it expands transversely and contracts during compression, i.e., they exhibit a negative Poisson's ratio [35; 36; 37; 38; 39]. Research in this field confirms that auxetic metamaterials outperform traditional honeycomb structures in terms of their energy absorption characteristics [40; 41; 42; 43; 44]. [PERSON] et al. [45] provided a detailed review of auxetic tubular structures (in general) and their state-of-the-art applications. Researchers focused on the possibilities of improving a vehicles' front end (bumper) by incorporating novel auxetic systems that could absorb crash-induced energy [46]. [PERSON] et al. [47] suggested adding auxetic energy absorbers in an automobile's front end (bumper) to improve pedestrian lower leg protection. The study concludes that pedestrian lower leg protection was improved significantly by optimizing the NPR structure. [PERSON] et al. [48] proposed an auxetic hierarchical crash box for front bumpers composed of an auxetic filling in a square, thin-walled tube. Numerical results proved that vehicle crashworthiness remarkably improved after adding the auxetic hierarchical crash box, with a specific energy absorption (SEA) of 29.26%, which was higher than that of a traditional crash box [48]. A study conducted by [PERSON] et al. [49] performed anti-ram bollard tests numerically using LS-DYNA software and tested different types of vehicles with different specifications/categories. For the M1 category of vehicles, those with a mass of 1500 kg, different speeds were used in the simulation to measure how bollards react to each of the crashes. It is important to highlight that the research [49] covered only hollow-steel pipe bollards with no additional cellular cores. The research concludes that \"_existing fixed bollard systems are not sufficient for the protection of hazardous roadside facilities and need to be improved_\" [49]. Based on the reviewed literature, hollow-steel bollards and honeycomb-core bollards were tested and examined by previous researchers, such as [49] and [30], respectively. However, to the authors' knowledge, the use of auxetic core explicitly in anti-ram bollards was not examined before and can be considered the novel part presented in this paper. Hence, the aim of this study was to numerically simulate a bollard crash test (following PAS 68:2013 [8]) and to check the deformations and energy absorption of the bollard with and without non-auxetic and auxetic cellular cores. The objectives of this research can be described as follows:  
* Numerical modeling of hollow-steel bollards during sudden vehicle impact. * Introducing a new cost-effective bollard with a honeycomb cellular core. * Improving the impact absorption of the steel bollard using an auxetic cellular core.
## 4 Simulation and Experimental Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Topology** & **Active-Clamped** & **Proposed Snubberless** \\ \hline \multirow{2}{*}{Soft-switching} & ZVS & ZCS \\  & (soft-switching at turn-on) & (soft-switching at turn-off) \\ \hline \multirow{2}{*}{Soft-switching range} & Limited range & Full range \\  & (soft-switching is lost with the source voltage variation) & (inherent soft-switching for the entire operating range) \\ \hline \multirow{2}{*}{Device voltage} & Higher voltage and variable with duty cycle & Clamped at reflected output voltage and duty cycle independent \\ \hline \multirow{2}{*}{Boost Capability (voltage gain)} & Voltage gain (boost) is compromised; & \\  & 20\% reduction at rated load & Natural boost gain \\ \hline \multirow{2}{*}{Device RMS current} & Circulating current is present that increases average and RMS value & 10\% reduction in the rms current due to the absence of active clamp \\ \hline \multirow{2}{*}{Device peak current} & 1.5\% input current & Same as input current (33\% less) \\ \hline \multirow{2}{*}{Transformer current} & Same as input current & Half of the input current (50\% less) \\ \hline \multirow{2}{*}{Efficiency} & High & 2\% improvement \\ \hline Power flow & Unidirectional & Bidirectional \\ \hline \multirow{2}{*}{Duty cycle variation} & Wide range (0 to 1) & Limited (0.5 to 1) \\ \hline \multirow{2}{*}{Transformer} & Higher turns ratio, higher kVA rating and volume & Relatively lower turns ratio, reduced kVA rating and volume (40\% less) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of proposed snubberless topology with active-clamped topology. Figure 19: Experimental results for \(v_{in}=12\) V at full load: (**a**) gate-to-source voltage \(V_{GS1}\) (20 \(V/\mathrm{div}\)), current across primary-side MOSFET \(i_{s1}\) (20 A/div), and voltage \(V_{AB}=50\) V/div; (**b**) gate-to-source voltage \(V_{GS3}\) (20 V/div), drain-to-source voltage \(V_{ds3}\) (200 V/div), and current across secondary-side MOSFET \(i_{s3}\) (2 A/div).
# Framework for Malware Triggering Using Steganography
## 3 Methodology  
This section of the paper describes the attack design and implementation. Figure 2 below illustrates the attack architecture. The primary goal of this study was to develop an attack that would exploit image steganography and target a specific location where malicious code is concealed in a digital image. When the stego-image reaches the target location, it will be triggered.  
Based on Stegosploit, we conducted three attack phases. The contact and exploit stages are the first and last stages of the attack, the same as the stages of the exploit delivery technique. The intermediate stage is the propagation stage, which differs from the redirection stage of the exploit delivery technique [12].  
This paper tries to answer these questions: first, may the proposed attack remain stealthy for a long time before reaching its target? Second, may the proposed attack trigger when it reaches the target based on location? Finally, can the image quality be distorted by hiding the malware inside it?  
The experiment can be summarized as follows. First, we surveyed the various geolocation services methods and chose the appropriate to encode it with the image in our attack. Then, we used steganography to encode the JavaScript exploit and geolocation code into a PNG image. In addition, an HTML + PNG polyglot file was created using the Stegosploit toolkit. Next, we uploaded the polyglot image into a vulnerable browser. The malicious code will start running immediately when the target opens the image. The intended attack was carried out on the target system based on location.  
The general procedure for carrying out the above attack is described in detail in the following sections.
## 1 Introduction
Primarily, Hop latency was determined by taking the busy-to-idle ratio of the data being distributed, followed by the creation of multicast decision tree to minimize the summation of the total forwarding period of the relays and the sum of blocking time of all nodes that are obstructed by using the transfer data of neighboring nodes. Additionally, the data rates of the relays were precisely modified to lessen the resource consumption such that several flows were enabled in the MANETs. Conversely, additional QoS metrics were essential to further improve the network throughput. To overcome this problem, the QSSM-M algorithm [6] has been designed, which reports the bandwidth, packets dropped, and jitter together with the latency determined from every nearby node for relays. Then, several QoS-satisfied multicast trees were formed,where all trees guarantee the fixed range of QoS criteria. So, the total latency and bandwidth use was lessened if guaranteeing the QoS to the present and desired flow. Moreover, a randomized code for the network is applied to reject the packets that are not relevant so that all destinations receive the novel coded packets through different routes. Nonetheless, the data transfer rate for the source was not properly decided because of using different transfer areas. To decide a proper data transfer rate for source node, the EECRM routing algorithm [7] has been presented, which uses the advantages of Deep Neural Networks. Here, the dilemma of multi-label classification for deciding the data rates was taken as a primary challenge. Then, different metrics such as payload size of transfer frames, transfer route efficiency, and throughput were measured whilst ensuring the predetermined false error rate. Further, such metrics were learned by the DCNN to predict the proper data rate for the source and achieve effective forwarding. Though it enhances the network throughput significantly, the network lifetime was not adequately increased because of high energy depletion. The EECRP-PSO method took the problem of energy utilization and life span as an optimized problem. The Particle swarm optimization was deployed for optimal usage of energy. However, the method could not prove to be scalable when more nodes are taken for experiments. A large deviation in the performance was noticed when the number of nodes was increased in the order of 400. In this research work, a machine learning-based routing technique has been presented for minimizing energy usage and for improving the life of a MANET. Here, a maximization problem is framed, keeping the objective to increase the lifetime of a network amidst maintaining the optimal use of energy and stability of the path. Second, a strategy is adopted for energy transmission in an asymmetric manner. Adaptive transmission of energy both at the origin and intermediate nodes is performed. The proposed method significantly reduces energy depletion and increases the lifetime of the MANET. The proposed method produces significant results in terms of 1-Hop Latency, End-to-End Latency, Success rate, Admission Rate, and Control Byte Overhead.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 6 Service-Oriented Middleware (SOM) Architectures Approaches for WSNs
### Sensors MiddleWare (SensorsMW)  
In [49], the SOM architecture is used for QoS configuration and the management of the WSNs. The authors presented Service-Oriented, adaptable, and flexible middleware (SensorsMW). This middleware supports the dynamic management of heterogeneous data. The middleware has the capability to hide the complexity of low-level sensor devices [49]. Once the SensorsMW abstracts the WSNs, it acts as a gathering service and easily integrates into the enterprise information system. The applications collect the sensed information by using a web service. Consequently, the SensorsMW allows high-level applications to configure a data collection level for the WSNs in a simple manner. This approach enables the application to collect data by using a web service, which can guarantee flexibility in the delivery of the data. Furthermore, this architecture enables applications to independently negotiate from run time by using a technique called the contract negotiation approach, based on a Service Level Agreement (SLA) [49]. SLA stops the application from requiring knowledge of the other QoS applications. The SLA enables the application to reconfigure and maintain the network within its lifetime. Every end-device node contains Crossbow MicaZ (Crossbow Technology, Inc., Milpitas, CA, USA) [49]. Every node has TinyOS 2.0 (TinyOS Alliance) [49]. The implementation only focuses on service level management and does not provide any mechanism to handle a secure execution or communication. Typically, in WSNs, a faulty node is factored into the performance of the system in order to generate the correct execution. Unfortunately, this approach does not take this fact into consideration. In addition, the resource management of the system does not support any node with low capacity. The details of QoS parameters, resource surveillance, scalability, and data evaluation are not provided.
# Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools +
## 8 Conclusions and Future Work  
We can draw the conclusion that inferential SIR-GN can produce vectors to represent the structural data of every node in a network. Additionally, an ML technique for botnet identification can be used with the graphical depiction of the nodes. The inclusion of a neural network classifier with inferential SIR-GN enables the identification of botnets, regardless of the topology used for training, which is a noteworthy advantage over models based on prior knowledge of the graph topology. We have also seen how quickly a botnet structure can alter, mostly as a result of the significant rise in P2P connections. The variety of botnet topologies has increased as a result of this circumstance. This means that, in contrast to methods that leverage GNNs, the usage of inferential SIR-GN in tandem with a neural network classifier will result in a very high botnet detection ability. Due to the difficulty of adaptability, GNN techniques may not be able to identify previously unknown botnet topologies. Another line of research to be followed consists of coupling our framework with emerging big data trends (e.g., [78; 79; 80; 81; 82; 83; 84; 85]).  
**Author Contributions:** Conceptualization, A.C.; methodology, A.C.; software, A.H. and C.G.; validation, A.C., A.H. and C.G.; formal analysis, A.C.; investigation, A.C.; resources, A.C.; data curation, A.H. and C.G.; writing--original draft preparation, A.C., A.H. and C.G.; writing--review and editing, A.C.; visualization, C.G.; supervision, A.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by project SERICS (PE0000014) under the MUR National Recovery and Resilience Plan funded by the European Union--NextGenerationEU.  
**Data Availability Statement:** The datasets generated during and/or analyzed during the current study are publicly available.  
**Acknowledgments:** The authors are grateful to [PERSON], [PERSON], and [PERSON] for their contributions to an earlier version of this research.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest. They also declare that they do not have any competing financial interests.
###### Abstract  
The aim of the present study was to analyse differences in acceleration and movement variability caused by adding whole-body vibration (WBV) and an implement (stick) while performing a slide-board (SB) skating exercise. A total of 10 professional ice-hockey players (age 20.4 \(\pm\) 2.07 years) participated in the study. Participants performed 30 s of lateral sliding on a slide vibration board (SVB). Four conditions were analysed: no vibration and no stick (NVNS), no vibration with a stick (NVS), vibration without a stick (VNS) and vibration with a stick (VS). Peak acceleration, mean acceleration and movement variability (MV) were analysed in the dominant and non-dominant legs in each condition. Peak acceleration was higher in the non-dominant leg (\(p\) < 0.01). However, MV was higher in the dominant leg (\(p\) < 0.01). Regarding differences between conditions, mean acceleration was higher in VNS and VS than in NVS (\(p\) < 0.05). Regarding MV (sample entropy), there were differences in NVNS compared to VNS and VS (\(p\) < 0.01) and in NVS compared to VNS and VS (\(p\) < 0.01), with the values being superior in VNS and VS. The addition of WBV during an SB skating exercise results in an increase in MV and mean acceleration. The dominant leg shows greater MV regardless of the addition of vibration and a stick during sliding on an SVB.  
entropy; ice hockey; whole-body vibration; skating; off-ice training; slide training +
Footnote †: journal: Applied Sciences  
Article