esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Channel Model
### MIMO Joint Fading Channel Model  
According to the above description of channel fading, the distribution of \(h(t)\) is the same as the joint distribution of \(h_{\rm{t}}(t)\)-\(h_{\rm{p}}(t)\). It is assumed that the effects of ocean turbulence and pointing errors are approximately independent of each other. Therefore, considering the combined effects of the Malaga turbulence and Rayleigh pointing errors, the PDF can be expressed as [7]:  
\[f_{h}(h)=\int_{0}^{A_{0}}f_{h|h_{\rm{p}}}\bigl{(}h|h_{\rm{p}}\bigr{)}f_{h_{\rm {p}}}\bigl{(}h_{\rm{p}}\bigr{)}dh_{\rm{p}}, \tag{16}\]  
where \(f_{h|h_{\rm{p}}}(h|h_{\rm{p}})\) is the condition PDF for a given channel fading \(h_{\rm{p}}(t)\) caused by pointing errors. Based on (07.34.21.0085.01) and (07.34.16.0001.01) in [29], the PDF of the received instantaneous channel fading in the MIMO system can be expressed as [6,8,31]:\[f_{h}(h_{mn})=\frac{A_{\rm g}^{2}}{2h_{mn}}\sum_{k=1}^{\beta}a_{k}\bigg{(}\frac{ \alpha\beta}{g\beta+\Omega^{\prime}}\bigg{)}^{-\frac{n+k}{2}}G_{1,3}^{3,0} \bigg{(}\frac{\alpha\beta}{g\beta+\Omega^{\prime}}\frac{h_{mn}}{A_{0}h_{c}h_{ d}}\bigg{|}_{\rm g}^{2^{\prime}+1}\bigg{)}. \tag{17}\]  
where \(G_{c,d}^{a,b}(\cdot)\) is the Meijer-G function [29], and the other parameters' meanings are the same as above.
# Uncertainty Theory-Based Resilience Analysis for LEO Satellite Communication Systems
## 3 Belief Resilience Measure  
We present a new belief resilience measure to overcome the limitations of the commonly used resilience measures introduced in Section 1.
## 4 Conclusions  
The development and method validation of a new smartphone-assisted method was successfully carried out, yielding an LOD of 0.2 ng, LOQ of 0.6 ng and a good precision (RSD of <20%) for a spotting technique. The accuracy of the developed method was compared to FAAS using a standard reference material and no statistical difference was found at a 95% confidence level. It was determined that for this method, a linear range of 0.2-4.5 ng of Fe was applicable. The developed method proved more sensitive than similar methods in the literature for paper substrate according to compared LODs. This study also concludes that, contrary to other substrates (specifically paper), the ferroin complex was not immobilized by polyacrylic acid and, in fact, yielded worse results because of it. Therefore, the specific substrate is important in determining the reagents to be used for Fe detection in such devices. In processing the images on ImageJ, the color thresholding function resulted in poorer results. This was in contrast to what has been reported in similar studies with paper as a substrate. Therefore, for silica substrate, adjusting the color contrast by setting it to 156-252 (Image > Adjust > Contrast) yielded the best results. Both inverted and un-inverted images yielded good results, with inverted results being slightly better. While most colorimetric studies that have used ImageJ software report color intensity in mean gray, this study not only explored integrated density as an alternative measuring tool but, in fact, concludes that according to the results, it statistically showed better precision of the method. We plan to apply the current method on-site to real environmental samples and use similar method development strategies for other metals of concern.  
Conceptualization, B.S.S., W.M. and V.O.; methodology, B.S.S., W.M. and V.O.; software, B.S.S.; validation, B.S.S., W.M. and V.O.; formal analysis, B.S.S., W.M. and V.O.; investigation, B.S.S., W.M. and V.O.; resources, W.M. and V.O.; data curation, B.S.S., W.M. and V.O.; writing--original draft preparation, B.S.S.; writing--review and editing, B.S.S., W.M. and V.O.; visualization, B.S.S., W.M. and V.O.; supervision, W.M. and V.O.; project administration, B.S.S.; funding acquisition, B.S.S. and W.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Botswana International University of Science and Technology (BIUST) Initiation Grant Project code S00138 and the University of Botswana's Department of Chemistry for laboratory space.  
Not applicable.  
The data presented in this study is readily available upon request from the corresponding author.  
Gratitude is extended to technicians at the Botswana International University of Science and Technology (BIUST)'s department of Chemical and Forensic Sciences and the University of Botswana's Department of Chemistry for their immeasurable assistance.  
The authors declare no conflicts of interest.
## 4 Model Development
### Solution Algorithm  
Figure 5 shows the flow chart of the entire data processing and transfer trip extraction by the proposed identification method. The left-hand side of the flow chart illustrates how the elapsed threshold time is derived from SC data and GPS data. Firstly, the SC data and GPS data are filtered to eliminate error records. Then, bus SC data and GPS data are joined together according to POS number, and one can estimate the passenger's bus boarding stations. Finally, the elapsed time threshold for each station and time period is determined based on the two rules.  
Figure 5: The algorithmic framework for metro-to-bus interchange behavior identification.  
Moreover, the right-hand side of the flow chart shows how to identify metro-to-bus transfer behavior by using collected SC data and GPS data. It is very time-consuming to estimate the elapsed time threshold for all city-level stations and time periods with the usage of citywide metro and bus datasets. For example, on a PC with a 2.2 GHz, Intel i5-8500 CPU with 8 GB RAM, and Windows 10, 64-bit operating system, it took 2.9 h to generate a metro-to-bus elapsed time threshold for all metro stations within a one-week dataset of Shenzhen, including 42.69 million SC records.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 5 Methods for Improving Photocatalytic Efficiency  
By investigating the mechanism of photocatalytic reaction, it is known that semiconductor photocatalysis shows the advantages of mild reaction conditions, high efficiency, and a wide application range. However, semiconductor photocatalytic reactions also suffer from drawbacks in practical applications such as narrow absorption spectra, low photon quantum efficiency, and an easy recombination of photogenerated holes. The recombination of photogenerated electrons and holes is unfavorable for photocatalytic reactions. Therefore, reducing the recombination of photogenerated electrons and holes is an effective way to improve photocatalytic efficiency [48; 222; 223]. Many methods have been reported for improving the photocatalytic efficiency of semiconductors such as precious metal deposition, semiconductor compounds, metal or non-metal particle doping, and surface dye sensitization.
## 2 Structure and Design
### Patch  
Standard patch antenna design principles [18; 19] are useful as an initial design point; however, the patch must be adapted to a non-TEM line. This is carried out by adding some distance from the rod to the end of the waveguide, denoted as _lshort_, and displacing the feeding point of the rod with respect to the edge of the patch, denoted as _Apatch_ (see Figure 3), which will be explained in the next subsection. The patch is a square patch (\(Lpatch=Wpatch\)), implemented using FR-4 substrate (0.8 mm height) with metallised walls (see Equation (1) from [18], which proposes equations to calculate the length (\(L_{patch}\)) and width (\(W_{patch}\)) of a square microstrip patch particularised to an air substrate (\(\epsilon_{r}=1\))). \(A_{patch}\) is the displacement of the centre of the rod with respect to the patch edge (see Figure 4).  
\[W_{patch}=\left(\frac{c_{0}}{2f}\right)\cdot\sqrt{\frac{2}{\epsilon_{r}+1}} L_{patch}=\frac{1}{2f\cdot\sqrt{\epsilon_{\text{reff}}}\cdot\sqrt{ \mu_{0}e_{0}}} \tag{1}\]  
Here, \(c_{0}\) is the the speed of light (299,792,458 m/s), \(f\) the resonance frequency of the antenna (10 GHz), \(\epsilon_{r}\) the relative permittivity (1 for air), \(\epsilon_{ref}\) the effective permittivity (see Equation (2)), \(\epsilon_{0}\) the permittivity of free space, \(\mu_{0}\) the magnetic permeability of a vacuum, and \(h\) the height of the substrate (0.818).  
\[\epsilon_{\text{eff}}=\frac{\epsilon_{r}+1}{2}+\frac{\epsilon_{r}-1}{2}\left( 1+12\frac{h}{W}\right)^{-1/2} \tag{2}\]  
Once \(Lpatch\) and \(W_{patch}\) are calculated, the integration can be accomplished.  
Figure 4: Frontal view without the top cover. In orange is the patch (copper), in light orange the rod (copper), in yellow (copper) the bot cover, and in dark yellow the central layer (copper) [**left**] and patch positioning [**right**].  
Figure 3: Inner cut of ESIW patch with parameters.
# Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation
## 3 Architecture and Communication Protocol
### Proposed Architecture
#### 3.1.3 End System Layer  
End system layer is the most heterogeneous layer of the architecture because it is composed of two sub-layers:  
* The vehicular layer is composed of VANET devices such as OBUs and RSUs. This layer is reachable by exploiting the capability of RSUs to communicate with other VANET devices, but it is also able to communicate with external networks. This layer is used to acquire data on traffic  
Figure 2: Interface integration in the heterogeneous architecture.  
and road status by monitoring flows: data is then filtered and aggregated by LRTM and sent to HRTM;
* The traffic controller layer is composed of smart devices such as traffic lights and road signs that have been improved by equipping them with an elaboration unit. It allows devices to communicate with other layers (to actuate decisions made by LRTM or HRTM layers).
# BSM-Data Reuse Model Based on In-Vehicular Computing
## 4 BSM Reuse Model: Our Proposal
### Architecture Design
#### 4.4.3 Data Reuse  
Next to the data maintaining function discussed above, access to data is a critical enabler for the efficient and wide reuse of BSMs into a multimodal transportation system. Hence, many data consumer use cases that may require different types of data delivery should be considered. Our model provides several data formats and it relies on different data delivery mechanisms to cater to these different use case requirements.  
* Data reshaping formats: we can imagine and design a bunch of solutions such as a suite of APIs, portals, and apps to turn the passive stored BSMs into an active and actionable dataset and make every data element count. Hence, to support that and make this valuable dataset available for sharing and consumption, we count on a handful of data formats, as shown in Figure 8, including JSON, XML, and CSV in order to provide standard data that developers, systems, and  
Figure 7: BSM modeling.  
applications can easily reuse. Neither type is better than the other, we simply provide the ability for developers, systems, and applications to select the one that meets their requirements the best.
* Data delivery:as we mentioned earlier, our model serves two types of vehicle data, hot data that refers to real-time data and cold data represented in offline data. Data delivery is a sort of service that allows for different transport agents to re-use the stored BSMs across the following methods. 1 Streaming: usually, hot data are better served using a push mechanism, which ensures minimal delay and packet loss. Besides, streaming is the ideal delivery mechanism for applications that require hot, rich, vehicle data. As a means to guarantee optimal and rational streaming, we count on our previous RTR approach that permits requesters to determine filters, like Data Element list, geo-fencing, and maximum latency, so they timely only get their desired data. 2 Data Query: we rely on this retrieval technique to open up to different data consumers' the possibility of making requests on our database to obtain desired data. Data query is, therefore, a pull mechanism to provide hot or cold data by having data requests.
* Data analysis: another way to reuse stored BSMs is by the application of emerging data analytic methods like machine learning. Machine-learning techniques can inspect our BSM dataset and make possible the patterns recognition (like real-time vehicle traffic, and driver behavior different road traffic conditions), decision-making, and/or future trends forecasting.
* Data visualization: from another perspective, the BSM dataset can feed various data visualization tools and techniques to provide monitoring data about vehicles on the road and support decision-making to significantly improve the efficiency of transport system operations. For instance, it is practicable to visualize different data elements according to time and geofencing limits.
# Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits
## 2 Experiment Setup  
In this experiment, a SmartCheck system, which is a novel condition monitoring tool produced by the Schaeffler Group, was used for vibration data collection. A piezoelectric  
Figure 1: Schematical representation of the experimental process.  
tric accelerometer and a temperature sensor were integrated into the SmartCheck system. Figure 2a shows the image of the SmartCheck system used in this experiment. The acceleration sensor in the SmartCheck system has a \(\pm\)50 g measurement range and a frequency range of 0.8 Hz-10 kHz. This frequency range is suitable for measuring most of the machine vibrations. The sampling frequency of the SmartCheck system was set to 25.8 K for high-resolution measurement.  
The sensor was attached to the top of the dill machine to measure vibrations (Figure 1). The sensor position was selected because it was the closest position to the motor of the drill machine and helped in the collection of vibration signals. Additionally, this position would not be disturbed when changing drill bits or moving samples. It ensured that the sensor was placed in the same location for all measurements and reduced measurement error.  
During the measurement, the drilling speed was controlled by a control panel and was set to 500 rpm. The feeding speed was approximately 3 mm/min. Each vibration signal was continuously collected for 1.27 s, and the collecting interval of two signals was 20-25 s. Each data set contains 325,120 data points. Figure 2b presents the 6061 aluminum block used in the experiment. The aluminum was fixed on the stage by a clamp for safety purposes and to avoid displacement during the drilling process. Figure 2c shows the drill machine used in this experiment.  
Figure 3 presents three drill bits with three artificially created defects and a drill bit without defects. Yellow circles show the defect positions. Figure 3a presents a notched blade edge. There is evident damage on the edge of the blade. Figure 3b presents a blunt edge in a drill bit, as shown in the yellow circle. The edge of the drill bit was polished by file tools, which made the blade not as sharp as a healthy drill bit. Figure 3c presents a defective drill bit with a blunt head. The top of the drill bit was flattened. A yellow circle highlights this in the image. These are the most common defects in drill bits caused by daily operations [23]. Lastly, Figure 3d presents a healthy drill bit without defects.  
Figure 2: (**a**) An image of the SmartCheck system, (**b**) an image of the aluminum block, and (**c**) an image of the drill machine used in the experiment.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 1 Introduction
Again, despite the use of the TCP/IP standard, protocols are proprietary for energy solutions, and it is not possible to deploy equipment from different manufacturers side by side in one network. New standards IEEE 1901 and ITU-T G.In provide a new solution for coexistence and high performance. The drawback of BB-PLC is the allowable frequency bandwidth, for example, if BB-PLC is used in medium voltage (MV) lines, its performance significantly decreases due to the need to control available frequency bandwidth (power spectral density). Millions of smart meters will be deployed in Europe in the near future. Vast amounts of data from such meters and other intelligent electronic devices in distribution network substations will require high-speed communications, therefore, the BB-PLC is starting to be the best solution. The contribution of this paper is three-fold. First, the proposal of the number and position of the repeaters for a real topology are proposed thanks to the simulation, and also supported by verification with real on-filed measurements. Second, the influence of the power line topological parameters, physical characteristics of the power line, noise, and distance on the throughput are revealed. Third, the results of simulations revealed limits of BPL technologies for particular grids (e.g., communication distances without repeaters, necessity of additional repeaters). In the power line it is quite hard to predict the PLC behaviour. Therefore, simulation offers an opportunity for understanding and performance assessment of the grid configuration before deployment (roll-out).
# Enhancing Cloud Security--Proactive Threat Monitoring and Detection Using a SIEM-Based Approach
## 3 Materials and Methods
### Microsoft Sentinel and Log Analytics Deployment  
Microsoft Sentinel is \"a scalable, cloud-native, security information and event management (SIEM) and Security Orchestration Automation and Response (SOAR) solution\" [29]. It collects data from all sources, including users, servers, and applications, in both cloud-based and on-premises devices [16]. It has built-in connectors to make it easier to integrate popular security solutions. Microsoft Sentinel can support open standard formats such as Common Events Format (CEF) and Syslog.  
An Azure Log Analytics workspace is \"a logical storage unit in Azure where all log data generated by Azure Monitor from various sources are stored\" [38]. It uses the Kusto Query Language (KQL) version to speedily fetch, consolidate, and analyze all data collected into Azure Monitoring Logs. To meet the objectives of this study, we deployed a Microsoft Sentinel instance with a log analytics workspace. To ingest data in our environment, we connected the VMs created in the previous section to the workspace using an Azure Monitor Agent (AMA). The AMA collects monitoring data from Azure virtual machines' guest operating systems and sends them to Azure Monitor (log analytics workspace) [39]. Microsoft Defender for Cloud is connected to our workspace to check industry and regulatory compliance status. Figure 4 depicts a selection of the data connectors that were utilized. In addition, to have more data ingested in our log analytics, we installed a log analytic agent--an operation management suite (OMS) on on-premises machines that collects data and sends them to a log analytics workspace in Azure. The agent configurations are adjusted to meet the nature of events that one wishes to gather and their severity. Figure 5 showcases the flow for data logging and processes.  
For a user to detect a threat in their cloud environment, they need to be notified when suspicious activity occurs. In this regard, Microsoft Sentinel uses analytics rules to correlate alerts into incidents [40]. These rules automatically search the environment for any suspicious activity and can be used either as-is or customized to fit the need of the organization. A playbook is set to automatically be executed if an alert is generated by Sentinel analytics rules. With the help of Microsoft solution, we ingested pre-recorded data [41] to enable several artifacts to simulate real-world scenarios. From that, we created a rule to detect malicious phishing activity from the real-world experience available in [42].  
Furthermore, Microsoft Sentinel workbooks offer a wide range of usage options. Starting from visual data representation to complex graphing and resource investigation maps, Sentinel has different types of workbooks. In this experiment, we focused on the Investigation Insight, Identity and Access, Data Collection, and Health Monitoring workbooks.  
Figure 4: Employed data connectors.
## 3 Results and Discussion  
### Influence of the Amount of Biocatalyst  
As a general trend, increasing the amount of immobilized enzyme in the reaction medium guarantees a boost in the reaction rate. Nonetheless, this effect is usually mini  
Figure 2: GC analysis of an intermediate sample of the esterification process between NPG and LA.  
mized at high concentrations of biocatalyst, as the number of active centers available may exceed the needs of the process and, therefore, lead to unnecessary expenses [32; 33].  
Figure 3 shows the influence of the concentration of Novozym(r) 40086 (a) and Novozym(r) 435 (b) at 70 \({}^{\circ}\)C, showing that in both systems, the reaction rate is significantly improved when increasing the amount of biocatalyst within the range studied. In this sense, doubling the concentration of Novozym(r) 40086 from 1.88% to 3.75% (\(w/w\)) has an important effect on the process: only a conversion of \(\sim\)20% is achieved after 24 h with the lowest quantity tested, while such value is attained after 6 h by using a 3.75% (\(w/w\)) of immobilized derivative. However, the best results are obtained with a concentration of 7.5% (\(w/w\)), with a final conversion of 91.2% being chosen as optimum. On the other hand, in Figure 3b it can be noticed that, even if increasing the amount of Novozym(r) 435 implies less reaction time to achieve a certain yield, a conversion of \(\sim\)90% is obtained with just one hour of difference when using 3.75 or 7.5%, therefore 3.75% has been selected. These results are similar to the ones reported by other authors during the esterification process of NPG with free fatty acids with 4% of commercial immobilized lipases at 40 \({}^{\circ}\)C [21].  
From the previous discussion, it is also obvious that Novozym(r) 40086 shows a lower activity than Novozym(r) 435, and this difference might be attributed to factors inherent to the enzymes themselves, either their substrate preferences or their resistance to temperature under reaction conditions (or a combination of them).  
According to the work of [PERSON] et al. [25], both enzymes are able to accept acyl donors of 12 carbon atoms. Consequently, the low activity of Novozym 40086 cannot be attributed to the acid. What is more, by reviewing the literature, several examples can be found proving the good performance of Rml and Calb biocatalysts in esterification processes with longer acids [33; 34; 35; 36]. Thus, regarding substrate specificity, it seems that the main difference in their activity could lie in their affinity for NPG.  
On the other hand, previous work from the authors has pointed out the great performance of Novozym(r) 435 and both Novozym' Rml biocatalysts, Lipozyme(r) RM IM and Novozym(r) 40086, during the solvent-free synthesis of monoesters at temperatures over 70 \({}^{\circ}\)C [33; 36; 37]. Nevertheless, in view of the results depicted in Figure 3, a negative effect of reaction temperature on Novozym(r) 40086 cannot be discarded, and the effect of this variable will be discussed in the subsequent section.
# Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking
## 4 Proposed Algorithms  
In this section, we analyze the characteristics of the server placement problem. Inspired by the ideas of transfer placement information between layers and server reuse, we developed the IIT-DOWN algorithm in the direction from the high-level layer to the low-level layer. Then, we found that the servers in the lower-level layer occupy the majority of the total number of servers, so we proposed the IIT-UP algorithm, which transfers placement information and detailed latency information from low to high. In these two algorithms, we used the relaxation technique of binary integer linear programming in choosing each layer's HM, since it is an NP-hard problem as well. Though the result is approximate, it can output a solution that is not far from the exact solution within polynomial time [52].