esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigation against DDoS Attacks on an IoT-Based Production Line Using Machine Learning
## 2 Materials and Methods
### IoT-Based Production Line  
The production line is the basic cell in the manufacturing industry and usually depends on the stability of the production process of factories, companies, and industrial chains. IoT production is a new production mode that combines IoT technology with production technology to carry out the production and the service process, as well as the dynamic perception, intelligent processing, and optimal management of production resources and information resources throughout the product life cycle [15]. The IoT-based production line uses IoT technology to monitor objects and processes interactively in real-time. For this purpose, it uses sensors, actuators, RFID, image recognition, and other methods.  
This case study handles a real production line (Figure 1). The production line is used by leading industrial producers of beverages and food and also by the chemical industry.  
The production line consists of five zones. Every zone forms a part of the production process. Zones are controlled by Siemens PLC and there are integrated IoT devices. They have the task of performing operations from the simplest to the most complex. In the first zone, materials are collected and dosed. In the second zone, after mixing and dosing the materials, the production process continues by filling the material into bottles, which are closed by a cap on a rotating table. The bottles are sorted using an RFID reader and then shipped by a transport system. The transport system loads the filled bottles into crates. The high-speed camera checks the correct filling of the containers with bottles. The third zone is used for dosing semifinished products for the production of bottle closures. The fourth zone is used to transport crates between individual stations using a conveyor belt. The fifth zone is used for unloading bottles from overflows and their subsequent transport to the unpacking station. It is a recycling station where the robot is located, and the pump is used to empty the unnecessary liquid contents of the bottles. The individual zones communicate via an Ethernet network [16].  
The production line, for efficient production, is equipped with an MES system based on the Wondervare platform. This system monitors and documents the production process. Based on this information, manufacturers can adjust the production conditions in order to improve the efficiency of the production process. Additionally, it includes the Historian Server subsystem, which serves as a high-speed database of historical information in real-time [17].  
Figure 1: The real production line tested in the case study.
# SEA and GATOR 10 Years Later
## 5 Function of the SEA and GATOR in Nutrient Sensing and Responding
### Overview of Amino Acid Axis of Signaling to mTOR1
RAG GTPases regulate the recruitment of TSC to the lysosome and its ability to interact with and inhibit RHEB in response to amino acid starvation, growth factors removal and to other stresses that inhibit mTOR1 [73; 74; 75]. Both RAGs and RHEB are necessary for mTOR1 activation at the lysosome, as the lone presence of either one is not sufficient. Accordingly, only when both the RAG GTPases and RHEB are inactive mTOR1 fully released from the lysosome [73]. The RAGs and RAGULATOR are conserved both in fission and in budding yeast (Figure 3B) [76; 77]. Thus, the orthologoue of RAGA/B, a protein called Gtr1 in yeast, forms a heterodimer with Gtr2, which is an orthologoue of RAGC/D. Similar to mammals, in order to activate TORC1, GTP-bound Gtr1 and GDP-loaded Gtr2 interact with trimeric _S. cerevisiae_ Ego1-Ego3 complex (Lam1-Lam4 in fission yeast) analog of RAGULATOR. Im1/Sea1 from the SEACIT serves as a GAP for Gtr1 in the absence of amino acids [11]. Interestingly, LARS1 in yeast is the GEF for Gtr1 [78], while in mammalian cells LARS1 was shown to be a GAP for RAGD [70], although a GAP activity was not confirmed in a later study from different laboratory [69]. Lst4-Lst7 complex, an orthologoue of mammalian FLCN/FNIP, is GAP for Gtr2 [79]. The GEF for Gtr2 in yeast and for RAGC/D in mammals is still not known. There are some notable differences between yeast and humans during amino acid signaling to mTOR1 (Figure 3). First, many amino acid sensors (e.g., SAMTOR, SESTRINs) are absent in yeast (see below) [1]. Second, \(\
u\)-ATPase in yeast, which interacts with Gtr1, seems to activate TORC1 in response to glucose [80]. Third, RHEB orthologoue in yeast _S. cerevisiae_ seems not to be involved in TORC1 signaling, although it is required for arginine and lysine uptake [81]. Fourth, _S. cerevisiae_ does not have TSC homologues, thus the entire branch of TSC/RHEB signaling is not conserved in this particular yeast. In contrast, _S. pombe_ has both RHEB and TSC, which are involved in mTOR1 activation. How _S. cerevisiae_ achieves full TORC1 activation at the vacuole without TSC/RHEB branch is currently not well understood.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Mobile Networks
#### 4.5.1 GSM  
The GSM technology uses a frequency of 850/900/1800/1900 MHz [74] and the Time Division Multiple Access (TDMA) and Frequency Division Multiple Access techniques to provide audio and data services. The channel bandwidth needed for 2G transmission is 20-200 KHz, with digital modulation utilised to improve voice quality over 1G technology. The General Packet Radio Service (GPRS) was brought onboard after 2G, and the Enhanced Data rate for GSM Evolution (EDGE) followed suit after GPRS. Both GPRS and EDGE are advancements of GSM technology, and are often termed as 2.5G [28] and 2.75G [115], with improved data rates of 117 Kbps and 384 Kbps, respectively. GSM/GPRS/EDGE are often used to remotely communicate telemedicine information from the CSU to the CGU for real-time remote monitoring. In [28], the authors successfully used it alongside other mobile network technologies for real-time wireless communication of medical video on an open-source telemedicine platform.
## 4 Conclusions  
This experimental work investigated the physical properties as well as the thermal insulation property of cement mortar containing EGA and TiO\({}_{2}\). The findings revealed that incorporating EGA into the mortar composite causing a significant decrease in density and compressive strength, which was attributed to the porous nature and low compressive strength of EGA. The results also demonstrated that as the EGA content increased, the workability and water absorption of cement composite increased. It is found that the increase in water absorption was due to the high porosity of EGA in comparison to NA. However, the beneficial effect of the EGA was the decrease in the heat-transferring rate of the cement composite, which indicates the feasibility of a potential reduction in energy consumption in buildings. Moreover, the results demonstrated that inclusion of TiO\({}_{2}\) into the cement composite partially compensated the water absorption and loss in compressive strength. However, it was revealed that addition of nTiO\({}_{2}\) into EGA-cement composites increased the heat transfer rate of the cement matrix and insulation properties as nTiO\({}_{2}\) acts as nanofillers and changes the pores structure in the cement matrix. It can be concluded that in terms of thermal behavior, substitution of NA with EGA decreases the heat transfer rate and consequently improves the thermal insulation properties of the cement mortar.  
Conceptualization, A.Y., W.T.; methodology, W.T., M.K., S.W. and C.F.; Investigation, W.T., M.K. and A.Y.; writing--original draft preparation, A.Y., M.K.; writing--review and editing, W.T., C.F., M.K., S.W. and A.Y. All authors have read and agree to the published version of the manuscript. This research received no external funding. The authors gratefully acknowledge the financial support provided by University of Newcastle (2017 UNIPRS and UNRS Central Scholarship). We also thank the support and cooperation of the staff of the concrete laboratory at the School of Civil Engineering, University of Newcastle, and Expanded Glass Technologies Pty Ltd. for providing EGA for this experimental investigation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mix**} & \multicolumn{3}{c}{**\(\Delta\) (T\({}_{\text{ave.}}\))**} & \multicolumn{3}{c}{**Heat Transfer Rate (\({}^{\circ}\)C/min)**} \\ \cline{2-7}  & **5 min** & **10 min** & **15 min** & **5 min** & **10 min** & **15 min** \\ \hline CS & 4.40 & 12.30 & 26.30 & 1.58 & 2.80 & 1.75 \\ CT & 4.10 & 16.40 & 28.10 & 2.46 & 2.34 & 1.87 \\ E50 & 3.40 & 14.00 & 24.00 & 2.12 & 2.00 & 1.60 \\ E50T & 4.30 & 15.60 & 26.40 & 2.26 & 2.16 & 1.76 \\ E100 & 2.50 & 11.80 & 20.30 & 1.86 & 1.70 & 1.35 \\ E100T & 6.20 & 15.60 & 20.70 & 1.88 & 1.02 & 1.38 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The average temperature differences for each mix.
## 3 Analytical Dynamic Modeling
### The Derivation of Matrix \(C\)  
There are two groups of constraint equations that need to be established: let nodes \(i(i=3.16)\) and \(j(j=2.17)\) represent a group, and \(m(m=12.27)\) and \(n(n=13.26)\) stand for another group. The key to establish the constraint equations is to utilize the kinematic relationships of the nodal degrees of freedom. As shown in Figure 2a, the motion of the rigid body can be described by the node \(k\), then the degrees of freedom of the nodes \(i\) and \(j\) can be expressed by \(q_{1}\), \(q_{2}\) and \(q_{3}\). The constraint equations can be obtained as:  
\[\begin{cases}d_{i1}=q_{1}-l_{y}q_{3}\\ d_{i2}=q_{2}\\ d_{i3}=q_{3}\\ d_{j1}=q_{1}\\ d_{j2}=q_{2}+l_{x}q_{3}\\ d_{j3}=q_{3}\end{cases} \tag{11}\]  
Eliminating the degrees freedom of the rigid body \(q_{1}\), \(q_{2}\) and \(q_{3}\), we can obtain three equations as:  
\[\begin{cases}d_{i1}+l_{y}d_{i3}-d_{j1}=0\\ d_{i2}+l_{x}d_{i3}-d_{j2}=0\\ d_{i3}-d_{j3}=0\end{cases} \tag{12}\]  
The same procedure can be performed to obtain another group of constraint equations, as shown in Figure 2b:  
\[\begin{cases}d_{m1}-l_{y}d_{m3}-d_{n1}=0\\ d_{m2}+l_{x}d_{m3}-d_{n2}=0\\ d_{n3}-d_{m3}=0\end{cases} \tag{13}\]  
Figure 2: The rigid body with displacement offsets for multipoint constraint equations (**a**) \(i\) and \(j\) group (**b**) \(m\) and \(n\) group.
## 1 Introduction  
Employing passive evaporative cooling technology as an energy-efficient approach to partially decreasing energy consumption in buildings is becoming popular due to improvements in materials and designs [1].  
Geopolymers, or inorganic polymers, have been touted as unique engineering materials that have the potential to form elements that are environmentally sustainable for the construction industry due to their respectable mechanical properties, low shrinkage, fire resistance, and low energy consumption [2; 3; 4]. Geopolymers are regarded as an environmentally friendly material, owing to their low manufacturing temperature (<100 \({}^{\circ}\)C) and low emission, which is six times less than CO\({}_{2}\) compared with standard cement [5]. Although the investigations of geopolymers are mainly focused on concrete applications, they are regarded as attractive replacement materials for evaporative cooling applications [1].  
The high water retention and excellent cooling effects due to the capillary lift of water and high surface area are regarded as crucial factors for evaporative cooling materials [6]. The compressive strength of geopolymers needs to be accounted for within the context of evaporative cooling. Geopolymer matrix materials, or binders, are an inorganic family of alkali-activated aluminosilicates. Geopolymerization is an alkaline activation of silica and alumina containing materials that produce an amorphous to semi-crystalline polymeric structure [7; 8].  
Another promising feature of the geopolymer is the fact that it can be prepared using waste materials [1].  
Researchers reported three important factors that greatly influence the performance of geopolymers, which are: ratio of alkali liquid to solid; the ratio of sodium silicate to sodium hydroxide (Na\({}_{2}\)SiO\({}_{3}\)/NaOH); and the concentration of sodium hydroxide [9; 10; 11]. Researchers pointed out that the concentration of NaOH significantly influences the mechanical strength of kaolin-based geopolymers [12].  
This study investigates the use of agricultural and industrial wastes and byproducts, such as rice husk, rice husk ash, palm oil fuel ash, and ground granulated blast furnace slag as resource materials to prepare green geopolymer pastes for the evaporative cooling application. The compositions of precursors on water absorption, apparent porosity, and compressive strength of the prepared geopolymer have been determined. Despite studies on porous geopolymers for evaporative cooling applications, investigation on the development of green geopolymers requires more work. This research developed new green binders of geopolymer using different agricultural and industrial wastes in an evaporative passive cooling system as a greener alternative. Moreover, the capillary rise properties of selected samples have been determined to confirm their viability as green geopolymer materials for evaporative cooling application.
# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 3 Concept Drift in Process Mining
### Categories of Concept Drift Analysis
#### 3.2.3 Putting all the Pieces Together  
In Reference [19], a generalization of these categories is presented by abstracting process flexibility classifications into three main aspects:  
* Where do changes occur (i.e., abstraction level of the change)?--This aspect focuses on whether a change occurs at the process type or the process instance level.
* What is being changed (i.e., subject of change)?--This aspect concerns the process perspectives affected by a change.
* How to characterise a change (i.e., properties of change)?--This aspect can be mapped onto the drift patterns and duration of a drift?  
Regarding the abstraction level of a process change, the authors of [20] distinguish between flexibility and variability. Moreover, the work in [20] introduces the concept of _flexibility by change_ which defines a condition where a certain change is applied to selected traces at runtime. In turn, this might qualify the change to be leveraged to the process type level, that is, to be propagated to all process instances following the same process variant. Furthermore, the authors of [20] introduces _variability by restriction_, which starts with providing a process model with all allowed behavior and restricting models that may be configured from this model by adding more behavior. On the contrary, _variability by extension_ begins with providing a process model whose behaviour can be extended by deriving other process models based on the behaviour explained by it. In general, mining flexible processes is challenging as the employed business process might be rather unstructured, resulting in spaghetti-like models. The authors of [23] describe this situation by having process models with a large number of nodes and interrelations resulting from the diversity of an event log.  
Another effort to characterise the levels of concept drift is introduced in the work in [24], which considers concept drift as the action of changing the associations between elements, labels, or references. Any change of the associations between elements is considered to be concept drift, whereas changes of labels and references are considered to be a conceptual replacement. Adopting this view, a concept drift would occur whenever a change affects the ordering relations (e.g., directly follows, parallel, causal dependency, choice) between a set of activities. Adhering to the same view, conceptual replacement takes place whenever a change happens to the name of an activity or the actual task represented by this activity. This viewpoint can directly relate to Semantic Web, and to some extent to process mining, as well.
# A Survey on Fault Diagnosis of Rolling Bearings
## 5 Datasets, Practices, Limitations/Challenges, and Future Research Trends
### Commonly Used Datasets and Practices  
In addition to the development of fault diagnosis methods, the collection and establishment of benchmark datasets are also necessary. The commonly used fault diagnosis datasets are Case Western Reserve University [159], IEEE PHM 2012 Data Challenge [160], University of Cincinnati [161], University of Ottawa [162], and Xi'an Jiao Tong University [163]. They are publicly available and state-of-the-art datasets in the rolling fault diagnosis community. These datasets contain a wide range of rolling bearing operation data, which are described in detail in the corresponding references. For the fault detection problem, a representative signal segment is subjectively intercepted from the collected rolling bearing data for analysis. The detection performance of the same method will vary with different intercepted signals. For the fault type recognition problems, these datasets cannot be directly used to test the effectiveness of the proposed methods. Data preprocessing may be needed to solve the task. For example, the original rolling bearing data of these datasets are often divided to form the training set and the test set to train and test the machine learning-based methods, respectively. In addition to these datasets, there are also some other fault diagnosis datasets that were used in the literature, but they are not publicly available. To make fair comparisons between existing methods, it is important to use the same experimental settings including data preprocessing and splitting. However, this is very hard to achieve at the current stage. On the other hand, to enrich the field of fault diagnosis, it is also necessary to develop/share good datasets of various rolling bearing fault diagnosis tasks, such as the ImageNet [164] dataset in the computer vision community.
## 2 Materials and Methods
### Statistical Analyses  
In order to assess the postharvest storage period of fresh potatoes, namely the freshness of raw tubers, various partial least square regression analysis models were tested using color, chlorophyll fluorescence components, R-G-B, R-G-NIR, and R-B-NIR imagingindexes, as well as spectra reflectance data both externally on the peel or internally in the flesh and multiple linear regressions (MLR) were applied. MLR is the linear modeling of the relationship between a dependent and more than one independent variables [28], but it often encounters multicollinearity issues among the variables and has to be used with caution, in order to avoid model overfitting. Therefore, only models with variance inflation factor (VIF), an indicator of the severity of multicollinearity, with values lower than 4 were selected in the present study. Partial least squares regression (PLSR) analyses, an advanced modern regression technique was further employed using only the identified important variables, in order to cross-validate the models. The number of latent variables were selected based on the lowest root mean square cross validation error (RMSECV) using the 'leave-one-sample-out' method.  
For the discrimination of the three potato cultivars, the partial least squares discriminant analysis (PLSDA) was used. PLSDA is a linear classification method which combines regression algorithms and discrimination techniques, which minimize the cross-validation error [29]. The performance of PLS was estimated according to the non-error rate (NER), an index of total correctly classified samples, according to the equations (Table 2).  
Moreover, advanced feature extraction methods were adapted, in order to detect specific wavelength regions that exhibit the most significant effect in the regression or discriminant analysis models, such as the genetic algorithm (GA). All data analyses were carried out using Microsoft Excel 2016, SPSS v. 25, MATLAB (Version R2017, The Mathworks Inc., Natick, MA, USA) and PLS Toolbox 8.6 (Eigenvector Research Inc., Manson, WA, USA).
## 3 Design and Implementation of the Smart Campus System  
### Architecture for Outdoor Applications  
The proposed communications architecture is depicted in Figure 2. As can be observed, it comprises three different layers. The layer at the bottom consists of the different IoT LoRaWAN nodes that are deployed throughout the campus. Such nodes communicate with LoRaWAN gateways that comprise the Fog Layer, since they also act as fog computing gateways, thus providing fast location-aware responses to the LoRaWAN node requests. Every fog gateway is essentially a Single Board Computer (SBC) that embeds Ethernet, Wi-Fi, and Bluetooth interfaces besides a LoRaWAN transceiver. Finally, the top layer is the cloud, where user applications run together with data storage services.  
Figure 2: Proposed LoRaWAN-based smart campus architecture.
## 2 Protocol Overview  
This section presents the frame structure and the protocol operation of the three FSA variants that are modeled and evaluated in later sections. In particular, the frame structure of FSA-ACK and DFSA is displayed in Figure 1a, whereas the FSA-FBP frame structure is displayed in Figure 1b.  
Regarding operation, for all FSA variants, we consider that communication between the end-devices and the network coordinator is structured in DCRs (Data Collection Round) composed of frames with \(m\) contention slots. In each DCR, end-devices have exactly one data packet and randomly select one of the slots available in each frame to transmit the data packet to the network coordinator. For FSA-ACK and FSA-FBP each frame \(F_{i}\) is sub-divided into a fixed number of contention slots, whereas for DFSA the number of contention slots \(m_{i}\) of each frame \(F_{i}\) is variable. Notice that for DFSA the number of contention slots per frame is adjusted to be proportional to the estimated number \(\hat{n}_{i}\) of end-devices that contend in every frame as \(m_{i}=\lceil\rho\cdot\hat{n}_{i}\rceil\), where \(\rho\) is a positive real number.  
With respect to slot duration, for FSA-ACK and DFSA it matches the duration of a data packet and an acknowledgement (ACK) packet, and includes the guard times (called IFS, Inter-Frame Space) between transmit and receive modes to compensate for signal propagation, processing delays and radio turn-around time between modes. Conversely, for FSA-FBP the duration of each slot is adjusted to allow a node to transmit a single data packet, as the coordinator transmits a FBP (FeedBack Packet) at the end of the frame to indicate the state of every slot and allow end-devices to maintain synchronization.  
It is important to mention that, regardless of the protocol variant, packets are transmitted without CCA (Clear Channel Assessment). This means that end-devices do not check the channel status (i.e., busy or idle) before transmission. As a consequence, a given slot in any frame can be in the following states: empty (when no packet is transmitted), success (when a single packet is transmitted) or collision (when two or more more end-devices have transmitted their respective packet). Based on the outcome of each slot, end-devices that have succeeded to transmit their data packet in a given frame will enter into sleep mode and stop contending in subsequent frames. Contrarily, end-devices that have collided in frame \(F_{i}\) will transmit again in frame \(F_{i+1}\).  
Finally, in Figure 2 we provide an example of the FSA-FBP protocol operation. As it can be observed, all end-devices try to transmit a data packet in Frame 1. The outcome is that end-device 1 and end-device 2 succeed, whereas end-device 3 and end-device 4 collide. Hence, end-device 3 and  
Figure 1: Frame structure of FSA (Frame Slotted ALOHA)-ACK (ACKnowledgements), FSA-FBP (FSA with FeedBack Packets) and DFSA (Dynamic FSA).  
end-device 4 will transmit a packet again in Frame 2. As both end-devices collide again, both will transmit again in Frame 3, where they succeed and the process ends.