esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
The implemented methodology is divided into three main steps (Figure 4). The first one (Step 1) aims to characterize the thermal performance of a constructive solution chosen as the reference (Scenario A), which is fully described in Section 2.1. The reference model was validated and duly calibrated through onsite indoor temperature and relative humidity data gathered with the building running in a free-flow basis. Monitoring sensors were placed in each thermal zone of the reference building, ensuring a continuous record of the air temperature and relative humidity. The position of the sensors inside the rooms was defined in order to avoid direct sun exposure from the glazed areas in accordance with ISO 7726 [41]. The monitoring acquisition system is logged at 10 min intervals and averaged hourly and has as accuracy of 0.5 \({}^{\circ}\)C and a resolution of 0.1 \({}^{\circ}\)C.  
After validating the reference case, different constructive solutions, representing different constructive periods, were simulated (Step 2). Solutions were defined as discrete parameters (materials and thicknesses were changed between scenarios) but the layout and internal loads of the reference case were maintained across simulations. The window solutions as well as outdoor doors were also maintained for a real understanding of the  
Figure 4: Schematic of the followed methodology.  
behavior on the opaque envelope level. Two scenarios were simulated to exploit two very distinctive constructive solutions, namely:  
* Scenario B--representing the period before 1950 characterized by high-thickness solid brick walls without insulation;
* Scenario C--representing the most recent thermal requirements announced in the recent Portuguese Decree-Law 101-D/2020 [42].  
These two scenarios alongside the reference one allow to represent the main construction techniques. Moreover, due to a growing tendency of prefabricated construction systems as faster and economic building solutions, a LSF structure was also modeled (enhanced solution--Step 3). PCM solutions are combined in the inner surfaces of external walls and ceiling to reduce the risk of overheating (due to the reduction of the thermal inertia) and maximize the potential of the charge and discharge process for the indoor environment. Lastly, the results of all scenarios are compared, and recommendations are listed to aid designers when choosing construction solutions, to deal with the lack of thermal inertia, as well as to demystify several issues in the research community. More details on the constructive solutions are presented in Section 2.3.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 1 Introduction
### Motivation  
We have targeted time-critical and emergency environments in our work. In such scenarios, data delivery must be performed in a timely manner, such that timely actions can be performed; otherwise, lives and properties may be lost [21; 22]. UWSNs for emergency applications must operate in harsh environments, where data collection must be performed in dynamic environments with high reliability while ensuring quick responses [23]. To achieve better network performances in such environments, several issues pertaining to UWSNs must be addressed [24]. Path generation is a complicated procedure owing to various operating constraints such as waypoint covering, collision detection and avoidance, and flight-time reduction. Identifying the safest and shortest path that ensures minimal energy consumption and delay is critical in the path planning of UWSNs in emergency environments. UAVs' path planning for self-directed exploration is a promising topic that has garnered significant attention [25]. However, we do not find any path planning algorithms in the literature that deal with emergencies' constraints and provide the shortest and safest path for efficient data collection from the ground sensors. UAV path planning in a 3D dynamic environment with static/dynamic obstacles and prohibited areas is challenging and not much studied. Hence, in this study, we have focused on path planning that involves traversing an emergency environment without colliding with environmental elements to achieve the predefined goal. Figure 1 shows an example of UAV path planning in a UWSN.
## 2 Results
### Content of Other Terpenes along Ripening in Italia and Bronx Seedless Table Grapes
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline  & **Linalol** & **Terpinen-** & **\(\alpha\)-** & **Hotrienol** & **Neral** & **\(\alpha\)-** & **Terpinol** & **Geraniol** & **Citronellol** & **Myrtenol** & **Nerol** & **Geraniol** & **Nerol** \\  & **Trialia** & 2.26 b & 1.15 b & 11.50 b & 1.25 b & 2.42 b & 1.65 a & 21.86 a & 74.48 b & 64.52 a & 21.95 a & 97.85 a \\ Bronx Seedless & 2.17 a & 1.05 a & 9.26 a & 0.79 a & 1.73 a & 1.80 b & 21.49 a & 61.98 a & 72.35 b & 23.17 a & 98.18 a \\
**Phenological stage (S)** & & & & & & & & & & \\ BBCH-7 & 1.59 a & 1.16 b & 7.83 a & 0.77 a & 1.56 a & 1.24 a & 16.35 a & 51.47 a & 49.09 a & 17.02 a & 70.30 a \\ BBCH-79 & 1.80 b & 1.14 ab & 8.71 ab & 0.86 b & 1.74 b & 1.40 ab & 18.18 ab & 57.24 ab & 55.57 a & 18.93 a & 79.59 ab \\ BBCH-81 & 2.04 c & 1.12 ab & 9.68 b & 0.95 c & 1.93 c & 1.58 b & 20.22 bc & 63.65 abc & 62.90 ab & 21.05 ab & 90.09 ab \\ BBCH-83 & 2.31 d & 1.09 b & 10.77 c & 1.06 c & 2.15 d & 1.79 c & 22.48 d & 70.78 b & 7.21 bc & 23.41 ab & 101.98 b \\ BBCH-85 & 2.61 e & 1.07 ab & 11.97 d & 1.18 e & 2.39 e & 2.03 d & 25.00 de & 78.71 cd & 80.60 cd & 26.03 bc & 115.44 cd \\
**BBCH-89** & 2.96 f & 1.06 a & 13.31 e & 1.31 f & 2.66 f & 2.30 e & 27.80 e & 87.52 d & 91.24 d & 28.94 c & 130.68 d \\ V & 0.0370 & 0.0003 & 0.0000 & 0.0000 & 0.0000 & 0.0190 & 0.6669 & 0.0049 & 0.0472 & 0.4956 & 0.9583 \\ S & 0.0000 & 0.1419 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0064 & 0.0001 \\ VContent of Some Terpenes, \(\beta\)-ionone, and Fatty Acids along Ripening in Italia and Bronx Seedless Table Grapes  
Table 3 shows the content of some other terpenes, \(\beta\)-ionone and fatty acids of Italia and Bronx Seedless table grapes obtained in six phenological stages of vine ripening. The most abundant terpenes found in Italia and Bronx Seedless grapes were geranyl acetone, (E)-nerolidol, cedrol, myrtenol and nerol, whereas the most abundant fatty acid was hexanoic acid (Tables 1-3). Variety factor significantly affected the contents of cedrol, geranyl acetone, hexanoic acid and 2-hexenoic acid. The phenological stage factor significantly affected all terpenes, \(\beta\)-ionone and fatty acids shown in Table 3. None of the volatile compounds were affected by the interaction between variety and phenological stage factors. Italia grapes presented lower contents of cedrol and 2-hexenoic acid, and higher contents of geranic acid, geranyl acetone and hexanoic acid compared to Bronx Seedless grapes. Grapes harvested at the BBCH-89 stage presented the highest content of octanoic acid compared to the rest of the phenological stages. These grapes also presented higher contents of cedrol, \(\beta\)-damascenone, geranyl acetone, \(\beta\)-ionone, hexanoic acid and 2-hecenoic acid compared to the grapes harvested at most of the stages except BBCH-85. Grapes harvested at BBCH-77 presented a lower content of geranic acid than the grapes harvested at BBCH-85 and BBCH-89.
# Online Algorithm for Deriving Heart Rate Variability Components and Their Time-Frequency Analysis
## 2 Materials and Methods
### Variational Mode Decomposition  
As mentioned in the Introduction, VMD-based HRV decomposition procedures outperform other approaches such as MBF, STFT, or EMD; therefore, this research focuses on adapting VMD to online HRV decomposition.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Component** & \(A_{am0}\) & \(A_{am}\) & \(A_{fm0}\) & \(A_{fm}\) & \(f_{am}\) & \(f_{fm}\) \\  & **(ms)** & **(ms)** & **(mHz)** & **(mHz)** & **(mHz)** & **(mHz)** \\ \hline HF & 25 & 15 & 240 & 60 & 1.05 & 0.96 \\ LF & 35 & 25 & 80 & 20 & 0.67 & 0.81 \\ VLF & 50 & 30 & 17.5 & 7.5 & 0.37 & 0.45 \\ ULF & 75 & 35 & 0.8 & 0.6 & 0.22 & 0.27 \\ \hline \end{tabular}
\end{table}
Table 1: Core parameters for synthetic HRV signal generation.  
VMD is a relatively new method enabling the optimal decomposition of a non-stationary signal into several intrinsic mode functions (IMFs) of different center frequencies [44]. It uses adaptive Wiener filters, and optimization is performed with the alternating-direction multiplier method. As a non-recursive approach, all searched modes are extracted simultaneously, and the final IMFs result from minimizing the sum of the spectral widths of all modes [44]. The original VMD procedure requires the predetermined number of searched modes (_maxIMFs_), and the incorrect setting of this parameter may result in their omission or mixing [50]. It is especially important since each of the non-stationary component of HRV can potentially contain several dominant frequencies in its characteristic range. Therefore, an adaptive VMD (AVMD) that can find the optimal number of IMFs has been recently proposed for the offline analysis of HRV [47]. The phenomenon underlying this method is the significant reduction in residuals with a justified increase in _maxIMFs_. At the same time, this parameter should be as low as possible due to the increasing complexity of AVMD with less and less improvement in the quality of decomposition for larger values. An additional condition, and in this work the only change from the original AVMD [47], is that at least one IMF is assigned to each HRV component based on its frequency range. If this is not the case, the algorithm continues to run. Assigning an IMF to a specific HRV component involves the use of the HT, commonly applied in conjunction with VMD [44]: the instantaneous frequencies are calculated, and their histogram is created; all frequencies are counted within the standard ranges corresponding to the HRV components, and finally, the IMF is assigned to the one whose range is most represented--this prevents the imposition of rigid frequency limits on individual components. The final step after finding the optimal _maxIMFs_ is to create the HRV components as the sum of all IMFs assigned to them [47].
# Challenges of Using Blockchain in the Education Sector: A Literature Review
## 3 Results
### Challenge 7: Lack of Adequate Skills  
Since there is a critical lack of blockchain engineers and those with expertise, with the implementation of blockchain in many sectors of the industry, the demand for qualified blockchain resources has increased [7]. In particular, human resources with a sound technological and mathematical base are a major challenge for the education sector. However, only one of the reviewed articles [20] reported the lack of adequate skills as a challenge.  
For the development of a blockchain-based solution, experts are needed for both growing organizations and established organizations seeking this technology [20]. In addition, high-level expertise is required to design a secure and trusted blockchain education system with complete functionality and outstanding usability [20]. Learners, educators, and other specialized parties in the chain have difficulty understanding blockchain technology, platforms, and smart contracts [7, 61]. It seems practical to assign the responsibility for development to computer engineers, but the solutions they develop are generally less comprehensive than the requirements of the educational stockholders [61].  
The lack of human resources and skilled professionals is recognized as a major barrier in organizations that are increasingly opposed to blockchain technology [68, 69, 70, 71, 72, 73, 74]. For example, in the education sector, several educational institutions are still wary about adopting blockchain technology because there is a lack of knowledge and skills needed to manage student data in a blockchain network. Therefore, this could be one of the reasons for the lack of confidence in this technology [20]. In addition, the number of available experts and professionals is affected by the type of blockchain technology used in educational solutions. For example, Ethereum has the biggest and most active developer community around the world, with skilled people from various domains actively contributing to the ecosystem [57].  
The key step for the successful implementation of blockchain technologies in educational institutions could be to train the academic and administrative staff on how blockchain technology can be used and maintained internally [20]. In addition, since the institution's administrative staff, not its academic staff, is considered to be the main actors that ultimately decide whether an institution would adopt a blockchain solution, raising awareness and educating academic governance staff about the benefits, application, and maintenance of blockchain solutions in education is considered an important step to achieving the use of blockchain technology in the higher education sector [20]. In addition, it is very important to understand their motivations for resisting this technology. For example, blockchain's novelty and/or the reduction of the costs associated with administration operations can make managers resist this technology [20]. Therefore, this can be addressed by appropriate training and by raising awareness about the benefits of blockchain [59].
# Multi-Modality Cardiovascular Imaging Assessment in Fabry Disease
## 5 Prognosis
### Transthoracic Echocardiography  
In a retrospective analysis of data from the Fabry Outcome Survey (FOS), the risks of cardiovascular and renal events in patients with FD established on ERT were assessed based on TTE measures. Comparisons were made between patients who were LVH+ and had impaired renal function and those who were LVH- with normal renal function at initiation of ERT. Cardiovascular events were defined as myocardial infarction, heart failure, arrhythmia, conduction abnormalities, and need for cardiac surgery. It is not surprising that cardiovascular and renal events were more common in the LVH+ and impaired renal function cohort, who were 57% more likely to have a cardiovascular end-point [43].  
The ability of advanced echocardiography to define future risk in FD was replicated in a retrospective study of 96 adults with FD and normal LVEF, that assessed the ability of standard echocardiographic parameters, as well as LV GLS, myocardial mechano-energetic efficiency, and strain-based myocardial work efficiency to predict major adverse cardiac events (atrial fibrillation, ventricular tachycardia, heart failure, and death). GLS is assessed by 2D speckle tracking echocardiography and has emerged as a technique to assess early impairment in LV function. Myocardial mechano-energetics efficiency by definition is the ratio of work delivered by the myocardium versus the amount of total energy produced per heartbeat and is assessed on ultrasound. In total, 14 events occurred over a median follow-up of 63 months, and after multivariate analysis, GLS, and myocardial work indices were independently associated with a poor outcome, although myocardial mechano-energetic efficiency was not predictive [44]. Of note, both LVM and diastolic dysfunction provided additional prognostic value in detecting patients at risk of major adverse cardiac events (Cox regression analysis) which supports previous work demonstrating this in FD [42; 45].
## 4 Conclusions  
The C:N ratio (a significant factor for methane fermentation) in the tested monosubstrates was 9.5 \(\pm\) 0.4 in the microalgae biomass and 39.6 \(\pm\) 1.7 in the _Miscanthus_\(\times\)_giganteus_ silage. A reasonable technological step was therefore to use appropriate proportions of these substrates to achieve the optimal C/N ratio. Appropriate values of the C/N ratio from the technological point of view were obtained when the microalgal biomass was between 20% and 40% dry organic matter of the substrate: 24.3 \(\pm\) 1.4 and 17.5 \(\pm\) 1.2, respectively. The highest unit productivity of biogas (628.00 \(\pm\) 20.05 cm\({}^{3}\)/gVS) and methane (380.69 \(\pm\) 28.55 cm\({}^{3}\) CH\({}_{4}\)/gVS) was recorded when microalgae biomass accounted for 40% of the feedstock. The highest concentration of methane in biogas at the level of 65.12 \(\pm\) 1.94% was observed during fermentation of microalgae biomass only. Successive increases in the proportion of _Miscanthus_\(\times\)_giganteus_ silage in the substrate decreased the methane content. The highest digestion level and removal of organic compounds, amounting to 71.84 \(\pm\) 2.12% and 67.11 \(\pm\) 1.45%, respectively, were recorded in the variant in which only the giant miscanthus silage was introduced into the fermentation chamber. Similar values were found for the substrate containing 40% to 60% of microalgae biomass. Bacteria (EUB338) dominated in the microbial environment in all variants. The highest share of methanogenic bacteria from the _Methanosarcinaceae_ and _Methanosaeta_ groups in the anaerobic bacterial community structure was found for the substrates with the share of microalgae at the level of 20% and 40%. These were, respectively, 15 \(\pm\) 2% and 10 \(\pm\) 1% for _Methanosarcinaceae_, and 14 \(\pm\) 2% and 11 \(\pm\) 1% for _Methanosaeta_. In order to enable the estimation of the produced biogas and methane as a result of co-fermentation of microalgae biomass with _Miscanthus_\(\times\)_giganteus_ silage, empirical equations were developed by the multiple regression method. It was found that the amount of biogas and methane was significantly influenced by variables such as the share of microalgae in the substrate and the feedstock C/N ratio. Research has shown that co-fermentation of microalgae biomass and _Miscanthus_\(\times\)_giganteus_ silage allows for high technological effects. An important factor determining the possibility of implementing this solution is the development of efficient, economically justified and environmentally friendly methods of feedstock production, mainly microalgae biomass. The main issue is the implementation of solutions consistent with the assumptions of a circular economy and that are conducive to reducing greenhouse gas emissions to the atmosphere.
## 1 Introduction  
Mobile communication is becoming commonplace and playing an irreplaceable role in our lives. It is facilitating not only voice communication but also high speed data communication. Millions of smart phones with 3G capabilities are sold every year and mostly used for browsing the Internet, banking applications and messaging, resulting in the expectations for the data revenue of mobile cellular network operators to exceed their voice revenue [1]. Especially, with the availability of the high data throughput in 3G, Long Term Evolution (LTE) and LTE-Advanced [2] mobile networks, many subscribers communicate more data than voice. The trend is that data communication, compared to voice communication, is dominating the mobile market. It is expected that mobile revenue will double by 2016, but traffic will increase tenfold due to increasing demand for mobile data communication [3]. As a result, in mobile cellular networks, data communication revenue is forecasted to exceed voice communication revenue [4]. Therefore, mobile operators capitalize on their data services and, in addition to being mobile voice carriers, mobile core networks tend to be used as Internet Service Providers (ISP). The investments made by mobile carriers on their IP based infrastructures and services are motivated mostly by their subscribers' increasing demand for mobile data communication. The acceptance of mobile networks as a venue for Internet access brings along new IP based threats on mobile core networks [5].  
Previous studies investigated several vulnerabilities in mobile 3G networks. [PERSON] [6] discussed some attacks on 3G networks and showed how unwanted traffic results in resource wastage. He also discussed four different attack models specific for cellular networks and evaluated their results for a more robust network design perspective [7]. [PERSON] _et al._[8] investigated attacks on the paging channel and presented their findings through simulations. [PERSON] [9] made valuable contributions to the field by discussing the vulnerabilities on cellular networks in detail and proposing countermeasures. [PERSON] _et al._[10] studied the randomization method to prevent DoS attacks on 3G cellular networks, again using simulations. Several DoS attacks on 3G networks were realized [11, 12, 13]. In this work, we particularly focus on measuring the effect of DoS and flooding attacks, which exploit paging [14] vulnerabilities, and realize our experiments in a real production mobile cellular network. Furthermore, we propose solutions to mitigate the existent vulnerabilities.  
The rest of the paper is organized as follows. Section 2 discusses the basic architecture of a 3G network and Section 3 analyzes possible threats in 3G as well as LTE networks. In Section 4, we conducted some attacks exploiting paging vulnerabilities on 3G networks and observed their negative effects. Section 5 discusses some countermeasures against the security threats mentioned. Finally, Section 6 is the conclusion.
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 3 Receiver for Optical Wireless Communication
### Quad Photodiode Array
#### 3.3.2 Experiment  
The diagram of the experimental setup is shown in Figure 8. In the experiment, the tunable laser source emitted light at 1530 nm with 12 dBm output power. Then, the light polarization was aligned to the TE mode for optimization of the transmission power of the modulator. The BERT modulated the signal with OOK NRZ PRBS 31. The light was then amplified with EDFA to compensate for the losses in the system, and later VOA was used for optical power sweep. The output fibre was placed very closely above the photodiode array as shown in Figure 8. The fibre was mounted to a six-degree-of-freedom platform such that the position and the angle of the fibre were controlled. The output of the photodiode was fed back to the BERT via a DC block to remove the bias voltage.  
In the experiment, two parameters of the photodiode array were verified: the field-of-view and the effect of the received power on the BER. The field of view was measured by changing the angle of the fibre with respect to the photodiode array. The maximum angle supported by the mounting is 16\({}^{\circ}\) relative to the normal incidence. This measurement was done by transmitting 1 Gbps at different angles and measuring BER. In the second measurement, the VOA was used to sweep the power for various bit rates, which was controlled by the BERT.