esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Contamination of Molasses Solutions  
First, 100 g of prepared molasses solution samples were inoculated with referent cultures mixed together: _Salmonella_ enteritidis ATCC 13076, typhimurium ATCC 14028, a mix of _E. coli_ ATCC 8739 and ATCC 25922 and a mix of _L. monocytogenes_ ATCC 13932 and 19111. Following reference, strains were used: _Salmonella_ typhimurium ATCC 14028, enteritidis ATCC 13076, _E. coli_ ATCC 25922 and ATCC 8739, _L. monocytogenes_ ATCC 19111 and ATCC 13932 (Microbiologics, St Cloud, MN, USA). Used microorganisms were chosen as indicators of food safety and process hygiene criteria according to Commission Regulation (EU) No 2073/2005 [16]. Storing and maintenance of the reference cultures from commercial sources were performed according to ISO 11133:2014 [37]. Freeze-dried reference cultures were stored in the refrigerator until the activation time. After opening the package, the subculture was prepared according to the producers' instructions. Storage was performed on slant nutrient agar (Himedia, India) at +4 \({}^{\circ}\)C of refrigerated temperature. Cultures were weekly refreshed by sowing to a new slant nutrient agar of the same producer, and after 28 days, cultures were safely removed. Working cultures were obtained from the related subcultures, via plating onto nutrient agar (Himedia, India) and incubation for 18 to 24 h at 37 \({}^{\circ}\)C. Next, 24 h old colonies (one or more of them) of the test microorganism working culture material were transferred by microbiological loop, under aseptic conditions, to the tube with sterile saline water. Then the tube was intensely shaken on the vortex. Using a densitometer (DEN-1, Biosan, Latvia), the respectively suspensions' densities were corrected to match the values of 2 McFarland standards, representing a bacterial concentration of 6 \(\times\) 10\({}^{8}\) cfu/mL, for each referent culture. Using this procedure, the initial suspension was obtained. From the initial test on the microorganisms' suspensions, a series of decimal dilutions in sterile saline water were prepared. Further, 1 mL of each dilution was transferred into a Petri dish and mixed with Tryptic Soya (TSA) agar (Himedia, India), in an effort to confirm the numbers of the inoculated test microorganisms. All samples of molasses' solutions were inoculated with 1 mL of 10\({}^{-2}\) dilution to obtain an approximative 360,000 cfu/mL of all test microorganisms.  
After the contamination procedure, molasses' solutions were homogenized, and samples were taken for the selected microorganisms' analysis at the beginning of the process, marked as 0 h [38].
# Bluetooth Low Energy Mesh Networks: A Survey
## 4 BLE Mesh Networks: Academic Solutions
### Routing-Based Solutions
#### 4.2.1 Static Routing Solutions  
The authors in [11] present a solution that creates a static tree topology over Bluetooth 4.0. This scheme includes three kinds of nodes: (i) the root node, which is a central device as defined in the BLE specification; (ii) intermediary nodes, which actually comprise two subnodes (one acting as a master for nodes located in a lower hierarchical level and the other acting as a slave for nodes of higher hierarchical level); (iii) and leaf nodes, which are set to be peripheral devices. The authors define a simple hierarchy addressing scheme with two-byte addresses. The addressing scheme allows five tree levels, requiring a bigger address space for deeper networks. The transmission of data from nodes to the root takes place by sending the data from one node to the next one at a higher hierarchical level, and the process is repeated until data reach the root. The root can also send data to other nodes; in that case, the path is determined based on the destination and intermediate node addresses. Note that addresses in this solution are designed to reflect the hierarchical level and location of a node within the network. This solution is suitable for data collection in Wireless Sensor Network (WSN)s where the root node is a sink node. However, being a tree-based solution, this scheme suffers from the single-node failure problem and lacks a mechanism to rebuild the network after a node or link failure. The authors measured the power consumption, latency, and range of this solution experimentally.  
Real Time BLE (RT-BLE) is another static routing solution designed over Bluetooth 4.1 and intended to enable bounded message delay for BLE mesh networks [12]. In RT-BLE, each node keeps a default route and an alternative route as a back up. RT-BLE connects subnetworks (comprised of a master and its slaves) in order to create an extended BLE network. However, there exist two limitations to network growth: (i) a node can establish a Link Layer connection with up to two masters and (ii) a master can establish a connection with at most another master, and, in this connection, the former shall play the slave role. In order to avoid an overlap of connection events for intermediate slave nodes that are connected to different masters, the authors used the Client Characteristic Configuration Descriptor (CCCD), a descriptor available in the GATT layer. CCCD acts as a switch and only allows one connection to be in active mode at a time, while the rest are kept inactive. The authors analytically calculate the latency in a subnetwork and the end-to-end latency between devices in different subnetworks. They also provided experimental inter-subnetwork results by using devices with the X-NUCLEO-IDB05A1 chip from STMicroelectronics.
# Feasibility of Non-Remanufactured Waste Bottle Glass as Supplementary Cementitious Material
## 2 Methodology
### Non-Remanufactured Waste Bottle Glass
#### 2.1.2 Laboratory Waste Glass Milling  
Waste glass raw samples were received in the laboratory and dried until a constant mass. Then, the samples were sieved with a 1 mm sieve to separate the waste glass powder from small visible amounts of paper, plastic, and fibres, as shown in Figure 1. The materials passed through a 1 mm sieve were used in the current research and named waste glass (WG), as shown in Figure 2. The scanning electron microscopy (SEM) images of WG obtained using a secondary electron mode are presented in Figure 3a.  
Figure 1: WG contaminants retained in the laboratory via a 1 mm sieve.  
Figure 2: WG passed through a 1 mm sieve.  
The use of waste glass as a cement replacement is better as the particles are finer, than cement. This is because the finer the powder, higher the pozzolanic activity. Thus, the WG was ground in the planetary mill RETSCH PM100 in the following steps: (a) grinding using 20 mm diameter zircon balls for 15 min at 350 rpm and (b) grinding the resulting material from the previous step using 1 mm diameter zircon balls for 15 min at 350 rpm speed. This material was named ground waste glass (GWG); the samples were then taken for physical analysis. The particle size analysis and the specific surface results (Blaine method), see Table 1, revealed that the ground material (GWG) was coarser than the original WG. It was concluded that fibres (see Figure 4) created tangled lumps during milling, compromising both the particle size analysis method and the Blaine test (Table 1). SEM observation confirmed the existence of fibres, as depicted in Figure 3b.  
Thus, it was decided to repeat the second step of the milling process using alcohol. Even though it is a more time-consuming process due to the drying needed after milling, previous research by authors obtained good results using this method. After 15 min of grinding with alcohol at 350 rpm, the sample was dried in an oven at 50 \({}^{\circ}\)C for 10 min and then mechanically sieved to remove the spheres. The SEM observation of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Materials** & **Cement** & **WG** & **GWG** & **AGWG** \\ \hline Density (kg/m\({}^{3}\)) & 3110 & 2280 & 2280 & 2280 \\ \hline Specific surface (cm\({}^{2}\)/g) & 3742 & 3462 & 3131 & 4779 \\ \hline d\({}_{10}\) (\(\mu\)m) & 1.42 & 4.45 & 4.57 & 2,92 \\ \hline d\({}_{50}\) (\(\mu\)m) & 13.50 & 25.96 & 32.96 & 18.34 \\ \hline d\({}_{90}\) (\(\mu\)m) & 40.52 & 127.00 & 197.00 & 82.83 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Density, specific surface, and particle size distribution of cement, WG, GWG, and AGWG.  
Figure 4: Fibre contaminants found in dry-milled ground WG (GWG).  
Figure 3: SEM (secondary electron mode) images of glass powders: (**a**) WG; (**b**) GWG; and (**c**) AGWG.  
resulting material, alcohol-ground waste glass (AGWG), revealed that even after grinding with alcohol, the waste glass contained fibres. However, the fibres seemed to be shorter, see Figure 3c.
## 7 Conclusion, Limitations, and Future Research Work
### Recommendation for Future Research Work  
The developed research/model helps both researchers and practitioners to predict accurate company performance. Some of the recommendations and future works that can enhance the model and the research, in general, are listed below:  
* Develop rubrics for survey participants to rate their opinion without bias.
* Collect quantitative information to validate the responses to qualitative questions.
* Include more quantitative financial factors to improve the accuracy of the model by assessing the overall performance.
* Reduce personal bias; multiple participants from the same organizations should participate in the survey.
* Use the sensitivity analysis to evaluate the effect of the omitted factors in the shortlisting process.  
* The study shows a need for the further investigation of critical success factors to select the optimum number and nature for modeling the organization's performance.
* There is a need for considering the self-confidence of decision-makers through the social network [50; 51].
* The results of this research will lead to a new generation of specific and accurate company performance models and fully automated models/systems that might partially replace expert opinion techniques.  
Writing--original draft preparation, Z.R.; writing--review and editing, Z.R. and E.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Simulation and Experimental Results of the Proposed System
### Experimental Results  
The proposed method was verified experimentally by conducting a real-time test study. A practical setup was developed: first, the LUO converter and controller circuit designs were developed as demanded by the simulation requirements. The experimental setup shown in Figure 15 includes this designed controller and converter, along with the few additional components like the voltage source inverter, autotransformer, rheostat and BLDC motor. In Figure 16a, the output voltage of the PV panel is shown. This voltage consists of higher-order ripple contents which are oscillatory. For reduce this problem, the voltage is fed to the modified LUO converter. Figure 16b shows the modified LUO converter output voltage. This constant and ripple-free voltage is achieved using the LUO converter and its optimized MPPT algorithm. This constant voltage is fed to the three-phase inverter which converts the DC voltage into AC voltage.  
Figure 14: Efficiency comparison of the different MPPT methods for the elementary and modified LUO converters (E-LUO and M-LUO).  
Figure 15: Experimental setup of the proposed system.  
Figure 17 shows the PWM pulses fed to the switching devices of the voltage source inverter. The PI controller manages the pulse width for speed compensation. The dsPIC produces PWM pulses with a switching frequency of 10 kHz.  
Figure 18 shows the position of the rotor signal from the three hall effect sensors which are mounted on the stator. Based on the rotor position signals, the electronic commutator switches are energised. Figure 19 shows the output voltage from the three-phase voltage source inverter, which also includes back emf. Due to the presence of this back emf, the supply voltage is trapezoidal.  
Figure 20 shows the BLDC motor output current waveform. The modified LUO converter maintains the current within a specific limit. Figure 21 illustrates the speed waveform of the BLDC motor using the closed-loop PI control technique. Here, the reference speed and actual speed are compared and shown in the graph. The PI controller makes the speed constant in terms of the load.  
Figure 16: (a) Solar panel output voltage waveform; (b) Modified LUO converter output voltage waveform.  
Figure 17: Voltage source inverter Pulse Width Modulation (PWM) pulses.  
Figure 19: The output voltage waveform of the three-phase inverter.  
Figure 20: Voltage source inverter-fed BLDC motor output current waveform.  
Figure 18: Hall sensor signals and stator current waveform of the BLDC motor.
# A Systematic Review of the Effects of Nutrient Intake in Handball Players on Exercise Performance
## 4 Discussion
### Fats  
Fats are a necessary component of the healthy diet of the handball athlete given their role as an essential element of the cell membrane, in the provision and absorption of fat-soluble vitamins, and as an important energy substrate in the aerobic metabolism used during sport [7; 10; 78; 100]. In contrast to CHO and PRO, the guidelines of different sports organisations regarding fat intake are reduced, recommending no more than 30% of energy intake from this macronutrient, with less than 10% and 1% respectively for saturated fatty acids and trans fatty acids, 7-10% for polyunsaturated fatty acids (especially omega 3 and omega 6) and the remaining percentage for monomsaturated fatty acids [10; 100]. The vast majority of studies included in this review did not comply with these recommendations [25; 26; 27; 28; 29; 35; 38; 39], exceeding total fat intake with values of up to 42% of energy intake in some of them [39]. Furthermore, the fat sources consumed by male and female beach and court handball athletes were dairy products [26; 27; 39; 47; 48], meals and sausages [18; 26], fish [18; 47], margarines [27; 45], olive oil and other vegetable oils [47; 49], eggs [26] and seeds and nuts [49]. These data are widely heterogeneous depending on the country of the study sample but show similar observations to data on intake and major fat sources in the European population [101]. Furthermore, they show the urgent need to seek better and more adequate nutritional planning for handball athletes in relation to fatty acid intake. Reduced fat intakes may be especially advisable at times close to matches or high intensity training, to avoid possible gastrointestinal discomfort [10; 102; 103], or fat loss protocols during specific training programmes or phases of the season [104].
## 3 Results
### Model Performance in Calibration Site-Years  
At the half-hourly scale, TL-LUEn showed slightly better performance than TL-LUE when data in all 85 calibration site-years were lumped together (Figure 2). With the increase of temporal scales from half an hour to 8 days, the difference of TL-LUEn and TL-LUE became almost indistinguishable. The improvement of both TL-LUE and TL-LUEn over MOD17 was obvious at all three temporal scales. At the half-hourly scale, the average RMSE of MOD17 were larger than that of TL-LUEn by 8.1 mg C m\({}^{-2}\) (30 min)\({}^{-1}\), and the corresponding average R\({}^{2}\) was lower than that of TL-LUEn by 0.050 (Figure 2a,b). At the daily scale, MOD17 output an average RMSE higher by 0.3 g C m\({}^{-2}\) day\({}^{-1}\) and R\({}^{2}\) lower by 0.051 than TL-LUEn (Figure 2c,d). As to the 8-day scale, the average RMSE of MOD17 was 1.0 g C m\({}^{-2}\) 8 days\({}^{-1}\) larger than that of TL-LUEn, and the corresponding average R\({}^{2}\) was 0.025 lower than that of TL-LUEn (Figure 2e,f).  
At three different temporal scales, the number of site-years in the same RMSE and R\({}^{2}\) classes was similar for TL-LUEn and TL-LUE, confirming their similar ability to simulate GPP (see Figure 2). MOD17 performed poorer than TL-LUEn and TL-LUE in most site-years, indicated by larger RMSE and smaller R\({}^{2}\). For example, at the half-hourly temporal scale, the number of site-years with small RMSE values (below 60 mg C m\({}^{-2}\) (30 min)\({}^{-1}\)), was 29 for MOD17, 40 for TL-LUEn, and 41 for TL-LUE, respectively. The R\({}^{2}\) of GPP simulated by MOD17 was mostly in the range of 0.5-0.8 (in  
Figure 2: The number of site-years within different root mean square error (RMSE) and R\({}^{2}\) classes (**left**) and the averages of RMSE and R\({}^{2}\) (**right**) of GPP simulated using the TL-LUEn, TL-LUE, and MOD17 models in 85 calibration site-years at half-hourly (**a,b**), daily (**c,d**), and 8-day (**e,f**) temporal scales, respectively.  
58 site-years) while the R\({}^{2}\) of GPP simulated by the TL-LUEn and TL-LUE mostly ranged from 0.7 to 0.9 (58 site-years for TL-LUEn and 59 site-years for TL-LUE).  
TL-LUEn performed better than TL-LUE for most vegetation types except CROP at the half-hourly and daily scale. However, it performed no better than TL-LUE for MF, NF and SHRUB at the 8-day scale (see Figure 3). Both TL-LUEn and TL-LUE outperformed MOD17 for most vegetation types at three temporal scales except for CROP at the half-hourly scale. The superiority of TL-LUEn and TL-LUE over MOD17 was most significant at forest sites.
## 5 Performance Analysis
### Energy Consumption of a Base Station  
Resource allocations in the time and frequency domains in the defined use cases are presented in Figure 6. Cooperation leads to a shorter active transmission period of the base station in all co-operative scenarios. The figure shows an example with two nodes (UEs) but the same model can be easily generalized to \(N\) users. The energy required for the transmission of data is the integral of the power consumption \(P(t)\) of the air interface over time  
\[E=\int_{t_{0}}^{t_{0}+T}P(t)\text{dt} \tag{12}\]where the transmission duration \(T\) is dependent on the transmission size \(D\) and data rate \(R\) of the used air interface. We can now define the energy consumption for all defined cases as follows.  
Case 1: Normal cellular case, data sent independently to \(N\) users. According to Equation (2) energy consumption is  
\[E=N_{\text{trx}}\times(P_{0}+\Delta_{\text{P}}P_{\text{tx}})\times(D/R) \tag{13}\]  
Case 2 and Case 3 look the same from the base station perspective, since it sends all the data to a single relay. Clear energy savings are achieved especially if the same data is of interest to multiple users in a D2D enabled network. Energy consumption is now defined as  
\[E=N_{\text{trx}}\times(P_{0}+\Delta_{\text{P}}P_{\text{tx}})\times\frac{1}{N} \bigg{(}\frac{D}{R}\bigg{)}+P_{\text{sleep}}\frac{N-1}{N}\bigg{(}\frac{D}{R} \bigg{)} \tag{14}\]  
which means that the base station is able to reduce its active transmission time to one \(N\)th of the time when compared with the Case 1 and then spend rest of the time in the sleep mode.  
Again, Case 4 and Case 5 are the same from the base station perspective. Since the data is divided into independent pieces, the total amount of data transmitted by the base station is actually the same as in Case 2 and Case 3. Assuming that separating the interesting data to independent pieces does not consume significant amount of energy, we can use the same model for the base station power consumption as in Equation (14).
## 5 Plane Size Requirements
The plot of the first error term in Figure 3a shows a convergence to zero with increasing plane size. The plot in Figure 3b depicts the second error term in the absolute power. For planes that capture more than 95% of the whole power of the reference plane, the FT estimate does not change by more than approximately 0.6 dB. For example, the \(16\times 16\)-plane in Figure 4 captures 99.4% of the power of the 80 \(\times\) 80-plane. The results reported in this section were obtained with simulated equivalent surface currents. They represent a conservative bound for the required plane size when currents are obtained from amplitude measurements and phase reconstruction, as described in Section 2. As the phase reconstruction uses the second and third measurement plane, some modeling errors due to overly small plane sizes are compensated partially by the inverse source reconstruction. This was verified  
Figure 4: Forward transformation (FT) to \(z=50\) mm based on simulated equivalent currents for the different plane sizes in Figure 2. The results from FT improve as the plane is enlarged, but results change very little for planes larger than \(16\times 16\) samples. Figure 3: Error in peak spatial-average power density (\(\text{psPD}_{tot}\)) for different sizes of the plane of equivalent currents (or the closest measurement plane) for the different evaluation antennas. (**a**) shows the dependence of the absolute error for the FT algorithm with respect to the simulated fields, and (**b**) shows the error caused by plane truncation with respect to the reference case with the largest plane size. by considering phase reconstruction in the calculations; the resulting dependence on plane size was less pronounced, and planes which captured 99.5% yielded results that could not be substantially improved by larger planes.
###### Abstract  
The fast development of communication technologies and computer systems brings several challenges from a security point of view. The increasing number of IoT devices as well as other computing devices make network communications more challenging. The number, sophistication, and severity of network-related attacks are growing rapidly. There are a variety of different attacks including remote-to-user (R2L), user-to-remote (U2R), denial of service (DoS), distributed DDoS, and probing. Firewalls, antivirus scanners, intrusion detection systems (IDSs), and intrusion prevention systems (IPSs) are widely used to prevent and stop cyber-related attacks. Especially, IDPSs are used to stop and prevent intrusions on communication networks. However, traditional IDSs are no longer effective in detecting complicated cyber attacks from normal network traffic. Because of this, new promising techniques, which specifically utilize data mining, machine learning, and deep learning, need to be proposed in order to distinguish intrusions from normal network traffic. To effectively recognize intrusions, the feature generation, feature selection, and learning processes must be performed delicately before the classification stage. In this study, a new feature selection method called FSAP (Feature Selection Approach) is proposed. In addition, a hybrid attack detection model called SABADT (Signature- and Anomaly-Based Attack Detection Technique) is suggested, which utilizes different classification metrics to recognize attacks. The proposed general method FSACM (Feature Selection and Attack Classification Method) is tested on KDD '99, UNSW-NB15, and CIC-IDS2017 datasets. According to the experiment results, the proposed method outperformed the state-of-the-art methods in the literature in terms of detection, accuracy, and false-alarm rates.  
cyberattacks; intrusion detection system; feature selection; classification; machine learning 2023
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Feature Extraction from Quad-Polarimetric SAR Data
#### 2.2.4 Ellipticity \(\chi\)  
Ellipticity \(\chi\) describes the polarization status of the scattered EM wave. From the Stokes vector, it can be calculated by:  
\[\sin(2\chi)=-\frac{s_{3}}{ms_{0}} \tag{11}\]where \(m\) stands for the degree of polarization of the received EM wave.  
The parameter \(\chi\) can be employed as an indicator of the scattering mechanism. For slick-free sea surfaces where Bragg scattering is dominant, the sign of \(\chi\) is negative. For oil-covered sea surfaces, as a more random scattering mechanism is present, \(\chi\) will be larger and can become positive [28]. This feature makes \(\chi\) a logical binary descriptor of slick-free vs. oil-covered areas.