esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of the Anomalous Noise Events Present in the Urban and Suburban Environments
### ANE Occurrences  
This first part of the study aims at determining the predominant types of ANE for each of the two recording scenarios. To that end, the ANEs distribution of occurrences is determined by computing the accumulated duration of each ANE subcategory within the real-life database. In Figure 8, the distributions of the sum of ANE durations are depicted. As it can be observed, sirens and sound of portals' structures ('stru') followed by the noise of vehicle horns, people, trucks and car brakes are the most observed subcategories considered as anomalous noise events, being the rest significantly-less probable. As the list of anomalous events subcategories have been defined (see Section 3.2) to account for all types of ANEs recorded either in the urban or suburban environments, it can be also observed that some ANE subcategories do not occur in the suburban area. For instance, no samples of airplanes, bikes, birds, chains, dogs, mega, thunder, tramways or wind were recorded during the suburban real-life recordings. On the contrary, some types of anomalous noise events were collected unexpectedly in this environment. For instance, highway operators talking while doing maintenance works were recorded (i.e., 'pepo' ANE subcategory), which is a somewhat rare event to collect in a suburban scenario such as a highway. As foreseen, a larger diversity of ANEs can be observed in the urban than in suburban soundscape.  
Regarding the probability of the recorded ANEs, it can be concluded from Figure 8 that sirens and the sound of portal's structures were the most frequent ANEs in the urban environment, while music, people talking and sounds of tramways and trains where the most represented events in the urban area. In a second order of occurrence, we can find brakes, horns, people talking and trucks in the suburban area, while in the urban context, sounds of airplanes, birdsongs, car brakes, doors, horns, sirens, structures, thunders and trucks can be observed. Finally, the less frequent sounds are sound of pressurized air (bust), door or impulsive-like sounds, and music in cars in the suburban soundscape, and sounds of bikes, opening bus or tramway door noises (bust), chains, dog's barks, noise of people reporting by the public address station (mega), and wind in the urban sound field.  
Figure 7: Examples of SNR labeling of three anomalous noise events. From left to right: (a) a low-salient ANE (people talking in the street, measured in a Milan street, with SNR = 1.1 dB); (b) a medium-salient ANE (the sound of an airplane measured in another Milan road, with SNR = 3.6 dB); and (c) a high-salient ANE (sound of a horn measured along the A90 motorway in Rome, with SNR = 9.7 dB). \(x\)-axis corresponds to time in seconds referenced to the start point of the recording.
# ABACO: A New Model of Microalgae-Bacteria Consortia for Biological Treatment of Wastewaters
## 2 Materials and Methods
### Biomass Concentration and Analytical Methods  
The biomass concentration (Cb) was measured by dry weight. For that, aliquots (100 mL) of each photobioreactors the culture were filtered through the Macherey-Nagel MN 85/90 glass fiber filters. Then, the filters were dried in an oven at 80 \({}^{\circ}\)C for 24 h. Standard official methods were used to analyze the composition of the piggery wastewater and the supernatants from microalgae-bacteria cultures. The phosphate was measured by visible spectrophotometry through the phospho-vanado-molybdate complex (phosphate standard for IC: 38364). The nitrate was quantified by measuring optical density at 220 nm and 275 nm (nitrate Standard for IC: 74246). The ammonium was measured according to the Nessler method (ammonium standard for IC: 59755). The chemical oxygen demand (COD) was determined by spectrophotometric measurement using Hach-Lange kits (LCI-400).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Piggery Wastewater** & **Arnon Medium** \\ \hline pH & 8.1 \(\pm\) 0.3 & 7.5 \(\pm\) 0.2 \\ COD & 2181.7 \(\pm\) 100.9 & 16.0 \(\pm\) 1.2 \\ Nitrogen-Nitrate & 56.5 \(\pm\) 2.7 & 140.0 \(\pm\) 4.5 \\ Chloride & 2060.2 \(\pm\) 23.5 & 78.9 \(\pm\) 2.1 \\ Potassium & 1800 \(\pm\) 1.6 & 325.1 \(\pm\) 6.3 \\ Calcium & 350.1 \(\pm\) 0.2 & 364.9 \(\pm\) 5.5 \\ Magnesium & 108.2 \(\pm\) 14.1 & 12.2 \(\pm\) 0.6 \\ Phosphorus-Phosphgate & 119.2 \(\pm\) 5.1 & 39.3 \(\pm\) 3.1 \\ Nitrogen-Ammonium & 1495.6 \(\pm\) 17.7 & 0.0 \(\pm\) 0.1 \\ Iron & 4.8 \(\pm\) 0.01 & 5.0 \(\pm\) 0.3 \\ Copper & 1.1 \(\pm\) 0.1 & 0.02 \(\pm\) 0.00 \\ Manganese & 2.6 \(\pm\) 0.0 & 0.5 \(\pm\) 0.02 \\ Zinc & 20.1 \(\pm\) 0.2 & 0.06 \(\pm\) 0.01 \\ Boron & 5.3 \(\pm\) 0.1 & 0.4 \(\pm\) 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average compositions of the culture medium and piggery wastewater used as the influent in the bioreactors. Concentrations expressed as mg \(\times\) L\({}^{-1}\).  
Figure 1: Laboratory photobioreactors used for performing the experiments.
## 5 Feasibility Study
### Performance Evaluation of Raspberry Pi Boards as Asterisk Server for LTC Applications
#### 5.1.3 Passthrough VoIP Testing Overview  
Figure 7 gives an overview of the passthrough VoIP testing procedure. In passthrough, the calling parties use the same codec to transform analog audio signals into a digital format suitable for transmission over the network. For passthrough performance evaluation, the testing script forces the embedded Asterisk server to originate a call to the MOH extension through the loopback interface of the board. This resembles a SIP trunk operation. The call origination by the script creates a communication channel with the Asterisk server (Channel #1). On the other hand, passing the call to the called party (MOH extension) is carried out by a SIP trunk interface defined in pjsip_wizard.conf configuration file that uses the loopback interface of the board (Channel #2). Since both Channel #1 and Channel #2 are configured to use the same audio codec (ulaw), a passthrough VoIP call is established between the script and the MOH extension. This configuration has two advantages: (1) It allows to perform the passthrough testing using a single Raspberry Pi board, and (2) It shortens the testing time; since each originated call actually represents two active channels. As a result, it reduces the testing time by 50%.
## Data
### Daily Sea Surface Temperature  
In the NLSST algorithm, [PERSON] et al. [17] and [PERSON] et al. [41] recommended climatological SST for a first-guess SST. However, climatology data should not be used as a first-guess SST in coastal areas where small-scale ocean features cause high SST variability. In order to compensate for this problem, the Operational Sea surface Temperature and sea-Ice Analysis (OSTIA) data of Met Office were acquired in order to remove the cloudy and cloud-contaminated pixels and to provide a first-guess SST in the NLSST algorithm. The OSTIA uses the optimal interpolation method to compose infrared and microwave satellite, and in situ, SST measurements [42], and provides global high-resolution (1/20\({}^{\circ}\)), gap-free SST, and sea-ice products on daily base [43; 44].  
Figure 2: An example of time series of in-situ drifter temperatures (\({}^{\circ}\)C) (**a**) before and (**b**) after QC procedure to eliminate abnormal temperatures in 2011. (**c,d**) present another example of the QC procedure.
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 5 Results
### Comparing the Predication Results with Previous Research  
This section compares the results obtained in this research with the previous research identified and discussed in Section 2. Several experimental results are compared, and the first comparison is performed with the research conducted by [44]. The model developed in this paper is referred to as 'User Model'. Figure 18 compares the performance of the user model with that of the models developed by [44].  
The comparison between the results of this research and the results presented by [44] reveals several key insights. This research demonstrated a well-balanced performance and achieves a recall of approximately 0.90, significantly outperforming all the configurations from [44], where the highest recall was 0.667. This highlights that the model developed in this research has a greater ability to correctly identify malware patterns. In terms of precision, the strict configurations from [44] (cost 20 and 30) achieved slightly higher values (0.989) compared to the precision of 0.93 of this research. However, the trade-off for this higher precision is a lower recall, resulting in a less balanced performance. The F-measure, which balances precision and recall, was notably greater in this research (-0.914), exceeding all configurations from [44], except for the strict configuration with cost 10 (0.787). These results indicate that the model developed for this research is highly effective and achieves a balance between detecting true positives and minimizing false positives, making it more robust for practical implementations.  
The results obtained by [45] are also compared with the user model, and Figure 19 compares these results. The comparison reveals that the model developed in this research had a TPR of 90%, which is competitive with or exceeds most methods tested by [45], except for the \"Zeus (Snort)--SBB\" and \"Zeus (Snort)--C4.5\", which both achieved a TPR of 98%. However, the model developed in this research minimized the false positive  
Figure 18: Results of [44] compared to the results of this research.  
rate (FPR), maintaining a consistent FPR of 5%, significantly outperforming methods like \"Zeus-1--SBB\" (27%) and \"Zeus-1--C4.5\" (17%). This highlights the robustness of the user model in accurately identifying true positives while reducing false alarms, making it highly effective in real-world scenarios. Overall, the user model provides a balanced approach with a strong performance in both detection and minimizing errors, positioning it as a reliable alternative to [45].
## 5 Simulation and Analysis
### Performance of the Routing Protocols with the Mobility Models
#### 5.2.5 Flooding  
When making use of flooding, the PDR (Figure 17) is again the highest under RWP, followed by EGM and GM. RD causes the worst performance, followed by ST. This performance of flooding is closely related to both the partitioning of the network and the network diameter. As can be seen in Table 2, RWP causes the least amount of partitioning (average number of components), followed by EGM and GM. ST and RD show the most partitioning. The same ranking applies to network diameter, with RD showing the highest (worst) diameter. Therefore, and once again, the ranking in PDR performance that we observe here could have been predicted. Furthermore, the PDR for flooding ranges from about 96% to about 99.2%, thus a range length of about three percentage points. This range length is five-times lower than that of both OLSR and GRP, three-times lower than that of RGR and about half that of AODV.  
Figure 16: End-to-end delay of OLSR under various mobility models.  
We can see that flooding is much less sensitive than actual routing protocols to the changes induced by the mobility models. However, we are not suggesting that flooding should be used for UAANET. It brings about an unsustainable amount of bandwidth waste, as we have 10-times (as observed in simulations) more transmissions per seconds than routing protocols. We are presenting flooding here for the sole purpose of establishing certain performance upper bounds. Now, we know for example that the upper bound for PDR for routing protocols to tend to is about 99.2% and that changing mobility models will lead to a PDR fluctuation of at least three percentage points.  
Table 3 summarizes the PDR of the protocols over different mobility models. The table ultimately shows that the ranking of the routing protocols with regard to PDR does not change with the mobility model used. The PDR for flooding is the upper bound. We have RGR showing the best PDR of all of the protocols, followed by AODV and OLSR. GRP shows the worst performance. This ranking is expected since we know that RGR can be seen as an enhanced version of AODV combined with a form of GRP.
## 4 Results and Discussion
### Impact on the Risk Matrix
Figure 7 summarizes the impact of Industry 4.0 technologies on failure consequences. Figure 7: Impact of maintenance technologies on failure consequences and risk matrix. Thus, in general, a change in the overall risk has not been observed, as there are several balancing and reinforcing factors introduced by the upgraded monitoring system and planned standby policy. Minor changes are observable at the component level in terms of failure likelihood or consequence; however, at the system level, and having modifications that have positive and negative impacts at the same time, the risk appears to be stable, while it is extremely dynamic. In total, the potential reduction in the likelihood and consequences of failure due to emerging practices could lead to a reduction in risk. It is worth mentioning that the risk matrix shall be reinforced and updated more frequently as emerging practices create a dynamic and systemic risk in the context. In addition, emerging practices introduce other types of risk, such as those related to cyber security. With regard to production-related consequences, emerging practices have provided more redundant configurations that ensure asset availability in the event of failure. In addition, early detection of failures has kept the level of damage and service to a minimum while offering an effective maintenance planning time and the use of opportunistic maintenance intervals. In addition, some emerging practices related to additive or manufactured spare parts on-demand have shortened the supply lead time. Regarding the consequences in terms of maintenance expenditures, some emerging practices had a target of reducing the \"time to maintain\" by having more supported (easy to access) and modular (easy to replace) maintenance and autonomous maintenance using robots. Although emerging practices are intended to reduce asset risks by design, capital and operating expenditures must be justified to realize and gain the benefits of these emerging practices. Investing in unmanned systems and smart maintenance programs could reduce the risk of the system while increasing the CAPEX, as shown in Table 13. However, some emerging practices, such as heavy redundant and instrumented systems, could increase system risk and maintenance expenditures.
## 4 Results Evaluation
### Model Validation
#### 4.2.1 Empirical Micro Validation  
Empirical micro validation typically deals with calibrating model's input parameters according to empirical data. The empirical data used for setting the values of input parameters of this model are drawn from recorded social data (RSD) provided by ITRC and empirical values (EV) of one of the underlying studies [64]. These empirically derived values are presented in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Level** & **Face** & **Empirical** \\ \hline Micro & Face micro & Empirical \\  & & validation & micro validation \\ Macro & Face macro & Empirical \\  & & validation & macro validation \\ \hline \hline \end{tabular}
\end{table}
Table 7: The relationship between validation type and validation level.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Development** & **Item of** & **Data** & **Provider** & **Derived Value** \\
**Symbol** & & **Change** & **Source** & & \\ \hline N & Theory [64] & Name & RSD & ITRC & 800 \\ N\({}_{\text{2G}}\) & Theory [64] & Name & RSD & ITRC & 440 \\ N\({}_{\text{3G}}\) & Theory [64] & Name & RSD & ITRC & 280 \\ N\({}_{\text{4G}}\) & Evidence & - & RSD & ITRC & 80 \\ Ad & Evidence & - & RSD & ITRC & 0.6 \\
4 GComP & Evidence & - & RSD & ITRC & 0.8 \\ MC\({}_{\text{I}}\) & Theory [64] & Name & EV & & 0.1 \\ MC\({}_{\text{EA}}\) & Theory [64] & Name & EV & & 2 \\ MC\({}_{\text{EM}}\) & Theory [64] & Name & EV & & 4 \\ MC\({}_{\text{LM}}\) & Theory [64] & Name & EV & & 6 \\ MC\({}_{\text{L}}\) & Theory [64] & Name & EV & & 8 \\ PAN & Theory [66] & - & - & TRUE or FALSE \\ SWN & Theory [67] & - & - & TRUE or FALSE \\ \hline \hline \end{tabular}
\end{table}
Table 8: Empirically derived parameters.  
As we alluded to above, there are many different ways to set (i.e., calibrate) the parameter values of an ABM. In our case, all RSD values were provided by ITRC. Empirical values (EV) of MC\({}_{\rm I}\), MC\({}_{\rm EA}\), MC\({}_{\rm EM}\), MC\({}_{\rm LM}\), and MC\({}_{\rm L}\) were directly drawn from [64], which in turn was calibrated from earlier data. In the case of network inputs, we used a different approach; because we were less sure of what inputs to use, we analyzed two variants of networks [66; 67] that have been reported to be representative of real-world social networks, and investigate the sensitivity of model's performance in each of two variants. Now the model's inputs are empirically validated and realized; the _empirical macro-validation_ should be implemented.
# Research on Temperature Compensation Method in Crankshaft Online Measurement System
## 4 Experiment
### Validation of Temperature Compensation
Obviously, the residual error decreased from \(-0.0282\) mm \(\sim\) +0.0018 mm before temperature compensation to \(-0.0058\) mm \(\sim\)\(-0.0008\) mm after temperature compensation. Regardless of the ambient temperature, crankshaft temperature just off the production line and calibration part temperature, the measurement results are basically the same. The effectiveness of the method is proved.
# Low-Dose Benzene Exposure Monitoring of Oil Refinery Workers: Inhalation and Biomarkers
###### Abstract  
Airborne benzene in workplaces has progressively decreased due to preventive actions and the redesigning of facility processes. Professionals who assess occupational exposure should select techniques to detect benzene levels comparable to ambient air exposure. Thus, sensitive biomarkers are needed to discriminate the effects of confounding factors, such as smoking or sorbic acid (SA). In order to identify sensitive biomarkers and to study their correlation with confounding factors, 23 oil refinery workers were enrolled in the study; their airborne benzene exposures and biomarkers were monitored. Urinary benzene (U-B), _t,t_-muconic acid (_t,t_-MA), and S-phenylmercapapturic acid (SPMA) were quantified. Urinary cotinine (U-C) and _t,t_-sorbic acid (_t,t_-SA) were evaluated to flag smoking and SA intake, respectively. The benzene measured in personal inhalation sampling ranged from 0.6 to 83.5 (median 1.7) \(\mu\)g/m\({}^{3}\). The concentration range of the biomarkers, U-B, _t,t_-MA, and SPMA, were 18-4893 ng/m\({}^{3}\), <10-79.4 \(\mu\)g/g creatinine, and <0.5-3.96 \(\mu\)g/g creatinine, respectively. Pearson tests were carried out; the best correlations were between airborne benzene and U-B (\(\mu\)g/L r = 0.820, \(p\) < 0.001) and between benzene and SPMA (g/L r = 0.812, \(p\) < 0.001), followed by benzene and _t,t_-MA (mg/L r = 0.465, \(p\) = 0.039). From our study, U-B and SPMA result to be the most reliable biomarkers to assess the internal number of low doses of benzene exposure, thanks to their specificity and sensitivity.  
Keywords:airborne exposure evaluation; benzene; biological monitoring; oil refinery
## 4 Pairwise Clock Synchronization under Exponential Delays  
In the literature, the exponential distribution is also commonly exploited to represent the random portion of delays in WSNs. The reasons behind choosing the exponential distribution include:  
* For the point-to-point hypothetical reference connection (HRX) between two nodes, a single-server M/M/1 queue can appropriately represent the aggregate link delay, where the random delays are modeled as independent exponential random variables [25].
* Experiment results were carried out in [26, 27] to verify the appropriateness of choosing the exponential distribution to characterize the random delays in WSNs.
* Among all distributions with a fixed mean in the support \([0,+\infty)\), the exponential distribution achieves the maximum differential entropy, and thus, it is the least informative.  
The most representative clock offset estimation methods for pairwise synchronization in the presence of exponential delays are the maximum likelihood estimator (MLE), the best linear unbiased estimator (BLUE) and the minimum variance unbiased estimator (MVUE), and they will be first reviewed in this section. The MLE for joint estimation of the clock offset and skew will be investigated later. Finally, an important statistical concept, referred to as the confidence interval for the clock offset, will be investigated.
## 3 Methods for the Characterisation and Evaluation of the Skid Resistance of Asphalt Pavements
### Pavement Macro- and Microtexture Measurement Methods
#### 3.2.2 Measurement of Pavement Microtexture  
(1) Laser measurement method  
At present, pavement microtexture is predominantly assessed using non-contact optical techniques. The most commonly employed instruments include circular texture meters (CTMs), linear laser scanners (LLSs), and laser texture scanners (LTSs) [59; 69; 70]. CTMs capture surface texture by following a circular scanning trajectory, whereas the LLSs operate along a linear path. A LTS is capable of simultaneously acquiring both the macrotexture and microtexture characteristics of a pavement surface.  
(2) Direct microscopic observation method  
The direct microscopic observation method enables the evaluation of the aggregate surface microtexture by carrying out a high-resolution visual inspection of surface features at the microscale [71], in which SEM can magnify the original image of the aggregate surface by a factor of 6000 [72], the texture condition can be directly binarised by software such as Fractalfox, and finally, the microtexture of the aggregate surface can be analysed [73].  
In addition, atomic force microscopy (AFM) has shown great potential for application during microtexture studies of pavements [68]. A number of researchers have conducted computer simulations of the molecular dynamics of microfriction between a tyre and an asphalt pavement. The atomic force microscope (AFM) was found to be an effective tool for validating simulated microtexture changes in the pavement. The AFM is shown in Figure 10.