esa-sceva/satcom-chunk-collection huggingface dataset:
# Antioxidant and Antimicrobial Properties of Selected Phytogenics for Sustainable Poultry Production
## 4 Selected Plant Sources of Antimicrobial and Antioxidant Compounds
### Searweeds  
Seaweeds are macroalgae that grow in the littoral zone of aquatic systems. They consist of green (_Chlorophyceae_), red (_Rhodophyta_), and brown (_Plueophyceae_) algae. There are 10,100 different kinds of seaweeds that have been identified worldwide. Seaweeds can be found in all types of marine settings; hence, their nutritional value varies greatly depending on the species, habitat, geographical origin, production region, season, harvest time, environmental and physiological fluctuations, and water temperature [125]. Seaweeds contain polysaccharides, proteins, amino acids, minerals, vitamins, and antioxidant chemicals [125]. [PERSON] et al. [126], reported that seaweeds are a rich source of bioactive compounds, such as polyphenols, which have antioxidant and antimicrobial properties and thus, can be used as a nutraceutical additive in poultry production [127].  
[PERSON] et al. [128] demonstrated that adding seaweed supplements at a rate of 5 g/kg improved the feed conversion efficiency. According to [PERSON] et al. [129], adding seaweed to broiler diets improved the quality of the meat by reducing the number of microorganisms in the digestive tract and boosting immunological function. [PERSON] et al. [130] investigated the impact of green seaweed (_Ulva_ sp.) meal on native chickens and found that its dietary addition boosted native chickens' feed intake and overall body weight gain but not their ability to convert feed into energy. Broiler hens supplemented with green seaweed at doses of 10 or 30 g/kg did not alter the growth or feed efficiency; however, birds given the higher dose (30 g/kg) showed increased dressing percentage and breast muscle yield compared to those given the control or 10 g/kg dose [131]. In another study, the inclusion of seaweed up to 150 g/kg in the diets of ducks had no negative impacts on the growth rate or carcass quality [132].
# Earthquake Magnitude and Frequency Forecasting in Northeastern Algeria using Time Series Analysis
## 2 Seismicity and Earthquake Database
Specifically, for the current study, this initial earthquake data file has been updated to December 2019, with data coming from the Spanish Instituto Geografico Nacional (IGN) and the Algerian Centre de Recherche en Astronomie, Astrophysique et Geophysique (CRAAG). It is important to note that in order to maintain the magnitude homogenization, this process was performed using the same relationships between reported magnitudes and moment magnitude that were used to generate the initial catalog [7]. The initial recorded events were described using several scales (surface-wave Ms, body wave m\({}_{\rm b}\), body wave from Lg phase amplitude m\({}_{\rm blg}\) and local duration M\({}_{\rm L}\) magnitudes). The next stage was to identify and remove any dependent event or non-Poissonian earthquake (foreshocks, aftershocks and swarms). In the present study, the method of [PERSON] [70]was applied using the particular temporal and spatial window introduced in the initial catalog [7]. Figure 1b shows the spatial distribution of shallow seismicity for earthquakes with magnitudes greater than Mw 4.0 using the declustered dataset. Figure 1c, on the other hand, shows the distribution of focal mechanism solutions and seismicity with magnitudes greater than Mw 4.0. Finally, the completeness of this catalog is performed at the base of a visual approach [71] in order to evaluate the threshold magnitude of the earthquake data file. Following previous studies [7; 72], this approach is applied to our data above different magnitude values: if the cumulative annual number of earthquakes over this magnitude is approximately linear, then the seismic catalog is complete and Poissonian for a given threshold magnitude during a specific period of time. The cumulative number of events with a magnitude above Mw 4.0, 4.5, 5.0, 5.5, 6.0 and 6.5 is depicted in Figure 2. It shows that magnitudes above Mw 4.0 and 4.5 appear roughly complete and Poissonian since 1920 and 1910, with rates of 7.7 and 4.2 events/year, respectively. However, approximately since 1870 and 1885, with rates of 2.1 and 0.81 events/year, magnitudes exceeding Mw 5.5 and 5.0 can be considered complete and Poissonian, respectively. For magnitudes greater than Mw 6.0 and 6.5, they are likely complete and Poissonian since 1860 and 1700, respectively, with rates of 0.21 and 0.08 events/year.
## 4 Conclusions  
We systematically investigated the effect of alkali treatment on a commercial AEM containing PVC as a backing and binder and discovered that the alkali treatment improved the burst pressure of the AMX membrane; in some cases, its burst pressure was almost double that of a pristine AMX.  
When a tensile test was conducted subsequently, it was discovered that the tensile stress decreased monotonically as the alkali treatment time progressed, whereas the tensile strain improved temporarily, i.e., it became easier to stretch and then decreased. A good correlation was discovered between the burst pressure and the area below the SS curve. It was interpreted that the area below the SS curve corresponded to the work up to the burst.  
We conducted a Soxllet extraction of the immersed membrane and analyzed the chemical structure of the PVC after treatment. The results indicated not only polyene formation, but also main chain cleavage and the formation of a cross-linking structure.  
Based on the findings as well as information from an organic chemistry textbook, we interpreted the reaction mechanism and developed a model that elucidated the change in the mechanical strength.  
Upon alkali contact, the PVC was dehydrochlorinated by the E2 mechanism and formed a double bond and hence a cross-linked structure. The SN2 mechanism progressed in parallel with the E2 mechanism, and Cl was replaced with OH. The reverse Aldol reaction, which involved oxidization with OH, caused the main chain to break.  
The AMX membrane is a composite of \"PVC backing\" and a \"CMS-DVB cross-linked structure containing a PVC binder\". The improvement in the burst strength of the AMX membrane after alkaline treatment is attributed to the cleavage of the main chain of the PVC. Further investigations using a test AEM without a PVC binder are necessary to validate the model.  
Conceptualization, S.D. and M.H.; methodology, S.D., Y.K., I.T. and M.H.; formal analysis, X.X.; investigation, S.D., Y.K., I.T. and M.H.; data curation, S.D.; writing--original draft preparation, M.H., M.Y. and T.I.; writing--review and editing, S.D., I.T. and M.H.; visualization, S.D., I.T. and M.H.; supervision, M.H.; project administration, S.D. and M.H.; funding acquisition, M.H. All authors have read and agree to the published version of the manuscript.  
This research was partly funded by SALT SCIENCE grant number 2018.  
**Acknowledgments:** The authors would like to thank [PERSON] of Asahi Kasei Corporation's Production Engineering Division for technical advice in interpreting the experimental data of mechanical strength, and [PERSON] of Yamaguchi University for scientific advice regarding chemical reactions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Integrated Robotic and Network Simulation Method
## 3 IRoNS Method
### Case-Study--Experimentation Design  
Once the Base Simulation passes the validation process, we assume that it is a valid simulation model and it can be used for designing the experimentation, which is a process of planning simulation cases to analyze the impact of changes in parameters and algorithms on simulation results.  
The initial goal is to define evaluation and observation parameters. Observation parameters are those used to track system states. Evaluation parameters are simulation performance criteria, which generally depend on the nature of the cooperative task that we are analyzing, and it can also consist of a combination of several observation parameters.  
To do the case-study planning, we use a _factorial 2K experimental design_, where \(K\) is the number of parameters used. This approach measures the impact on the results when changing one parameter value or algorithm and can be combined with statistical analysis by confidence intervals when assessing the results. The \(2K\)_factorial design_ has an increasingly demanding cost as the number of parameters and experiments increases, but it is very straightforward when these numbers can be bounded to a small size [22].  
In this formulation, we must choose two levels (values) for a parameter or use two test algorithms, adopting '\(-\)' and '\(+\)' for their representation. In this case, by convention, '\(-\)' is used for the parameter standard value or standard algorithm, i.e., the ones used in Base Simulation, and '\(+\)' to indicate the test value or test algorithm that we are comparing with.  
These results can be organized in an experimentation table, as indicated in Table 1 for an example with two factors (\(K=2\), indexed by \(f=1\dots\)\(K\)). In this case, there are four possible factor combinations, indexed by \(k=1\dots\)\(2^{K}\), and their respective results R\({}^{k}_{l}\). Moreover, we should explore \(j=1\dots\)\(n\) different simulation situations (samples), under the same operational parameters, leading to \(n\) experimental tables and, in this case, \(n\times 2^{K}\) simulations. For the cooperative robotic tasks addressed in this work, as an example, this can be achieved using different values for robots' initial positions and by varying their initial communication topology.  
The number of samples has a key role when building the statistical analysis by confidence interval, thus it is recommended to simulate at least 15 different samples to obtain satisfactory results using t-student distribution and 30 samples when a normal distribution behavior is desired [22]. A higher number of samples may improve results, as it may reduce the confidence interval length, however it can be time consuming and not always possible.
## 4 Experimental Results  
This section is entirely dedicated to an exhaustive set of experimental test and evaluation of the implemented functionality of a sidecar object for the integration and synchronization with Google Cloud IoT Platform. The environment used to perform the tests on the sidecar had the following characteristics: (i) Dell XPS 15 9560 equipped with Intel Core i7-7700 HQ CPU 2.80 GHz x 8 RAM 16 GB Ubuntu 18.04.2 64-bit and (ii) WiFi connectivity with high-speed internet access (largely enough to meet the maximum data requirements of a single sidecar object).  
In the following test it has also been used a Mosquito MQTT Broker, running in a local docker container, and a custom Java program that emulated the behaviors of a real smart object in terms of data generation. The section explains minutely all the tests performed (considering and averaging through multiple runs for each configuration) on the sidecar object, analyzing and motivating in-depth the obtained results. The metrics that will used are summarized in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metrics** & **Dimension** & **Description** \\ \hline Startup Time & [ms] & Time in milliseconds required by the sidecar object and its internal components to startup \\ \hline Message pass-through delay & [ms] & The pass-through delay as the time elapsed between the arrival of the message inside the SmartObjectSubscriber and the execution of the asynchronous publish by the GoogleCloud-TelemetryPublisher \\ \hline Message Ack Delay & [ms] & The elapsed time between the asynchronous publish performed by the TelemetryPublisher and the server acknowledgment of that specific publish \\ \hline Queues Sizes & Adimensional & Outbound and Recovery Queues sizes, for the Telemetry Message Recovery tests \\ \hline Heap Memory Size & [MBytes] & Allocated memory for the Java Heap \\ \hline Heap Memory Usage & [MBytes] & User memory of the allocated Java Heap \\ \hline CPU Usage Percentage & Adimensional & Percentage of used CPU by the object \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental evaluation’s metrics.
## 5 Discussion
### Applicability Across Scenarios  
The adaptability of MAMRS is a crucial factor in its success across diverse mining environments. The modular design of the system allows for easy customization and retraining to meet the requirements of various scenarios:  
**(1)**: **Algorithm Adaptability:**  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Collector** & **Start Time** & **End Time** & **Location** & **Number of Images** & **Image Collection Time (min)** & **Reading** & **Image Collection (Images/ min)** & **Reading** & **Overall Efficiency (Readings/ min)** \\ \hline Human & 09:00 & 09:45 & \begin{tabular}{c} Power \\ distribution \\ room \\ \end{tabular} & 42 & 30 & 15 & 1.4 & 2.8 & 0.93 \\ Robot & 09:00 & 09:45 & \begin{tabular}{c} Power \\ distribution \\ room \\ \end{tabular} & 98 & 25 & 20 & 4.0 & 4.9 & 2.12 \\ Human & 10:00 & 10:50 & \
\begin{tabular}{c} Control \\ room \\ \end{tabular} & 45 & 40 & 10 & 0.9 & 4.5 & 0.9 \\ Robot & 10:00 & 10:50 &
\begin{tabular}{c} Control \\ room \\ \end{tabular} & 120 & 36 & 14 & 3.3 & 8.6 & 2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of meter collection and reading efficiency.  
The deep learning models (ResNet18, YOLOv5, and U2-net) can be retrained with data from new environments, such as low-light or high-dust areas, to handle additional meter types and challenging conditions.  
**(2) System Scalability:**  
The quadruped robot's modular design enables the integration of advanced sensors like thermal cameras and LIDAR to enhance navigation and meter recognition in adverse environments.  
**(3) Future Scenario Testing:**  
Planned experiments in mine tunnels and extraction areas will further validate the system's adaptability and highlight potential areas for optimization.  
By addressing these aspects, MAMRS can serve as a reliable solution for automated inspections in diverse and dynamic mining environments.
# Further Advances in Atrial Fibrillation Research: A Metabolomic Perspective
## 2 Application of Metabolomics in Atrial Fibrillation
### Energy Metabolism and Its Influence on Atrial Fibrillation  
Cardiac tissue can use different sources for energy production. However, after a meal, 60% to 70% of ATP production in myocardial tissue is due to beta-oxidation of fatty acids. Thus, fatty acids play a vital role in the energy balance of the heart [26]. In this context, two medium-chain fatty acids, e.g., decanylcarnitine and octanoyllcarnitine, have been associated with cardioembolic stroke, mainly caused by AF [26], as we explained before. Decenylcarnitine and other fatty acids have also been linked to impaired glucose tolerance in previous studies [27].  
Glucose metabolism also plays a very important role in energetic requirement of heart tissue. A recent microarray analysis form transmural ventricle tissue in patients with permanent AF revealed ventricularization of gene expression and up-regulation of transcripts involved in metabolic activities, including several enzymes related to glucose metabolism, suggesting a link between a change in glucose metabolism and AF [28].  
Among these enzymes, we can find triose phosphate isomerase and glyceraldehyde-3-phosphate dehydrogenase. These two glycolytic enzymes perform a positive regulation as part of the reprogramming of the human atrial transcriptome in permanent AF [14].
## 3 Results and Discussion  
### Encapsulation Yield and Encapsulation Efficiency of Algae Pigment Microparticles  
Encapsulation of algae pigments by the PGSS process was studied using extracts prepared with different extraction techniques. The conditions of pressure and temperature for the PGSS were selected to ensure a temperature above the melting point of PEG 4000 (62 \({}^{\circ}\)C) to provide good processability. As shown in Table 1, the yield of collected microparticles in the collecting vessel was from 61.60% to 73.73%. The discontinuous operating approach used in the study caused a reduction in average encapsulation yield because some of the initial pigment/PEG mixture was left in the reactor and/or on the spraying tower's walls. The yield of collected particles was highest with WAT+PEG as the initial mixture. The EE of pigments in PEG varied in the ranges from 56.98 to 95.00%, from 65.48 to 83.40%, and from 43.02 to 62.31%, for chlorophyll a, chlorophyll b, and carotenoids respectively, and the results differed depending on the used extraction process. The pattern was the same with all pigment classes; the lowest EE was for raw algal material, followed by extract obtained with an ethanol-water solution and conventional extraction, while EE for SCO\({}_{2}\)E was the highest. The extracts due to differential solubility interacted with the PEG differently. Pure PEG 4000 has low polarity and therefore polar (SCO\({}_{2}\)E) extract is more soluble in PEG. These results showed good agreement with prior research employing the PGSS method to load active compounds into microparticles in terms of encapsulation efficiency [20; 21].
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 2 Unveiling the Evolution of AI: From Symbol Manipulation to Deep Learning Networks
### From Symbol Manipulation to Conversational Agents  
In the early days of AI, the manipulation of symbols using predicates and logical propositions formed the basis of the technology. Progress in AI was a result of progress in manipulating symbols, which also pushed the boundaries of computing languages [8; 9].  
An early and noteworthy instance of a conversational agent is ELIZA, which originated in the mid-1960s [10]. This agent employed a domain-specific script to establish a conversational style, such as that of a \"DOCTOR\", and engaged users in a \"therapist\" conversation by mostly restating their remarks as questions, thus prompting the user to provide further information and continue an ongoing dialogue. Some users reported benefiting from these therapy sessions and sought continued access to the system, a phenomenon known as \"the ELIZA effect\". Considering that this occurs despite the strong reliance of each question on the preceding answer and the transparent manner of restatement, it becomes apparent that more sophisticated platforms like ChatGPT [11] are poised to gain widespread acceptance as human conversational partners among a substantial portion of individuals.  
Integrating sophisticated symbolic manipulation capabilities, the Lisp family of programming languages has established its prominence. However, it is intriguing to note that ELIZA, despite this trend, was crafted using a distinct language called MAD (Michigan Algorithm Decoder) [12]. This language was enriched with the integration of an accompanying package named SLIP (Symmetric List Processor), effectively infusing Lisp-like features into its functionality. Such a departure from Lisp's convention highlights the innovative approach undertaken in ELIZA's development.  
In this unconventional choice of language, ELIZA's creators navigated their way towards designing a conversational agent capable of interactive dialogue. The augmentation of MAD with SLIP not only exemplified a creative adaptation but also paved the way for a system that initiated the journey toward explainable AI and human-computer interaction. This strategic departure from the established norm enabled ELIZA to simulate therapeutic conversations effectively, setting the stage for modern developments in the domain of interpretation techniques for AI systems.
## 3 Feature Extraction Algorithm for Industrial Control Protocol
### Frequent Item Extraction Based on Jaccard Coefficient  
A large number of N-Gram data units are obtained after dividing the protocol data through the N-Gram model, but few data units are useful for classification. The data units that contribute to protocol classification are meaningful and frequent, so these N-Gram data units need to be further screened in order to obtain frequent data units. The Jaccard coefficient [25] can be used to compare the similarity between sets of N-Gram data units. In this paper, the Jaccard coefficient is used to find out the threshold for screening and filtering out the N-Gram frequent data units that are helpful for classification. The larger the Jaccard coefficient, the higher the similarity between sets of N-Gram data units. At this time, the data units contained in the set are most likely to be frequent and useful.  
The process of calculating the Jaccard coefficient is shown in Equation (3). The data units obtained by dividing are randomly divided into two sets, A and B. The similarity between sets A and B can be seen as the ratio of the intersection and union between the two sets.  
\[J(A,B)=\frac{|A\cap B|}{|A\cup B|}=\frac{|A\cap B|}{|A|+|B|-|A\cap B|} \tag{3}\]  
Then, calculate the frequency of occurrence of subunits in each set separately and sort them from large to small, recorded as Equations (4) and (5):  
\[\{A_{1}:f_{A1},A_{2}:f_{A2},\ldots,A_{n}:f_{An}\} \tag{4}\]  
\[\{B_{1}:f_{B1},B_{2}:f_{B2},\ldots,B_{n}:f_{Bn}\} \tag{5}\]  
Figure 2: Segmentation of 2-Gram.  
Among them, \(A_{i}\), \(f_{Ai}\), \(B_{i}\), and \(f_{Bi}\) represent the subunits in A and B and their frequency of occurrence, respectively. In order to make Jaccard more suitable for the needs of this paper, it is modified as shown in Equation (6):  
\[J(A,B)=\frac{\sum_{i=1}^{n}(f_{Ai}*f_{Bi})}{\sum_{i=1}^{n}{f_{Ai}}^{2}+\sum_{i= 1}^{n}{f_{Bi}}^{2}-\sum_{i=1}^{n}(f_{Ai}*f_{Bi})} \tag{6}\]  
Different data units appear at different frequencies in the set. Select different frequencies as thresholds to filter out subunits with low frequencies in the set and then calculate the Jaccard coefficients of the two sets at the corresponding frequencies. We can obtain a list of Jaccard coefficients; this paper selects the frequency value \(f\) corresponding to the first maximum value of the Jaccard coefficient as the screening threshold for retaining as many useful data units as possible, and the frequency of a subunit that is greater than \(f\) is considered as a meaningful and frequent data unit.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 3 BGP Anomalies and Attacks
Regarding the granularity of BGP hijacks, attacks such as same-prefix hijacking, subprefix hijacking, and AS path poisoning demonstrate the varied and increasingly sophisticated methods attackers use [6]. The subprefix hijack, for instance, exploits the BGP's trust model by announcing a more specific prefix, thereby rerouting traffic. Path poisoning attacks leverage the BGP's loop prevention mechanism, selectively inhibiting route propagation by including specific ASNs in the path. While such attacks are detectable through certain measures, the emergence of more stealthy and surgical interception techniques, including the strategic use of BGP communities' attributes, presents ongoing challenges in ensuring secure and reliable routing [34; 36; 37]. Protocol manipulation-based BGP attacks are those where attackers exploit the BGP's decision-making mechanisms [6]. Techniques such as altering the Multi-Exit Discriminator (MED) or exploiting timers such as Route Flap Damping (RFD) and the Minimum Route Advertisement Interval (MRAI) allow adversaries to influence route selection and destabilize networks [37; 38]. Data misuse attacks are explained through attack methods such as denial of service (DoS) and route leaks [6; 39; 40]. Lastly, recent advanced and evasive BGP attacks have been described in the literature, where attackers equipped with in-depth knowledge of BGP infrastructure execute sophisticated and stealthy operations, often evading detection and exploiting the public collector and monitor's infrastructure and their limitations [15].