esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The WBANs Communication Stack
### Medium Access Control Layer
#### 4.2.2 Key Attributes for Application-specific WBAN MAC  
With regards to the energy consumption and lifetime of the WBAN devices, according to IEEE 802.15.6 TG6 Technical Requirements [43], the device should operate while supporting a battery life of months or years without intervention, whereas others may require a battery life of tens of hours due to the nature of the applications and/or physical constraints on the size of the devices. For example, cardiac defibrillators and pacemakers have a lifetime of more than 5 years, whereas swallowable camera pills typically have lifetime of 12 h. Most non-medical applications have stand-by power requirements of 100-200 h and active power requirements of several hours.  
Most of the WBAN-based MAC layer protocols and surveys have considered energy consumption as the most important design constraint [44, 45, 46]. For example, for classical health monitoring applications such as ECG, the battery lifetime of the devices are limited between 3 to 5 days [47] while further optimization can extend it up to more than 20 days [48] which is reasonable for health monitoring systems if it is non-invasive system.  
However, for applications such as rescue, workers safety and other critical operations discussed in this paper, the lifetime is one of the concerns, however, it is not the most important issue. On the contrary, it is the reliability of the successful transmission of packets as well as within the required latency, which are the most crucial requirements for the target WBAN-based applications discussed in this paper. Therefore, the MAC layer has to take care of these two important constraints for the specific applications explored in this paper.
# Approaches and Challenges in Internet of Robotic Things
## 2 IoRT: An Overview
### Applications of IoRT  
For the past few years, IoRT has been a rapidly growing field. IoRT applications interlinked with the Internet are found in every field. Examples include transferring resource-intensive activities to the cloud, accessing huge quantities of data, and exchanging data with other robots [3]. Some application fields are manufacturing, agriculture, healthcare, education, and surveillance [24]. The electronics industry is using the IoRT widely. In the modern era, robots do the work of humans in every sector, such as healthcare robots, agricultural robots, and home and hotel robots [23]. IoRT is significantly developing in terms of the revolution of numerous application fields. Hence, new techniques are emerging and required [2]. The human standard of living has been affected by the Internet of robotic technologies in numerous ways. Several manufacturers use robotics to do sophisticated, critical, and difficult jobs, including welding, product assemblage, product testing, packing, and quality control. Preprogrammed robotics has aided and improved industries to never-before-seen levels of precision and 24/7 operational capability. Robotics became more efficient as network technologies were merged, allowing them to perform in unstructured situations [2; 40]. Figure 5 describes the overall percentage of IoRT in different fields such as the health sector, agriculture, manufacturing, and surveillance, giving us a brief idea of the latest use of IoRT in all sectors. Figure 6 classifies the robots on the basis of application areas, requirements, and features [4]. The IoRT physical operation classifications used by IoRT include ground and underground, space and planetary exploration, marine and underwater, hybrid location operations, and aerial. Each class has its own set of capabilities [22; 41].  
Figure 5: IoRT market usage [1; 3].
## 3 Numerical Analysis Results and Discussion under Static Loading
### GRS Wall Potential Failure Surfaces and Reinforcement Behavior
#### 3.4.2 Reinforcement Behavior  
When the failure develops, the geosynthetic (extensible) reinforcement tends to elongate and may be deformed at the point at which it intersects with the failure surface. This results in an increase of tensile force in the reinforcement [4]. In this study, the distribution of reinforcement forces at seven elevations (seven reinforcement layers) within the GRS abutment are plotted in Figures 13 and 14 for the GRS-IB (Case 1) and GRS-IBS (Case 2) models, respectively. The seven elevations that correspond to the reinforcement layer numbers 2, 4, 8, 11, 14, 18, and 21 are taken into consideration because of its significant behavior as observed from Figure 12. There are several points of comparison between the Case 1 and Case 2 reinforcement behavior as shown in Figures 13 and 14, respectively. The results show that the reinforcements in Case 2 exhibited greater forces than those in Case 1. This can be attributed to the great bridge surcharge loads that is directly supported by the Case 2 GRS abutment at the bearing bed (bridge seat). Hence, it can be observed that upper reinforcement layers exhibited greater forces than those at lower layers. On the contrary, the lower reinforcement layers for Case 1 exhibited greater forces than those at the upper layers. Moreover, the maximum reinforcement forces in Case 1 occurred mostly at 0.80 m from the wall facing, while the maximum reinforcement forces in Case 2 occurred mostly at the connection point of the reinforcement and block facing, which is 0.5 m from the wall facing. It can be inferred that the connection point of the block facing and reinforcement at 0.50 m from the wall facing is critical. The difference in stiffness and unit weight between  
Figure 12: Locus of maximum reinforcement strain and potential failure surface.  
the block and backfill soil may have attributed to behavior of the reinforcement at the said connection point. Consequently, the differences in settlement between the block facing and backfill soil, which are evident at the top and base of the wall, resulted in an increase of reinforcement forces or strain at the said connection point. Lastly, both models also exhibited great tension near the end of the reinforcements, which indicates the limiting shear stress for pullout.
## 4 Design of Fault Activation Slip Monitoring System
### Monitoring Method
#### 4.2.1 Monitoring Method of Fault Displacement Change  
(1) Multipoint displacement monitoring method  
The two-point multipoint displacement meter is used to monitor the dislocation displacement of the hangingwall and footwall of the fault. During installation, the sensor and the shorter anchor head are placed on the footwall of the fault so that, when the hangingwall of the fault moves towards the goat, the shorter anchor head and the sensor remain relatively stationary, and the displacement measured by the longer anchor head is the fault dislocation displacement. Therefore, it is only necessary to read the displacement of the longer anchor head crossing the fault when measuring the displacement of fault dislocation. A schematic diagram of the installation of the multipoint displacement meter is shown in Figure 4.  
(2) Mark line observation method  
The mark line observation method is simple for field operation, convenient in application and can meet the observation requirements. When using this method, it is necessary to set up a marked observation line at the exposure of the fault on the roadway or chamber wall. When the fault slips, the marked line will slip along with the fault, and the marked observation line will move. The three-dimensional displacement of the staggered marked line on fault plane L is the actual slip amount of the fault. The schematic diagram of the mark line observation method is shown in Figure 5.  
The relative increase in fault shear stress or the decrease in normal stress caused by mining may cause fault activation. However, according to the currently retrieved literature, there are few reports on an observation method for the fault stress state in the ground pressure activity and rock movement monitoring caused by underground mining. Therefore, this paper explores the observation method for the fault stress state and analyzes the variation law of the fault stress state in the mining process.
## 2 Theoretical Background
\[T=\frac{1}{3}\frac{I_{1}}{\sigma_{VM}} \tag{2}\]3  
The damage model constants define the actual shape of the fracture surface with respect to \(T\) and \(X\), for each material. They have to be derived for a given material through a calibration procedure based on multiaxial mechanical tests, characterized by different stress states. Typical tests are tensile tests on a smooth round bar (RB) and on a round notched bar (RNB) to investigate stress states characterized by high \(T\) and \(X\) equal to unity, plane strain, and torsion tests, to generate stress states with low values of \(T\) and \(X\), compression tests to analyze the negative domain of \(T\) and \(X\), or other multiaxial tests such as tensile-shear or tensile-torsion tests to induce more complex stress states usually in the low regime of \(T\). To calibrate a specific model, hence having an accurate overview of the material ductility, a minimum number of mechanical tests characterized by adequately different stress states is required. Global information such as load and displacement at failure are recorded during the tests. On the other hand, local information relative to the critical point of the specimen such as stress state and plastic strain, not directly measurable, is evaluated via an inverse procedure taking advantage of finite element (FE) analysis, reproducing the exact test conditions and retrieving \(T\) and \(X\) and strain evolution up to the experimentally observed instant of fracture. Finally, a minimization algorithm is employed to find the best fit material constants that minimize a cost function reported in Equation (15), which in this case represents the standard deviation error (SDErr) between the input data and the results provided by the damage models. The optimproblem function within the MATLAB R2022b software was used to solve this minimization problem. As a result, the best combination of calibration constants needed to achieve the best match between the fracture surface and the input points having coordinate \((T,X,\varepsilon_{f})\) is identified. The term \(\varepsilon_{f,i}^{model}\) corresponds to the accumulated equivalent plastic strain at the onset of failure predicted by damage model, while \(\varepsilon_{f,i}\) is the accumulated equivalent plastic strain obtained via FE. The letters \(N\) and \(i\) indicate the total number of test types performed and the \(i\)-th test, respectively. \[T=\frac{1}{3}\frac{I_{1}}{\sigma_{VM}} \tag{2}\]4  
It is worth noting that the calibration constants of the Hosford-Coulomb model could be directly identified through expressions related to the strain at failure of mechanical tests [35; 36]. However, the constants can also be derived through the minimization of the cost function described above, as done by several authors in their work [37; 38; 39; 40; 41]. Here, the latter method was adopted.
## 4 Implementation
### Experimental SNR Measurement  
The SNR values were measured to determine the relationship between the communication distance, SNR value, and exposure time. By controlling the camera's exposure time, the desired communication distance and BER performance can be achieved. In the experimental SNR measurement, we deployed several measures with communication distances of 5-20 m.  
We implemented the proposed system using a Point Grey camera with rolling shutter mode on the receiver side and a LED matrix (5-V, LED matrix 8 \(\times\) 8, 8 \(\times\) 8 cm\({}^{2}\) rectangle) on the transmitter side shown in Figure 5. Moreover, we determined the performance of the proposed system by controlling the various values of camera exposure time (100-750 \(\upmu\)s) at different communication distances.  
The experiment was conducted at distances of 5, 10, 15, and 20 m with the LED statuses set to both \"ON\" and \"OFF to measure SNR values.\" With the \"ON\" status, the SNR was calculated as the signal power of the LED and is shown as a red line with different communication distances. However, the SNR is considered background noise when the LED is in the \"OFF\" status. In this case, the SNR is displayed as a blue line with a different distance. In both transmission statuses, the SNR (dB) was calculated based on the experiment using the following:  
\[\text{SNR}_{\text{dB}}=20\times\log(\frac{\sqrt{\frac
## 3 Methodology and Proposed Innovation
### The Composition of the Quality of the User's Application Key Experience Metric  
The composite metric \(\langle PJ,L,RNR^{norm},RCR^{norm}\rangle\), which enables the enumeration strategy to favor paths with better performance characteristics, is conceptualized for the purpose of this study. All participating metrics describe a specific aspect of performance, the additive combination of which lead to the multidimensional optimization of the user's QoE, while successfully serving incoming VNRs. To enhance its robustness, \(\langle PJ,L,RNR^{norm},RCR^{norm}\rangle\) is designed as the Quality of the User's Application Key Experience (QUAKE) metric, denoted by Equation (19). QUAKE is a weighted principle that comprehensively describes the performance profile of network paths, oriented toward the VNR's specifications according to user-centric requirements.  
\[QUAKE=w_{1}\times PJ+w_{2}\times L+w_{3}\times RNR^{norm}+w_{4}\times RCR^{norm} \tag{19}\]  
where \(w_{1}+w_{2}+w_{3}+w_{4}=1\) and \(w_{i}>0,i=\{1,2,3,4\}\). By applying normalized weights on the additive synthesized components, the capability of predefining the degree of influence each primary metric has on performance calculations is activated. Hence, any orchestrating authority can determine the prevailing performance elements and adjust their effect on the output result.
## Experimental Results
### Comparative Study of Ground-Level and Above-Ground Deployment
#### 5.1.5 PRR and RSSI Distribution  
Figure 6 shows the CDF of the PRR (Figure 6a-c) and RSSI (Figure 6d-f) values measured for the two deployments. These results are shown for radio links of length 3 m, 6 m, and for all existing links in the network. As in the case of Figure 6, these results also confirm the degraded quality of a link in a ground surface deployment compared to a height deployment.  
For example, as shown in Figure 6, nearly 80% of the messages were received with a RSSI less than \(-65\) dBm at ground level, while, in a deployment at height, more than 80% of the messages were received with RSSI higher than this value. The differences were even more visible when one considered just links of distance 6 m, where the ranges of the measured RSSI values in the two deployments were actually disjoint.  
Figure 7 gives the distribution of the PRR of radio links in the network according to their quality (see Figure 4) and deployment, taking into account all the network links. Regardless the deployment conditions, these results show that most of the radio links in the network were good with very few intermediate links (see Figure 4). These results are consistent with the theoretical ones presented in [17] and in Section 3, and with other experimental results [14]. We note, however, that almost 10% of links at ground level were poor, while all links in the case of a deployment at height were either intermediate or good (with almost 95% good links).  
\begin{table}
\begin{tabular}{l c c} \hline  & **Ground** & **Height** \\ \hline \(AVG_{|PRR_{AB}-PRR_{BA}|}\) & **0.0505** & 0.04375 \\ \hline \(MAX_{|PRR_{AB}-PRR_{BA}|}\) & 0.55 & **0.6** \\ \hline \(AVG_{|\overline{RSSI}_{AB}-\overline{RSSI}_{BA}|}\) & 1.67 & **2.67** \\ \hline \(MAX_{|\overline{RSSI}_{AB}-\overline{RSSI}_{BA}|}\) & 4.7 & **14.001** \\ \hline \%_Unidirectional_ & **8** & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Link asymmetry for deployments at height and at ground level.
# Chaos Based Cryptographic Pseudo-Random Number Generator Template with Dynamic State Change +
## 4 One-Time Pad Stream Cipher Application
### Results Analysis
The middle correlations reflect the uniform distribution of pixel values when enciphering using the random-like behavior enabling parameters \((a;b)=(1.76;0.1)\) or the \(E_{1}\) entropy builder, as well as \(E_{1}\) and \(E_{2}\). The right up corner demonstrates a pattern in the scatter plot, when using \(E_{2}\), indicating dependence in the values of enciphered pixels. The right bottom corner (update with \(E_{2}\)) also shows unsatisfactory results, because the two gray-levels existing in the original low entropy level image were maped to only a few different intensity levels. Aiming to investigate the information obtained when deciphering with the wrong key, we changed each of the parameters and the initial state values at the receiving end. The computation revealed that the entropy is the same in this case as for the encrypted versions demonstrating that even the smallest change of \(10^{-15}\) of any of the components of the seed will not reveal any information regarding the clear input image. \begin{table}
\begin{tabular}{c c c} \hline
**Update Period** & **Entropy for Image in Figure 10** & **Entropy for Image in Figure 11** \\ \hline
10 & 7.99 & 7.99 \\
20 & 7.17 & 0.24 \\
70 & 7.99 & 7.99 \\ \hline \end{tabular}
\end{table} 
Table 5: Entropy values for encrypted images with \(E_{2}\) and update period of 10, 20 or 70. Figure 12: Histograms of encypherings using \(E_{2}\) and update period of 10 (**left**), 20 (**middle**), 70 (**right**).
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 4 Discussion  
The aim of this study was to define requirements for a wildfire monitoring satellite system explicitly to support wildfire management. To do so we surveyed experienced wildfire managers with various specialties in order to better capture their needs in the form of the six FMFs. These functionalities were then used to guide the definition of the WFS User Requirements based on current scientific and technical capabilities.  
This study does not describe all factors or analyses considered throughout the process of defining the User Requirements for WFS. Some decisions were made in defining the scope and thus impacted the entire process. For example, the assumption of a LEO satellite mission was taken as the baseline. However, it could be argued that Highly Elliptical Orbiting (HEO) satellite constellations are better suited for the Canadian AoI (e.g., [89; 90]), although they are far more costly.  
Further, many active- and post-fire products exploit the availability of Short-Wave infrared (SWIR) spectral bands for overnight active wildfire detection and burn severity mapping. Although this spectral band has been considered it was not deemed essential to deliver the FMFs so it was not included as an essential requirement for the mission, though it does remain a goal.  
The greatest challenge throughout this process has been defining threshold requirements. It is a simple task to identify the ideal system, payload, and detector. However, determining the limit beyond which a system will no longer be functional is challenging. There is little precedence for marginal systems which can be drawn from. Definition of end-user \"usefulness\" happens gradually and there is rarely a single threshold of usefulness, particularly when introducing new capabilities. Ultimately, numerous trade-off analyses must be carried out by the Space Team and the User and Science Team where prioritization of the various goal and threshold requirements is necessary for mission development.  
The inclusion of trade-off criteria that underpin these requirements is essential as budgetary, technical, and practical limitations will inevitably limit the ability to achieve all of the goal requirements. For example, finer spatial resolution will either reduce the swath and therefore coverage or increase the number of pixels and data volume, challenging the latency requirements. Balancing the consequences of competing requirements is difficult to prescribe a priori. Generally, the trade-off criteria hold coverage of the AoI and IR payload performance as the highest priorities, although overall ability to meet the FMFs is the guiding need. As such, the requirements presented here are a documentation of process at this point in time, but are expected to evolve throughout the mission. The intent is for these requirements to be interpreted in close coordination with the User and Science Team throughout the full mission development.
# Unlocking Wellness: Probiotics as Key Drivers in Functional Food Innovation and Health Promotion
## 4 Application of Probiotics in Functional Foods
### Dairy Products
#### 4.1.1. _Probiotic Fermented Milk_  
As research into the fermentation characteristics of probiotic bacteria has deepened, a variety of probiotics have been added to fermented milk, including _B. bifidum_, _L. buggaricus_, _L. casei_ and also _L. acidophilus_, _L. fermentatum_, and _L. plantarum_[80]. Fermented milk not only possesses excellent nutritional composition but also effectively regulates the balance of intestinal microorganisms and enhances immune and digestive functions [81]. Additionally, such products promote more effective absorption of various minerals and vitamins and help reduce body fat content [82]. Dairy products enriched with probiotics not only retain the nutritional value of raw milk but also introduce a variety of micronutrients during the fermentation process [83], thereby enhancing the body's absorption and utilization of proteins. Notably, probiotics can effectively reduce aflatoxin levels in dairy products [84]. [PERSON] et al. [85] formed various experimental groups by adding _L. acidophilus_ DSMZ 20079, _L. rhamn_s GG, and _B. bifidum_ DSMZ 20456 to Aflatoxin M1 (AFM1)-enriched contaminated milk and analyzed the AFM1 levels after one day storage. The results showed that all three groups exhibited binding capacity for AFM1, with the highest binding capacity observed in the _Bifidobacterium_ group (40.14 \(\pm\) 0.38%). Regular consumption of probiotic-containing dairy products may reduce AFM1 exposure and improve overall health.