esa-sceva/satcom-chunk-collection huggingface dataset:
# CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario
###### Abstract  
The forthcoming 6G will attempt to rewrite the communication networks' perspective focusing on a shift in paradigm in the way technologies and services are conceived, integrated and used. In this viewpoint, the Internet of Medical Things (IoMT) represents a merger of medical devices and health applications that are connected through networks, introducing an important change in managing the disease, treatments and diagnosis, reducing costs and faults. In 6G, the edge intelligence moves the innovative abilities from the central cloud to the edge and jointly with the complex systems approach will enable the development of a new category of lightweight applications as microservices. It requires edge intelligence also for the service evaluation in order to introduce the same degree of adaptability. We propose a new evaluation model, called CoKnowEMe (context knowledge evaluation model), by introducing an architectural and analytical scheme, modeled following a complex and dynamical approach, consisting of three inter-operable level and different networked attributes, to quantify the quality of IoMT microservices depending on a changeable context of use. We conduct simulations to display and quantify the structural complex properties and performance statistical estimators. We select and classify suitable attributes through a further detailed procedure in a supplementary information document.  
6G networks; QoS; Evaluation Systems; Internet of Medical Things; Edge Intelligence; microservices +
Footnote †: journal:  
_Future Internet_
## 3 Methodology
### Machine Learning Models
#### 3.2.5. KNeighborsClassifier  
KNeighborsClassifier is an instance-based learning algorithm where predictions are made based on the \(k\)-nearest neighbors in the training dataset. It is simple and effective for small datasets with clear patterns. The given equation describes the KNeighborsClassifier:  
\[\hat{y}_{\text{KNeighbors}}(x;D)=\arg\max_{y}\sum_{i\in\mathcal{N}_{k}(x,D)}I(y_ {i}=y) \tag{14}\]  
where \(D\) is the training dataset, \(\mathcal{N}_{k}(x,D)\) denotes the \(k\) nearest neighbors of \(x\) in \(D\), and \(I(\cdot)\) is the indicator function.  
The prediction is made by a majority vote for classification or averaging for regression among the \(k\)-nearest neighbors.  
\[d(x,x^{\prime})=\sqrt{\sum_{i=1}^{n}(x_{i}-x_{i}^{\prime})^{2}} \tag{15}\]  
\[\hat{y}=\text{mode}(\{y_{i}\mid x_{i}\in\mathcal{N}_{k}(x)\}) \tag{16}\]  
where \(\mathcal{N}_{k}(x)\) is the set of \(k\)-nearest neighbors.  
\[\hat{y}=\frac{1}{k}\sum_{i\in\mathcal{N}_{k}(x)}y_{i} \tag{17}\]  
We evaluated each model using metrics such as accuracy, precision, recall, and F1-score to ensure a comprehensive assessment of their performance in detecting DDoS attacks. Here are the formulas for these metrics:  
Accuracy \[=\frac{TP+TN}{TP+TN+FP+FN}\] (18) Precision \[=\frac{TP}{TP+FP}\] (19) Recall \[=\frac{TP}{TP+FN}\] (20) F1-Score \[=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}\] (21)  
where \(TP\) is true positives, \(TN\) is true negatives, \(FP\) is false positives, and \(FN\) is false negatives.  
These models collectively enhance the federated learning system's capability to learn from decentralized data while maintaining privacy and improving predictive performance.
# Network Tower Sharing Analysis in Greece: A Structure-Conduct-Performance Approach
## 5 Limitations and Future Research  
Changes in market structure began in 2021, but the full extent and significance of these changes require more time to become clear. While the timeframe since 2021 is too short to gather sufficient quantitative data, the proposed qualitative findings offer valuable insights into expected market trends in the coming years. Additionally, it is important to note that the exact number of macro and micro sites for each operator is not publicly available; only approximate range values of market shares can be accessed. This limitation makes it challenging to assess small yet meaningful changes in the market.  
For future research, it is recommended to continue monitoring the telecommunications market to observe incumbents' behavior and responses, as well as the ongoing expansion of mobile networks by all operators in Greece following the proposed network tower-sharing analysis. Additionally, as the market has not yet fully stabilized after TowerCo's entry, further developments are expected in the coming years. Leveraging quantitative methods or statistical models to assess the impact of tower-sharing on market performance more rigorously, once sufficient data becomes available, will offer valuable insights. Moreover, conducting a comparative study with other countries that have similar market structures or have implemented network sharing under different regulatory frameworks is also advised, as this could generate useful findings for policymaking across all involved nations.  
An intriguing question that arises concerns the incumbent's approach to negotiating commercial agreements with alternative operators for infrastructure sharing (collocation agreements). Until recently, such agreements were difficult to establish, as the proposed commercial terms largely favored the incumbent. However, the presence of an independent TowerCo is expected to introduce greater competition, prompting the incumbent to reassess its infrastructure-sharing strategy and policies. This shift aims to enhance competitiveness with the new TowerCo while minimizing potential losses in wholesale revenue.  
Conceptualization, V.A., A.K. and D.V.; methodology, V.A., A.K. and D.V.; investigation, V.A.; resources, V.A.; data curation, V.A.; writing--original draft preparation, V.A. and A.K.; writing--review and editing, V.A. and A.K.; visualization, V.A.; supervision, A.K. and D.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to acknowledge the Department of Business Administration--University of West Attica and the Department of Informatics and Telecommunications--National and Kapodistrian University of Athens, for their support to the research.  
The authors declare no conflicts of interest.
# Study on the Stability and Control of Gob-Side Entry Retaining in Paste Backfill Working Face
## 3 Analysis of Stability Factors for GER with Paste Backfill
### Movement Characteristics of Overlying Strata Under Different Backfill Ratios  
Filling the gob with paste material reduces the open void distance, limits the roof's rotational subsidence space, restricts the rotation and fracture of overlying strata, and alters their movement characteristics. The backfill ratio is used to describe the interaction between the backfill material and the movement characteristics of the overlying strata [43].  
Figure 3: Layout of working face.  
The movement characteristics of the overlying strata are classified under different backfill ratio conditions as follows:  
1. When the backfill ratio is low, the open void distance in gob and the roof subsidence space are large. As the working face advances, the support provided by the backfill material is limited. The immediate roof and main roof successively fracture and collapse, causing intense manifestations of mining stress. This results in severe deformation of the gob-side entry, leading to ground subsidence and limited protection for the surface environment, buildings, and water systems. The movement characteristics of overlying strata under low backfill ratios are illustrated in Figure 4a.
2. With an increase in the backfill ratio, the backfill material in gob provides stronger support for the overlying strata. Although the immediate roof still experiences fracture and collapse, the combined support from the backfill material and the immediate roof causes the main roof to bend and subside rather than fracture. This reduces surface subsidence caused by mining activities and enhances the stability of the gob-side entry. The movement characteristics of overlying strata under moderate backfill ratios are illustrated in Figure 4b.
3. As the backfill ratio continues to increase, the open void distance and roof subsidence space in gob become extremely limited. The backfill material effectively restricts the subsidence of both the immediate roof and main roof, preventing them from fracturing. The movement characteristics of overlying strata are dominated by bending and subsidence without the formation of fractures or collapse zones. This results in excellent stability of the gob-side entry and significantly contributes to the protection of the surface ecological environment. The movement characteristics of overlying strata under high backfill ratios are illustrated in Figure 4c.  
Figure 4: _Cont._
## 7 Discussion
### Future Research  
In future research, we will further optimize the prediction algorithm for transmission arrival, establish a suitable transmission arrival probability model, and improve the accuracy of predicting the number of transmission requests that arrive within the control delay to ingestion. At the same time, we will consider selecting a more suitable transmission path during the rate adjustment process to avoid congestion of hotspot nodes, balance network load, and improve the network's overall bandwidth utilization and throughput.
## 4 Food Quality and Safety Evaluation  
Because of its potential to inactivate foodborne pathogens and extend the shelf-life of food products, CP, an exceptional state of matter, has been explored for a broad range of potential uses in the food processing industry. CP has already shown guarantee as an efficient antimicrobial intervention for food contact surfaces. Despite these potential benefits, applying CP to improve and enhance food quality and safety takes time due to food production systems' complex and variable character.  
Most research has concentrated on evaluating the overall appearance of CP-treated food products, and their sensory and physicochemical properties. Before drawing definite conclusions about the advantages of plasma technology, more attention must be  
Figure 4: Mechanisms of bacterial inactivation with plasma reactive species [85].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Microorganism** & **Food Matrice** & **Plasma Type** & **Results** & **References** \\ \hline _Salmonella_ & Korean Rice Cakes & DBD & _Salmonella_ growth is reduced by & [81] \\  & & & 3.9 \(\pm\) 0.3-log CFU/g. & \\ \hline _Bacillus cereus_ & Red pepper powder & DBD & \(\geq\)6.0-log reduction & [82] \\ \hline _Enterococcus faecalis_ & Fresh pineapple juice & Plasma jet and surface & \multirow{2}{*}{8.2-log reduction} & [83] \\  & & dielectric barrier discharge & & \\ \hline _Escherichia coli, Listeria_ & & & & \\ _monocytogenes, Staphylococcus_ & Milk & DBD & 98.75-100\% fatality rate & [84] \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._paid to the stability of delicate food ingredients such as vitamins and other bioactive constituents [86].These are important quality characteristics that determine food's nutritional value and safety.  
The harmful impacts of the CP procedure on the organoleptic and nutritional characteristics of foods pose significant obstacles to the advancement of the method. The presence of OH radicals in CP causes oxidative damage in meat, which reduces validity and shelf-life due to lipid deterioration and rancidity development [16]. The same type of oxidation has been reported in cereal products [87].  
Consequently, any treatment process used on products containing high levels of lipids and fats must always be carefully studied and optimized to minimize the oxidation effect, which can degrade quality aspects [88]. Table 4 summarizes some research findings on the effect of CP on fruit quality.
## 3 Results and Discussion
### Optimization of the HS-SPME Method
#### 3.1.3 Extraction Time  
Extraction time is essential for SPME equilibrium abilities [15]. The HS-SPME extraction time was investigated at 20, 30, 40, 50, and 60 min to find the optimum extraction time. As shown in Figure 1C, when the extraction time increased from 20 to 40 min, VOCs from 62.71% increased to 67.35%. Our previous studies showed that 40 min extraction time best separates VOCs from rice [19]. Thus, we chose the same extraction time as [PERSON] et al. [19] (40 min) in this study.
## 4 Description of Search Algorithms
### Overview of Moth-Flame Optimization Algorithm

Table 4: Optimal value for PI controller gains using CSA, WOA, MOA, DA, MOD-FPA and FPA. #### 5.1.2 Simulation Result for Speed Tracking Task with Constant Load and Nominal Parameters Condition

The Figure 4 shows the closed-loop tracking response for full load (2 N.m) condition with set speed of 500 rpm. The performance parameter of the speed response is shown in Table 6. From these results, it is clear that MOD-FPA controller has better time domain specifications in speed response and performance indices. Parameters such as overshoot, MSE, JISE, JITAE and steady state error are favoring only for MOD-FPA controller. Figure 4: Simulation result for speed tracking task with constant load and nominal parameters condition. Figure 3: Speed tracking task without load and nominal parameters condition. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Algorithm** & **MSE** & **JISE** & **JIAE** & **JITSE** & **JITAE** & **Rise Time** & **Overshoot** & **Settling Time** & \(\epsilon_{\infty}\)** & **Overall Error Rate** \\ \hline MOA & 0.59069 & 0.11814 & 0.04121 & 0.00899 & 0.00042 & **0.00561** & 0.39198 & **0.01111** & 0.00005 & 1.1601 \\ FPA & 0.38809 & 0.07762 & 0.03409 & 0.00058 & 0.00037 & 0.00564 & 0.26066 & 0.01117 & **0.00001** & 0.77823 \\ WOA & 1.18869 & 0.23775 & 0.05947 & 0.00178 & 0.00056 & 0.00561 & 0.46571 & 0.01113 & 0.00003 & 1.97103 \\ DF & 0.55037 & 0.11008 & 0.0404 & 0.0083 & 0.00043 & 0.00562 & 0.33686 & 0.01115 & 0.00004 & 1.05577 \\ CSA & 0.48287 & 0.09688 & 0.03757 & 0.00073 & 0.00039 & 0.00562 & 0.33492 & 0.01114 & 0.00003 & 0.96986 \\ MOD-FPA & **0.18572** & **0.03715** & **0.02329** & **0.00028** & **0.00024** & 0.00564 & **0.20369** & 0.01117 & **0.00001** & **0.4672** \\ \hline \hline \end{tabular}

* The best results of each study and comparative analysis are highlighted in bold in the corresponding table.

\end{table} 
Table 5: Performance analysis for speed tracking task without load and nominal parameters condition. #### 5.1.3 Simulation Result for Varying Load at Constant Speed and Nominal Parameters Condition

To validate the effectiveness of controllers, PMSM is subjected to sudden load changes also, and the response is analyzed. The speed response characteristics for varying load conditions are observed for this case. The speed is set at 500 rpm, load torque is varied from no load to full load at 0.065 s, and 0.051 s, later load torque is varied from full load to zero load. Figure 5 shows the speed response for sudden load changes condition and performance parameters are shown in Table 7. The total performance indices are less for MOD-FPA controller than other considered controllers. Moreover, performance indices such as recovery time, overshoot, and steady state error are also in support of MOD-FPA controller. 5.1.4 Simulation Result for Varying Set Rotor Speed and Load Torque, at Nominal Parameters Condition

To make the comparative analysis of the dynamic performance of the algorithms under different operating conditions, the PMSM is subjected to varying set of conditions with parameters at their
## 10 Knowledge Gaps and Future Directions
### Need of New Technologies  
There is also a need for integrative, multi-omics approaches and advanced imaging technologies to unravel the dynamic and multitrophic interactions within the rhizosphere, and to translate this knowledge into practical strategies for microbiome engineering and sustainable agriculture [194,195]. Future research into plant-microbe interactions within the rhizosphere should focus on unraveling the intricate metabolic and chemical signaling networks that govern root exudate-mediated microbiome assembly and function. Advancements in high-resolution metabolomics, multi-omics integration, and gene-editing technologies will be essential for dissecting the diversity and specificity of root exudate compounds and their roles in recruiting beneficial microbes or deterring pathogens [104,192]. Investigating systemically induced root exudation processes, such as SIREM, and the long-distance signaling mechanisms between roots and shoots will provide deeper insights into how plants fine-tune their belowground interactions in response to environmental cues and microbial colonization [73; 196].  
Additionally, future directions should include the development of targeted microbiome engineering strategies, leveraging synthetic microbial communities, host-mediated selection, and ecological engineering, to enhance plant resilience, productivity, and sustainable disease management in agricultural systems. Understanding and harnessing interkingdom signaling and multitrophic interactions in the rhizosphere will be pivotal for designing next-generation biofertilizers, biopesticides, and sustainable crop management practices.
## 2 Infrastructure, Instrumentation and Calibration Methods
### PFAC Infrastructure and Instrumentation  
The location and setting of the Gavdos/Crete Permanent Facility for Altimetry Calibration, in the center of the eastern Mediterranean, offers a unique location for the present generation of multi-mission altimetry calibration (Figure 1).  
Figure 1: The ground tracks of Sentinel-3A, Sentinel-3B, Jason-3, HY-2B, SARAL/AltiKa over the Permanent Facility for Altimeter Calibration in west Crete, Greece (**a**). The established Cal/Val sites in Gavdos and Crete (**b**); the star denotes another coastal tide gauge site of “SUG1” to be established in 2020. The CDN1 transponder Cal/Val on the mountains of mainland Crete (**c**,**d**) and the sea-surface Cal/Val sites (GVD7 and GVD8) at the Gavdos harbor (**e**,**f**).  
The PFAC consists of three permanent coastal Cal/Val sites (GVD8 at Gavdos island, CRS1 and RDK1 at the south and west coast of Crete, respectively, Figure 1b) and one transponder Cal/Val site on a mountainous area in the mainland of Crete (Figure 1a). A detailed presentation of these Cal/Val sites has been provided in [12]. Table 1 presents an overview of each Cal/Val site in terms of the altimeters supported, the calibration method employed and its main instrumentation.  
These instruments are accessed and controlled remotely. Their field observations are transmitted to, via satellite and/or GPRS communications link, and directly archived at the PFAC Operations Control Center at the Technical University of Crete, Greece. Details of the data processing techniques and analysis of raw measurements are given in Section 3.  
Two more calibration sites are currently under development: A sea-surface-elevation Cal/Val site at the south coast of Crete (SUG1, Figure 1) and a second transponder Cal/Val site (GVD1) on Gavdos island to support Sentinel-6 Cal/Val activities. A summary of the new GVD1 site is provided in Section 6.
## 3 Results and Discussion
### SERS Spectral Difference Among Various Encephialic Regions
Figure 6: BP-AuNSs-based SERS analysis of the different encephalic regions. (**a**) The mean SERS spectra extracted from four different encephalic regions (CTX, cerebral cortex; HYP, hypothalamus; TH, thalamus; Hi, hippocampus) in left or right hemisphere. (**b**) The mean SERS spectra of brain tissues in left and right hemispheres, as well as the SERS difference spectrum of left versus right hemisphere. (**c**) SERS difference spectra of left versus right hemisphere in four encephalic regions. The detail difference spectra in various encephalic regions were displayed in Figure 6c. The SERS spectra collected from HYP showed little difference, which might be attributed to the weak enhancement of BP-AuNSs in this region. The spectral deviations in other three regions (CTX, TH and Hi) were generally similar to that in the averaged difference spectrum. In CTX region, Raman signals of 1105, 1210 and 1230 cm\({}^{-1}\) were dominated in the right hemisphere, while the Raman peaks at 1455, 1524 and 1564 cm\({}^{-1}\) were predominant in the SERS spectrum of CTX tissue in the left hemisphere. Besides, a new peak at 815 cm\({}^{-1}\), assigned to O-P-O stretching in phosphate [35], appeared, mainly distributed in the right hemisphere, also observed in Hi region. However, this band mostly transferred to the left hemisphere in TH region. The SERS spectra in Hi region underwent the greatest change from the left to the right hemisphere, which mainly appeared in the Raman range 1370-1700 cm\({}^{-1}\). Moreover, a prominent peak at 1154 cm\({}^{-1}\) (C-C stretching in carotenoids, C-C/C-N stretching in protein [6]) was primarily situated in the spectral line of right hemisphere. Therefore, the SERS spectral analysis revealed the different biochemical components among the various encephalic regions, as well as the difference between the left hemisphere and the right hemisphere, which might supply the molecular structural basis to explain the discrepant physiological and psychological functions of these brain structures.
## 9 Physical Considerations
### Commercial Sensors  
There are two types of probes to use here: distance and height detectors. The former allow us to measure the car-obstacle distance to determine when to slow down. Two seconds before that encounter, the Skyhook controller works to elevate the chassis. The height of the bump is measured by a height device. These probes are photoelectric sensors based on light-based detection. Within this class, color sensors are key. This type of device  
Figure 12: Path following idea to automate the vehicle trajectory.  
is useful when bumps/humps are painted yellow or white. The latter depends on the regulations of each country/region. When the distance to the target changes, the received light intensity also changes, as happens when passing over speed-calming structures [55]. The latter source also described laser 2D displacement sensors. Their performance is enhanced by an easy setup plus data storage via a PC. For instance, the mid-range LJ-G080 sensor measures in the z-axis, \(8\pm 2.3\) cm, while the long-range version, LJ-G200, reaches \(20\pm 4.8\) cm [56]. Another type of apparatus was described in [57]. This family of components works according to a pulsed red laser diode. They are analog and RS 485.  
Although the instruments just described may be adapted for vehicle applications, in [58], specialized optical laser height sensors were developed for car mounting. HF laser height sensors are designed for non-contact distance measurement for vehicle dynamics testing. These instruments range from 10-90 cm, depending on the type. They are produced according to the principle of optical triangulation. This means that a visible red laser is focused on the road surface. Then, a light is projected on the target, and the reflected light is collimated on a linear CCD matrix (a CCD device is an integrated circuit that contains a certain amount of bonded or coupled capacitors). The distance to the object is determined knowing the position of the point of light on the CCD matrix. The sensor output is directly proportional to the measured height. The way to place sensors on a sedan car was explained in [58]. This rule has been proposed for GCs and LSVs (see Figure 13).