esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Formulation
### Determination of the Passing Zone Length  
It is possible to regulate the moment the CAV queue enters the adjustment zone's passing zone. If the CAV queue enters the passing zone within limited speed range \([v_{0}^{L},v_{0}^{L}]\), the size of the passing zone must be set reasonably in conjunction with the length and direction of the variable virtual waiting zone in order to protect the CAVs' capacity to pass the stop line of the variable virtual waiting zone without stopping, all the while maintaining the speed limit. In this study, we refer to [PERSON]'s research results on the impact of travel distance on the design of CAV trajectories. It should be emphasized that CAV queue adjustment in the adjustment zone, especially lane-changing behaviour, crowds the HV lane under oversaturated traffic conditions, and thus may cause delays in HV passage. The adjustment zone will lead to inaccurate HV delay prediction in 4.2.1, especially close to the HV stop line. The CAV trajectory planning approach, however, may and will lead to an unrealistic passing zone. Therefore, the following equation can be used to determine the passing zone's maximum length \(L_{P-max}\):  
\[L_{P-max}=\frac{v_{max}^{2}-v_{0}^{L^{2}}}{2a^{L}}+\left(X_{j}^{min}-\frac{v_{ max}-v_{0}^{L}}{a^{L}}\right)v_{max}-L_{C} \tag{46}\]  
This research focuses on cooperative control of signals and car movements at lone intersections, and makes the assumption that there are enough lane lengths for zoning in the problem at hand. The minimum passing zone length must also be quantified in addition to the maximum passing zone length study. If the passing zone is long enough to provide the proper trajectory planning model, the CAV queue should be allowed to pass without halting at the stop line in front of the variable virtual waiting zone. Equation (47) can be used to express \(L_{P-min}\):  
\[L^{min}=\frac{v_{0}^{L^{2}}}{2*a^{L}} \tag{47}\]
# Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
## 2 Materials and Methods
### Methodology
#### 2.1.1 Analysis of Spaces via the BIM Model--Phase 1
Three sets of data are imported into an AI analysis system based on an ML algorithm, namely random forest. This system is programmed to identify patterns and anomalies in the data. The ML algorithm is employed for the comparison of the sensor data with the access logs of the badge system. To illustrate, if a sensor identifies the presence of an individual in a specific zone of the building, the AI assesses whether the corresponding access badge was used to gain entry to that zone concurrently. Subsequently, the AI performs an analysis of the discrepancies between the two datasets, identifying any notable differences, such as a presence detected by the sensors without a corresponding badge use, which are then subjected to further investigation. A comprehensive report of discrepancies and matches is generated and disseminated to security managers. In the event of discrepancies, corrective action can be taken, such as manual verification or the updating of access protocols. This integrated approach of data collection, integration, and comparison through an AI system ensures a high level of security and accuracy in the validation of presence and access in buildings.
## 2 Simulation Model of CMIs with the CC Method
### Calculating the Winding DC Resistance  
In this article, the mean length per turn of toroidal core-based inductors \(l_{\text{T}}\) is assumed, as shown in Figure 5. Thus, \(l_{\text{T}}\) is obtained from the following Equation (7), where \(OD\), \(ID\), and \(HT\) are the outer diameter, inner diameter, and height of the toroidal core, respectively. In addition, \(d_{\text{o}}\) is the outer diameter of the wire, including an insulation coating.  
\[l_{\text{T}}=OD-ID+2 HT+4d_{\text{o}} \tag{7}\]  
Figure 5: Mean length per turn of toroidal inductors.  
It is commonly understood that the winding DC resistance, \(R_{\text{w,DC}}\), can be expressed by Equation (8). In this equation, \(\rho_{\text{Cu}}\) denotes the resistivity of copper, \(d_{\text{w}}\) represents the diameter of the conductor, and \(N\) refers to the number of turns.  
\[R_{\text{w,DC}}=\rho_{\text{Cu}}\frac{4 Nl_{\text{T}}}{\pi d_{\text{w}}^{2}} \tag{8}\]  
The resistivity depends on temperature \(T\); thus, the resistivity of copper is also given by  
\[\rho_{\text{Cu}}(T)=\rho_{\text{Cu}}(20\ ^{\circ}\text{C})[1+\alpha_{\text{Cu}}(T -20)] \tag{9}\]  
Here, \(\rho_{\text{Cu}}(20\ ^{\circ}\text{C})\) is the resistivity of copper at 20 \({}^{\circ}\text{C}\) (1.724 \(\times\) 10\({}^{-8}\) (\(\Omega\cdot\text{m}\))), and \(\alpha_{\text{Cu}}\) is the temperature coefficient of copper (0.00393 (1/\({}^{\circ}\text{C}\))). In this article, the winding DC resistance of the winding was calculated, where T was set to the room temperature (25 \({}^{\circ}\text{C}\)).
# Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing
## 2 Related Work
### Microservices Architecture  
Cloud systems based on VMs, which, by their nature, are self-contained units capable of independent operation, have in the past tended toward more monolithic system architectures [2]. However, with the trend toward containerization in the infrastructure, a concurrent trend is being seen in the industry moving toward microservices-based system architectures [3].  
Microservices architecture \"is a cloud-native architecture that aims to realize software systems as a package of small services, each independently deployable on a potentially different platform and technological stack, and running in its own process while communicating through lightweight [APIs]\" [14]. Microservices architecture \"aims to decompose a monolithic application into a set of independent services which communicate with each other through open APIs or highly scalable messaging\" [15].  
In [3], the authors identify several advantages of the microservices architecture, including simplification and acceleration of deployment through modularity, language and programming framework heterogeneity, and simplification of correctness and performance debugging. In [16], the author notes cost reduction, quality improvement, agility, and decreased time to market as benefits of the microservices architecture.  
The increasing use of microservices is, in part, based on their correspondence with the containerization model at the infrastructure level, with \"each microservice accommodated in a single container\" [3].  
A key innovation in the microservices architecture is that of the stateless container. While stateful containers also exist, in which state information is kept in the container between calls to the microservice, stateless microservice containers are designed such that no state information is kept in the container between calls to the microservice [17]. Any stateful information required to supply the desired service is abstracted out of the microservice into a separate, persistent repository such as a database [17].  
Stateless containers are favored by cloud orchestrators due to their simpler synchronization and the ability to restart and initialize them from a clean state [17]. They thus \"facilitate the architectural decomposition of microservices into functional, stateless components supported by persistent data storage with defined scope and intention\" [6] (Figure 2).  
One of the advantages of stateless containers is that different instances of the same container are functionally and statefully equivalent [6]. This property of stateless containers makes them particularly suitable for at-scale replication and use in redundancy-based fault tolerance and reliability strategies [6].  
Figure 1: Comparison of virtual machine (**left**) and container (**right**) architectures.
## 9 Discussion, Contributions, and Future Directions
### Knowledge Modeling and AI Integration  
A key element of the framework is the incorporation of continuous knowledge modeling through Python-based machine learning (ML) and AI libraries (e.g., TensorFlow, Scikit-Learn). This integration enables both predictive maintenance and real-time process optimization. Additionally, intuitive user interfaces empower operators to query, interpret, and refine knowledge models without extensive technical expertise, bridging the gap between automated data processing and human decision-making. Real-time streaming and caching techniques further enhance data aggregation, paving the way for interactive visual analytics in high-throughput production settings.
## 2 Materials and Methods
### Response Surface Parameters
#### 2.2.6 Height Thermal Camera--\(d_{height}\)  
The distance measured from the sensor of the thermal camera to the surface of the sample to be inspected is represented by the height of the thermal camera. This parameter has no influence on the temperature difference of the sample surface, but it is utterly important while performing thermal inspections. In order to measure a defect accurately, the size of the hotspot has to be at least 3 pixels in diameter. This rule of thumb results in a correlation between the height of the thermal camera and the minimal size of the detectable defect. This parameter can be calculated using the Field Of View of the camera as well as using the focal length of the lens. In this research the focal length is used; the calculation using the Field Of View (FOV) can be found in [6].  
As can be seen in Figure 5, we can use opposite angles in order to describe the correlation between the pixel pitch \(p\) and the minimal size of a defect at a certain camera height \(d\).  
\[h=d*\frac{p}{f} \tag{2}\]  
with \(h\) size of the inspected region by half a pixel, \(p\) the pixel pitch and \(f\) the focal length of the lens. As we are interested in the dimensions of a region captured by one whole pixel the calculated size \(h\) has to be doubled.  
\[H=2*h \tag{3}\]  
In order to determine the minimal dimensions of a defect to be detectable at a certain camera height one should know how many pixels are needed in order to perform a decent thermography measurement. A rule of thumb indicates that a defect has to be bigger than a square of \(3\times 3\) pixels, therefore the diameter of a round hole has to be at least 3 times the region measured by a single pixel.  
\[D\geq 3*H \tag{4}\]  
Figure 5: Schematic representation used to calculate the minimum defect diameter in order to be detectable for a certain camera height \(d\). Using the detector pitch of the camera and the focal length of the lens the region recorded by one single pixel \(H\) can be calculated.  
Combining Equations (2) and (4) results in following equation:  
\[D\geq 6*d*\frac{p}{f} \tag{5}\]  
with \(D\) being the minimal diameter of a defect in order to be detectable for the camera at height \(d_{height}\) above the sample to be inspected.
## 3 Results
### Introduction of the Data Set  
This data set, which included the most essential and central part of our work, contained statistical indicators of the quality mode of the network, which were collected by the transmitter and receiver base stations in real time. These data were averaged every hour and sent to the operators. The available data for conducting this research was the result of an hourly average of the events and the statistical status of the network, which was made available to the researcher in cooperation with the mobile telecommunication company (Hamrahe Avval).  
These data contained statistical information on network quality measurement indicators at the covered points. Since the cellular network was very dynamic and complex and the volume of generated data was very high, we started the work by choosing one of the worst base station controllers in Tehran. This controller is located in District 8 of Tehran and covers a considerable part of this area. The data contained 64,114 records. Among the 70 indicators and features of this data, 14 indicators were selected by experts to achieve the goal of this research. The characteristics and impact of each on the network's quality and the cause of the occurred fault in the network were investigated.  
Table 2 provides the evaluated the statistical characteristics of these indicators. Since the values of different indices have different distributions, the minimum-maximum normalization method was used to normalize the data.  
The histogram of the CDR call drop rate indicator, which shows the drop rate in calls, is shown in Figure 7. As is clear in the figure, for some samples, the value of this index was higher than the threshold and, therefore, it indicated a problem in the records.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**The Indicator’s Name** & **Mean** & **Standard Deviation** \\ \hline CSSR & 0.950793 & 0.106 \\ \hline CDR & 0.0412 & 0.068 \\ \hline HSR & 0.832 & 0.2735 \\ \hline HTr-Rate * & 0.3408 & 0.4048 \\ \hline SDCCH-Congestion-Rate & 0.0555 & 0.18109 \\ \hline TCH-Congestion-Rate & 0.0401 & 0.11137 \\ \hline SDCCH\_DR & 0.0448 & 0.1404 \\ \hline SDCCH-MHT & 0.0707 & 0.076 \\ \hline SDCCH-Assign-Success-Rate & 0.4676 & 0.4249 \\ \hline TCH-ASSIGN-FAIL-RATE & 0.0478 & 0.0672 \\ \hline RX-QUAL-DL & 0.8485 & 0.1394 \\ \hline RX-QUAL-UL & 0.4080 & 0.4847 \\ \hline Random-Access-Success-Rate & 0.6765 & 0.4295 \\ \hline TCH-Availability & 0.9366 & 0.1233 \\ \hline \hline \end{tabular}  
* The HTr_Rate feature is the ratio of the traffic carried on the half-rate channel to the total traffic of that station.  
\end{table}
Table 2: Statistical status of the used indicators.
# Combination of Variable Loads in Structural Design
## 2 Materials and Methods
### Independent vs. Dependent Load Combination
In the current Eurocodes [1,2], permanent and variable loads are always combined independently in the ULS safety factor calculation, resulting in a design that is approximately 10% less safe regarding the dependent calculation. Additionally, combination rules (8.13a,b) and (8.14a,b) contribute further to this discrepancy, leading to an additional 10% reduction in safety. Permanent loads and variable loads, as well as most variable loads, are combined with each other dependently. However, some variable loads like wind and imposed loads are independently combined with each other. In this study, snow load is combined with another variable load approximately considering only the mid-winter period when the snow load is constant each year, making this combination dependent. Snow loads at the beginning and end of winter are disregarded. This assumption obviously results in lower bound combination loads and combination factors. An alternative calculation is also presented here, assuming the loads are combined independently for the entire winter period. The dependent combination results in higher combination factors if the mid-winter period with constant snow load constitutes half of the snow load period. Consequently, the dependent combination is more critical. Additionally, the independent combination is clearly inaccurate as the snow load remains constant for a prolonged period in mid-winter. Combining snow load with another variable load is analogous to combining permanent load and variable load. In multi-storey buildings, the combinations of imposed loads on different floors should be considered dependent, since these loads are essentially partial components of the overall imposed load on the entire building. When the imposed load on the entire building aligns with the target reliability, it constitutes the maximum design load, which is then uniformly or almost uniformly distributed across all floors. This means that all floors simultaneously bear a high load, rendering load reduction inapplicable. The current practice of treating these combinations as independent leads to inconsistencies. For example, consider a building with a ground floor supporting two apartments, one on the first floor and another on the second floor. If load reduction is applied to the imposed loads of these apartments, the combination results in contradictions. If both apartments were on the first floor, no load reduction would be applied, yet the overall load on the ground floor remains the same in both scenarios. Furthermore, applying load reduction independently to imposed loads on various floors results in unrealistically low combination loads in multi-storey buildings. While load reduction can be applied to imposed loads, it should be based on the imposed area rather than the number of floors.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 5 Analysis of Resilience in Existing Works
### Analysis of Existing Works That Used SDN or MSA Individually in Meeting Resilience Requirements
#### 5.4.2 MSA Technology: Pros and Cons  
As the number of nodes (i.e., patients) increases in the IoMT networks, resources also have to scale up to meet the high-throughput and low-latency requirements of the networks. Microservices provide decentralization to the IoMT networks, as they can be applied on edge servers or devices [81]. It reduces the latency and operates in real-time, as it is near the source of the data. Security and privacy are also preserved, as authorization and authentication are other important features of microservices [96]. Microservices distribute the system into different sets of services or applications; for this reason, it can avoid the single point of failure and provide flexibility to the user.  
However, implementing a microservices architecture on each edge server or device will be costly, as the microservices architecture has a resource-intensive nature [97]. It requires memory to store the data and a user interface to represent the data [98]. Microservices produce multiple entry points into the network; hence, it requires strong security on each point.  
Implementation of a machine learning application programming interface with MSA can mitigate these issues. It analyses the entire IoMT network data and detects the high-risk points of failure. Microservices architecture can be implemented only on those IoMT edge servers; hence, the cost of implementing the MSA can be reduced. Also, ML can be applied on those IoMT edge servers so that it can analyze the stored data and detect anomalies.  
After investigating all of the above existing works, we can conclude that the integration of these three technologies, SDN, ML, and MSA, can potentially provide resilience in data communication in the IoMT networks.
###### Abstract  
This paper investigated the use of unmanned aerial vehicles (UAVs) for the delivery of critical goods to remote areas in the absence of network connectivity. Under such conditions, it is important to track the delivery process and record the transactions in a delay-tolerant fashion so that this information can be recovered after the UAV's return to base. We propose a novel framework that combines the strengths of cipher block chaining, physical layer security, and symmetric and asymmetric encryption techniques in order to safely encrypt the transaction logs of remote delivery operations. The proposed approach is shown to provide high security levels, making the keys undetectable, in addition to being robust to attacks. Thus, it is very useful in drone systems used for logistics and autonomous goods delivery to multiple destinations. This is particularly important in health applications, e.g., for vaccine transmissions, or in relief and rescue operations.  
unmanned aerial vehicles (UAVs); cipher block chaining (CBC); delay tolerant networking (DTN); cryptography; physical layer security (PLS); rural connectivity Article
# Order-Based Identification of Bearing Defects under Variable Speed Condition
###### Abstract  
Condition monitoring of rotating machinery plays an important role in reducing catastrophic failures and production losses in the 4.0 Industry. Vibration analysis has proven to be effective in diagnosing rotating machine failures. However, identifying bearing defects based on vibration analysis remains a difficult task, especially in non-stationary operation conditions. This work aims to automate the process of identifying bearing defects under variable operating speeds. Based on an order analysis technique, three frequency domain features: Spectrum peak Ratio Outer (SPRO), Spectrum peak Ratio Inner (SPRI), and Spectrum peak Ratio Rolling element (SPRR) are updated to perform with non-stationary signals. The updated features are extracted from vibration data of a real ball bearing system. They are retained to build a predictive multi-kernel support vector machine (MSVM) classification model. Therefore, the effectiveness of the proposed features is evaluated based on the performance of the constructed classifier. The updated features deployed have proven their effectiveness in identifying bearing: outer race, inner race, ball, and combined defects under variable speed conditions.  
bearing vibration analysis; defects identification; variable speed; classification
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 4 Traffic-Aware Clustering-Based Routing Protocol (TACRP)  
The TACRP protocol was mainly developed to improve traffic management in VANETs. The systematic framework is shown in Figure 2. It mainly includes the Cluster Head (CH), Cluster Members (CMs), Road Side Units (RSUs) and Traffic Management Unit (TMU). The complete process of the TACRP protocol is elaborated below.
## 4 Discussion
### Further Application of Respiration Model  
The EMGPP model (Equation (1) in [40]) is a further application of the PAR balance method by replacing isoprene (one dominant species of BVOCs) with GPP from an empirical model of BVOC emissions (EMBE [70]. All of the above empirical models were applied/transformed from the PAR balance equation:  
\[\mathrm{PAR}=\mathrm{C}_{1}e^{-\mathrm{a}}1^{\mathrm{ISOm}}\times\cos(Z)+ \mathrm{C}_{2}e^{-\mathrm{kWm}}\times\cos\left(Z\right)+\mathrm{C}_{3}e^{- \mathrm{S/Q}}+\mathrm{C}_{0} \tag{6}\]  
The ratio \(\mathrm{PAR_{TOA}}/(\mathrm{C}_{1}+\mathrm{C}_{2}+\mathrm{C}_{3}+\left|\mathrm{ C}_{0}\right|)\) between the measured PAR (\(\mathrm{PAR_{TOA}}\)) and the calculated PAR (\(\mathrm{C}_{1}+\mathrm{C}_{2}+\mathrm{C}_{3}+\left|\mathrm{C}_{0}\right|\)) at the top of the atmosphere (TOA) can be used to calibrate the PAR sensor [102]. To further apply the principle of PAR balance, the Re (DR) model (Equation (3) in [41] was developed to deal with an energy balance during nighttime. The ratio between \(\mathrm{C}_{1}+\left|\mathrm{C}_{0}\right|\) (the calculated total longwave radiation emitted by the atmosphere and the surface) and the calculated longwave radiation emitted by vegetation (using Stefan-Boltzmann law) can be used to calibrate longwave radiation sensors, and reasonable results were found [41]. The meaning of \(\mathrm{C}_{1}\) and \(\mathrm{C}_{0}\) is fully discussed in studies [70,102]. In brief, the energy balance method (i.e., PAR, longwave radiation) has shown reasonable simulations of BVOCs and carbon flux components (GPP, Re, and NEP), as well as the application potential in the calibration of longwave radiation sensors.  
It should be noted that a newly purchased PAR sensor was used in the solar radiation system ([PERSON]'s group) at the Qianyanzhou station, and routine maintenance (sensor surface cleaning, horizontal keeping, etc.) was conducted every day to obtain reliable data [70]. To evaluate the performance of the PAR sensor, a similar PAR empirical model as Equation (6) without an isoprene term (\(e^{-\mathrm{a}}1^{\mathrm{ISOm}}\)) was developed for relatively clear sky conditions, using monthly averages from May 2013-December 2016 (n = 9, mean S/Q = 0.443). The ratio of the mean PAR to visible radiation (400-700 nm) from 2013-2016 (measured by [PERSON]'s group) was 4.97 mol MJ\({}^{-1}\) (n = 9). The relative bias between the summation of all coefficients and PAR\({}_{\rm TOA}\) (530.5 W m\({}^{-2}\)) was 8.47%. It indicated that the PAR sensor had better stability from 2013-2016 under careful daily maintenance [70; 102], and PAR data were reliable in the use of empirical model development and further application in GPP, Re, and NEP in the Qianyanzhou subtropical forest region.