esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Standardization of 5G Network Slicing Energy Efficiency KPIs
### General Artificial Intelligence-Based Process for Optimizing the Energy Efficiency of 5G Sliced RAN
It has been shown that global traffic prediction achieved better results, by 35%, in terms of energy consumption reductions and revenue loss due to QoS degradation in comparison to local traffic prediction. The problem of the appropriate selection of network resources is reflected in the possible over-provisioning costs due to reserving more resources (network elements, network functions, virtual machines, etc.) than needed. Conversely, under-provisioning costs are reflected in the increased network operator expenses for the degradation of QoS. At present, matching resources to VNFs is mostly a reactive process that uses hysteresis thresholds to ensure the efficient utilization of network resources. Thus, the usage of AI in the allocation of network resources among NSs is still in its early stages. Some preliminary results related to the usage of AI for optimizing resource allocation among NSs are shown in paper [47], where the authors propose an AI-based asymmetric Long Short-Term Memory (LSTM) resource allocation algorithm. The cost function of the proposed algorithm incorporates the cost penalty and takes into account network resource allocation costs and QoS degradation. It was shown in [47] that the proposed prediction algorithm in the NFV scenario with four network function virtual interfaces led to cost improvements of 40%. Another AI-based, proactive, and automated data analytic tool that optimizes network resource utilization among NSs is proposed in [48]. An AI-based machine learning solution has been developed that exploits space- and time-independent correlations of mobile traffic and provides MNOs with information about the capacity needed to satisfy the demands of every network slice. The proposed solution computes outputs at a data center level through a customized loss function that predicts capacity needs, and thus enables MNO to reduce the possibility of resource overprovisioning or underprovisioning. Nevertheless, the usage of AI in the allocation of network resources among NSs to improve network energy efficiency is still in its infancy. There is a need for additional investigations that will result in the development of new AI-based algorithms and techniques that will enable the integration of AI in the allocation of network resources among NSs, with the aim of optimizing network energy consumption.
## 2 Materials and Methods
### Data Contraction Detection Methods
#### 2.2.3 Root Mean Square  
The RMS [22; 23] is a statistical concept that measures how far the signal differs from the mean--that is, the standard deviation of the input signal. In EHG, the mean value is zero due to the constant component being removed from the data using a high-pass filter. Therefore, the RMS is obtained by:  
\[RMS=\sqrt{\frac{1}{n}\sum_{i=0}^{n}x_{i}^{2}} \tag{6}\]  
where \(x_{i}\) is the input signal sample, \(i\) is the sample number, and \(n\) is the number of samples in the window. Typically, the application of the RMS operator to EHG consists of a sliding window operation. The same window length of 70 s was used for the reasons already presented above.
###### Abstract  
A decision support system (DSS) was developed to help reduce food waste at traditional food retailers while selling fresh horticultural products, but also to promote food safety and quality. This computational tool includes two major functions: (1) the prediction of the remaining shelf life of fresh horticultural product, namely lettuce, onion, carrot, and cabbage based on its microbial growth status, governed by extrinsic and intrinsic parameters (temperature, water activity and pH, respectively). The remaining shelf life of the studied horticultural products is determined by using the online predictive food microbiology tool-- the Combined Database for Predictive Microbiology (Combase). The time to reach the infectious doses of bacteria considered in the study for each of the four horticultural products are predicted; (2) the calculation of the dynamic price of the produce that should be set each day, depending on the predicted end of the marketing period to increase the demand and potential for sale to the final consumer. The proposed dynamic pricing model assumes a linear relation with the remaining shelf life of the analyzed vegetable to set the selling price. The shelf life determined by the DSS for optimal storage conditions is, in general, conservative, ensuring food safety. The automatic dynamic pricing gives new opportunities to small retailers to manage their business, fostering profit and simultaneously contributing to reduce food waste. Thus, this decision support system can contribute to the sustainable value of reducing food waste by providing information to small grocers and retailers on the safety of their perishable status depending on storage conditions and allowing them to suggest a fair price depending on that quality.  
decision support system; shelf life; predictive microbiology; dynamic pricing; comBase; horticultural products; food waste; food quality; food safety; sustainability Article
# Current Challenges Supporting School-Aged Children with Vision Problems: A Rapid Review
## 3 Overall Context of Vision Screening
### Web and Smartphone-Based Tools  
With the advent of modern technologies, several improvements have been made in visual function testing, leading to robust, time-efficient, reliable, and evidence-based screening tools [71]. Studies show the reliability of automated vision testing tools for objective measurements of eyes, including autorefractors, photo screeners, and smartphones-based devices [56]. Such tools can offer a broad range of vision tests, such as refractive errors, visual acuity, visual efficiency skills, color vision, stereovision, contrast vision, visual field test, and testing the oculomotor behavior of the eyes.  
Today, health communities often use smartphone technologies in telemedicine for vision screening. Due to COVID-19, teleophthalmolog is being used by researchers for measuring visual acuity remotely [72]. Most of the available applications use optotypes for vision screening. \"Peek Acuity\" [73] uses the optotype letter \"E\" (different sizes, and rotated at different angles) for the visual acuity test. The Freiburg Vision Test (FrACT) [74] is a corresponding web-based application that also uses similar optotypes (often \"E\" or \"C\" of different sizes, and rotated at different angles) to evaluate visual acuity, and has significant sensitivity among preschool-aged children [75].  
The use of \"E\" or \"C\" optotypes in web-based applications increases the objectivity of the test when the child cannot recognize the letter and there is no learning effect from one eye to the other. The web-based tests are easily accessible, validated, store data to electronic patient record, and can be shared remotely. Several computerized vision screening systems are portable and often provide a self-assessment environment without a need for the supervision of vision experts [76].  
The main disadvantage of the tests is the presentation of one optotype without visual crowding effect (added distractors around a central target), which is very important in the assessment of visual acuity, the calculation of crowding ratio, and the diagnostics of amblyopia, dyslexia, learning disabilities, and ADHD [77; 78]. Figure 3 shows the different types of technologies supporting vision screening.
## 2 Optimization without Consideration of the Reliability Criteria
### Objective Functions and Constraints for Market Settlement without Reliability Criteria
#### 2.1.1 Mathematical Model of the Revenue Stream from a Resource Portfolio with a WPG and BESS  
The profit from resource coordination between the WPG and the BESS can be mathematically represented as follows:  
\[M_{t}^{W-B}=PCC_{t}^{w}\cdot SMP_{t}+\left(REC_{1}^{w}+\omega^{w}\cdot REC_{5}^ {w}\right)\cdot X^{w}-P_{wb,t}^{loss}\cdot c_{wb},\quad\forall\ wb,t \tag{2}\]  
\[P_{wb,t}^{loss}=\sum_{wb}\left(P_{wb,t}^{B2G}+P_{wb,t}^{W2B}\right),\quad \forall\ t \tag{3}\]  
The \(PCC_{t}^{w}\) term represents the energy measured at the point of common coupling in the portfolio with the WPG and the BESS. \(REC_{1}^{w}\), \(REC_{5}^{w}\), \(\omega^{w}\) and \(X^{w}\) represent the energy eligible for an REC settlement without and with the multiplier, the multiplier for the portfolio with the WPG and the price of the REC in the bid. \(P_{wb,t}^{loss}\cdot c_{wb}\), \(P_{wb,t}^{B2G}\) and \(P_{wb,t}^{W2B}\) represent the O&M cost due to its operation, the energy discharged from the BESS, and the energy charged into the BESS from the WPG, respectively.  
\[PCC_{t}^{w}=P_{wp,t}^{W2G}+P_{wb,t}^{B2G}-P_{wb,t}^{W2B},\quad\forall\ wp,wb,t \tag{4}\]  
The output eligible for SMP revenue can be expressed as Equation (4) above. \(PCC_{t}^{w}\), the output metered at the coupled point, is the sum of the energy injected into the external grid from the renewable represented by \(P_{wp,t}^{W2G}\); the BESS is represented by \(P_{wb,t}^{B2G}\), which excludes the energy charging the BESS.  
\[REC_{1}^{w}=\sum_{t}\left(P_{wp,t}^{W2G}-P_{wb,t}^{W2B}\right),\quad\forall\ wp,wb \tag{5}\]  
Equation (5) represents the energy eligible for \(REC_{1}^{w}\) without the multiplier. The energy eligible for the multiplier is the total energy generated from the WPG during all the scheduling periods.  
\[REC_{5}^{w}=\sum_{t\in TD}\left(\sum_{wb}P_{wb,t}^{B2G}\right) \tag{6}\]  
Equation (6) represents the energy eligible for an REC settlement with the multiplier for the portfolio with the WPG and the BESS. The variable TD represents the seasonal interval specified by the SO which indicates the periods eligible for the multiplier; therefore, energy can be discharged from the BESS in accordance with the market settlement for the AO and coordinated with the WPG.
## Dynamic Key Sharing and Fuzzy Adaptive Route Selection
### Dynamic Authentication Key Sharing
A session key can be created using pairwise key establishment protocols, as in [39], to securely transmit the encrypted generation key, i.e., \(E_{n_{i}}\left(key_{j}^{n_{i}}\right)\), to the chosen verification node. Alternatively, the e-CH may instruct all member nodes to calculate their new generation keys and share with the verification nodes chosen for the current round until e-CH performs another verification node selection procedure when the current round expires. Two association nodes, i.e., a member node in the source cluster and an intermediate CH, need to share an authentication key over multiple hops based on one of the id-based schemes [39; 40; 41; 42; 43]. All these schemes have comparable computational overhead and communication cost [16]. The average number of verification keys stored by an intermediate CH is less than or equal to \(c\cdot(d_{max}-d_{i})\). where, \(d_{max}\) is the distance between the BS and the farthest CH. there are \(4\cdot 2h\) number of CHs that are \(h\) hops away from the BS for a uniform distribution of CHs. Initially, a CH that is \(h+j\) hops away from the BS selects an intermediate CH\({}_{i}\) that is \(h\) hops away with a probability: \(\frac{d_{i}}{d_{i}+l}\). The average number of CHs that are \(h+j\) hops away from the BS select one CH that is \(h\) hops away from the BS as a verification node on the path is: \(\frac{8\cdot(d_{i}+j)}{8\cdot d_{i}}\). Every CH selects \(c\) different paths and the average number of verification keys a CH that is \(h\) hops away from the BS stores for the CH that is \(h+j\) hops away from the BS is \(1\). The total number of verification keys is less than or equal to \(\sum_{j=1}^{d_{max}-d_{i}}c\). Secure cryptographic one-way hash functions can be used with techniques such as salting, key stretching, chaining, etc., to make it difficult for an attacker to compute or find the one-way hash chain key in any rainbow table [44]. However, a discussion of these techniques is beyond the scope of this paper. The compromise of one-way hash function bears a resemblance to the compromise of a node in the sensor network that uses the same function to generate future authentication keys. If an attacker gets hold of the one-way hash function by compromising a node, the hacker can generate another key using the same function. Our proposed scheme uses the cluster-based organization which limits the degree to which compromised nodes could misuse their keys, as it becomes useless for nodes belonging to different clusters to conspire. Every report must be validated by s distinct votes using keys generated by the nodes belonging to a single cluster. The adversary must gather s distinct keys generated by s different one-way hash functions in a single cluster to successfully create a false report that eludes intermediate verification nodes. It also makes the identification and exclusion of compromised nodes easier. After a false vote is detected, the node which generated the same vote and the cluster it belongs to can be found and then isolated from the network.
## 2 Mathematical Model
### Thermal Energy Incident in a PV Cell  
The energy loss due to heat convection can be obtained from:  
\[Q_{conv}=S_{p}H(T_{C}-T_{a}) \tag{5}\]  
\(H\): Convective heat transfer coefficient  
\(T_{a}\): Ambient temperature  
The electrical power generated is given by:  
\[Q_{elect}=\eta GS_{c} \tag{6}\]  
\(\eta\): Module efficiency  
\(S_{c}\): Total surface area of PV cells in a module  
The thermal energy transferred from the PV cells to the heat transfer fluid (HTF) is determined from the heat balance across the PV cell and HTF in terms of the heat transfer mechanisms, conduction, convection, and radiation, as follows [1; 17; 18; 19; 20; 21; 22; 23]:  
\[Q_{conduction}=Q_{convection}-Q_{radiation} \tag{7}\]  
The heat transfer by conduction is calculated as follows:  
\[Q_{conduction}=\frac{K_{P_{v}}\times(T_{c}-T_{m})}{L_{cell}} \tag{8}\]  
\(T_{m}\): Module back-surface temperature  
\(K_{P_{v}}\): Thermal conductivity of PV cell  
\(L_{cell}\): Length of a PV cell  
To close the energy balance in Equation (7), the heat transfer by convection and by radiation is determined, respectively, using the convection heat transfer coefficient and emissivity PV cell Stefan-Boltzmann constant. Readers interested in the calculation of the heat balance in Equation (7) are advised to consult [PERSON] [23].  
The finite-difference formulation was used to determine the heat transfer fluid temperature as follows for each element using the following equation. The heat transfer fluid tube is divided into \(nTE\) elements:  
\[T_{f}=T_{f\_in}+\frac{\partial Q}{m_{water}C_{p}}\times t \tag{9}\]  
\(t\): Time  
\(T_{f\_in}\): Fluid temperature at the inlet and \(t\) represents the time step iteration  
The thermal energy transferred from the PV cell to the heat transfer fluid is obtained by:  
\[Q_{thermal}=\dot{m}\times C_{p\_water}\times\left(T_{fHx+1}-T_{f\_In}\right) \tag{10}\]\(Q_{\text{\it Thermal}}\): Energy from the thermal process  
\(T_{\text{\it H}H\text{\tiny x}+1}\): Fluid temperature at thermal element 1  
The energy transferred to heat transfer fluid is calculated by the integration of the aforementioned equations along the length of each tube. In this analysis, the mainstream temperature of the heat transfer fluid flow was considered as the average temperature of the inlet and outlet of each finite-difference element:  
\[Q_{in}=\dot{m}w\times C_{p\_water}(T_{f+1}-T_{f}) \tag{11}\]  
\(\dot{m}_{w}\): Water flow  
\(T_{f+1}\): Water temperature at the next element  
\(C_{p}\): Specific heat of HTF  
Finally, the PV-thermal hybrid system energy conversion efficiency for harnessing energy from solar radiation is given:  
\[\eta_{SH}=\frac{P(t)+Q_{thermal}}{Q_{in}} \tag{12}\]  
where \(Q_{in}\) and \(Q_{in}\) are the solar thermal heat transferred to the HTF and solar irradiance. The respective values are given by Equations (1) and (12). In addition, _P(t)_ is the PV solar electrical output and is defined by Equation (2).
# Digital Twin for Human-Robot Collaboration in Manufacturing: Review and Outlook
## 3 Review of DT in HRC
### Approaches in DT
#### 3.3.2 Layer Based Approach
Therefore, various data mining and machine learning techniques are used in the connection layer for process planning, optimization and tracking changes [82]. An improvised model consisting of five layers, i.e., two more layers, namely data and service, on top of the aforementioned layers has been proposed [21]. In this design, the data layer is the core of the system, which interacts via the communication layer to the physical, virtual and service layers in this 5-dimension system. A five-layer approach which is similar to [21] is used in the design of DT for dual manipulator cooperation units [83]. Another five-layer approach is created by dividing the layers into the physical layer, digital layer, data layer, knowledge layer, and social layer [84]. Here, the social layer is similar to the service layer in the 5-dimensional architecture previously mentioned. The service/social layer integrates different services, such as customer relationship management, manufacturing execution system and enterprise resource planning system. The knowledge layer integrated with artificial intelligence capabilities is the brain of the DT. A five-dimensional architecture framework of a reconfigurable DT model using the LBA discussed in [85; 86] is similar to that of the SBA. The architecture consists of four layers, namely the physical, model, data and service layer, which are similar to the concept of SBA, namely design, engineering, operations and service. Finally, an 8-dimensional model considering design elements such as hardware, software, IT, computational model, information and intelligence with a focus on intelligence, simulation and model richness is discussed in [87]. This 8-dimensional model is for enabling intelligent DT systems for smart factory applications. The growth of CPS has contributed significantly towards the realization of building a DT model using an LBA [88]. The data required in the LBA can be grouped into two categories, such as stationery and real-time data. The stationery or non-volatile data are the information that is time-invariant, such as the mechanical design or CAD drawings, and the real-time or time-variant data includes the updates received from sensors on dynamic changes occurring in the shop floor environment [88; 89]. An architecture for manufacturing cells in Industry 4.0 that utilizes the IoT gateway, cloud databases and open platform communication (OPC) to collectively combine and transfer data from the physical model to its virtual counterpart is discussed [90]. Table 3 provides a summary of the literature that utilises LBA based approach in various application.
## 7 Outcomes
### RQ2: What Is the Performance of Anomaly Intrusion Detection in IoT Using Deep Learning Techniques?
#### 7.3.2 Computational and Resource Constraint  
The computational complexity can be considered one of the prominent obstacles in the area of IoT security and deep learning. The usage of IoT devices requires a low battery and CPU power. Hence, the computational time in IoT devices should be quick, and the operation should be straightforward [59]. For better performance, it is more effective to mitigate the IoT computation to the edge of the cloud. There is one particular study [60] that emphasized analyzing the implementation of an algorithm that focuses on producing a lightweight computation system. The distributed computing and distributed algorithms provide better computational optimization by distributing the tasks overs multiple nodes, which improves the efficiency [54; 61].
## 4 Experiments
### Quality Control and Error Correction  
In this study, we performed a simple analysis of the noise distribution [13]. For the camera noise, we can use the elimination of outliers or the estimated noise distribution to redefine the random variable. In this scheme, we used the estimated noise distribution, as shown in Figure 10. The large scanning range of camera in this experiment caused the large noise and the low signal-to-noise ratio of the dark field images, which eventually led to an unsatisfactory HR image. To improve the reconstruction quality of the images and reduce the effect of the noise, we used each low-resolution image to subtract the estimated noise distribution as a new intensity image, which was used as the initial input for super-resolution reconstruction.  
In our experiments, we obtained image sequences at different positions using camera scanning. The camera's movement error may lead to LR image position misalignment and the loss of information during the imaging process. For image position misalignment, we aligned the image sequence using a fast sub-pixel registration algorithm [12].
## 4 System Model
### Loss Path Model with Ground Reflection  
Therefore, we consider a first-order reflection on the ground (Figure 10) with two trajectories:  
* Direct line;
* With a reflection on the ground.  
This \"ground reflection\" model is suitable because the transmitter and receiver antenna are not placed at the same heights. The Hi-Rail pickup antenna is at h1 = 2.5 m, while the locomotive antenna is h2 = 4.572 m or 15 feet.  
Figure 10: Ground reflection Train-Hi-Rail pickup.  
Figure 9: Train–Hi-Rail pickup (LOS).  
The expression of the range becomes:  
\[d=\left[\frac{Pt.Gt.Gr.h2^{2}h1^{2}}{Sr}\right]^{1/4} \tag{3}\]  
The sensitivity of the receiver located on the Hi-Rail pickup is:  
\[Sr=\text{Eb.Rb}=\text{Eb}/\text{No. Rb.No}=(\text{Eb}/\text{N0}).\text{Rb. (k.Tsys)} \tag{4}\]  
where Eb is the energy of a bit, Rb is the bit rate, Tsys is the noise temperature of the receiver, and k is the Boltzmann constant. For XTend modules, when the tests will be achieved, Rb will be set at 125 kbps with XCTU software, which afford to set the radio frequency modules' parameters. Eb/No is the signal-to-noise ratio obtained from the bit error rate (BER) [12]. The modulation type for the XTend radio-frequency modules is non-coherent FSK type.  
The MATLAB modeling [13] of the communication between the locomotive and the Hi-Rail pickup with a FSK modulator [14], a FSK demodulator, and an additive Gaussian noise gives the curve of the Bit Error Rate (BER) as a function of the signal-to-noise ratio (Figure 11). The telecommunication system is considered reliable for this modulation type if the bit error rate is set at 0.0001, so a signal-to-noise ratio of 12.5 dB. Therefore, Equation (4) gives a sensitivity of 1 \(\times\) 10\({}^{-15}\) Watt. Then, the range from Expression (3) is 37.93 km. This shows a strong reduction in the range of the rail telecommunication system compared to an ideal range obtained with the \"Loss Path\" model. However, it is necessary to further specify the system's range, considering the multiple reflections of the transmitted signal.