esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We study the interaction of a quantum dot in the Voigt configuration with a laser pulse and particularly analyze the potential for rapid spin initialization by putting the quantum dot near a molybdenum disulfide (MoS\({}_{2}\)) monolayer. The MoS\({}_{2}\) monolayer influences the spontaneous decay rates of the quantum dot, leading to anisotropically enhanced decay rates, for the quantum dot's electric dipole moments parallel and perpendicular to the layer. By solving the relevant density matrix equations, we find that high spin initialization fidelity is obtained at short times. The fidelity is significantly higher than when the quantum dot is in free-space vacuum. We examine two different cases of the interaction of the quantum dot with the applied optical field. First, we use a continuous wave laser field and determine for various quantum dot--MoS\({}_{2}\) layer distances the field strength that leads to acceptable fidelity levels. The effect of the quality of the MoS\({}_{2}\) material on the fidelity of spin initialization is also examined. We also study the interaction of the quantum dot with a laser pulse and apply numerical optimal control to obtain the time-dependent field strength, which leads to maximum final fidelity for short time intervals. The latter approach gives beneficial results in comparison to the continuous wave field excitation.  
quantum dot; spin initialization; Voigt geometry; optical pumping; optimal control; spin dynamics; Purcell effect +
Footnote †: journal: Applied Physics Letters
# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
## 6 Experiment and Evaluation
### Prediction Accuracy Comparison  
We compare our models with several well-known existing models, including  
1. **UserMean**: this method uses the average value of each user as the prediction value.
2. **ItemMean**: this method uses the average value of each service as the prediction value.
3. **UPCC** (user-based PCC) [37]: the user-based collaborative filtering algorithm using Pearson correlation coefficient ([PERSON] et al., 1994).
4. **IPCC** (item-based PCC) [38]: the item-based collaborative filtering algorithm using Pearson correlation coefficient ([PERSON] et al., 2001).
5. **WSRec**[36]: This method linearly combines the results of UPCC and IPCC to produce a hybrid result ([PERSON] et al., 2009).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attributes** & **Numbers** \\ \hline the number of users & 339 \\ the number of services & 5828 \\ the number of invocation records & 1,974,675 \\ the number of user countries & 30 \\ the number of service countries & 73 \\ average value of response time & 0.81 \\ average value of throughput & 44.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of data.  
* **LACF**[39]: A collaborative filtering algorithm that integrates the location information of users and services ([PERSON] et al., 2012).
* **SVD**[40]: the singular value decomposition model ([PERSON] et al., 2009).  
The experimental results are shown in Table 3 (response time dataset) and Table 4 (throughput dataset). From Tables 3 and 4, we have the following observations.  
1. The proposed models SL-RW, UL-RW and HL-RW all achieve smaller MAE and NMAE than the baseline models, almost in all cases of training set densities.
2. Along with the increase in training set density, the prediction accuracy also becomes higher. The reason is that more training data can provide more invocation records to improve the prediction of similarity computation and neighbor selection.
3. The improvement achieved by our models are significant based on the paired \(t\)-test (\(p<0.001\)).  
datasets. The other parameter \(K\) is set to the default value, and the results are shown in the following Figure 6.  
Figure 6 shows that for throughput data, the NMAE values first become smaller, and then reach an optimal point at \(\lambda=0.9\) in most density cases. We can also see that the NMAE value of response time data reaches the optimal point at \(\lambda=0.6\) in most cases. Recalling Equation (20), it indicates that in the hybrid model, the UL-RW model takes a more important role than the SL-RW model.
# Survey for Soil Sensing with IOT and Traditional Systems
## 4 Inference Method and Evaluation Metrics
### Method for Spectral Information Analysis  
In the analysis phase for multivariate spectral data collected from spectrometers, several methods are frequently employed to construct regression models. These models aim to predict the concentration level of target elements based on input data (predictors). Initially, we will delve into two prominent linear regression techniques: Principal Component Regression [71] and Partial Least Square Regression [72; 73; 74]. Following these, we will discuss the Uninformative Variable Elimination (UVE) method that is grounded on PLSR [36; 75; 76]. Subsequently, we will introduce the machine learning-inspired method, Least Squares Support Vector Regressions, offering an alternative perspective on spectral data analysis.
# Investigations on an Ancient Mortar from Ulpia Traiana Sarmizegetusa Archaeological Site, Romania
## 3 Results  
### XRD Analysis  
The XRD analysis results in Figure 4 show that the mortar is rich in the feldspar mineral albite (NaAlSi\({}_{3}\)O\({}_{8}\)--ICDD PDF4+#00-009-0466), indicating an igneous origin (granite, locally available) of the siliceous component in the original mortar mixture. Quartz (SiO\({}_{2}\)--ICDD PDF4+#01-070-3755) and calcite (CaCO\({}_{3}\)--ICDD PDF4+#04-023-8700) follow albite in similar percentages, while a small amount of kaolinite (Al\({}_{2}\)O\({}_{3}\) -2 SiO\({}_{2}\) -2H\({}_{2}\)O--ICDD PDF4+#00-058-2028) was found. Kaolinite may be used as an ingredient deliberately or accidentally released in the original mortar admixture and/or may be a contaminant accumulated over time.  
Starting from the mass percentages provided by XRD, namely, albite (NaAlSi\({}_{3}\)O\({}_{8}\)) 36.8%; calcium carbonate (CaCO\({}_{3}\)) 28.7%; quartz (SiO\({}_{2}\)) 28.4%; and kaolinite (Al\({}_{2}\)Si\({}_{2}\)O\({}_{5}\)(OH)\({}_{4}\)) 6.2%, the value of 158.152 mg/'virtual mmode' of the homogenized mortar can be reached. This can be used to estimate the mass percentage per element: Na--5.37%, Al--8.48%, Si--26.78%, O--49.68%, Ca--7.27%, C--2.27%, H--0.15%.  
The value of the mass percentage of carbon contained in the 'lumps and bumps' (henceforth called lumps), measured by elemental analysis, was 5.36%C. The nitrogen percentage of 0.1%N determined simultaneously with carbon enters the background threshold during thermal combustion in the EA (see Section 2.2.) and demonstrates that there is no contamination with organic material containing nitrogen inside lumps.  
The value obtained for %C in lumps is 2-2.5 times higher than that for the homogenized mass of mortar, a value determined by XRD, namely 2.27%C (within the limits of experimental errors of the methods, and the material's inhomogeneity). But it is also 2-2.5 times lower than that for pure calcite (12%C).  
A possible explanation is that lumps contain calcite/calcium carbonate together with other chemical elements, unidentifiable to the EA (such as Na, Al, Si, O, Ca, H), or, most likely, the material subjected to graphitization is a mixture of calcite from the time of hardening in construction, rehydrated calcite, secondary calcite, and/or geogenic calcite, together with some impurities from mortar, mentioned before.  
The other analyses will bring new information to help define the chemical composition of the lumps as correctly as possible. This information is vital in the selection of the dating strategy and the interpretation of radiocarbon results.
## 4 Experiments
### Result  
#### 4.3.1 Experimental Results of Multi-Scale Organ Information Dataset  
Synapse Dataset  
As illustrated in Table 1, we employed the Dice with HD95 to evaluate the performance of each model within the Synapse dataset. Subsequently, the Dice and HD95 mean columns are followed by the Dice for different organs, where GB represents the gallbladder, KL and KR are the left and right kidneys, PC is the pancreas, SP is the spleen, and SM is the stomach. MINTFormer achieves an excellent result in Synapse, with the best Dice and lowest HD95. TransCascade and Missformer exhibited a 2.06% and 2.78% improvement in Dice, respectively, and a 3.31 and 4.17 improvement in HD95. Concurrently, MINTFormer also demonstrated the most optimal Dice for the gallbladder, left and right kidneys, pancreas, and stomach. This illustrates that MINTFormer is capable of effectively capturing the multi-scale information of both large and small organs. To facilitate a more intuitive evaluation of the segmentation performance of MINTFormer, we present the segmentation results of the Synapse dataset across different models. Figure 10 illustrates the segmentation outcomes of Synapse in MINTFormer, Missformer, SwinUnet, and TransUnet. As illustrated in Figure 10a, certain models exhibit deficiencies in accurately identifying minute objects within the segmentation process conducted on the Synapse dataset. The two labeled organs in Figure 10a are the gallbladder and the aorta. These organs occupy a relatively limited area of the feature map and are of a relatively small size. Upon examination of the highlighted regions, it becomes evident that other models have made erroneous predictions. For instance, Missformer incorrectly identifies the gallbladder as other types of organs, while SwinUnet mislabels the aorta. Additionally, TransUnet mislocalizes the
## 4 Methodology
### Finite Element Modelling  
The validation process for the finite element model of the analyzed opening, featuring 4626 nodes and a combination of 20-noded cubic and 10-noded tetrahedral elements, is a three-stage endeavor. Initially, the assessment is centered on the viability of modifications made to the test specimens to enable their connection to the testing machine. This stage entails a comparison of parameters related to displacement, with a minimal difference of 4.5%, as well as the identification of the location of maximum von Mises stress, as illustrated in Figure 11.  
Figure 11: Opening \(a\). (**a**) Maximum stress location. (**b**) Maximum displacement value and location (in mm).  
Figure 10: Loading conditions. (**a**) Hydrostatic pressure, (**b**) nodal load due to weight of containers in upright vessel condition, (**c**) nodal load due to weight of containers in standard heel vessel condition.  
In the second stage of validation, the test specimens' surfaces are meticulously prepared through a sequence of steps involving cleaning with acetone and applying contrasting white and black paint to create a random mesh pattern. This mesh serves as the basis for measuring strains and displacements during the loading process. The digital image correlation (DIC) technique is employed, using the Ncorr(r) open source 2D MATLAB(r) R2023b software, following the method outlined by [PERSON] et al. to perform this validation [31]. The DIC results are subsequently compared to the Finite Element Method (FEM) outcomes, particularly in terms of displacements. This comparison reveals a 7.1% variation in results for a standard load of 130 kN, as depicted in Figure 12.
## 2 Background and Motivation
### SFC Chaining Requirements  
When it comes to the chaining of network functions in an SFC environment, the IETF SFC draft [35] provides an architectural framework that captures all the components that are required for SFC implementation in service provider networks.  
There are some useful assumptions that need to be considered when creating a chain of network functions: (1) different network functions present their own configuration and description challenges, thus, creating a generalised description for all service functions is not trivial; (2) the implementation environment of the network function affects the list of functions that can exist in a particular domain; (3) the logic employed for the chaining of service functions is not fixed, that is, it is peculiar to any given administrative domain and the requirements of service(s) to be delivered to end users; and (4) the invocation of any service chaining criteria depends on the administrative domain in which the service functions are deployed [35].  
In terms of chaining requirements, although there are domain-specific requirements that need to be in place when deploying SFCs (based on the network administrative domain), there are also general requirements for the components that are found in most SFC deployments [36]. Irrespective of the network environment(s), _global_ components must be in place. These components are the service classifier, which is placed at the entry point of the network (to classify ingress flows). Flow classification helps with the decision-making process of the orchestrator in terms of traffic steering across the service chain.  
The service function forwarder (SFF) is another component that forwards received traffic to the right service function (SF), and can be embedded on a physical network component or deployed as a virtual component along the service function path (SFP), which is based on the classification of ingress traffic performed by the SC [37]. The SFF is also responsible for handling any return traffic that needs to be forwarded back to a specific service function or service classifier in the service chain [35].  
Another component is the SFC proxy, which is often optional in SFC implementations and used in scenarios where other components (SF and SFF) are unable to communicate in the chain [25]. SFC deployments can be fully deployed without the use of any form of proxy component, that is, when SFC-unaware service functions are not deployed in the network infrastructure. In situations where SFC-unaware service functions are part of the service chain, an SFC proxy is used to add or remove encapsulation information; thus, these are considered as _logical_ components of the SFC architecture [35]. Figure 3 depicts some of the core components of the IETF SFC architecture.
## 4 Conclusions  
Algorithms to elaborate the simulated data in order to improve the control of the manufacturing process are very important and they need to be developed. Sheet forming is a widely used process to manufacture at low cost. The thinning of the stretched sheet needs to be kept under control, because it is strongly connected with the product quality. This work presents a simulation model and a geometric algorithm to evaluate the thickness distribution of a sheet, once stretched over a punch. It was developed for the numerical model because it allows measuring of the minimum distance that may be measured perpendicularly to the profile tangent, whereas the traditional methods calculate the distance between two opposite nodes of the mesh. The minimum distance is the critical parameter characterizing the sheet thinning.  
A coordinate measuring machine, which is a general-purpose device suitable to control free form surfaces, was used in this paper to validate the simulation model. It acquires a set of points on the outer and inner surfaces of a stretched sheet for different steps of the forming process. The proposed geometric algorithm was modified to be applied to calculate the AA6060 sheet thickness from the measurement points on a generic 2D section of the sheet stretched over a hemispherical punch.  
The comparison between numerical and experimental results showed a good agreement. A greater discrepancy between the results was noted where contact occurs between the sheet and punch. In particular, the maximum value of the percentage difference module between the thickness values due to numerical and experimental approaches along the 61 points on the inner and outer surfaces of the stretched sheet was calculated. This percentage difference ranges between a minimum of 4%, when \(\mu\) = 0.1 and the punch stroke was equal to 20 mm, and a maximum of 9%, when \(\mu\) = 0 and the punch stroke was equal to 20 mm. This maximum value is probably due to the model adopted to describe the plastic behaviour of the material.  
The obtained results show the sheet thickness trend, the manufacturing signature left by the forming process on a stretched sheet; this be used to estimate the geometric deviations of the assemblies constituted by sheets, such as those of automotive of aerospace applications.  
Conceptualization, G.G. and W.P.; methodology, G.G. and W.P.; software, A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 13: Comparison between the results due to geometric algorithm and node-to-node method when (**a**) \(\mu\) = 0, (**b**) \(\mu\) = 0.1, and (**c**) \(\mu\) = 0.2.
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 4 Results and Discussion  
This section of the paper presents the tests performed to assess the efficiency of the developed ANN-based rotational speed control. A complete wave-to-wire model on MATLAB/Simulink of the oscillating-water-column system has been implemented for performance evaluation of the suggested control and a comparison with the uncontrolled case has been carried out.  
The implemented OWC model has been configured using the parameters obtained from real measurements at the NREIDA wave power plant. These parameters were provided by the Basque Energy Agency (EVE) and are indicated in Table 1.
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 3 Aggregated Control Strategy for Multi-Type Loads in Industrial Parks
### Real-Time Predictive Control of Multiple Types Loads in Industrial Parks
#### 3.3.3 Model Parameter Tuning  
In predictive control algorithm, the parameters that need to be tuned include the sampling period, optimization horizon, control horizon, error weighting matrix, and control weighting matrix, as well as correction parameters. Below are the tuning methods for each parameter:  
* Optimization horizon \(P\): Needs to match with the intra-day rolling optimization horizon, and covers the delayed response time and dynamic response time of the controllable load. Due to the intra-day rolling optimization horizon being 4 h, and the  
Figure 3: Control block diagram of the real-time predictive control.  
sum of delayed response time and dynamic response time of the controllable load not exceeding 4 h, the optimization horizon is selected as 4 h.
* Control horizon \(M\): Needs to satisfy \(M\leq P\). With a smaller \(M\), the computational workload for model solving decreases, but the tracking performance worsens. To achieve precise control of controllable loads, the control horizon is selected as 3 h.
* Sampling time \(T\): With a comprehensive consideration of hardware conditions, response dynamic characteristics, optimization horizon, control horizon, model dimensions, and control scale, the sampling time is selected as 15 min.
* Error weight matrix \(\Lambda\) and control weight matrix \(\Gamma\): As the deviation assessment coefficient is consistent across all control time intervals, the error weight coefficient can be selected as \(\alpha_{1}=\alpha_{2}=\ldots=\alpha_{p}=1\). The control weight coefficient can be initially set to 0, and if there is excessive variation in control action, adjust the control weight coefficient to a higher value step by step.
* Feedback correction parameters \(h_{i}\): The selection of feedback correction parameters will affect the anti-interference capability and robustness of the control model. To improve the anti-interference capability, select \(h_{i}=1\).
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 4 MODY Subtypes and Their Treatments
### Abcc8-Mody (mody 12)  
MODY 12 is caused by heterozygous ATP binding cassette subfamily C member 8 (_ABCC8_) gene mutations. _ABCC8_ encodes for sulfonyl-urea receptor 1 (SUR1), a subunit of an ATP-sensitive potassium (K-ATP) channel found in \(\beta\)-cell membranes [40; 65]. _ABCC8_ is responsible for the secretion of insulin, which controls blood sugar levels [95]. _ABCC8_ gene mutations can result in congenital hyperinsulin, which can be caused by dominantly inherited inactivating mutations. As a result of activating mutations or recessive loss-of-function mutations, _ABCC8_ gene mutations can lead to permanent or transient neonatal diabetes (PNDM or TNDM, respectively) [95]. The majority of patients with MODY 12 are misdiagnosed as having diabetes of a different kind and are mistreated with insulin, resulting in poor control and hypoglycemia episodes [34]. [PERSON] et al. proposed that in adulthood, all _ABCC8_ mutation carriers could be switched to sulfonylureas [96].
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.7 Named Data VANET Protocol (NVP)  
NVP was presented in [37], a novel routing protocol for VANET based on the named data network (NDN). The authors enhanced the routing path by using a new distance metric in the protocol, preventing the shortcomings of the hop-count-based metric. It also uses an incremental and adaptive broadcast strategy according to vehicle density.  
The authors propose a novel transmission-cost-estimation method as follows (Equation (7)):  
\[Cost=\begin{cases}(\frac{seqnum_{1}-seqnum_{t-w}}{count(t-w,t)})&,count(t-w,t) \
eq 0\\ \infty&,count(t-w,t)=0\end{cases} \tag{7}\]  
Seemingly, the higher the value of the transmission cost is, the worse the link quality is. Due to that, this metric can more accurately calculate link quality between communication nodes compared to the minimum hop-count metric [37]. Taking into account the scenario in Figure 9, we can notice vehicles between the source (s) and destination (D); A, B, and C are the forwarding nodes. However, even when A has the lowest number of hops, that route is not considered due to the block of the building causing packet losses. So path B-C-D was established because it offers better link quality.  
Another improvement is the dynamic adjustment of backoff time according to vehicle density due to scenarios in a VANET. The authors separated the scenarios into three categories based on vehicle  
Figure 9: Hop-count-based metric without line of sight (LOS) [37].  
density: sparse (\(0<N<=4\)), normal (\(4<N<=7\)), and dense (\(N>7\)). The new back-off time could be calculated with the following equation:  
\[wait\_time=factor*max(0,d-(dstDist-j)). \tag{8}\]  
In Equation (8), factor represents a waiting factor; \(dstDist\) represents transmission cost between source and destination. Finally, the authors found the optimal waiting factors of 2.4, 3.6, and 5 in sparse, normal, and dense conditions, respectively.  
To evaluate the proposal, virtual urban scenario V-city was designed to generate real-world traffic and set up a coordinative testbed integrating SUMO with NS-3. Simulation results indicated that the proposed protocol was more appropriate for VANET scenarios than AODV.
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 5 EE-LoRa Algorithm  
In this section, we will present our algorithm, named EE-LoRa, that derives the best SF and TP for each node in a way to improve the energy efficiency of LoRaWAN networks. First, we compute the optimal node distribution among different SFs in Section 5.1. Then, we adjust the SNR thresholds in Section 5.2 to satisfy the obtained optimal node distribution. Next, a power-control step is applied in Section 5.3. Finally, these nodes will be notified about their new configuration as described in Section 5.4.