esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions
All authors have read and agreed to the published version of the manuscript. This research was funded by the European Space Agency (ESA) grant number 4000128640/19/NL/PG/pt, ESA MAP project TOPDESS. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the large dataset. The work has been carried out through a fruitful and effective collaboration between the University of Parma and the University of Poitiers. The Authors would like to acknowledge the European Space Agency (ESA) support. The Authors would also like to thank [PERSON] for fruitful discussions. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**  
**Symbol** **Quantity**  
_Amp_  Heat flux amplitude  W/m\({}^{2}\)  
_cp_  Specific heat at constant pressure  J/kg\(\cdot\)K  
_cvt_  Coefficient of variation (time) %  
_cvt\({}_{sld}\)_  Mean of the coefficient of variations over the whole device %  
_cvt\({}_{std}\)_  Standard deviation of the coefficients of variation over the whole device \(E\)  Restoration error %  
\(f\)  Heat flux frequency  Hz  
\(F\)  Discrete Fourier transform -  
\(H\)  Transfer function (Gaussian filter) -  
_henv_  Overall heat-transfer coefficient between the channel wall and the  W/m\({}^{2}\)K  
surrounding environment by both natural convection and radiation  
\(k\)  Thermal conductivity  W/m\(\cdot\)K  
_L\({}_{niab}\)_  Length of the adiabatic section  m  
_l\({}_{channel}\)_  Channel height  m  
\(M\)  Number of time samples of the generic thermographic temperature -  
distribution  
\(N\)  Number of axial coordinates of the generic thermographic temperature -  
distribution  
_num_  Total number of channels -  
\(O\)  Number of y coordinates of the generic thermographic temperature  distribution  
\(q\)  Convective heat flux per unit surface  W/m\({}^{2}\)  
\(Q\)  Electrical power input to the evaporator  W  
_qav_  Heat flux averaged over time  W/m\({}^{2}\)  
_Qconduction_  Power transferred by conduction between evaporator and condenser  W  
_qexact_  Synthetic heat flux  W/m\({}^{2}\)  
_Qnet_  Net power input to the evaporator, considering heat dissipation to  W  
the environment \(q_{restoral}\)  Restored synthetic heat flux  W/m\({}^{2}\)  
_rcut\({}_{-off}\)_  Ratio between time and space cut-off frequencies -  
_Renv_  Overall heat-transfer resistance between the external tube wall and the  m\({}^{2}\)\(\cdot\)K/W  
surrounding environment \(R_{eq}\)  Equivalent thermal resistance  W/\({}^{\circ}\)C  
\(s\)  Thickness of the thin-wall of the channel  m  
_Sopper_  Full metallic cross-section  m\({}^{2}\)  
_S'eva_  Non-insulated evaporator outer surface  m\({}^{2}\)  
\(t\)  Time  s  
\(T\)  Temperature \({}^{\circ}\)C  
_Tenv_  Environmental temperature  K  
_Tf_  Filtered experimental temperature  K  
_TFPHP_  Device thickness  m  
_u, v, w_  Frequency components  rad\({}^{-1}\)  
_u, v, w_  Cut-off frequencies  rad\({}^{-1}\)  
_WFPHP_  Device width  m  
_xint_  Half of the channel height  m  
\(z\)  Axial coordinate  m
## 5 Experiments
### Experimental Results
#### 5.2.2 Prediction Accuracy Comparison  
As depicted in Table 2, the precision of wind speed prediction for STGPT2 UGAN and benchmarks is compared on the DSWE 1 and NREL datasets. Several conclusions can be drawn: (1) Owing to discrete learning modules and manual feature selection, classic statistical models ARIMA and SVR are inferior to the deep learning benchmark LSTM. (2) The prediction precision of CNN-LSTM shows that introducing the spatial block into the LSTM urges the model to further enhance the prediction accuracy. (3) The prediction precision of GCN-LSTM and GAT-Informer outperforms CNN-LSTM. The commonality in the former models is that they treat a turbine network as an undirected graph, entirely maintaining spatial relationships (e.g., the dependencies between distant turbines) when extracting spatial dependencies. (4) The LLM-based benchmarks FPT and LLM4 TS are superior to these spatio-temporal benchmarks (e.g., GCN-LSTM and GAT-Informer). The underlying reason is that the LLM obtains abundant intrinsic knowledge by pre-training, which enables us to predict more reasonable wind speed sequences. (5) In contrast to the FPT and LLM4 TS, STGPT2 UGAN adopts STGPT-2, which integrates spatial attention and temporal attention into GPT-2 to capture spatio-temporal correlations. Meanwhile, STGPT2 UGAN develops a GAN to offer an adversarial training scheme, which incorporates unsupervised training and supervised training to optimize STGPT-2's training, thus attaining a higher prediction accuracy in the future 1-12 h.  
To evaluate whether STGPT2 UGAN outperforms the baselines in prediction accuracy, we conduct a statistical significance test. Specifically, we first propose two hypotheses (e.g., the null hypothesis and the alternative hypothesis). The null hypothesis refers to the notion that there is no improvement in the prediction accuracy of STGPT2 UGAN compared to the baselines. The alternative hypothesis refers to the notion that the prediction accuracy of STGPT2 UGAN outperforms baselines. Then, we use a \(t\)-test to compare the difference in prediction accuracy between STGPT2 UGAN and baselines. After the \(t\)-test, we can obtain a \(p\) value, which is equal to 0.038. Since the \(p\) value is less than the significance level (\(\alpha=0.05\)), the null hypothesis is rejected and the alternative hypothesis is accepted. Hence, this proves that STGPT2 UGAN outperforms baselines in prediction accuracy.  
Figure 3: Training error (**left**) and validation error (**right**) in the DSWE 1 and NREL datasets. The MSE function is set as the error evaluation metric.
## 5 Validation of RPAL SCAOF in Simulations
### Simulation Setup and Designated Scenarios
#### 5.2.1 Topology  
The majority of existing WSN deployments in field tests adopt a star topology (e.g., a coordinator node as a central node in ZigBee PRO network). A single A-LLN is a centralized architecture, but it is also possible to divided it into a number of smaller structures or extend it with other LLNs through the inter-domain manner of RPL. A-LLNs are expected to be a part of the smart agriculture infrastructure in futuristic scenarios, and there should be a central unit (_i.e._, an A-LLN border router or edge router) that plays the role of a network coordinator among the A-LLN nodes and the external \"things\". Thus, our study is focused on the routing optimization in a single A-LLN so the simulation will be also dedicated to a centralized architecture.  
Figure 4: Protocol stack for evaluating the RPAL routing model.  
The assumptions of a grid-like network topology as well as a tree-like network topology are common simplifications. In some cases, these two types of topologies could be equal for DODAGs organization of RPL framework. The difference in them is that the main consequence of the former is a near-uniform distribution of the number of node degree (_i.e._, neighbors' count). Distance Loss Unit Disk Graph Medium (UDGM) in Cooja is often used to build simplified scenarios configuring radio coverage and distance loss as fixed parameters [37; 38; 79; 80], and here UDGM is adopted to evaluate RPL and RPAL.  
Since realistic crop fields are mostly organized in the adjacent regular parcels, the shapes of them could be rectangle or quadrangle (see Figure 1), so they are similar to grid topology. Considering the network scalability as well as the low density of the A-LLN, the number of sensor nodes is defined as 20 and 30. The sensor node locations are decided to be the junctions of the edges of adjacent grids or the centers of the grids according to the above assumptions. Namely, they are uniformly distributed around but the sink node is placed at the boundary of this topology.
## 2 Materials and Methods
### Improved LK-SVD Sparse Dictionary Learning Method  
#### 2.3.1 Sparse Dictionary Learning  
The main idea of dictionary learning is to use the dictionary matrix to linearly sparsely represent the original samples [22], as shown in Equation (1):  
\[Y=DX \tag{1}\]  
where \(D\in R^{m\times k}\) represents the over-complete dictionary; \(X\in R^{k\times n}\) represents the sparse matrix; \(Y\in R^{m\times n}\) represents the original samples; \(m\) and \(n\) are the dimension and number of samples, respectively; and \(k\) is the number of dictionary atoms. The essence is to find \(X\), making \(D\) as sparse as possible.  
Generally, sparse representation mainly includes two parts: sparse coding and dictionary updating [23]. In this study, OMP [24] was used to sparsely decompose the input information and calculate the reconstruction error; the improved LK-SVD algorithm was used to construct and update the dictionary.
# CFD Analyses: The Effect of Pressure Suction and Airflow Velocity on Coronavirus Dispersal
## 4 Discussion  
By evaluating the velocity profiles, it was discovered that the velocities of both entrances were recorded at their lowest levels, thereby removing the velocity component as a role in COVID-19 distribution in those areas. Furthermore, the flow through the aisles (a, b, and c) generally maintained a consistent profile and was unaffected by the velocity profiles of the primary sections, indicating that no additional kinetic energy was gained from the primary sections (patient section, operating section, and physician section), implying that COVID-19 is less likely to be transferred through those aisles. Other aisles, on the other hand, have higher levels of velocity.  
Figure 10: Turbulence kinetic energy profile for the 1 m and 1.7 m planes at two suction levels (0.85 and 0.7 bar).  
Figure 9: Velocity profiles for the 1 m and 1.7 m planes at two suction levels (0.85 and 0.7 bar).  
The zone with high dispersion and mixing levels was identified by plotting the turbulence kinetic energy, which corresponds to the locations where COVID-19 is more likely to spread than other places. The operating and patients' sections were identified as such. Surprisingly, the turbulence level was found to be higher at 1 m above the ground, which corresponds to the height of medical benches (i.e., the level of patients' respiration). This also indicates that COVID-19 transmission is more frequent among patients lying down than among employees standing up in the patients' area.  
Airflow from the patients' section exits into the physician section, according to velocity streamlines. As a result, it was suggested that the suction level in the physician section should be reduced while the suction level in the patients' section is increased. Alternatively, the airflow velocity in the patients' section might be lowered while other factors (such as temperature) are taken into account to maintain an appropriate degree of air conditioning.  
Finally, it should be noted that the findings of this research are relative, not absolute, in the sense that the high/low-risk locations were found in relation to other sections of the building.
## 4 Methodology
### Implementing Gully Erosion Susceptibility Model
The sensitivity and specificity were calculated using the following equations:  
\[\text{Sensitivity}\ =\ \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
\[\text{Specificity}\ =\ \frac{\text{TN}}{\text{FP}+\text{TN}} \tag{9}\]  
The ROC curves were created both for the testing and training datasets to evaluate the models' predictive performances and investigate their fit to the training observations. The AUC-ROC curve was calculated to quantify the effectiveness of the model [70]. The values of AUC can range from 0.5 (randomness) to 1 (perfect discrimination). The closer the value of AUC is to 1, the higher the model's predictive capability [66; 70]. According to [PERSON] and [PERSON] [71], the predictive ability of a model based on the AUC value can be classified as follows: poor (0.5-0.6), moderate (0.6-0.7), good (0.7-0.8), very good (0.8-0.9), and excellent (0.9-1). Additionally, to reveal the overall predictive ability and robustness (i.e., model response when the training and testing set change) of the MaxEnt method, the mean and standard deviation were computed for each performance parameter. Using a GIS-based spatial analysis, implemented in ArcGIS software, the maps of probability of gully spatial occurrence, provided by 10 calibration models, were averaged to yield a single gully erosion susceptibility map. This susceptibility map, characterised by values of probability in each pixel that vary from 0 to 1, was categorised into five classes using the equal interval method (0.0 to 0.2, 0.2 to 0.4, 0.4 to 0.6, 0.6 to 0.8, and 0.8 to 1.0). These classes were defined, from 0 to 1, as follows: very low, low, moderate, high, and very high. In addition, for assessing the consistency of the susceptibility classes, the index of relative gully erosion density (R) method was employed [72]. The R index was calculated using Equation (10). \[\text{R}\ =\ \frac{(\text{x}_{\text{i}}/\text{X}_{\text{i}})}{\sum\text{x}_{ \text{i}}/\text{X}_{\text{i}}}\times 100 \tag{10}\]  
where \(\text{x}_{\text{i}}\) is the percentage of gullies for each susceptibility class and \(\text{X}_{\text{i}}\) is the percentage of pixels of each susceptibility class. If the values of the R index are higher in the very high and high susceptibility classes, the efficiency of the map can be defined as good. Finally, exploiting the 10 replicate calibration models, the standard deviation for the probability estimates of each pixel was also computed, allowing the uncertainty in model prediction to be evaluated quantitatively. The standard deviation is a good indicator of model stability [73; 74]. Specifically, a narrow standard deviation indicates lower uncertainty, suggesting that several model iterations do not bias the accuracy.
## 3 Results  
### Difference in \(c_{s}\) Estimates between Machines In Vitro  
Representative images of the phantom inclusions acquired using both machines are shown in Figure 3b. Table 1 lists the \(c_{s}\) estimates in the phantom measured by each observer, symmetric percentage differences \(c_{s}\) estimates in between machines, and the nominal values provided by the phantom manufacturer. Aixplorer gave between 0.2% to 3.06% greater \(c_{s}\) estimates than the S2000. The CVs of the \(c_{s}\) estimates were <0.1 for all regions of the phantom and both machines (see Table 1). Figure 2 shows example images of the lesions contained in the phantom acquired using both machines  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Background** & **Inclusion 1** & **Inclusion 2** & **Inclusion 3** & **Inclusion 4** & **Mean (SD)** \\ \hline Nominal \(c_{s}\) & 2.90 & 1.60 & 2.20 & 3.90 & 5.20 & \\ \hline \multicolumn{6}{c}{Aixplorer: \(c_{s}\) estimates (ms\({}^{-1}\))} \\ \hline Observer 1 & 2.50 & 2.00 & 2.60 & 3.80 & 4.10 \\ Observer 2 & 2.60 & 1.90 & 2.50 & 3.70 & 4.30 \\ Observer 3 & 2.50 & 1.80 & 2.50 & 3.60 & 4.10 \\ Mean & 2.53 & 1.90 & 2.53 & 3.70 & 4.17 \\ CV & 0.02 & 0.05 & 0.02
# Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots
## 5 Conclusions  
We propose two loss models for a mobile hotspot that accommodates new and handover users. To favor handover users but at the same time not substantially increase the CBP of new users, we propose the probabilistic BR policy where new calls may enter the reservation space with a certain, predefined, probability. In the first loss model, the probabilistic BR policy is applied only to new calls during the stop phase of a vehicle. In the second loss model, a generalization is considered, where the probabilistic BR policy can be applied not only during the stop phase but also during the moving phase. Both models are analytically described with the aid of two-dimensional Markov chains, while iterative algorithms are proposed for the determination of the steady-state probabilities and the various performance measures such as CBP and system's utilization.  
As a future study, we will extend these models to include the case of quasi-random traffic, where a finite number of users may generate calls in the system not only during the stop but also during the moving phase. Additionally, we intend to study the case of multiservice traffic where users generate traffic (either random or quasi-random) and have different bandwidth-per-call requirements, i.e., a user may request more than a single b.u. in order to be accepted in the system. Such extensions have been considered in the literature in wired/wireless networks but not in the case of mobile hotspots [28, 29, 30, 31, 32, 33, 34, 35, 36].  
**Author Contributions:** Conceptualization, I.P.K., I.D.M. and P.G.S.; methodology, I.P.K., I.D.M. and P.G.S.; software, I.P.K. and I.D.M.; validation, I.P.K. and I.D.M.; writing--original draft preparation, I.P.K., I.D.M. and P.G.S. writing--review and editing, I.P.K., I.D.M. and P.G.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
b.u. Bandwidth unit  
AP Access point  
BR Bandwidth reservation  
CAC Call admission control  
CBP Call blocking probabilities  
CS Complete sharing  
NB NodeB  
QoS Quality of service  
WLAN Wireless local area network  
**Notation**  
\(B_{s,new}\) CBP of new calls (stop phase)  
\(B_{m,new}\) CBP of new calls (moving phase)  
\(B_{new}\) Total CBP of new calls  
\(B_{s,h}\) CBP of handover calls (stop phase)  
\(B_{s,h}^{*}\) CBP of handover calls given stop phase  
\(B_{s,s,new}\) CBP of new calls (stop phase--generalized loss model)  
\(B_{g,m,new}\) CBP of new calls (moving phase--generalized loss model)  
Total CBP of new calls (generalized loss model)  
\(B_{g,s,h}\) CBP of handover calls (stop phase--generalized loss model)  
\(B_{g,s,h}^{*}\) CBP of handover calls given stop phase (generalized loss model)
## 1 Introduction  
Since its discovery in 1921, insulin has proven to be one of the medical community's greatest weapons against the growing scourge of diabetes mellitus. One of the largest burdens in terms of morbidity and mortality in the Western world, its prevalence is also rising in Asia and India. Diabetes creates a huge financial burden in terms of illness and through lost work hours [1]. Acute and chronic complications of diabetes are common; diabetes is the most common cause of (a) kidney failure, (b)blindness in adults under the age of 65 and (c) non-traumatic limb amputation. Chronically elevated blood glucose predisposes to these chronic complications [2].  
Type 1 diabetes mellitus is a disease that is characterized by autoimmune destruction of the pancreas' islet beta cells and often presents at an early age. Type 1 diabetes eventually leaves the patient completely insulin deficient, thus generating a risk for diabetic ketoacidosis. Historically, before the discovery of insulin, many patients with type 1 diabetes were confined to hospital wards with little hope of recovery from the ketoacidotic state. Later, the discovery of insulin allowed rapid recovery from comatose states [3].  
Today, the management of type 1 diabetes remains a challenge, despite commercially-available recombinant human insulin and its analogs. In addition to the loss of insulin, many persons with type 1 diabetes have insufficient release of the counter-regulatory hormone glucagon, leading to a propensity for prolonged hypoglycemia during insulin treatment. This issue appears to be a greater problem in persons who suffer repeated bouts of hypoglycemia and can be improved by assiduously avoiding hypoglycemia. Due to endogenous endocrine pancreatic dysregulation, tight glycemic control with subcutaneous depot insulin regimens and subcutaneous infusion pump treatment often eludes even the most experienced diabetologist. Though these treatments have greatly enhanced diabetes care over the last few decades [2], hypoglycemia remains a very commonly encountered barrier to intensive control [2; 4].  
Continuous insulin infusion using traditional pump technology requires input of pre-determined basal and bolus delivery rates, insulin sensitivity (correction) factors and insulin to carbohydrate ratios. Capillary glucose monitoring must be carried out four to six times per day for use as the basis of insulin dosage calculations. With such \"open-loop\" systems, the susceptibility to abnormally high or low glucose values is quite high. For this reason, the concept of closed loop glycemic control, in which continuous glucose monitoring is coupled to a fully automated insulin delivery device, without human input, has been an attractive idea. Patients would welcome the freedom from the need to make insulin dosing decisions many times per day. Preliminary studies using closed loop treatment have been shown to lead to improved diabetes control [5; 6].  
The present article reviews the history, structure and clinical applicability of commonly used control algorithms in modern closed loop insulin-delivery systems.
## 6 Case Study  
### Domain Knowledge Elicitation  
An experiment using structured interviews has been conducted to apply the results of this research study. The experiment occurred in the domain of sustainable practices in agriculture and farming. Three experts, namely, E1, E2, and E3, who have each ecological crop production in the Mediterranean biogeographic region of Murcia (Spain), were interviewed separately. They were asked to provide some of the most relevant concepts that should be considered in crops to be managed in a sustainable fashion. The interviews took place during May and October 2023.  
For E1, there are two key concepts to be considered in the management of sustainable practices in crops. These concepts are the specific type of plant in question and the irrigation system being used. When this expert was requested to supply more information on which factors related to the irrigation system he considers in crop management, he mentioned that often, the way through which the water comes to their crop is very important. Also, E1 indicated that the features of the source of irrigation were the most important factor for crops, in that the production success is dependent on the source of water (e.g., from pool, river, desalination plant, from the rain, etc.). When this expert was asked to specify the features of these concepts, he provided the following information, grouped by the concepts underlying the information on concepts as described above:  
* Crops: size.
* Plants: variety, age.
* Irrigation system: cost, technology.
* Irrigation pipeline: length.
* Surface irrigation: covered area. List 1. Concepts provided by expert E1  
By proceeding similarly with Experts E2 and E3, List 2 and List 3 below present the characteristics of each of the concepts according to experts E2 and E3, respectively. In this respect, in the third interview, E3 pointed out that in irrigation systems, a very important issue that should be considered is the reliability of such systems. He also stated that among irrigation systems, there are two main categories, namely, surface irrigation and drip irrigation.  
* Crops: size.
* Plants: variety, annual production.
* Land: pH level
* Water supply: cost, technology.
* Irrigation pipeline: flow.
* Sprinkler irrigation: capacity. List 2. Concepts provided by expert E2
* Crops: size
* Plants: variety.
* Land: pH level.
* Irrigation system: cost, technology, reliability.
* Irrigation pipeline: flow.
* Drip irrigation: automation, wet bulb. List 3. Concepts provided by expert E3
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 4 Framework Architecture  
This section concentrates on the architecture of the presented framework. We demonstrate the primary parameters configurable using the framework, followed by an explanation of how to measure energy consumption in an IoT setting. Finally, the parameter setup is embedded into a hyperparameter optimization setting, which serves as an automated tool to search for the best parameter combinations.