esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Hispabot-COVID-19 Dataset  
With the aim of providing information to citizens and facilitating contact with public institutions, the Spanish government launched in March 2020 Hispabot-Covid19, a conversational assistant [30] developed to answer frequently asked questions related to the pandemic originated by COVID-19 and its implications in Spain. The system received more than 350,000 queries between April and June 2020. The Hispabot-Covid19 assistant accessed official sources, such as the Spanish Ministry of Health and the World Health Organization, to provide information. The assistant also incorporated the information published in the Spanish BOE (official Spanish state bulletin) regarding the application of the State of Alarm and the Plan for the Transition towards a New Normality. The assistant did not require or analyze personal data.  
A total of 164 intents were defined to report on various issues, such as: attention telephone numbers, symptoms, vulnerable groups, transmission, prevention, coexistence with infected people, and conditions for quarantine and isolation, among many others. Users' utterances were classified to provide a response associated to each one of them.  
All dialogues were conveniently recorded in the system. With the aim of continuously retraining and improving the assistant, new training phrases were processed and new question categories were incorporated daily. Besides, misunderstandings were detected and corrected, and the knowledge base was accordingly restructured with the information updates provided. The proposal we present in this paper was validated upon a subset of the official Hispabot-Covid19 dataset, whose main features were summarized in Table 1.  
In addition, we also present a histogram representation in Figure 1 with the amount of samples available per each category (in blue). Specifically, the name of the most relevant categories (i.e., the most frequent ones, those with 100 or more samples) were highlighted in bold above the corresponding line. Information about the average sample length per class is also provided (in orange). Mean values are also displayed (in dotted lines) for both data series.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Param** & **Value** \\ \hline Number of samples & 7532 \\ Number of intents & 164 \\ Number of entities & 55 \\ Different words & 7658 \\ Average number of words per utterance & 6.16 \\ Average number of entities per utterance & 0.6 \\ Average number of words per entity & 1.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main features of selection of Hispabot-Covid19 corpus used in paper.
###### Abstract  
Several applications, from the Internet of Things for smart cities to those for vehicular networks, need fast and reliable proximity-based broadcast communications, i.e., the ability to reach all peers in a geographical neighborhood around the originator of a message, as well as ubiquitous connectivity. In this paper, we point out the inherent limitations of the LTE (Long-Term Evolution) cellular network, which make it difficult, if possible at all, to engineer such a service using traditional infrastructure-based communications. We argue, instead, that network-controlled device-to-device (D2D) communications, relayed in a multihop fashion, can efficiently support this service. To substantiate the above claim, we design a proximity-based broadcast service which exploits multihop D2D. We discuss the relevant issues both at the UE (User Equipment), which has to run applications, and within the network (i.e., at the eNodeBs), where suitable resource allocation schemes have to be enforced. We evaluate the performance of a multihop D2D broadcasting using system-level simulations, and demonstrate that it is fast, reliable and economical from a resource consumption standpoint.  
Keywords:proximity services; device-to-device; multihop; resource allocation; mobile networks; vehicular networks; simulation +
Footnote †: journal:  
_Future Internet_
## 3 Two-Stage Stochastic Scheduling Model for the Hybrid AC/DC Distribution Network
### Constraints for the Two-Stage Stochastic Programming
#### 3.3.2 Operation Constraint of the Energy Storage System  
\[y_{i,t}^{\text{ch}}+y_{i,t}^{\text{dis}}\leq 1, \tag{37}\] \[0\leq P_{i,t,s}^{\text{dis}}\leq y_{i,t}^{\text{dis}}p_{\text{ max}}^{\text{ESS}},\] (38) \[0\leq P_{i,t,s}^{\text{ch}}\leq y_{i,t}^{\text{ch}}p_{\text{ max}}^{\text{ESS}},\] (39) \[E_{i,t+1,s}^{\text{ESS}}=E_{i,t,s}^{\text{ESS}}+\eta^{\text{ch} }P_{i,t,s}^{\text{ch}}-\eta^{\text{dis}}p_{i,t,s}^{\text{dis}},\] (40) \[0.2E_{\text{max}}^{\text{ESS}}\leq E_{i,t,s}^{\text{ESS}}\leq 0.9E_{ \text{max}}^{\text{ESS}},\] (41) \[\sum_{t}\left|y_{i,t+1}^{\text{ch}}-y_{i,t}^{\text{ch}}\right|\leq y _{\text{max}}^{\text{ESS}},\] (42) \[\sum_{t}\left|y_{i,t+1}^{\text{dis}}-y_{i,t}^{\text{dis}}\right| \leq y_{\text{max}}^{\text{ESS}}, \tag{43}\]  
where \(y_{i,t}^{\text{ch}}\) and \(y_{i,t}^{\text{dis}}\) represent the charging and discharging status of the energy storage system; \(\eta^{\text{ch}}\) and \(\eta^{\text{dis}}\) are the charging and discharging efficiency; \(P_{\text{max}}^{\text{ESS}}\) is the maximum charging/discharging power; \(E_{\text{max}}^{\text{ESS}}\) is the maximum storage capacity; \(E_{i,t,s}^{\text{ESS}}\) is the storage capacity of ESS at time \(t\) under scenario \(s\); \(P_{i,t,s}^{\text{dis}}\) and \(P_{i,t,s}^{\text{ch}}\) represent the discharging and charging power output of ESS at time \(t\) under scenario \(s\); and \(y_{\text{max}}^{\text{ESS}}\) is the maximum charging/discharging number in one day.  
It can be seen that the charging/discharging state of ESS noted as \(y_{i,t}^{\text{ch}}\) and \(y_{i,t}^{\text{dis}}\) is not affected by wind power fluctuations under different scenarios, which remains unchanged during the whole scheduling period. On the other hand, the charging and discharging power output under wind power scenario \(s\) are defined as \(P_{i,t,s}^{\text{ch}}\) and \(P_{i,t,s}^{\text{dis}}\), respectively, which indicates that the charging/discharging power of ESS depends on the possible wind power scenarios. As a result, the charging/discharging state of ESS is regarded as the decision variables in the first stage, which is determined before the possible wind power scenarios happened. Moreover, the charging/discharging power output of ESS is considered as the decision variable in the second stage, which is used to alleviate the wind power fluctuation in real time.
## 3 Framework Design and Deployment
### Proposed Framework
#### 3.4.3 Federated Averaging with XAI Integration
The local loss function \(L^{k}(M^{k},x_{t}^{k})\) is modified using an importance score \(\alpha_{t}^{k}\): \[L^{k}_{\text{XAI}}(M^{k},x_{t}^{k})=\alpha_{t}^{k}\cdot L^{k}(M^{k},x_{t}^{k})\] (4) This prioritization helps the model adapt to challenging data points, fostering a more robust learning process and reducing the impact of recurrent errors. * To address edge cases, the framework adopts a prototype and criticism approach. The loss function emphasizes data that XAI identifies as either prototypical or problematic, allowing the model to focus on these cases. This weighted function is given by: \[L^{k}_{proto-crit}=\beta L^{k}(M^{k},x_{proto})+\gamma L^{k}(M^{k},x_{crit})\] (5) Here, \(\beta\) and \(\gamma\) prioritize training on essential data, enhancing the model's ability to manage edge cases effectively. * The framework's integration of XAI establishes a self-optimizing feedback loop that continuously refines the model based on real-time insights. This iterative process, represented as: \[M_{i+1}=\frac{\sum_{k=1}^{N}W_{i+1}^{k}\Big{(}M_{i+1}^{k}+\Delta M_{\text{XAI }}^{k}\Big{)}}{\sum_{k=1}^{N}W_{i+1}^{k}},\] (6) incorporates XAI-based adjustments, making the model more resilient and adaptable to dynamic conditions in IIoT environments.
## 4 Simulation Studies
### Simulation Studies and Discussion
#### 4.2.3 Objective Function and Constraints  
The objective function for the EC problem is defined in (15). The constraints on start-up costs and production range of units are specified in (16) and (18), respectively.  
\[F_{objective}=min\left\{\sum_{t=1}^{T}\left[\sum_{i=1}^{N_{c}}\ carrier_{i}^{t}\times price_{i}+\sum_{i=1}^{N_{g}}SC_{i}^{t}+ \sum_{i=1}^{N_{g}}C_{i}u_{i}^{t}\right]\right\} \tag{15}\]  
\[SC_{i}^{t}=\left\{\begin{array}{ll}SC_{i},\ u_{i}^{t}>u_{i}^{t-1}\\ 0,\ else\end{array}\right. \tag{16}\]  
\[P_{gi}^{min}\leq P_{gi}\leq P_{gi}^{max} \tag{17}\]  
\[\sum_{i=1}^{N_{g}}P_{gi}^{t}=load^{t} \tag{18}\]  
where \(T\) is the study period, \(N_{c}\) is the number of various carriers, \(carrier_{i}^{t}\) in the need for the \(i\)-th carrier at the \(t\)-th hour, and \(price_{i}\) is its price, \(N_{g}\) is the number of units, \(SC_{i}^{t}\) is the start-up cost for the \(i\)-th unit in the \(t\)-th hour, \(C_{i}\) is the fixed cost for the i-th unit and \(u_{i}^{t}\) is the status (on or off) of it in the \(t\)-th hour, \(P_{gi}\) is the production of the \(i\)-th unit, and \(load^{t}\) is the electricity demand in the \(t\)-th hour.  
The EC problem solution steps are shown in Algorithm 1. The flowchart of the EC solution method using the FOA algorithm is also shown in Figure 1.  
Figure 1: Energy Commitment (EC) flowchart.
## 2 Constraints and Challenges of Agricultural Sprayer UAV
### Obstacles on Farmland  
Due to geographical position and seasonal changes, the agricultural sector faces uncontrollable weather changes such as strong wind, drought, freezing wind, fog, etc. Thus, for the same geographical position, the agricultural UAV faces different situations, and may change its flight parameters, such as motor speed, and PID gains, in order to control the stability and position [85; 86]. Moreover, sprayer UAVs are relatively heavier than other UAVs due to the liquid load [87; 88]. During spraying operation, droplet deposition is the primary concern for an agricultural UAV, and it is directly related to the flying parameters such as flying velocity and altitude [89]. These parameter settings are selected by operators depending on the plant growth and types, terrain, and topography of the farmland, etc. [90; 91; 92].  
With regards to these spraying issues, the safety of the sprayer UAV should always be ensured. Because of the low altitude flying, obstacles are more specified on farmland, such as ladders, pump houses, electrical substations, power lines, telephone towers, lighting towers, groups of trees, scattered trees, flying birds or bats, etc. Example obstacle images (satellite image) are shown in Figure 6. To avoid these kinds of obstacles successfully and intelligently, it is essential to analyze the features of the obstacles on farmland. So, we categorized all the possible obstacles on farmlands, and their details are in Table 1.  
Figure 4: Liquid shift and center of gravity (CG\({}_{1}\), CG\({}_{2}\)) position [75].  
Figure 5: Slosh impact comparison: (**a**) free moving liquid vs. (**b**) included vertical and horizontal grill with 30% liquid [79].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
Figure 6: Farmland obstacles, satellite view—(**a**) house [93]; (**b**) electric tower [94]; (**c**) big bush [95]; (**d**) trees [96].  
After categorizing all possible obstacles on farmland, we can see some of these obstacles and the global detection system can detect the bigger size obstacles, and some need to be detected by the local detection system [70]. The local detection and obstacle avoidance operations need real-time analysis, intelligent identification, potential area detection, the suitable path, and so on [97]. Thus, the sprayer UAV needs a suitable detection sensor or sensor fusion; thus, obstacle detection sensors are discussed in the next section.
## 2 Materials and Methods
### The Ground Segment
#### 2.2.1 IT Platform  
The IT platform used to store and process the SAR datasets is installed in the IREA-CNR laboratories in Naples, Italy (see Figure 3). It consists of 22 nodes, each of which is equipped with 2 CPUs. Each CPU (AMD(r) EPYC 7513, 32 c, 2.6 GHz, 128 M cache 200W TDP) is equipped with 32 cores. Each node is equipped with 2 TB (3200 MHz) of RAM and with 12 16 TB 7K SAS 12 Gbps 512e equal to 192 TB RAW disks. The 22 nodes are interconnected through two 25 Gb/s networks. The platform is configured with the openSUSE 15.4 operating system. The specifications of this IT platform are very well tailored to the implementation of parallel programming techniques (multi-node and multi-thread), which are crucial for the efficient application of the airborne SAR data processing chain, as described in Section 2.2.4.  
Figure 1: (**left**) MIPS radar module. (**right**) Rack of the MIPS system.  
Figure 2: The X-band (**left**) and L-band (**right**) antenna layouts of the MIPS system mounted on Cessna 172 aircraft.
# Investigation of High-Q Lithium Niobate-Based Double Ring Resonator Used in RF Signal Modulation
## 3 Results and Discussion
### Analysis of Double Ring Structure
Both are calculated at \(k_{1}=k_{2}=k_{3}=0.1\). When it meets the critical coupling condition, the output power of the through port drops to zero. At this time, the relationship between transmission coefficient and transmission loss is shown in Equation (11). \[t_{1}=t_{3}A_{1}\bigg{|}\frac{t_{2}-A_{2}}{1-t_{2}A_{2}}\bigg{|} \tag{11}\]  
Figure 8a shows that with the value of \(A_{1}\) decreases from 1 to 0.98, the power of input light coupled into the outer ring resonator decreases by about 35%, and the Q-factor of the resonant peak of the inner ring decreases by an order of magnitude. Figure 8b shows that without waveguide loss in the inner ring, the influence of the outer ring waveguide loss on the transmission characteristics is insignificant; the inner ring waveguide loss has a greater influence on the transmission spectrum than that of the outer ring. Figure 9 shows the influence of the transmission coefficients of ring-ring waveguide (\(t_{2}\) ) and ring-straight waveguide (\(t_{3}\) ) on the transmission spectrum of the double ring resonator. Figure 9a shows that with the transmission coefficient increasing from 0.7 to 0.9, the optical power coupled into the outer ring resonator decreases by about 18%. It is helpful to reduce the interference of outer ring to inner ring transmission characteristics. Figure 9b shows that compared with \(t_{3}\), the increase of \(t_{2}\) will offset the resonance peak of the outer ring, but the effect on the Q-factor of the inner ring is not as obvious as that of \(t_{3}\). Figure 8: (**a**) The transmissivity of the double ring resonator at different wavelengths and the field attenuation (\(A_{1}=A_{2}\)) and (**b**) the transmissivity of the double ring resonator at different wavelengths and the field attenuation of outer ring (\(A_{1}\)). Both are calculated at \(t_{2}=t_{3}=0.9\). The resonance peak in the middle is the inner ring resonance peak, and the resonance peak on both sides is the outer ring resonance peak. Figure 9: (**a**) The transmissivity of the double ring resonator at different wavelengths and the transmission coefficient (\(t_{3}\)) and (**b**) the transmissivity of the double ring resonator at different wavelengths and the transmission coefficient (\(t_{2}\)). Both are calculated at \(A_{1}=A_{2}=0.99\).
## 3 Results
### Health Benefits
#### 3.2.3 Diabetes Mellitus  
From 78 studies, only 10 were randomized control trials that evaluated the specific varieties of beans and their impact on glucose control in diabetes patients. Beans may help reduce steep post-prandial glucose levels [79]. The explanation of these metabolic benefits is their high content of fibers that range from 4.6 g in fava beans up to 9.6 g fiber in navy beans and 6.2 g in 100 g cooked chickpeas [80]. These ingredients may lead to a lower risk of type 2 diabetes mellitus [81]. EFSA considers a daily intake of 25 g or more dietary fiber sufficient in reducing the complications of diabetes mellitus [82] and may help regulate post-prandial glucose levels [83]. [PERSON] et al. (2015) demonstrated in patients with metabolic syndrome, including diabetes, that meals containing black beans led to reduced postprandial insulin concentrations [84], and that could happen even with 100 g of legumes in the daily diet of people with type 2 diabetes [85]. The FREDIMED study [85] results were the same as the last study of [PERSON] et al. (2019). In a randomized controlled study where participants consumed one serving of legumes per day [86], it was observed that those who consumed beans experienced a reduction in blood sugar levels and lower blood pressure compared to the control group, which consumed more whole wheat fiber. In addition to the recommended daily intake of 40 g of dietary fiber [87], another important consideration for individuals with prediabetes, type 1 diabetes, or type 2 diabetes is the glycemic index (GI) of foods [88]. Among legumes, soybeans have the lowest GI (16 \(\pm\) 1%), followed by beans (GI 24 \(\pm\) 4%), chickpeas (GI 28 \(\pm\) 9%), and lentils (GI 32 \(\pm\) 5%) [89].  
In a study by [PERSON] et al. (2018), which examined pinto, black, and kidney beans at a dose of 225 g/day in individuals with diabetes, it was found that could help in glucose stability [90]. Increasing fiber intake (15 g to 30 g/day) is a usual recommendation for diabetics [91] and is necessary as part of dietetic diabetic management. Possible mechanisms involved in blood glucose homeostasis regulation include phytolectins, phytates, and amylase inhibitors, which play crucial roles in diabetes management [92].  
Furthermore, the mechanism may be linked to the reduction of TNF-\(\alpha\) and IL-6, as well as lower post-prandial insulin levels in patients who received products derived from Adzuki beans for 4 weeks [93].
## 2. Stray Current Corrosion Mechanism and Interference Characteristics
### Corrosion Mechanism and Interference Characteristics of AC Stray Current  
The AC stray current corrosion mechanism is also electrochemical in nature; however, scholars have not yet reached a consensus on the AC stray current corrosion mechanism. The more supported theories now include the rectification model, alkalization mechanism, AC depolarization theory, autocatalytic mechanism, oscillation model, AC corrosion mechanism model, passivation damage, and pitting AC corrosion theory. The basic reaction of AC corrosion is the same as the DC stray current corrosion reaction above.
## 3 Results and Discussion
### Sensitivity Analysis  
The geothermal field prediction model in this study is primarily influenced by the thermal conductivity and specific heat capacity at constant pressure of the rocks. To simplify the simulation process, the lithologies in the study area are divided into granite and sedimentary rock. It is known that the thermal conductivity of granite in the study area is 2.7929 \(\pm\) 0.34 W/(m\(\cdot\)K), while the thermal conductivity of sedimentary rocks ranges from 1.3 to 2.5 uW/m\({}^{3}\), with a data range of 20%. Given that the model spans over seventy kilometers, for the sake of simplicity, this study focuses the sensitivity analysis on the area around the SZK03 temperature measurement well, where the temperature is highest. The thermal conductivity and specific heat capacity at constant pressure of the strata in the SZK03 well area will be increased and decreased by 20%, in order to summarize the impact of each parameter on the results. A design verification scheme, shown in Table 3, was formulated, and the impact of each scheme on the model will be calculated accordingly.  
As illustrated in Figure 11, the effect of specific heat capacity at constant pressure on the temperature field is minimal. The temperature curve in plan 2 shows only a slight change compared to the original scheme. The parameter that has a greater impact on the temperature simulation is the thermal conductivity of the rocks. It can be observed that as the thermal conductivity increases, the temperature at depth in the SZK03 well rises accordingly.  
Figure 10: Temperature profile of surrounding rock along the tunnel and thermal damage classification.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Option** & **Thermal Conductivity** & **Heat Capacity at Constant Pressure** \\ \hline
1 & +20\% & 0\% \\
2 & 0\% & +20\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sensitivity analysis scheme.
# Hyperlipidemic Rabbit Models for Anti-Atherosclerotic Drug Development
## 2 Hyperlipidemic Rabbit Models
### Gene-Manipulated Rabbits  
#### 2.3.1 Transgenic (Tg) Rabbits  
Hyperlipidemic rabbits were also generated by the overexpression of human apo B-100, E, and C-III genes in the liver. Human apo B-100 Tg rabbits resulted in a 3-fold increase in plasma TC and TG levels compared with those in non-Tg rabbits, and the majority of the plasma TC was distributed in the LDL, with striking enrichment of TG content [40]. [PERSON] et al. generated Tg rabbits overexpressing the human apo C-III gene. Apo C-III Tg rabbits showed 3-fold higher plasma TG levels than non-Tg rabbits (191 in Tg vs. 59 mg/dL in non-Tg), although TC and HDL-C levels were not changed. Lipoprotein analysis revealed that increased TG in apo C-III Tg rabbits was distributed in CM and VLDL [41]. Tg rabbits expressing human apo E3, the most common isoform in humans, and apo E2, a variant associated with type III hyperlipoproteinemia, were also reported. Tg rabbits expressing higher levels of human apo E3 (>20 mg/dL) showed marked combined hyperlipidemia characterized by an increase in both LDL and VLDL [42; 43]. Overexpression of human apo E2 in rabbits exhibited 8-fold increases in plasma TC and 15-fold increases in plasma TG compared with non-Tg rabbits [44].
###### Abstract  
Despite its conceptual uncertainty, resilience is mostly about the measurement of capacity. Current studies confirm the importance of resilience measurement and the necessity to support policy makers with a measurement mechanism. A holistic approach considering the measurement of different resilience domains interactively and concurrently is the critical element in this endeavor. In parallel with the rise of popularity of resilience in international organizations, NATO has initiated a project with the objective to discover whether the resilience capacity of a country can be evaluated in a dynamic way via a prototype model execution. The implemented model running both baseline (without any shock) and extraordinary scenarios (with strategic shocks), clearly demonstrates its capacity to represent quantitatively the resilience related factors of a country in the complex operational environment. Moreover, the outputs of the model substantially comply with the resilience concept existing in the literature and NATO applications. One of the main strengths of the model is its almost infinite capacity to create various scenarios and make what-if analysis limited only by the current number of endogenous parameters of the model. It allows studying the secondary and the third order effects of events introduced in scenarios. The user interfaces (input and output dashboards) of the model help decision makers modify the values of selected endogenous parameters, see and compare the time-based values of the resilience factors, and doing so to evaluate risk related to the Area of Operations. Subject matter experts have validated the model and identified the main areas of improvement. The further development brings more countries to the model and implements an aggregation mechanism for output values of both resilience capacity and risk functions. The model will form the core of the NATO Resilience expert system.  
NATO resilience; resilience measurement; system dynamics; dynamic modeling +
Footnote †: journal: Applied Sciences