esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The article presents interesting PCM-based accumulation technology. We have shown the possibilities of using the PCM in the non-standard active system. The main benefit of the technology is the combination of PCM and the available sources for the active heating and cooling mode. The primary aim of PCM is passive increasing of the storage capacity of an object, especially in light constructions. An additional feature is the application of the PCM in the active system. One of the advantages is that it is possible to use standard energy sources and renewable energy sources. Moreover, the biggest advantage and non-standard solution is thermoelectric cooling of the PCM-based panels in combination with the photovoltaics. So, the technology demonstrates that it is possible to use solar radiation to cool buildings during hot months. This solution is not commonly used and it can be placed for new knowledge and improvements of the HVAC systems. The article shows the results of the measurement technology in active cooling modes. The active cooling mode using with the heat pump worked quite well without any problems. The thermoelectric cooling had the problem with overheating of the thermoelectric assemblies. The problem was solved and the technology is able to work in this mode, too. On the other hand, the measurements show that the efficiency of the cooling is not so high and there is place for improvements, for example, by forced convection. These measurements indicate the possible further application of the proposed combination of the photovoltaics (or other renewable energy sources), thermoelectric cooling and PCM. We have shown the energy balance of the energy gains from the photovoltaics and consumption of the technology was positive. In the future, system efficiency could be improved by utilisation of the waste heat from the thermoelectric assembly.  
For example, it can by used to preheat domestic hot water, swimming pool water or stored to thermal energy storage, etc.  
The manuscript is collective work of both authors. Specific contributions of [PERSON] are literature research, work on the experiment, measurement and colection of data and writing of the first draft. [PERSON] was experiment supervisor. The final manuscript was approved by all authors.  
This research and APC were funded by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT-7778/2014) and also by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089 and also by the Internal Grant Agency of Tomas Bata University under the project No. IGA/CebiaTech/2018/001.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ACAlternating current COPCoefficient of performance DCDirect current DHWDomestic hot water EEREnergy efficiency rating HPHeat pump HVACHeating, ventilation and air conditioning LONLocal operating network PCMPhase change material PVPhotovoltaic TECThermoelectric cooler TEGThermoelectric generator TESThermal energy storage
# Development Status and Service Performance Preliminary Analysis for BDSBAS
## 3 Message User Algorithms and Service Performance Evaluation Methods
### Message User Algorithm
\(C\) is the relative clock-ephemeris correction covariance matrix reconstructed with the parameters \(E_{i,j}\) broadcast in MT 28 message, \(scale\) is the scale exponent also broadcast in an MT 28 message. Degradation value \(\delta\)UDRE is applied to model the variance for the measurement residual error \(\sigma_{fit}^{2}\). If MT 7 and MT 10 are successfully received, the variance for the measurement residual error \(\sigma_{fit}^{2}\) will be computed with \(\delta\)UDRE as well as the degradation parameters obtained from MT 7 and MT10. Otherwise, \(\sigma_{fit}^{2}\) will be computed with \(\delta\)UDRE as well as the default value. The detail computation methods of the measurement residual error variance \(\sigma_{fit}^{2}\) are shown in [19]. Then, the position results and corresponding protection levels are calculated based on the weighted least squares solutions, as expressed as Equation (2). \[X=(G^{T}WG)^{-1}G^{T}WL \tag{2}\]  
where \(X\) is the unknown position parameters, \(G\) is the observation geometry matrix, \(L\) is the observations vector, \(W\) is the weight matrix which can be expresses as a diagonal matrix, at least four satellites are visible and useful to ensure that the first term is inversed. \[W=diag\left(\frac{1}{\sigma_{1}^{2}},\cdots\frac{1}{\sigma_{n}^{2}}\right) \tag{3}\]  
where \(\sigma_{i}^{2}\) is expressed as  
\[\sigma_{i}^{2}=\sigma_{i,fit}^{2}+\sigma_{i,UIRE}^{2}+\sigma_{i, tropo}^{2}+\sigma_{i,air}^{2} \tag{4}\]  
In the Equation (4), \(\sigma_{i,fit}^{2}\) is the variance for the measurement residual error of satellite I, \(\sigma_{i,UIRE}^{2}\) is the variance of residual of ionospheric model correction, \(\sigma_{i,tropo}^{2}\) is the variance of the residual of tropospheric model correction, and \(\sigma_{i,air}^{2}\) is the variance of the observation noise. The protection level equations are described in detail by [26].
# Enhancing Conversational Agent Development Through a Semi-Automatization Development Proposal
## 5 Application Example
### Participants  
A case study involving 100 prospective students interested in the Postgraduate Program in Information Sciences (PIS) was conducted to evaluate the postgraduate chatbot. The participants, selected based on their alignment with the PIS entry requirements, represented the main target audience of the program. Data collection involved a user satisfaction survey and systematic documentation of all user-chatbot interactions. To minimize bias, no information about the PIS program or chatbot functionality was provided beforehand; Participants received only the chatbot's WhatsApp contact information and a survey link devoid of contextual details.
## 2 Model
### Matte-Grade Prediction Model Based on BP Neural Network
#### 2.2.2 Model Algorithm Process  
The nonlinear relationship fitting based on the BP neural network is usually completed through three steps: BP neural network construction, BP neural network training, and BP neural network prediction. The training process is completed by forward propagation and error backpropagation. The forward propagation direction is input layer \(\rightarrow\) hidden layer \(\rightarrow\) output layer, and the state of neurons in each layer only affects neurons in the next layer. If the desired output is not obtained in the output layer, it will turn toward the error backpropagation process. By alternating the two processes, the error function gradient descent strategy is implemented in the weight and threshold vector space, and a set of weights and thresholds are searched dynamically iteratively to minimize the error between the predicted value and the expected value so as to complete the process of information extraction and memory. According to the structure of the BP neural network of matte grade and the relationship between matte grade and various factors, the algorithm process is determined as shown in Figure 2.  
The specific algorithm is described as follows:  
1. Small weights and thresholds are randomly assigned to the network.
2. The training samples (input and output data) were imported, the network output values were calculated and compared with the expected output values, and the local errors of all layers were obtained.
3. The network is trained, and the weight and threshold of each layer are adjusted by iterative formula. For the output layer and the hidden layer, the weight change from the \(i\)th input to the \(k\)th output is as follows:  
Figure 1: A schematic diagram of the BP neural network prediction model for matte grade.  
Figure 2: Flow chart of the BP neural network algorithm for matte grade.  
\[\Delta w_{ki}(t)=-\eta\frac{\partial E(t)}{\partial w_{ki}(t)} \tag{3}\]  
The threshold changes to:  
\[\Delta b_{ki}(t)=-\eta\frac{\partial E(t)}{\partial b_{ki}(t)} \tag{4}\]  
Among them, \(E(t)\) is mean square error; \(w(t)\) and \(b(t)\) are weights and thresholds at time \(t\), respectively; \(\Delta w(t)\) and \(\Delta b(t)\) are weights and threshold adjustment values at time \(t\), respectively; \(\eta\) is the learning rate.  
The total error sum of squares is calculated. If its value is less than the set target value or the number of iterations reaches the set value, then the model stops. If neither is reached, the network training will continue to iteratively adjust the weights and thresholds until the conditions are met.
## 4 Conclusions  
The thermal response test in situ in a borehole is both time consuming and expensive; particularly owing to the testing simplification in the complex construction field, it can only involve the summer case, and the long-term engineering and environment effect could not be comprehensively considered and evaluated. The objective of this present research program is to develop a large-scale laboratory modeling facility which could simulate the operation of GSHP system both in summer and winter cases. Preliminary analysis of the thermal balance problem is discussed. Some outcomes are concluded as follows:  
1. Temperature variation induced by summer or winter case almost linearly increase with the elevated initial temperature difference between the circulation fluid and the ambient ground soil. However, the heat transfer rate could approach a balance by heat equilibrium in heat exchange. From this aspect, higher circulation temperatures could not always result in better efficiency of GSHP system.
2. From the comparison of temperature variation ratio and time ratio values in summer and winter circulations, Shanghai muddy clay is proved to be a geo-material with strong heat storage, but poor heat release ability.
3. Thermal balance of heat dissipated to and extracted from muddy clay can hardly be achieved during the alternation between summer and winter cycles without other external effort and appropriate management. This is explained by the irreversible and asymmetric heat conduction characteristics based on monitoring data and also the second law of thermodynamics.
4. The thermal mattress effect in muddy clay soil will potentially occur after single long-term operation of GSHP system in the absence of corresponding management and involving the environment evaluation.  
All these conclusions are provided the valuable instructions for the incoming large-area utilization of GSHP system in Shanghai. GSHP system should not be long-term operated as an independent energy supply system and it is best to be joint-designed together with another energy system.  
Methodology, validation, investigation, writing-original draft preparation, J.Z.; conceptualization, writing-review and revision, Y.T.; engineering background materials and data check, P.W.; literature analysis, Z.L.; figure and table re-editing, C.W.  
This research received no external funding.  
The research work herein was supported by the National Natural Science Foundation of China (Grant No. 41702299), Shanghai Sailing Program (Grant No. 02302360185) and Talent Program, Tongji University (2015 KJ010). The authors are deeply indebted to these financial supporters.  
The authors declare no conflict of interest.
# Status of Pure Electric Vehicle Power Train Technology and Future Prospects
## 4 Electric Propulsion
### Permanent Magnet Brushless DC Motor
#### 4.3.3 Permanent Magnet Synchronous Motor (PMSM)  
Permanent magnet synchronous motors have sinusoidal magnetomotive force (mmf), voltage, and current waveforms. When the sinusoidal distribution of the air-gap flux and stator windings is arranged, the machine operates as a synchronous machine. The rare earth magnet material in this motor drive helps to increase the flux density in the air-gap, the motor power density, and torque-to-inertia, and thus can be operated over a wide constant power speed range. The most common type of magnet materials that have been used in PM machines are ferrites, samarium cobalt (SmCo), and neodymium-iron-boron (NdFeB) [94; 95; 96; 97; 98; 99]. The working mechanism is identical to BLDC motor except the sinusoidal wave form of the back EMF.  
The major advantages of PMSM are:  
* efficiency is higher compared to brushless DC motors,
* absence of torque ripple when the motor is commutated,
* better performance with the higher torque,
* reliable and less noisy,
* performance is high in both higher and lower speed of operation,  
Figure 9: Torque–speed capabilities of phase-advance angle control of PM BLDC motor.  
* easy to control due to lower inertia of the rotor,
* heat dissipation is efficient,
* smaller in size.
# Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach
## 3 Green Communication in the Internet of Things: A Hybrid Bio-Inspired Intelligent Approach
### Energy Model  
The IoT nodes set their states between transmitter and receiver before the start of the transmission period. The total energy (\(E_{S}(y,z)\)) consumed to send \(y\) bit of packet is twofold (a) energy (\(E_{T}\)) exhaust in transceiver circuit to process the packet (b) energy (\(E_{A}(y,z)\)) consumption takes place to amplify the \(y\) bit of packet over \(z\) distance either in free-space energy (\(\partial_{fg}\)) or multipath energy (\(\partial_{mp}\)) corresponds to threshold distance \(z_{0}\) similar to the first order radio model [3] as follows:  
\[E_{S}(y,z)=E_{T}+E_{A}\;(y,z)=\left\{\begin{array}{cc}y\;E_{T}+y\;\partial_{ fs}\;z^{2},\;if\;z<z_{0}\\ y\;E_{T}+y\;\partial_{mp}\;z^{4},\;if\;z\geq\;z_{0}\end{array}\right. \tag{1}\]  
Additionally, energy (\(E_{r}\)) consumed by IoT node to receive \(y\) bit of packet as follows:  
\[E_{R}(y,z)=yE_{T} \tag{2}\]  
We consider \(N\) IoT nodes are evenly distributed into \(W\) clusters such that \(N/W\) nodes are allocated to each cluster. Further, CHs use the TDMA slotted period to get data from their member IoT nodes, where IoT nodes are able to transmit the sensed data to their CH only in wakeup mode after that switch to sleep mode. The total energy (\(E_{P}\)) exhaust by each CH in a single transmission period is the energy payoff in (a) (\(E_{g}\)) gathering the data from its member's node and (b) (\(E_{e}\)) transmitted the received data to the edge node as aggregated data following the multipass model can be written as:  
\[E_{P}=E_{g}+E_{e} \tag{3}\]  
where, \(E_{g}=yE_{T}\Big{(}\frac{N}{w}-1\Big{)}\), and \(E_{e}=yE_{ay}\frac{N}{W}+yE_{T}+y\partial_{mp}\;z_{pe}^{4}\) is consumed energy in the multipass model. \(E_{ay}\) represents the data aggregation energy and \(y_{ze}\) denote the distance between CH and edge node. The energy consumed (\(E_{c}\)) by child IoT nodes to transfer gathered data to their parent CH uses a free space model over average distance (\(z_{cp}=(1/2\pi)\big{(}R^{2}/w\big{)}\), \(R\) is diameter of network) between parent and child node in one round as follows:  
\[E_{c}=yE_{T}+y\partial_{fs}z_{cp}^{2} \tag{4}\]  
Overall, each cluster exhausts the total energy is the summation of energy consumed by parent CH and its child IoT nodes during one round as follows:  
\[E_{cluster} =E_{P}+E_{c}=y\Big{(}2\;N\;E_{T}+NE_{ay}+w\;\partial_{mp}\;z_{pe}^{4 }+w\;\partial_{fs}z_{cp}^{2}\Big{)} \tag{5}\] \[=y\Big{(}2\;N\;E_{T}+NE_{ay}+w\;\partial_{mp}\;z_{pe}^{4}+w\; \partial_{fs}\;\frac{1}{2\pi}\;\frac{R^{2}}{w}\Big{)} \tag{6}\]
# Analysis and Research on Secondary LT Coding Anti-Eavesdropping Scheme Based on LT Code Degree-1
## 3 Program Performance Analysis  
In this section, we examine the security capacity, decoding complexity, and decoding overhead for the double LT method to assess the security and reliability of system information transmission.
###### Abstract  
This paper is concerned with the design of synchronized large-scale chaos random number generators (CRNGs) and its application to secure communication. In order to increase the diversity of chaotic signals, we firstly introduce additional modulation parameters in the original chaotic Duffing map system to modulate the amplitude and DC offset of the chaotic states. Then according to the butterfly effect, we implement modulated Duffing map systems with different initial values by using the microcontroller and complete the design of large-scale CRNGs. Next, a discrete sliding mode scheme is proposed to solve the synchronization problem of the master-slave large-scale CRNGs. Finally, we integrate the aforementioned results to implement an innovative secure communication system.  
chaotic Duffing map system; butterfly effect; large-scale CRNG; discrete sliding mode; secure communication +
Footnote †: journal: Applied Physics Letters
## 3 Results
\begin{table}
\begin{tabular}{l c} \hline \hline MV repair & 6 (22) \\ Annuloplasty ring & 6 (100) \\ Posterior leaflet resection & 3 (50) \\ Artificial chords & 2 (33) \\ MV replacement, prosthesis & 21 (78) \\ Biological & 20 (95) \\ Mechanical & 1 (5) \\ Extensive annular decalcification & 5 (19) \\ Concomitant TV Surgery & 6 (22) \\ RAP & 20 (74) \\ EAC & 7 (26) \\ TTC & 19 (70) \\ CPB (mins) & 147 (127–173) \\ Cross-clamp (mins) & 106 (84–119) \\ Conversion to stemotomy & 1 (4) \\ Redo for early failure & 1 (4) \\ Re-exploration for bleeding & 2 (7) \\ Permanent PM implantation & 3 (11) \\ Low cardiac output syndrome & 4 (15) \\ Acute kidney injury & 1 (4) \\ Dialysis & 2 (7) \\ Vascular complication & 1 (4) \\ Stroke & – \\ Perioperative myocardial infarction & – \\ Mechanical ventilation (hours) & 15 (10–22) \\ ICU stay (days) & 1 (1–1) \\ Hospital-stay (days) & 7 (7–9) \\
30-day mortality & 2 (7) \\ FU MV regurgitation, moderate or more & 2 (7) \\ FU patient prosthesis mismatch (n=21) & \\ None or mild & 19 (90) \\ Moderate & 2 (10) \\ Severe & – \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Intraoperative variables and postoperative outcomes (n = 27). Extensive annular decalcification was unavoidable in 5 patients (18%) because of the impossibility to pass the sutures through the calcific bar in extremely severe MAC. No cases of intraoperative death, atrioventricular disruption, or circumflex coronary artery injury occurred. Conversion to sternotomy was necessary in one case due to extremely unfavorable chest anatomy, with the impossibility to safely gain sufficient exposure of the ascending aorta to ensure adequate cross-clamp and safe myocardial protection. Residual moderate periprosthetic leak requiring early reoperation on the first postoperative day, and permanent heart block requiring permanent pacemaker implantation were reported in one and in three patients, respectively. Importantly, stroke rate was zero. Two patients died, accounting for an operative mortality of 7.4%. The causes of death were multiorgan failure and septic shock on postoperative day 24 in the former, and multiorgan failure on postoperative day 14 in a patient with pre-existing severe chronic kidney disease and cirrhosis. Both displayed severe degree MAC and required MV replacement. Residual MV regurgitation at discharge was absent to mild in all patients. At follow-up, no death was observed. One patient developed moderate regurgitation following MV repair, and another patient required redo surgery for significant periprosthetic leak 14 months after surgery. Actually, this adverse event occurred in the same patient who had initially required redo surgery on postoperative day 1. At early reoperation the leak had been primarily closed through the right mini-thoracotomy with additional pledgetted sutures, but the later procedure was carried out via sternotomy and the patient underwent MV re-replacement.
## 6 Application Scenarios  
### Description of the Social Context of Pepita  
Based on the features selected for the design of Pepita, one of the possible applications is as a mediator of remote communication. This involves a paired robots configuration, which means that each user will communicate using one identical robot. The current implementation is driven by the user's tactile gesture (hug/pulling the tail), and it communicates with the user using projected avatars. These avatars are displayed by a running application in the robot's smartphone. This feature allows for easily assigning different avatars to the different users, as a kind of ID. The robot will display its avatar with a happy expression every time the user hugs it. At the same time, every time a robot is hugged, it is translated as a message and displayed on the paired robot. As time passes without a user hugging a robot, the avatar will change to showing a sad expression. The same robot will display the partner's avatar every time it receives a hug from the paired robot. Similarly, as time passes without receiving more messages, the avatar of the paired robot will change to display a sad expression. It is expected that the users will be aware and feel motivated to interact with the robot to improve its affective state, which is also a message for the partner. Since this type of communication does not contain any detailed content, the purpose of the message is open to interpretation.  
Pepita is envisioned to be placed in two scenarios. On the first one, the robot works as a standalone communication device, and each message works as a kind of presence indicator (Figure 13A). The second scenario involves Pepita working as a complement of verbal communication, and, in this context, Pepita works as a tangible emoticon (Figure 13B). With this, it is expected to enhance the transmission of affective messages and foster a sense of co-presence.  
Two user scenarios are proposed to understand how Pepita mediates expressions of affection in remote communication. The first one reflects the use of Pepita as a standalone communication device, and, on the second one, Pepita works as a complement of verbal communication:  
1. [PERSON] is a college student living away from her family. Every morning before going to the university, she leaves a message to her parents by hugging her robot. [PERSON] observes how her avatar displays a happy expression and then sets the robot on the sofa. She comes back home in the evening and observes her parents' avatar displaying a sad expression, which she understands as \"I received a hug from my parents a long time ago\", she takes her Pepita and hugs it, observing her avatar appearing with a happy expression, and conveying to her parents that she is now home.
2. On the weekend, [PERSON] is talking with her mother by phone sharing stories of the recent days. She takes her robot and hugs it sending a message. Her father, who is in the living room watching TV, observes their robot reacting showing [PERSON]'s robot's \"happy\" avatar. Then, he asks to speak to her and say hello. While talking to her, he hugs back.