esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Main SMEP Elements
### Zone Compression History  
In every compression, the sensor data \(S_{c,0}\) in the zone 0 is selected unconditionally in the compression interval sequence in the zone. In fact, \(S_{c,0}\) in the zone 0 plays a role as a base in sensor data selection by the first application of _selection by minimum error_ rule in the zone 0. We call such a sensor data like \(S_{c,0}\) the _base sensor data_ in the zone of _DataQueue_. In other words, a base sensor data is the first sensor data positioned in the first interval in a zone of _DataQueue_ and it has a role as a base in applying the first _selection by minimum error_ rule in the zone. By the way, a problem is that there is no base sensor data in all zones except the zone 0 so to prevent from selecting a new sensor data from the first interval in the first zone for the compression. To resolve this problem, we make the last selected sensor data in the previous zone be a base for the compression of a sensor data sequence after that zone. In spite of doing that, another problem occurs if there is among zones no history about sensor data selected in every compression. Figure 10 shows such an example. In this figure, the sensor data sequence between the zone 1 and the zone 2 should be compressed and the numbers of compressions in the zone 0 and in the zone 1 are different as 2 and 0, respectively. In this case, however, there is no way for compression without the last selected sensor and absolute position in the zone 0 as a historical sensor data in the 1 st compression. Because there is no base sensor data for the 1 th compression. For this reason, we introduce _ZoneCompHistory_, an array, as the very data structure to keep the historical information about zone compressions. As shown in Figure 10, the rows in the _ZoneCompHistory_ array corresponds to zones of _DataQueue_ and its columns correspond to the number of compressions. Moreover, its value is a pair of the value and absolute position of the sensor data selected in the zone through compression.  
As an example, in Figure 10, _ZoneCompHistory[0,1]_ tells the sensor data with 28 as its value and 7 as its absolute position had been selected as a last sensor data in the zone 0 through the 1 st compression. This sensor data is the very base for the 1 st compression of a sensor data sequence between zones 1 and 2.
## 4 Experimental Results  
### Motor Data Acquisition  
The motor used in this paper is a HY-DZ2200-PM (Ningbo Huayuan Machinery Technology Co., Ltd., Ningbo, China) three-phase variable frequency permanent magneto-synchronous motor. The performance parameters are as follows: rated power 2.2 kW, rated speed 1500 r/min, and motor poles 10. The experimental platform is shown in Figure 12. An experimental motor is a particular unit with four sets. Different combinations of connecting taps on the lead-out line of motor winding can be simulated through different degrees of the turn-to-turn short-circuit fault of the synchronous motor. The data acquisition was carried out for the No. 1-4 motors at 0%, 2%, 5%, 7%, and 10% turn-to-turn short-circuit faults under the three operating conditions as follows: 15 units per motor, 100 samples per group, totaling 6000 samples.  
Four thousand five hundred samples of No. 1-3 motors were used for the training of generative and diagnostic networks, and 1500 samples of No. 4 motors were not used for network training as generalization proof. The validity of the model was judged through the following three aspects:  
1. The validity of the generated sample of the turn-to-turn short-circuit fault current of a permanent magnet-synchronous motor;
2. Improve the accuracy of the fault diagnosis after sample set expansion;
3. The generalization capability for different motor diagnostics.
## 3 Results and Discussion  
Numerical validation based on the comparison of experimental measurements and numerical results for the velocity and temperature fields were first carried out in the central, sagittal planes of the thermal plumes. As previously mentioned, the thermal plumes were characterized with and without the functioning of the general ventilation system using infrared (IR) thermography, arrays of thermocouples, and particle image velocimetry (PIV) measurements. Some studies have shown that the higher the temperature differences between the surface of the manikin and the air in the environment, the higher the velocity of the thermal plume [59]. For an increase in ambient air temperature of 6 \({}^{\circ}\)C (20 to 26 \({}^{\circ}\)C), a decrease in peak velocity of the thermal plume of a standing subject of 30% was observed, with no influence on the shape of the convective boundary layer [60]. Based on PIV measurements, [PERSON] et. al. [61] studied the thermal plume generated by a seated manikin in a room with variable air temperature. The authors varied the velocities in the enclosure, obtaining airflows transversal to the thermal plume characterized by velocity magnitude values of up to 1 m/s. In this case, the airflow generated by the thermal plume was no longer observable. Consequently, in the present study, the validation of the numerical thermal plume results by their experimental counterparts will be done using a test case the general ventilation system was turned off for both the experimental and numerical of approaches.  
In a second step, we compared velocity distributions obtained at different locations of the transverse planes of the flow issued from the PV nozzle with the experimental results from [52].
###### Abstract  
Most liquid food flavours such as essential oils are volatile and prone to degradation in the presence of oxygen, light, moisture and high temperatures. Microencapsulation of volatile ingredients prior to use in food or beverages is a commonly used process to limit loss and degradation of flavours and aromas during processing and storage. Here, peppermint essential oil was microencapsulated via complex coacervation using a combination of bovine serum albumin and gum Acacia as wall materials. The resulting core-shell microcapsules were chemically crosslinked with a modified food-grade starch, and subsequently spray dried, resulting in dry microcapsules which could be easily redispersed in aqueous solutions. Microcapsule formation and stability, as well as microencapsulation yield of peppermint oil, were investigated as a function of polymer concentration, core material load/wall thickness and crosslink concentration. The crosslinked peppermint oil microcapsules were spherical and mononuclear both before and after spray drying and redispersion, whereas control coacervate samples without crosslinker did not withstand the spray drying process. Microencapsulation yield as analysed by GC-MS showed no loss of peppermint oil during or after complex coacervation, and 54% loss after spray drying for the best combination of Polymer.Oil ratio and crosslinker concentration used here, indicating good overall protection of the core material.  
Keywords:complex coacervation; bovine serum albumin; gum Acacia; peppermint oil; microencapsulation; spray drying; food grade crosslinker +
Footnote †: journal: Applied Sci.
# Generalized Kinetic Monte Carlo Framework for Organic Electronics
## 3 kMC Framework for Organic Materials and Devices
### Particles and Transitions
#### 3.3.1 Charge Carriers
The electrostatic potential at node \(i\) can be calculated by:  
\[U_{i}=\frac{1}{4\pi\epsilon_{0}\epsilon_{r}}\sum_{n}\sum_{j=1}^{2N}{}^{\prime} \frac{q_{j}}{\left|r_{ij}+\bar{n}\right|}\,, \tag{14}\]  
where the outer sum corresponds to the periodic replica in the \(x\)-, \(y\)- and \(z\)-direction and the inner one sums over all charged particles \(q_{j}\) inside the box \(\bar{n}\). The prime in the inner sum notes that self-interactions are excluded. Here, \(\epsilon_{0}\) is the vacuum permittivity and \(\epsilon_{r}\) is the permittivity of the material. The Miller-Abrahams rate provides a generic model for the hopping process modeling the contribution of tunneling and thermal activation by the parameters \(a_{0}\), \(\gamma\) and the Boltzmann term, respectively. It is used if hopping is mainly caused by single phonon-assisted tunneling between sites of energy \(E_{i}\) and \(E_{j}\). Using [PERSON] theory, one implicitly accounts for molecular details including \(I_{ij}\) and \(\lambda\). It is frequently used in multiscale studies, as for given organic molecules, the molecular positions and transfer integrals need to be calculated by molecular dynamics and quantum chemical calculations, respectively [42; 43; 79; 80]. [PERSON] theory accounts for multiphonon hopping processes. Recombination of charge carriers is possible when an electron and a hole are located at neighboring sites. Charge recombination is one of the primary loss processes in OSCs besides exciton decay. When recombination occurs, the two particles are removed, and the sites are free again.
## 3 The Proposed Scheme
### The Data Recovery Phase  
In this phase, if there is some network packet loss or EHR data loss of the patient \(U\) after they have performed the EHR migration phase, then the e-health system of the hospital \(W\) cannot obtain the full \(U\)'s personal EHR, so \(U\) can ask the \(HCA\) to deal with this situation.  
* First, when a patient migrating to a hospital \(W\) that does not receive the \(EHR_{U}\) from the server \(V\) after querying the system of hospital \(W\), then \(U\) can ask \(HCA\) to resolve the situation with some evidence \(Sig_{V}(S_{HCA},S_{U},Agree_{V->W})\) with signatures \(S_{HCA}\), \(S_{U}\) and \(Agree_{V->W}\). After verifying the above signatures successfully, \(HCA\) can fetch the \(EHR_{U}\oplus r_{U}^{\prime\prime}\) to the server of hospital \(W\) with \(AE_{pk_{W}}(r_{U}^{\prime\prime})\).
* \(W\) can then decrypt \(AE_{pk_{W}}(r_{U}^{\prime\prime})\) to obtain \(r_{U}^{\prime\prime}\) and fetches the patient's EHR file \(EHR_{U}\) from the above \(EHR_{U}\oplus r_{U}^{\prime\prime}\) by applying \(\oplus\) with \(r_{U}^{\prime\prime}\). At this time, this situation is solved with \(HCA\)'s help if needed. This phase's scenario shows in the Figure 3.  
Figure 3: The data recovery phase.  
Figure 2: The EHR migration phase.
# Centralized Energy Prediction in Wireless Sensor Networks
## 4 Centralized Energy Consumption Prediction
### Implementation  
The implementation was conducted using Contiki-3.0 [40], a widely used open-source operating system for WSN, and IT-SDN version 0.4 [9; 42] (Available at [[http://www.larc.usp.br/users/cbmargi/www/it-sdn/](http://www.larc.usp.br/users/cbmargi/www/it-sdn/)]([http://www.larc.usp.br/users/cbmargi/www/it-sdn/](http://www.larc.usp.br/users/cbmargi/www/it-sdn/)), last time accessed 12 August 2021). IT-SDN is an open-source SDWSN framework implemented in our group, with results showing that its performance is similar to RPL for topologies up to 289 nodes.  
Previous works used time steps of 1000 ms [17] and 100 ms [43]. Moreover, we decided to use a time step of 1 ms to obtain a more accurate probability matrix. For example, ContikiMAC [41] channel checking takes 0.884 ms plus the radio turning on and turning off time. Thus, time steps of 1000 ms and 100 ms lose information in our evaluation environment.  
The communication between sensor nodes and the controller was implemented using the IT-SDN communication stack. IT-SDN [9; 42] is an SDWSN framework composed of three communication protocols: the Southbound protocol, the Neighbor Discovery protocol, and the Controller Discovery protocol. The Southbound protocol defines the message formats for communication between the WSN and the controller. The WSN nodes use the Controller Discovery protocol to find a route to the controller and the Neighbor Discovery protocol to collect neighborhood information. The controller uses the neighbor information to define the routes according to the policies programmed. Then these routes are configured in the WSN nodes.  
The Southbound protocol has six packet types: flow request, flow setup, flow ID register, acknowledgment, neighbor report, and data packet. Moreover, we created two more packets to address energy and prediction information communication: one packet exclusively to send energy information (i.e., from sensor nodes to the controller), and another packet exclusively to send the controller's reply (i.e., from the controller to sensor nodes). The energy information packets are routed using a flow identifier and the reply packets are source-routed from the controller.  
All the energy information packets contain a byte specific for prediction mechanism identification. Thus, in the case of having more than one prediction algorithm, the receiver will know how to process the packet. For example, running the prediction on the WSN nodes and running the prediction on the SDN controller. Each case requires different information to be shared, thus using this identifier we inform which one we are implementing. Furthermore, using this identifier we are able to mix both approaches. Table 2 shows packets' sizes in bytes for both cases.
## 3 Modelling of the Bus Transportation Network
### Definitions  
Several specific terms which are used for the modelling of bus transportation network are introduced to ease the comprehensibility of the presented method (see Figure 2). The first components of a bus network are bus stops where the bus will stop during the day to board or alight passengers. The travel of a bus from one stop to another is named as a segment. A succession of segments is a route defined by its ordered sequence of stops.  
Figure 1: Flow chart of the modelling approach with the inputs and the computation steps.  
A route can belong to various bus lines that are official paths with intermediate stops between one or several terminals at the end of a line.  
Public transport along a line is characterized by a timetable which indicates the times when a bus will stop at each bus stop. Based on the timetable, bus operators define the different bus trips that are combinations of routes and time sequences. This means that a bus can run through the same route several times during the day; however, each time will represent a different bus trip. Eventually, the succession of trips represent the daily service. Services can vary according to the period of the year (e.g., weekdays, weekends, or bank holidays). A service is composed of commercial trips and deadheading ones such as travelling from a bus depot to the beginning of a line without carrying passengers. These terms are illustrated on Table 1, which is the timetable of the bus line \"ARC Express\" in Compiegne, France [34]. The first column shows the stop sequence for bus line ARC Express, from the \"Gare\" terminal to the \"Aramont\" terminal. Then each column is a bus trip, starting at a different time. It can be noticed that bus trips 1, 2, 3, 6, 10, and 11 correspond to the same stops sequence, as well as trips 4, 5, and 7. Therefore, 11 trips are represented on the table with only 4 routes. These trips are assigned to the services of two buses, represented on the first row. It is worth noticing that studies may use a different vocabulary to present the bus transportation network from a passenger point of view.
# AAGA: A Priori Adaptive Splitting Algorithm for the Split Delivery Vehicle Routing Problem
## 2 Problem Definition and the Proposed Solution
### A Priori Adaptive Splitting Algorithm (AASA)  
The a priori splitting rule described by Equations (1)-(4) is a fixed rule, i.e., a single splitting rule is applied to all customers regardless of their demand and location. However, the information regarding the customers' demand and location can be leveraged in the vehicle routing decisions towards reducing the total travel cost as well as the size of the resulting CVRP instance. We propose an adaptive splitting rule based on the location and demand information that is shown to effectively improve the solution. In the following, we first present the motivation behind the main building blocks of our proposed splitting rule. We then explain the AASA algorithm.
# Nucleobase-Containing Polymers: Structure, Synthesis, and Applications
## 4 Applications of Nucleobase-Containing Polymers in Materials Science
### Metal-Nucleobase Coordination  
In nature, metal ions play important roles in the structure and function of nucleic acids. A wide variety of investigations into transition metal ions' binding to nucleic acids have been utilized extensively, such as in disease diagnostics [124; 125; 126]. Other than applications in biology, the ability of nucleic acids to store and transmit information has been explored in creating nanostructures and molecular electronic devices [127; 128; 129]. [PERSON] et al. [130] introduced 8-hydroxyquinoline (Q) into PNA oligomers and investigated the properties of duplexes formed by these oligomers in the presence of Cu\({}^{2+}\). Their results demonstrated that the Cu\({}^{2+}\)-containing PNA duplexes have higher thermal stability than the unmodified PNA, and furthermore, with the help of metal ions, PNA duplex pairs exhibited high tolerance to mismatches.  
Recently, coordination polymers and metal organic frameworks (MOFs) have attracted a lot of attention from researchers [131; 132]. [PERSON] et al. [133] presented a simple method to obtain single polymeric chains from crystals of [Cd(6-MCP)\({}_{2}\)-2H\({}_{2}\)O]\({}_{n}\) (6-MCP = 6-mercapto-purinate), and this particular 1D MOF exhibited significant similarities to DNA-based systems (Figure 14). [PERSON] et al. [134] synthesized a 3D coordination polymer using the interaction between adenine and Cu\({}^{2+}\). X-ray analysis indicated in this covalent 3D network: the centered Cu\({}^{2+}\) ions are coordinated  
Figure 13: TEM images of the Au–UCNP clusters: (**A**) AuNP–UCNP, (**B**) 700 AuNS–UCNP, (**C**) 980 AuNS–UCNP and (**D–F**) the corresponding optical images; Images of electric field amplitude: (**A**) AuNPs, (**B**) AuNSs with 700 nm plasmon resonance and (**C’**) AuNSs with 980 nm [123]. Reproduced with permission. Copyright Royal Society of Chemistry, 2015.  
by tridentate \(\mu\)-N3, N7, N9 adenine ligands. This 3D coordination polymer has relatively large, nanometer-sized tubes and exhibited overall antiferromagnetic behavior.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 4 Simulation Setup and Performance Evaluation
### Performance Evaluation
#### 4.2.1 PU Detection through Sensing Module  
The probability distribution of PU detection through the sensing module of PUTPOSH is shown in Figure 5. The energy recognition method is used to sense the PU arrival. The detection prospect depends on the signal-to-noise ratio (SNR). To measure the efficiency of any strategy, the detection probability is considered an important factor. However, the existing techniques in CR did not deliberate the detection probability. In this experiment, the detection probability is calculated to make sure that PUTPOSH works well with the changes in SNR. According to Figure 5, the PU recognition probability remains lofty with the higher values of the SNR (i.e., values larger than 0.8). This is due to the fact that energy recognition works proficiently well with the superior standards of SNR [37, 53]. CRU acquires the link to deliver data for a particular communication is recognized as the overall time. The best state for CRU is to attain the minimum time for data delivery with minimum delay. PUTPOSH intelligently switches between reactive and proactive handoff decision in order to reduce the delivery time of data and consequently the threshold value of PU interarrival is extracted. With the lower value of SNR, the energy detector is unable to distinguish between PU signal and the noise. The solution to this problem is the use of sensing techniques that are based on eigenvalue centered methods. Eigenvalue-centered methods are simple and based on the low-complexity spectrum methods that are capable to distinguish between PU signal and the noise with lower SNR. The eigenvalue centered energy detection scheme performs in a decent way alongside the low-frequency selective channels and with the noise uncertainty. With the incorporation of the eigenvalue-based method, the performance of PUTPOSH is well-organized and efficient.
## 3 Results and Discussion
### Analysis of Migration Tests by LC-MS\({}^{n}\)  
As was seen in previous studies carried out by [PERSON] et al. [6] and [PERSON] et al. [38], the number of detected oligomers increased with the ethanol concentration. In our study, the most drastic conditions were simulated, and EtOH 95% was used to perform the migration tests. In the present work, all combinations above 1000 Da were disregarded because it is generally recognized that compounds above this mass are not important from a toxicological point of view, based on the fact that they are not typically absorbed through the gastrointestinal tract. Moreover, when comparing the ionization mode, it was considered that the negative ionization mode did not bring any significant additional information since more intense, related signals were usually observed in the positive mode, in accordance with previous findings reported by [PERSON] et al. and [PERSON] and co-workers, who detected few linear oligomers in the negative mode [8; 65]. Therefore, only the oligomers obtained in positive mode are displayed in Table 4.  
Figure 4: MALDI mass spectrum of the acetonitrile extract of sample CM1: intensity [a.u.] vs. _m_/_z_. The Cramer decision tree based on the molecular structure of the compound was used to estimate the toxicity of the identified compounds (Table 4). For this purpose, the software Toxtree v3.1.0 (Ideaconsult Ltd., Sofia, Bulgaria) was used. The Cramer scheme consists of 33 questions where each answer leads to another question or to a final classification into one of the three classes of toxicity: substances with simple chemical structures and for which efficient modes of metabolism exist are classified in class I (low toxicity), substances which possess structures that are less innocuous than class I but do not contain structural characteristics suggestive of toxicity like those substances in class III are classified in class II (intermediate toxicity), while class III (high toxicity) is assigned to substances with chemical structures that permit no strong initial impression of safety and may suggest a significant toxicity by having reactive functional groups [66]. Several homologous series (n = 3-8) of polyester oligomers were tentatively identified in the migration tests of the samples, more than those listed in Table 4, since the isomeric forms of the oligomers eluted at different retention times. These isomers can arise from
## 4 Testbed Results  
In this section, we move on to discuss results from a smart grid experimental testbed. This uses a Laptop PC as a network controller and low-cost Raspberry Pi computers to emulate client-side devices that can implement advanced smart grid applications, such as demand response. This system makes use of the UK internet network to emulate a practical smart grid system.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### Supporting Functions
#### 3.1.3 Block Cipher  
The block cipher includes the encryption algorithm Enc (\(K_{1}\), \(m\)) and decryption algorithm Dec (\(K_{1}\), \(c\)). Enc (\(K_{1}\), \(m\)) encrypts plaintext \(m\) with key \(K_{1}\) and its output is a ciphertext bit string \(c\). Dec (\(K_{1}\), c) decrypts ciphertext \(c\) using key \(K_{1}\) and its output is either a plaintext bit string \(m\) or the message \"error.\" The bit length of the key \(K_{1}\) is denoted by \(K_{1}\_len\). The SM9 standard requires SM4 [33] to be used as the block cipher.