esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermostable D-Allulose 3-Epimerase for Long-Term Food-Compatible Continuous Production Systems
## 3 Results and Discussion
### Long-Term Continuous D-Allulose Production in Packed-Bed Reactor  
As a result of the above findings, 30 g of immobilized TcDAEase was loaded into a packed-bed reactor (Figure 10a) under fixed reaction conditions (60 \({}^{\circ}\)C, pH 8.0, 5 mM Mg\({}^{2+}\), 500 g/L D-fructose, and a flow rate of 30 mL/min) for long-term production testing. The recombinant TcDAEase exhibited a substrate conversion rate of 30%, comparable to most reported DAEases (26-32.9%; Table 2), indicating sufficient catalytic efficiency for potential industrial application. The total volume of substrate solution delivered to the reactor was approximately 43.2 L per day. Over 150 consecutive days of operation, the system produced a cumulative total of 756 kg of D-allulose, corresponding to 89.7% of the theoretical maximum (842.2 kg) yield. This discrepancy could be attributed to multiple factors, including potential side reactions such as non-specific isomerization [32] of D-fructose into undesired products like D-glucose or D-mannose under suboptimal pH or temperature [19]. In addition, gradual loss of enzyme activity during prolonged operation and minor handling losses throughout the process may also contribute to the observed deviation.  
Throughout the trial, the reactor exhibited stable hydraulic performance with no detectable pressure buildup or clogging, indicating efficient mass transfer and the mechanical robustness of the immobilized matrix. As shown in Figure 10b, the specific activity of TcDAEase gradually decreased by about 30% (from 6.5 to 4.5 U\(\cdot\)g\({}^{-1}\)), and the substrate conversion rate declined from 26.0 \(\pm\) 1.2% to 22.5 \(\pm\) 1.1%. These results confirm the long-term operational stability and excellent performance of the immobilized TcDAEase system for continuous, food-grade D-allulose production.  
This system notably outperformed previously reported immobilized enzyme platforms. For instance, [PERSON] et al. [22] achieved a 60-day operation using 200 g of immobilized D-tagatose 3-epimerase (70 U\(\cdot\)g\({}^{-1}\)), yielding only 20 kg of D-allulose at pH 7.0. [PERSON] et al. [13] reported continuous production using DAEase from _Arthobacter globiformis_ immobilized on an ion-exchange resin, which produced 215 kg of D-allulose per liter of catalyst over four months at 55 \({}^{\circ}\)C and pH 7.5.  
Figure 10: Continuous bioconversion of D-fructose to D-allulose by immobilized TcDAEase in a packed-bed reactor. (**a**) Schematic of the reactor system (30 g immobilized enzyme, 60 \({}^{\circ}\)C jacket temperature). (**b**) Operational stability over 150 days: Residual activity (\(\blacktriangle\)) and conversion rate (\(\blacksquare\)) were monitored under continuous conditions (30 mL/min, 500 g/L D-fructose, pH 8.0, 5 mM Mg\({}^{2+}\)).  
Collectively, these findings underscore the scalability, durability, and industrial viability of the TcDAEase-based biocatalytic system for large-scale, continuous D-allulose manufacturing under food-compatible conditions.
# High Dielectric Design of Polymer Composites by Using Artificial Neural Network
## 3 Results & Discussions
### Influence of Variables on Dielectric Constant
#### 3.3.2 Effect of Operating Frequency  
In general, the dielectric constant of a dielectrics comes from the result of complex interactions of a total of four polarizations: (i) the electronic polarization between the nucleus and electrons of an atom, (ii) the ionic polarization between a cation and an anion, (iii) the directional polarization according to the orientation of the permanent dipoles, and (iv) the space charge polarization caused by the movement of charge carriers (electron, hole, ion, and vacancy) to form a space charge [54]. For these polarizations to appear, a displacement of the charged particle must occur. When the applied electric field is alternating current, polarization can occur only when the displacement of the particle follows the change in the direction of the electric field. Therefore, as the charged particles are heavier, they cannot respond to high-frequency electric fields, and the dielectric constant decreases gradually. Although it is important to predict the dielectric constant with increasing frequency because of these characteristics, it has not been studied much [55; 56].  
To further confirm the accuracy of the ANN model with increasing frequency, three polymer composites were considered: P(VDF-TrFE-CFE)/BaTiO\({}_{3}\) nanoparticle, P(VDF-HFP)/TiO\({}_{2}\) nanowire, and P(VDF-HFP)/BN nanosheet. The normalized \(\varepsilon_{r}\) as a function of volume fraction with the increasing frequency range from 100-100 kHz is shown in Figure 9a-d. It can be seen that the trends of the volume fraction and frequency-dependent \(\varepsilon_{r}\) for the three composites are predicted fairly well. The normalized \(\varepsilon_{r}\) of P(VDF-TrFE-CFE)/BaTiO\({}_{3}\) nanoparticle and P(VDF-HFP)/TiO\({}_{2}\) nanowire increases with the volume fraction, while P(VDF-HFP)/BN nanosheet decrease with the volume fraction, which agree  
Figure 8: Predicted dielectric constant depends on different filler’s dimensions as a function of filler volume fraction.  
with Figure 8. When we compared the experimental and predicted values, there is no big difference with increasing frequency until 100 kHz. From the above, the ANN models can well show high accuracy between predicted \(\varepsilon_{r}\) and experimental \(\varepsilon_{r}\) at various frequency ranges.
## 2 Aims and Objectives
### Environmental Conditions at 600 km Altitude  
At an altitude of 600 km the satellite is orbiting around the upper limit of Earth's thermosphere. This region of the thermosphere is at a relatively low altitude [20] compared to the orbiting altitude of larger medium Earth orbit and geostationary orbit craft which operate at around 20,000 km and 36,000 km respectively [23].  
The MSISE-90 model of Earth's upper atmosphere gives the following properties of air at an altitude of 600 km: See Table 2.  
The following values (Table 3) apply to air at a 600 km altitude relative to sea level; with mean solar activity rather than low solar activity, and will be used from this point forwards to give a fair representation of the conditions the CubeSat will encounter. At this altitude, solar activity has a significant impact upon the relative small amount of oxygen due to the intense amount of solar radiation [3]. This altitude is above the \"Karman Line\" meaning astronautics should be applied instead of aeronautics. The upper thermosphere is too thin to support conventional aircraft and vehicles must travel at orbital velocities [7].
## 5 Results  
The experiment was run on 900 documents classified into the nine main classes, each represented by 100 documents. The recommendations were compared to the ground truth UDC codes provided in the dataset. Similarities between the recommended and ground truth UDC codes were measured with the Levenshtein similarity, Jaro-Winkler similarity, and adjusted hierarchical similarity. The titles, keywords, and abstracts in English and Slovenian were used in the experiment. This was done to compare the performance in different languages, with English being a very well-resourced language and Slovenian being a traditionally resource-constrained language, due to scarcity of text corpora and available large language models. The evaluated models were grouped by their parameter sizes into groups. Large models, which consist of 70B parameters, and smaller models, which consist of 4-7B parameters, were grouped into two distinct groups. For some proprietary models, the size of parameters is unknown, so we grouped those into a separate group. We present the language-specific results for all groups of models in the following subsections.
## 3 Validation of the Proposed Approach
### Verification of Crack Growth Path Prediction
Taking the example of the D specimen, crack growth obtained via the Abaqus results propagates until meeting the second hole. However, the crack growth established using the proposed approach and the experimental method path correlated well with reaching the upper hole. Furthermore, overall, the crack paths predicted using the proposed method accord well with the experimental findings for various configurations. Notably, the propagation angle of the fracture affected by the hole position and the location of the crack ending is coherent with the experimental results. A consistent deviation in comparing the Abaqus results with the test results may lead to inaccurate results when assessing fatigue life. Overall, making use of the PMMA specimens, it has been successfully demonstrated that the approach presented in this study retains a high degree of reliability for crack growth path prediction.
# Multilevel Bipolar Electroforming-Free Resistive Switching Memory Based on Silicon Oxynitride
## 3 Results and Discussion  
### FTIR Measurement of the SiO\({}_{x}\)N\({}_{y}\) Film  
The composition of the PECVD-deposited silicon oxynitride films depends on four major factors, (1) the N\({}_{2}\)O gas flow rate, (2) the presence of N\({}_{2}\) gas, (3) the ratio of NH\({}_{3}\) in the gas mixture, and (4) the deposition temperature. As the literature suggests, the H-Si bond is weaker than the Si-O bond. The bond dissociation energies for H-Si and Si-O are 298.49 and 798 kJ/mol, respectively. Consequently, when the flow rate of N\({}_{2}\)O and N\({}_{2}\) in the gas mixture is increased, oxygen replaces the hydrogen bound with silicon, and subsequently dominates in the film [30; 31]. The resulting characteristics of the silicon oxynitride films appear to be more similar to oxide layers than nitride films [32].  
It has also been reported that with a low flow rate of NH\({}_{3}\) and high deposition temperature (\(>\)200 \({}^{\circ}\)C), less hydrogen is incorporated into the deposited layer, and the electrical properties of the films are improved [33; 34].  
The results of the Fourier transform infrared (FTIR) absorbance spectroscopy of the SiO\({}_{x}\)N\({}_{y}\) film are shown in Figure 1. The most significant peaks at 850 and 820 cm\({}^{-1}\) are assigned to the Si-N bond in silicon nitride, but is probably a combination of Si-N and Si-O-Si bonding in oxynitride [30; 31]. The broadening of the Si-H absorption peak (2200-2300 cm\({}^{-1}\)) is a result of the distortion of the S-H bonds, caused by the accommodation of both oxygen and hydrogen in the silicon oxynitride film [30]. The characteristic peak of N-H bonds (3300-3400 cm\({}^{-1}\)) is absent, which indicates that hydrogen is only present in a small amount [32]. The FTIR spectra analysis confirmed that the characteristics of the SiO\({}_{x}\)N\({}_{y}\) film were dominated by oxygen and nitrogen, with little hydrogen [34].
## 3 CSES Optimization Framework with Dual-Time-Scale Coordination  
### System Description  
With the high penetration of RES, issues such as source-load mismatch and difficulty in RES accommodation have adversely affected the economic and operational efficiency of AC/DC distribution systems. Unlike conventional individual ESS approaches where each subnetwork has its own independent ESS, the proposed CSES framework implements a shared energy storage paradigm where subnetworks collectively utilize a large-scale CSES. By coordinating the charging and discharging operations across multiple subnetworks, the framework exploits the complementary nature of RES patterns. For instance, when excessive RES occurs in one subnetwork, the surplus energy can be stored and later dispatched to other subnetworks experiencing power deficits. This approach enhances overall RES utilization and addresses the source-load mismatch issue.  
Figure 1 illustrates the AC/DC distribution system with CSES connecting to N subnetworks through tie lines. The system comprises multiple AC subnetworks connected through a medium-voltage DC ring network [1; 30]. Each AC subnetwork is based on the IEEE 33-node topology and contains various components, including distributed PV, WT, as well as local loads. The subnetworks are interconnected via a multi-terminal DC ring network operating at a rated voltage of \(\pm 10\) kV, which serves as the backbone for energy exchange. All subnetworks share the capacity of the shared energy storage, allowing for more efficient use of storage resources compared to individual approaches. This enables the system to leverage temporal and spatial differences in RES and load across subnetworks.
## 3 Properties and Characteristics of LoRaWAN Technologies
### Spreading Factor  
In LoRaWAN, spreading factors (SF) are critical in establishing an optimal trade-off between data throughput and transmission range. LoRaWAN offers six spreading factors, which range from SF7 to SF12, that allow adaptability to specific application requirements. Higher SF values, such as SF12 [23], provide coverage ranges of up to 45 km in rural regions and more than 5 km in urban settings, but at a cost to the data rate. The number of chips required for representation and the length of each symbol are determined by the spreading factor. A lower SF results in shorter symbol duration and higher data rates, but reduced resistance to noise. Higher SF values, on the other hand, improve interference resistance but result in lower data rates [20].  
The choice to balance the intended data rate and range according to the requirements of the application is based on preference and environment. All base stations within a certain range receive every message from an end device (ED), which enhances the communication reliability of LoRaWAN. However, this requires the setup of several base stations, which raises the cost of network infrastructure. Spreading factor values are usually set between 7 and 12, and their modification provides a flexible way to handle transmission distance as well as data rate and energy consumption [24]. This allows it to be used in a variety of situations, such as high-attenuation long-range communications and environments with high interference. Thorough testing and optimization based on specific use cases produce the ideal spreading factor, which is essential for effective performance [25].  
The nominal bitrate (Rbit) in bits per second can be expressed mathematically as seen in Equation (1) using the parameters (Spreading Factor--SF, Bandwidth--BW, Coding Rate--CR) as follows [26]:  
\[R_{bit}=SF.\frac{BW}{2^{SF}}CR \tag{1}\]  
This formula considers the Spreading Factor (SF) and Bandwidth (BW) to calculate the bitrate, incorporating the Coding Rate (CR) in the numerator. The Spreading Factor contributes to the duration of each symbol, while the Bandwidth influences the frequency range. The Coding Rate determines the encoding efficiency that impacts the time on air for data transmission. The formula in Equation (1) shows the relationship between these parameters in determining the nominal bitrate in LoRa communication [20].
## 2 Key Concepts and Enablers for 5G Private Networks
### Neutral Hosting and RAN Sharing  
One of the disruptive concepts that has evolved in the telecom industry during recent years is neutral hosting. Unlike the traditional individual deployment and operation by each MNO, a third neutral party builds and operates part of the network, offering either private or public connectivity. Hosted clients, such as MNOs, civil protection entities, or other private operators, will be able to lease the neutral host to supply their services at the network edge. In this context, one of the potential strategies for reducing deployment and operation costs is network sharing through neutral hosting [35]. The idea is to have a single network infrastructure that is owned by a third party and leased to interested operators. The neutral hosting model is especially suited to infrastructure owners of large campuses that need both indoor and outdoor coverage. Utilizing a new mid-band spectrum for 5G deployments results in hyper-dense radio networks that are very costly to deploy and operate. Therefore, the ability to share this infrastructure among multiple public and non-public network operators not only reduces relevant costs, but also results in flexible network topologies.
# A Multiple Access Protocol Based on Gray Forecast for Satellite Network
## 2 Combined Free/Demand Assignment Multiple Access  
The CFDAMA-GF protocol proposed in this paper is based on a classic CFDAMA protocol. The combined free/demand assignment access to the CFDAMA communication protocol is based on the original TDMA frame that combines the demand assignment distribution with free allocation. The basic principle of CFDAMA is in accordance with a user's reservation request for allocation, and remaining timeslots are freely poll assigned to all active user terminals [17].  
A reservation table and a free allocation table are stored in a satellite database. Table 1 shows that the reservation table contains the user ID number of the current timeslot request, the number of applied timeslots and the priority. The free allocation table shown in Table 2 includes all of the user ID numbers and the number of allocated timeslots for each user.  
\begin{table}
\begin{tabular}{c c c} \hline
**User ID** & **Application for the Timeslot Number** & **Priority** \\ \hline User 0 & User 0 timeslots & User 0 priority \\ User 1 & User 1 timeslots & User 1 priority \\ User 2 & User 2 timeslots & User 2 priority \\ \(\cdots\) & \(\cdots\) & \(\cdots\) \\ User \(n\) & User \(n\) timeslots & User \(n\) priority \\ \hline \end{tabular}
\end{table}
Table 1: Reservation table.  
After a user sends an initial request, the satellite initializes reservation and free allocation tables in accordance with the received request information. In terms of the number of timeslots that each user applies for prioritizing, the more the timeslots there are, the higher the priority will be. In terms of the reservation schedule, the satellite allocates each user according to individual needs from top to bottom. Demand assignment indicates that the satellite allocates few timeslots for the current user to apply for the number of timeslots. After the allocation of one user ends, the user's information is deleted from the reservation table. In the free allocation table, the user's information is moved to the lowest side, which runs in a loop until the demand assignment allocation table is empty. In the demand assignment, the satellite allocates one timeslot from top to bottom for each user in the free allocation list until no remaining timeslots exist. After the time slot assignment is completed, the satellite sends the allocated number of timeslots to the corresponding user. If the reservation table is not empty after the demand assignment is allocated, then certain users do not have a timeslot allocated demand assignment. These users' requests become accumulated in the next frame for processing.
## Sensor Bus Standards
### Serial Peripheral Interface  
Serial Peripheral Interface (SPI) is a three wire serial bus for 8-bit data transfer applications [3]. Figure 2 represents a configuration of the SPI bus. Two of the three lines transfer data and the third is the serial clock. Similar to I\({}^{2}\)C bus, devices on the bus are defined as masters and slaves. A master  
Figure 1: Typical I\({}^{2}\)C bus configuration.  
initiates an information transfer and generates the clock. Each slave device on the bus is controlled by a chip select line, which is a parallel line for each bus node in addition to the three SPI bus signals.  
The two data lines are unidirectional and the clock signal is generated by the master. The _dout_ line carries data from the master (microcontroller) to the slave (sensor) while _din_ carries data from the slave to the master. The SPI bus employs a simple shift register data transfer scheme. Data transfer is usually performed in eight or sixteen bit blocks. Information on the SPI bus can be transferred at a rate of near zero bits per second to 1 Mbits per second.  
Four clock modes are defined for the SPI bus by clock polarity and clock phase settings. The clock polarity determines the level of the clock idle state and the clock phase determines the clock edge that places new data on the bus. Any hardware device capable of operating in more than one mode will have some methods of selecting the value of these mode control settings. This multi-mode capability combined with the simple shift-register architecture makes the SPI bus very versatile and allows many non-serial devices to be used as SPI slaves.  
SPI is an attractive bus for smart sensor microsystems. Two separated data lines and the simple shift register data transfer scheme make the hardware of an SPI interface much more simple than that of the I\({}^{2}\)C bus. However, an individual node enable line is needed to select the device connected on the bus. Since select lines are generally provided by the master (microcontroller), this greatly limits the number of the sensor nodes in the system, and consequently the size of the sensor network. Also, unlike I\({}^{2}\)C, the SPI bus does not allows new sensors to be easily added to the system without any additional external interfacing. An SPI smart sensor system could not be modified or upgraded simply by \"clipping' or unclipping the sensor to or from the bus without affecting any other circuits on the bus. Finally, as with the I\({}^{2}\)C bus, SPI has no control signal for new-sensor and/or interrupt detection which makes it unsuitable for many applications including environment monitoring and event detection.
## 3 PSS Signal Detection at UE  
As discussed earlier, the detection of the PSS signals is generally performed by applying a matched filter between the received signal and the transmitted PSS waveforms. The maximized value of the matched-filter output determines the delay propagation between the BS and UE. However, it should be noted that this process becomes more difficult as the noise density increases. Therefore, it is necessary to establish a threshold value as a reference for assessing the level of matched-filter output values. The threshold value is also a function of the signal-to-noise ratio (SNR) to make sure that the output levels of the matched filter are realized even under high-density-noise circumstances. With these characteristics, it is appropriate to apply the concept of Generalized Likelihood Ratio Test (GLRT) [30; 31] as the detection algorithm at the UEs. The concept of GLRT has been shown in [32; 33; 34; 35; 36] for detections of signal embedded in Gaussian noise with unknown covariance matrix. Recently, [PERSON] et al. [37; 38; 39] investigated the signal detection problem in partially homogeneous disturbance with structured interference. They extended the well-known generalized multivariate analysis of variance to obtain the maximal invariant statistic for the problem and developed several detectors for the hypothesis testing. In this section, we discuss the formulation of the GLRT algorithm, which is categorized as Constant False Alarm detection based on the work in [21; 30; 31]. We also discuss the calculation of test values that are used to assess the performance of the GLRT detector.