esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We simulate the transmission of K+ ions through the KcsA potassium ion channel filter region at physiological temperatures, employing classical molecular dynamics (MD) at the atomic scale together with a quantum mechanical version of MD simulation (QMD), treating single ions as quantum wave packets. We provide a direct comparison between both concepts, embedding the simulations into identical force fields and thermal fluctuations. The quantum simulations permit the estimation of coherence times and wave packet dispersions of a K+ ion over a range of 0.5 nm (a range that covers almost 50% of the filter domains longitudinal extension). We find that this observed extension of particle delocalization changes the mean orientation of the coordinating carbonyl oxygen atoms significantly, transiently suppressing their 'caging action' responsible for selective ion coordination. Compared to classical MD simulations, this particular quantum effect allows the K+ ions to 'escape' more easily from temporary binding sites provided by the surrounding filter atoms. To further elucidate the role of this observation for ion conduction rates, we compare the temporal pattern of single conduction events between classical MD and quantum QMD simulations at a femto-sec time scale. A finding from both approaches is that ion permeation follows a very irregular time pattern, involving flushes of permeation interrupted by non-conductive time intervals. However, as compared with classical behavior, the QMD simulation shortens non-conductive time by more than a half. As a consequence, and given the same force-fields, the QMD-simulated ion current appears to be considerably stronger as compared with the classical current. To bring this result in line with experimentally observed ion currents and the predictions based on Nernst-Planck theories, the conclusion is that a transient short time quantum behavior of permeating ions can successfully compromise high conduction rates with ion selectivity in the filter of channel proteins.  
potassium ion channel; KcsA channel; selectivity filter; quantum wave packet; coherence time; biological quantum coherence
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
## 2 Design and Geometrical Consideration
### Overview of Representative Volume Elements
#### 2.2.1 Strut-Based  
Struts-based unit cells are RVEs with a specific arrangement of struts. Such examples of unit cells are presented in Figure 1. An emerging type of investigated strut-based unit cells are the ones used in bi-auxetic structures, from the Greek word \"auxetos\" meaning \"that may increase\". Bi-auxetic structures are structures with a negative Poisson's ratio [80], which means that when stretched in one direction, the structure expands in all other directions, whereas when compressed, the opposite is expected to happen. Auxetic materials have traditionally been fabricated through the use of foams; with the advent of AM such structures can be better tailored based on the intended application. Auxetic materials can be found in a few applications such as shock and sound-absorbing materials for vehicles and aircraft [80]. Other works, such as by [PERSON] and [PERSON] [81], have studied strut-based unit cells with hollow struts; hollow struts are not widely used as of now in the AM domain. The manufacturing of hollow struts still poses a problem in AM processes due to challenges in removing trapped powder or support materials within the struts.  
\begin{table}
\begin{tabular}{l|l|l|l} \hline  & **Change of RVE Morphology** & **Volume Fraction Change of RVE** & **Scaling \& Rotation of RVE** \\ \hline Boundary induced variation & [28] & [29] & [29; 30; 31; 32; 33; 34; 35; 36] \\ \hline Load induced variation & [37] & [30; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49] & [34; 35; 36; 37; 38; 39; 50; 51; 52; 53] \\ \hline Random and external sources induced variations & [54; 55; 56] & [55; 57; 58] & [33; 55; 59; 60; 61; 62; 63; 64] \\ \hline No variation & & [4; 6; 11; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79] \\ \hline \end{tabular}
\end{table}
Table 1: Cellular structure classification based on geometrical degrees of freedom and representative examples.  
Sometimes, the strut-based unit cell can just be one strut. For the classification in this review, when a CS is made of a truss network but does not contain any clear strut-based unit cell (an arrangement of strut replicated throughout the design space) then the RVE is considered to be a strut. An example is given by [PERSON] et al. [39], whereby they describe a design methodology based on layout optimization where nodes distributed across the design space are interconnected by potential members. Those final members of the structures are chosen to minimize the total structural volume. Following this step, the size optimization algorithm uses the updated layout as the ground structure. Finally, in the last step, every member is resized to ensure that none of the members buckle [39].
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 2 State of the Art
### Standardization  
An important aspect is the standardization of these technologies. The most recognized standards for NPLC are ITU-T G.Inem [10], IEEE 1901.2 [11], PRIME [12; 13] and G3-PLC [14; 15]. The BPLC systems are supported by fewer standards, e.g., IEEE P1901 [16; 17]. The first BPLC standard, approved in 2010, aimed to achieve more than 100 Mbps PHY rate in 100 MHz band for access networks and home area networks.  
The most widespread BPLC technology in the commercial sphere are the standards/ recommendations of the HomePlug Powerline Alliance [18]. The HomePlug Powerline Alliance has been involved in standardization since 2000. The first series of recommendations HomePlug 1.0 (created according to the first BPL standard--TIA-1113 [19]), HomePlug GreenPHY (mostly used in e-mobility for charging stations), HomePlug AV and HomePlug AV2 are commercially used for HAN as plug-in adapters to extend the internet range.  
The latest standard for BPLC technology is ITU-T G.hn [20]. This is made up as a set of specifications G.996x, which are the physical (PHY) layer ITU-T G.9960, link layer ITU-T G.9961, management ITU-T G.9962 (G.hn-mgm), MIMO (Multiple-Input-Multiple-Output) extensions ITU-T G.9963 (G.mimo), PSD management and setting ITU-T G.9964 (G.hn-psd),coexistence with other PLC systems ITU-T G.9972 (G.cx), and remote management ITU-T G.9980 (G.cwmp). These recommendations are described in detail in [21; 22]. The baseband for BPLC modems is 2-50 MHz or 2-100 MHz, and the passband is 100-200 MHz. High-frequency bands are also specified for a coaxial cable transmission in the 350-2450 MHz band. As with IEEE 1901.2 and HomePlug, the OFDM (Orthogonal Frequency Division Multiplexing) modulation technique is used. The number of OFDM carriers is 256, 512, 1024, 2048 or 4096. The carrier spacing for PLC is approximately 24,414 kHz. Bigger spacing for the phoneline or coax cables is also defined. Every single carrier can be modulated by 4096-QAM--12 allocated bits. The allowed medium access methods are CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) and TDMA (Time Division Multiple Access).  
Smart grid and smart metering systems require standardized systems where the best option can be considered to be the latest G.hn standard. Today, the P1901 standard is still the most widespread for smart grid and smart metering applications.
###### Abstract  
With an increased focus on reducing greenhouse gas emissions, energy saving is of great importance in all sectors of society. EU directives set targets for member states to reduce energy use in buildings. Energy saving in historic buildings requires special measures, balancing energy-saving renovations against the preservation of heritage values. Traditional constructions are open to vapor diffusion and generally work differently from modern constructions. Modern materials in traditional constructions sometimes damages the original material as they are usually diffusion-tight. The aim of this study was to investigate whether hemp-lime could be used as an insulation material to improve the energy efficiency of historic timber building envelopes with a rendered facade in Sweden. The objective was to determine the actual energy savings for space heating. An additional objective was to determine the actual thermal transmittance and to study thermal buffering through in-situ measurements in a full-scale wall renovated with hemp-lime. Two full-scale wall sections were constructed at the Energy and Building Design laboratory at Lund University: A traditional post-and-plank wall with a lime render (80 mm), and a post-and-plank wall with a hemp-lime render (90 mm). Energy use for space heating was monitored continuously over a period of one year. The wall with a hemp-lime render required 33% less energy for space heating than the traditional post-and-plank wall with a lime render. This was accomplished without changing the framework, appearance or material in the render and without drastically changing the hygric properties of the facade. From the gathered data, the thermal transmittance (U-values) for both walls was calculated using two different methods, one based on material properties and the other based on energy use data. For both walls, thermal transmittance based on actual energy use data during the heating period was lower than what was expected from their material properties. This indicates that more material properties than thermal conductivity and material thickness need to be taken into account when performing energy use calculations. With hemp-lime, a renovation can be accomplished without damaging the timber structure and wooden slats, and it can be done with local traditional materials and building methods with no difference in appearance to a traditional lime render. This allows for heritage values to be preserved, while also allowing the building to comply with modern standards and with increased thermal comfort and reduced energy use.  
Keywords:hemp-lime; lime render; energy efficiency; thermal insulation; retrofitting; cultural heritage +
Footnote †: journal: Applied Physics Letters
## 4 Experimental Results
### Quantitative Evaluation  
#### 4.2.1 Learning Phase  
We start by studying the learning phase. Figure 9 demonstrates the Dice Coefficient and the Dice Loss Training curves of the lung's segmentation model, respectively.  
As the figure shows, the plots of training and validation loss decreased to a point of stability which confirms the good performance of the proposed model on unseen data. Figure 10 demonstrates the Dice Coefficient and the Dice Loss Training curves of the infection segmentation model respectively for the different folds.  
The curves clearly confirm the good performance of the proposed model on unseen data in the different folds, as the training and validation loss decreased to a point of stability. Figure 11 demonstrates the Loss Training curve of the classification model.  
Figure 9: Dice Coefficient and Dice Loss Training curves of the lung’s segmentation model.  
Figure 10: (a) Dice Coefficient and Dice Loss Training curves of the infection segmentation model using fold 1. (b) Dice Coefficient and Dice Loss Training curves of the infection segmentation model using fold 2. (c) Dice Coefficient and Dice Loss Training curves of the infection segmentation model using fold 3. (d) Dice Coefficient and Dice Loss Training curves of the infection segmentation model using fold 4.
# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 3 Framework for Security Monitoring and Multi-Domain Trust Management
### Customizing Security by Software-Defined Networking  
The framework utilizes SDN to isolate traffic flows that are related to different applications or users. Consequently, different monitoring, inferencing, and control functions may be applied for different applications. The approach provides scalability to 5G monitoring in the sense that it allows to use more computationally expensive algorithms as different algorithms may be dedicated to different data flows. For instance, some IoT applications may be more vulnerable to availability threats and hence analysis on an IoT-specific slice may focus more on such threats and less on threats related to privacy or tracking of a terminal's location.  
The framework includes a micro-segmentation enabler that is used to: (1) control and create virtualized network slices; (2) control access to SDN; (3) collect monitoring information from SDN; and (4) enable cooperation between SDNs in different domains. The enabler parses traffic statistics available from software switches and sends them in a format supported by the broker. The enabler can support different authentication mechanisms. The cooperation between different slices in single administrative domain is sustained by creating secure tunnels between them.  
Deep traffic analysis is enabled by redirecting particular data flows to traffic inspectors. Different inspectors may be applied to examine headers and data payloads in order to detect protocol non-compliance, malicious content, or other policy violations. Packet inspectors need to be delivered decryption keys for encrypted data payloads that they are authorized to inspect.
# DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism
## 6 Conclusions  
At present, there are many problems in DGA domain name classification. For example, it is difficult to obtain DGA algorithm by reversing malware samples, and the WHOIS information caused by the implementation of GDPR can no longer be easily obtained. Based on the research of DGA algorithm and DGA domain name, we propose a DGA domain name classification method based on LSTM with attention mechanism. This method no longer reverses the malware sample, nor does it use context information such as WHOIS of the domain name, and only uses the character sequence of the domain name. For the character sequence of the domain name, each domain name is converted into a matrix of fixed dimensions by padding and embedding. Then it use the LSTM with attention mechanism algorithm to construct the DGA domain name classification model to achieve fast and accurate classification of domain names. The experimental results show that combining the attention mechanism with the LSTM can effectively classify DGA domain names. Therefore, the DGA domain name is associated with the network attack, and the response time of the attack incident is shortened. The method takes into account the weights of different characters in different positions in the DGA domain name and has higher classification accuracy than the simple LSTM algorithm.  
Conceptualization, Y.Q. and B.Z.; methodology, Y.Q.; software, Y.Q.; validation, Y.Q.; formal analysis, W.Z.; investigation, Y.Q. and H.W.; resources, B.Z.; data curation, Y.Q. and H.W.; writing--original draft preparation, Y.Q.; writing--review and editing, B.Z. and A.K.S.; visualization, H.W.; supervision, B.Z. and W.Z.; project administration, W.Z. and B.Z.; funding acquisition, B.Z. and W.Z.  
This work is supported by the Key Research and Development Program for Guangdong Province, 2019B010136001, and the Peng Cheng Laboratory Project of Guangdong Province, PCL2018 KP004 and PCL2018 KP005.  
**Acknowledgments:** We gratefully acknowledge the helpful comments and suggestions of the reviewers and editors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 3 Methods
### Parameter Estimation  
The estimation of parameters in this study involved three distinct optimization frameworks, each tailored to balance model complexity, computational efficiency, and statistical accuracy. The primary parameters estimated were student abilities (\(\theta\)), item difficulties (b), and time-effect coefficients (\(\lambda\)), all of which were optimized through different methodologies based on model type.
# Understanding Neutrophil Dynamics during COVID-19 Infection
## 2 Materials and Methods
### Parameter Estimation
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Description** & **Mild** & **Severe** & **Reference** \\ \hline \(\gamma_{e}\) & Decay rate of CD8 T-cells & 0.5/day & 0.5/day & [64] \\ \(\gamma_{c}\) & Decay rate of proinflammatory mediators & 1.1/day & 1.1/day & [69] \\ \(\gamma_{g}\) & Decay rate of \(IL-10\) & 6.2/day & 6.2/day & [70] \\ \(\gamma_{m}\) & Decay rate of alveolar macrophages & 0.05/day & 0.05/day & [63] \\ \(\gamma_{a}\) & Rate of necrosis of apoptotic neutrophils. & 1.66/day & 1.66/day & [62] \\ \(A\) & Cycles of tissue damage & 1 & 4 & – \\ \(\alpha\) & Tissue damage magnitude & 1 & 1 & – \\ \(\alpha_{m}\) & Macrophage recruitment rate & 0.7 & 0.7 & – \\ \(\beta_{a}\) & Neutrophhire where proinflammatory. release is half-max. & 1 & 1 & – \\ \(\beta_{e}\) & Reg. cells where CD8 T-cell response is half-max. & 1 & 1 & – \\ \(s_{g}\) & T regulatory cells recruitment rate & \(g(0)\gamma_{g}\) & \(g(0)\gamma_{g}\) & – \\ \(s_{e}\) & CD8 T-cells recruitment rate & \(e(0)\gamma_{e}\) & \(e(0)\gamma_{e}\) & – \\ \(k_{g}\) & Anti-inflammatory production rate & 15 & 15 & – \\ \(n_{0}\) & Initial concentration of active neutrophils & \(0.5\times\) data & 0.5 \(\times\) data & [13] \\ \(a_{0}\) & Initial concentration of apoptotic neutrophils & \(0.5\times\) data & 0.5 \(\times\) data & [13] \\ \(m_{0}\) & Initial concentration macrophages & \(0.3\times 10^{9}/\text{L}\) & \(0.3\times 10^{9}/\text{L}\) & [13] \\ \(c_{0}\) & Initial concentration proinflammatory mediators & \(10^{9}/\text{L}\) & \(10^{9}/\text{L}\) & – \\ \(g_{0}\) & Initial concentration anti-inflammatory mediators & data & data & [13] \\ \(e_{0}\) & Initial concentration of CD8 T-cells & data & data & [13] \\ \(k_{a}\) & Max. level of proinflammatory. pop. recruited after damage & 0.6 & 0.6 & – \\ \hline \end{tabular}
\end{table} 
Table 1: Fixed parameter values and initial conditions. \[J(\theta_{i})=\frac{1}{\max\limits_{t_{j}\in T}\{(n+a)_{data}(t_{j})\}} \left(\sum_{t_{j}\in T}\left((n+a)(t_{j},\theta_{i})-(n+a)_{data}(t _{j})\right)^{2}\right)^{1/2}\] \[+\frac{1}{\max\limits_{t_{j}\in T}\{e_{data}(t_{j})\}}\left(\sum_ {t_{j}\in T}\left(e(t_{j},\theta_{i})-e_{data}(t_{j})\right)^{2}\right)^{1/2} \tag{4}\] \[+\frac{1}{\max\limits_{t_{j}\in T}\{g_{data}(t_{j})\}}\left(\sum_ {t_{j}\in T}\left(g(t_{j},\theta_{i})-g_{data}(t_{j})\right)^{2}\right)^{1/2},\]  
where \(n(t_{j},\theta_{i})\), \(a(t_{j},\theta_{i})\), \(g(t_{j},\theta_{i})\), \(e(t_{j},\theta_{i})\) are the theoretical solutions of model Equation (3) at times \(t_{j}\) for parameters \(\theta_{i}\); \((n+a)_{data}(t_{j})\), \(g_{data}(t_{j})\) and \(e_{data}(t_{j})\) are the neutrophil, IL-10 and CD8 T-cell data at times \(t_{j}\) as given in [13], and \(t_{j}\in T=\{2,5,8,11,14,17\}\) days post hospitalization. The best parameters estimates are given in Table 2.
# Simultaneous Determination of 12 Marker Components in Yeonkyopaedok-san Using HPLC-PDA and LC-MS/MS
## 3 Results and Discussion
### Quantitative Analysis of the 12 Marker Components in YPS Samples Using HPLC-PDA  
The 12 marker components (3-CQA, CFG, LIQA, FA, NAR, 5-MVS, NAG, NHES, OPDH, ARC, GA, and 6-GIN) were analyzed in the YPS samples using the developed and validated HPLC analytical method. Quantification of these components was carried out at 254 nm for GA; 275 nm for LIQA; 280 nm for NAR, NAG, NHES, ARC, and 6-GIN; 290 nm for 5-MVS; 300 nm for CFG; 310 nm for OPDH; 320 nm for FA; and 325 nm for 3-CQA, based on the \(\lambda_{\rm max}\) of the ultra-violet (UV) spectra. The peak of each analyte was identified by comparing the UV spectrum and the retention time of the reference standard. The amounts of the 12 marker components in the three batches of the YPS samples were calculated using the calibration curves of each analyte. The amounts of these analytes in the samples were found to be in the range of 0.14-9.00 mg/g (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Compound**} & \multirow{3}{*}{
\begin{tabular}{c} **Con.** \\ **(ug/mL)** \\ \end{tabular} } & \multicolumn{3}{c}{**Intra-Day (n = 5)**} & \multicolumn{3}{c}{**Inter-Day (n = 5)**} & \multicolumn{3}{c}{**Repeatability (n = 6)**} \\ \cline{3-10}  & & **Observed** & **Precision** & **Accuracy** & **Observed** & **Precision** & **Accuracy** & **RSD (\%) of** & **RSD (\%) of** \\  & & **Con.** & **(\%)** * & **(\%)** & **(\%/mL)** & **(\%)** & **Time** & **Feak Area** \\ \hline \multirow{3}{*}{3-CQA}
## 7 Summary, Conclusions, and Discussion  
We presented the idea of exploiting spatial side information on fingerprints from adjacent positions relative to a target position with the purpose of minimizing the positioning error in fingerprinting-based positioning. We proposed increasing the complexity on the positioning device side by implementing a 2D sensor array that would allow us to collect physically adjacent RSS fingerprints. Our primary goal was to learn whether using spatial side information on RSS fingerprints, by means of an ideal 2D sensor array, could lead to a justifiable gain in terms of minimizing the positioning error.  
To put our work into context, we provided a differentiation between fingerprinting in uplink and downlink cases, the possible uses of side information, and a classification of the possible positioning data aggregation domains. The literature related to the topic of our research was surveyed.  
Then, we developed the concepts related to fingerprinting-based positioning with spatial side information in downlink and presented three pattern-matching methods for estimating the position by processing fingerprints with spatial side information. Two methods were based on regression, implemented with FFNNs, and one method was based on the classification of fractions of the positioning area, implemented with a CNN. In one method, positioning data aggregation was carried out in the position domain. In the other two methods, positioning data aggregation was carried out in the fingerprint domain.  
The idea of exploiting spatial side information on fingerprints, along with the proposed pattern-matching methods, was benchmarked using a system simulator based on Monte Carlo methods. The simulator creates a scenario for positioning, generates synthetic fingerprints based on an indoor radio channel model, constructs arrangements of the fingerprints (tiles) that are equivalent to samples of the 2D sensor array, implements the pattern-matching methods, estimates the positions with each method, and calculates the positioning error for each one of the proposed methods. The cases without and with spatial side information were benchmarked.
# Analysis of the Development Status of eLoran Time Service System in China
## 2 Principle of eLoran Time Service System
The operation control center sends standard time frequency signals to the time service broadcasting, time service monitoring, and differential enhancement stations to ensure the consistency of their time frequency signals. And it transmits the integrity monitoring, satellite ground fusion monitoring, and differential correction data generated by the time service monitoring and differential enhancement stations to the time service broadcasting station [5; 6]. Figure 1: Working principle of eLoran time service system.