esa-sceva/satcom-chunk-collection huggingface dataset:
# Basis Functions for a Transient Analysis of Linear Commensurate Fractional-Order Systems
## 6 Basis Functions for Integer- and Fractional-Order Commensurate Systems: One-to-One Correspondence  
Starting from the possible forms of the decomposition of the transfer function into partial fractions for real and complex poles (41), (42), (46), and comparing the formulas of the corresponding waveforms for the IOS and FOS in Tables 2-4, we arrive at the summary of basis functions given in Table 5 in the \"Basis Functions\" columns. There are specific connections between these functions. These connections, which are different for single and multiple poles, are best seen by comparing the contents of the \"Generated from\" columns, which reveal from which general \"generating function\" these basis functions are generated. Let us first compare the part of Table 5 for integer-order systems with the \"Basis Functions\" column for fractional-order systems. If we restrict ourselves to simple poles, then the basis function \(e^{at}\) of the IOS, i.e., the classical exponential function, corresponds to the basis function \(\widetilde{e}_{\alpha,\alpha}(a,t)\) of FOS, i.e., fractional exponential function (26). Undamped functions of the sine and cosine type are projected into the fractional domain as fractional sine and cosine functions of type (36) and (35). Note that all of these above basis functions of FOS can be expressed using a two-parameter MLF of type \(E_{\alpha,\alpha}\). For multiple poles, the basis functions of IOS are given by the product of the integer power of time, \(t^{k}\), and the classical exponential function or its imaginary or real part. For FOS, they are the products of the non-integer powers of time \(t^{ak}\), the non-integer powers of time \(t^{\alpha-1}\) and the \(k\)-th derivatives of the function \(E_{\alpha,\alpha}\) or its imaginary or real part. For the purely imaginary poles, the \(k\)-th derivatives of the functions \(\sin_{\alpha,\alpha}\) and \(\cos_{\alpha,\alpha}\) multiplied by the terms \(t^{\alpha-1}\) correspond to the classical sine and cosine functions for IOS.
## 3 Experimental Results  
### Lateral Load-Displacement Relationship and Failure Modes  
Figures 6 and 7 show the lateral load-drift ratio relationships and failure modes of the tested specimens. The energy equivalent elastic-plastic (EEEP) curves for the specimens are shown in Figure 6. The key parameters (the yield strength V\({}_{y}\), yield displacement \(\Delta_{y}\) and ultimate displacement \(\Delta_{u}\)) of the EEEP curves were defined in accordance with the American Society for Testing and Materials (ASTM) E2126-19 [29], as shown in Figure 8.  
The integrated member specimens (IP, IP-IS, IP-OS) all showed local buckling at the bottom of the panel when subjected to loading force. The failures of the specimens were concentrated at the bottom of the panel, because the force exerted at the top of the panel by the actuator was lower than the force exerted by the jig at the bottom of the panel. Furthermore, failure occurred at greater displacements for the stud-reinforced specimens (IP-IS and IP-OS) than the non-reinforced specimen (IP). Finally, the screw pull-out failure of the SSP specimen occurred at a drift ratio of 4.0%. The screw is a point at which the load-carrying capacity changes dramatically, rendering it vulnerable to failure. However, no equivalent significant failure events were observed in the integrated member specimens.  
Figure 6: Lateral load–drift ratio relationships and failure modes of specimens: (**a**) The steel-sheathed panel (SSP) specimen, (**b**) The integrated panel-inner stud (IP) specimen, (**c**) The integrated panel-inner stud (IP-IS) specimen, (**d**) The integrated panel-outer stud (IP-OS) specimen.
## 4 Evaluation
### Verification of the Proposed Localization System Accuracy  
We implemented a positioning device using a Raspberry Pi 4 and DWM1000 UWB module in a real experimental environment. Detailed specifications for the positioning module implementation and experiment are shown in Table 1. Each hardware component has the following characteristics.  
The Raspberry Pi 4 is a powerful computing device with a 1.5 GHz quad-core CPU and up to 4 GB RAM, making it ideal for rapid data processing and extensive sensor processing via GPIO pins. However, the Linux-based operating systems we utilized have common security vulnerabilities and require regular updates and strong security measures.  
The DWM1000 module, which uses the DW1000 UWB chipset, has a distance measurement accuracy of several tens of centimeters and shows precise location tracking performance, making it suitable for use in experiments sensitive to spatial data. While the module itself is not vulnerable to traditional malware, integration with other systems requires secure data transmission protocols to prevent wireless signal interference or tampering, highlighting the need for encryption and security practices.  
To evaluate the performance of the proposed localization system, we first verified the positioning performance in an environment without malicious attacks. Using the implemented positioning device, we set the anchors and tags and performed the 3D localization of three tag nodes. The ranging and positioning modules mentioned earlier were employed for indoor localization.  
Figure 4 and Table 2 show the results of the localization performance experiment. The enhanced positioning module demonstrated the excellent performance of the UWB system in real-world environments where noise can occur. Each of the three tag nodes showed localization errors of 10.50 cm, 10.82 cm, and 13.10 cm and standard deviations of 5.10 cm, 4.41 cm, and 6.07 cm, respectively. Thus, the proposed localization system exhibited outstanding performance when there were no external attacks.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Description** \\ \hline Computing Board & Raspberry Pi 4 \\ Operating System & Ubuntu MATE 22.04 \\ UWB Module & DWM 1000 \\ Based Standard (UWB) & IEEE 802.15.4a/z \\ Utilized Channel & UWB Channel 5 (6240.0-6739.2 MHz) \\ Sampling Clock Frequency & 63.8976 GHz \\ Modulation Scheme & BPM/BPSK \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications for positioning module implementation and experiment.  
Figure 4: The 3D positioning experiment in a multi-tag environment of the proposed positioning system.
# IECI: A Pipeline Framework for Iterative Event Causal Identification with Dynamic Inference Chains
## 3 Methodology
### Semantic-Role Guided Causal Inference Graph
#### 3.2.3 Causal Relation Graph Construction  
Based on the above causal prediction results of event pairs, the SRCIG constructs a document-level event causal graph. The graph's vertices are the events, and the edges represent the causal relationships between event pairs. The edges are constructed using the following strategy. For each pair of events \(\left(e_{i},e_{i}\right)\), the model computes the causal prediction probability. If the score \(p_{ij}^{(r)}\) of a relationship type in its causal prediction probability exceeds a threshold \(\theta\), a directed edge is added to indicate the existence of the relationship. The types of edges include intra-sentence edges and inter-sentence edges.  
To avoid the problem of missed detections caused by fixed thresholds, SRCIG introduces a dynamic threshold adjustment mechanism based on the density of events. This mechanism automatically adjusts the threshold \(\theta\) according to the number N of recognized events. The specific formula is as follows:  
\[\theta=\theta_{base}-\lambda\cdot N, \tag{24}\]  
where \(\theta_{base}\) is the initial threshold and \(\lambda\) is the decay factor.  
This strategy reduces the threshold in long documents with many events, avoiding the omission of true causal relationships due to overly strict standards. In contrast, for short texts, a higher threshold is maintained to ensure the accuracy of predicting relationships.
# Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging
## Appendix B Comparative Truncation Threshold Determination Methods  
In this section, the details of the following methods used for comparative experiments are described: (1) fixed truncation threshold and (2) [PERSON]'s unbiased risk estimator (SURE).  
1. According to the direct singular value sequence (\(\mathbf{s}\)), the fixed truncation threshold is commonly set as \[k_{\text{f}}=\min\{k\mid\mathbf{s}_{k}\leq 0.01\cdot\mathbf{s}_{\text{max}}\},\] (10) where \(\mathbf{s}_{\text{max}}=\mathbf{s}_{1}\). Then, the recovered target is \[\hat{\mathbf{x}}_{k_{\text{f}}}=\sum_{j=1}^{k_{\text{f}}}\frac{\mathbf{u}_{j} ^{\text{T}}(\mathbf{y}+\mathbf{e})\mathbf{v}_{j}}{\mathbf{s}_{j}}.\] (11)
2. SURE has long been adopted as the state-of-the-art threshold selection mechanism [40]. First, the noisy BD signal (\(\mathbf{y}+\mathbf{e}\)) is projected onto an orthogonal matrix (\(\mathbf{U}\)) obtained via the SVD of measurement matrix \(\mathbf{A}\) to obtain the amplitude (\(\mathbf{c}=\mathbf{U}^{\text{T}}(\mathbf{y}+\mathbf{e})\)), which is then reinforced by a specified value (\(t\)), i.e., the soft threshold [46]. \[\eta_{t}(\mathbf{c})=\text{sgn}(\mathbf{c})(|\mathbf{c}|-t)_{+},\] (12) where \(\text{sgn}()\) is the signum function and \((*)_{+}=\max(*,0)\). The SURE function denotes the cost as follows: \[\text{SURE}(t,\mathbf{c})=N_{\text{mea}}\sigma^{2}-2\sigma^{2}\#\{j:|\mathbf{ c}_{j}|\leq t\}+\sum_{j=1}^{N_{\text{mea}}}(|\mathbf{c}_{j}|\wedge t)^{2},\] (13) where \(\#\{j:|\mathbf{c}_{j}|\leq t\}\) is the number of amplitudes with an absolute value of no more than \(t\), and \(a\wedge b=\min(a,b)\). The noise power (\(\sigma^{2}\)) can be estimated via the median absolute deviation in practical experiments [46], or it can be specified beforehand in simulations. Note that the value of the cost of \(\text{SURE}(t,\mathbf{c})\) only changes when threshold \(t\) changes from one amplitude \(|\mathbf{c}_{j}|\) to another; thus, iterating all amplitudes would produce the optimal \(t_{\text{opt}}\) as follows: \[t_{\text{opt}}=\operatorname*{arg\,min}_{0\leq t\leq\sqrt{\sigma^{2}\log N_{ \text{mea}}}}\text{SURE}(t;\mathbf{c}).\] (14) Moreover, the universal upper bound of \(t\) is recommended [46; 47]. Finally, the recovered target is expressed as follows: \[\hat{\mathbf{x}}_{\text{SURE}}=\sum_{j=1}^{N_{\text{mea}}}\frac{\eta_{t_{ \text{opt}}}(\mathbf{c}_{j})\mathbf{v}_{j}}{\sigma_{j}}.\] (15)
## 3 Hierarchical Graph Learning for Next POI Recommendation
### Hierarchical Graph Construction  
Our hierarchical graph construction module builds a three-level graph structure to capture POI relationships at different scales. The overall architecture of HGL is illustrated in Figure 3. The three levels of the graph structure are as follows:  
* Base-level graph: This level captures direct check-in transitions. Each node in this graph represents a check-in, and edges between nodes indicate direct transitions between different places.
* Region-level graph: This level models area-based interactions through spatio-temporal clustering. We define a region as a group of spatially proximate check-ins that are frequently visited together. Nodes in this graph represent regions, and edges between nodes indicate interactions between regions.
* Global-level graph: This level captures broader mobility patterns across the entire city. Each node represents a major geographical area or significant landmark that influences citywide movement flows. Edges between nodes indicate transition frequencies between these macro-level entities, enabling the model to understand high-level movement patterns that transcend individual regions.  
Based on the definitions above, we now introduce the construction process of the three-level graph.  
Figure 2: Overview of the proposed hierarchical graph learning framework for next POI recommendation. (**a**) Check-in embedding module: raw spatio-temporal and semantic data are transformed into unified representations through a pretrained embedding model. (**b**) Hierarchical graph learning module: multi-level graph message passing is performed using multi-head attention, including bottom-up aggregation from check-in to regional and global levels, as well as intra-layer message propagation among regional/global nodes.
## 3 Results and Discussion
### Effect of Space Time  
The effect of space time (in the 1.25-20 g\({}_{\rm cat}\) h (mol\({}_{\rm C}\))\({}^{-1}\) range) on CO\({}_{\rm x}\) conversion, product yield and CO\({}_{2}\) conversion has been studied under the following reaction conditions: Feed, ternary mixtures of H\({}_{2}\) + CO + CO\({}_{2}\) with CO\({}_{2}\)/CO molar ratios between 1/3 and 1; temperature, 275 \({}^{\circ}\)C; pressure, 30 bar; H\({}_{2}\)/CO\({}_{\rm x}\) molar ratio, 3. Results in Figure 4 show the influence of space time on the reaction indices for a ternary feed with a CO\({}_{2}\)/CO molar ratio of 2/3, as an example of the data obtained for the different feeds studied. It is observed that an increase in space time has a favorable effect on CO\({}_{\rm x}\) conversion (Figure 4a) and DME yield (Figure 4b). It should be highlighted that at these conditions CO\({}_{2}\) conversion is positive within almost the entire range of space time studied (Figure 3c), with a maximum value (10%) at 2.5 g\({}_{\rm cat}\) h (mol\({}_{\rm C}\))\({}^{-1}\).  
On the other hand, the comparison of these results in Figure 4 with those regarding the syngas feed [35] reveal that significantly higher values of space time are required when co-feeding CO\({}_{2}\), to obtain similar values of CO\({}_{\rm x}\) conversion and DME yield. In addition, as observed when studying the influence of reaction temperature (Figure 2), pressure (Figure 3) and space time (Figure 4), under the conditions corresponding to the maximum value of CO\({}_{2}\) conversion, low DME yield is attained. The explanation is that the unfavorable effect of the presence of CO\({}_{2}\) in the feed over the WGS reaction (Equation (3)), leads to the attenuation of the methanol synthesis reactions (Equations (1) and (2)), and its subsequent dehydration to DME (Equation (4)), due to the increase of the water content in the reaction medium.  
Figure 4: _Cont._
## 5 Internet of Medical Things
### IoMT Systems Architecture
#### 5.3.6 Application Layer  
The application layer plays a vital role in granting healthcare professionals access to the data collected by the IoMT system. It encompasses a range of applications, including electronic health records (EHRs), mobile apps, and web-based portals. Through the application layer, healthcare professionals can monitor patients' health status, offer real-time feedback, and make informed decisions regarding diagnosis and treatment.
# Low-Latency Short-Packet Transmission over a Large Spatial Scale
## 5 Simulation Results
Figure 5: The overall blocklength versus error probability curves. Figure 6: The overall normalized energy versus error probability curves. Figure 7: The optimal \(\epsilon\) curves w.r.t the number of relays \(L\).
## 4 Discussion
### Fractioned Plant Concentrations and Extractions
Thus, despite Co grain-concentration differences, all grains can be used for animal feed. Concentration values from Pb and in all treatments (Figure 3e) are below the limits established by the EU for complete animal feed (5 mg Pb kg\({}^{-1}\), 12% moisture [13]) and human consumption [14]. For macronutrients (N, P, K, Mg and S), sewage sludge treatments were only able to increase cob P concentration (vs. MIN), which might be interesting for animal feed when the entire cob is milled or just returned to the soil. For sulphur, concentrations in different parts of a maize plant varied from 0.7 to 0.8 g S kg\({}^{-1}\) in cobs, 0.9 to 1.1 g S kg\({}^{-1}\) in grain and 1.1 to 1.3 g S kg\({}^{-1}\) in the rest of the plant. Around 1 g S kg\({}^{-1}\) is the average value for maize fodder meal [60]. However, S availability requires further research as our concentrations were a bit lower that values recorded for mature plants [63; 64]. In extractions, the significant differences found may affect future management strategies (Figure 4). Nitrogen extractions, from 200 kg N ha\({}^{-1}\) (SPC) to 223 kg N ha\({}^{-1}\) (MIN), alert us about potential N mining in the MIN treatment (Figure 4a) if organic matter is not replaced (Table 1). Moreover, it adds an advantage to the use of SS as a source of organic matter (Table 1) because of the additional available N when organic matter is mineralized. This is in addition to the other benefits to physical soil-quality properties found by [65]. From heavy metals, despite the higher soil-extractable Cd (Figure 2) in SS, cob Cd extraction was reduced in SS (Figure 4f). Mineral P fertilizers are also a Cd source (although concentrations vary according to the fertilizer origin [66]). The ordinary superphosphate is also a S source, and S can help in Cd mobility in calcareous soils [67], which might explain this contradictory behavior of higher Cd cob extraction in MIN. For sulphur, the extraction of S in grain tended to decrease with SS. The uncertainty about S's full availability might be related to the tendency toward high N concentration in grain in the MIN treatment. The N/S ratio in grain was 8.7, 11.3 and 11.7 for MIN, SPC and SNC, respectively. Sulphur favors N absorption [68]. Furthermore, sulphate uptake is also positively influenced by ammonium-N [64]. In the cited experiment [64], ammonium-N is applied in an MIN topdressing at a time of maximum S uptake, thus favoring both N and S uptake. Furthermore, in the MIN treatment, the ordinary mineral superphosphate (0-20-0-11S) is a source of sulphur as it has sulphates (mainly gypsum) in its formula [69]. Thus, in the MIN treatment, 55 kg S ha\({}^{-1}\) is applied annually, which fully covers S demand (Figure 4b). In sludges, this interaction between nitrogen and sulphur might be limited, as N and S in SS are mainly in organic forms [70] and their plant availability requires mineralization.
# Development and Characterization of High-Fiber, Gluten-Free Pasta for Celiac Disease Patients
## 3 Results and Discussion
### Chemical Composition of the Pasta Treatments
#### 3.1.3 Protein Content  
The percentage of protein content of the control sample and pasta treatments for the control (13.45), T1 (6.70), T2 (9.56), T3 (10.79), T4 (12.79), T5 (12.07), and T6 (13.84), respectively. The percentage of protein content in the gluten-free pasta treatments varied significantly (\(p<0.05\)) from each other. The differences in crude protein content might be due to compositional differences in the flour blends [25]. Gluten refers to a family of proteins, mainly glutenin and gliadin (prolamins), a storage protein in the endosperm of cereal grains such as wheat, rye, and barley and found in different amounts in each cereal [26]. As can be seen, T1 contains the least protein content compared to all other treatments because corn starches have a smaller amount of protein as compared to rice [27]. The percentage of protein increased significantly as the percentage of flaxseed and lupin flour incorporated into the samples increased. This could be due to the lupin and flazseed flour protein content, which contains about 40-45% protein in lupin [28] and about 28-30% protein in flaxseed [29]. Our results showed that control pasta (100% wheat) was high in protein due to the protein content in wheat. There was no significant difference in the protein content between T6 and the control, while T6 was significantly higher in protein content compared to other treatments.
# High Reliability Evaluation and Lifetime Prediction of 50 GHz Athermal AWG Module
## 6 Conclusions  
The temperature dependence of the center wavelength in the 96-channel (50 GHz-spacing) athermal AWG module developed based on a new temperature compensation  
Figure 13: Transmittance and insertion loss changes after accelerated life test; (**a**) Transmittance and insertion loss for each wavelength at 71 \({}^{\circ}\)C; (**b**) Transmittance and insertion loss for each wavelength at 83 \({}^{\circ}\)C; (**c**) Transmittance and insertion loss for each wavelength at 95 \({}^{\circ}\)C.  
board shape and patent satisfied the \(\pm\)0.05 nm range in all channels in the temperature range of \(-\)40 \({}^{\circ}\)C\(-\)85 \({}^{\circ}\)C, and the insertion loss variation was also less than \(\pm\)0.5 dB.  
As a result of performing reliability tests based on Telcordia-GR-1209 and GR-1221, the temperature dependence of the center wavelength satisfied the \(\pm\)0.022 nm range, and the insertion loss variation was also less than \(\pm\)0.2 dB. According to the accelerated life test by high-temperature stress, the predicted mean life of the AWG module was 3.22 \(\times\) 10\({}^{5}\) (about 36.7 years), which is more than enough to guarantee the 10-year service life required for optical communication components.  
The developed athermal AWG module showed sufficient performance and reliability to be applied to 5G mobile networks such as reducing the power consumption and volume of DWDM network systems and enabling easy management.  
Conceptualization, K.-S.Y., C.-H.Y. and I.J.; Software, K.-S.Y.; Formal analysis, K.-S.Y., Y.-S.K.; Data curation, K.-S.Y., Y.-S.K.; Writing, K.-S.Y.; Review, K.-S.Y., C.-H.Y., K.-S.L., Y.-S.K. and I.J.; Visualization, K.-S.Y.; Funding acquisition, K.-S.L., I.J. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by an Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government [21 RK1100, (Honam Area) Post Pandemic Regional Strategic Industry Support Project] and Chonnam National University (Grant number: 2020-3815).  
Not applicable.  
The authors declare no conflict of interest.
# Improved Interpolation and Anomaly Detection for Personal PM\({}_{\mathbf{2.5}}\) Measurement
## 2 Methods
### Context-Aware Anomaly Detection  
Once we finished the bandwidth selection for interpolation and optimal bandwidth, we developed another algorithm for detecting anomalies in the time-series data. Many techniques have been presented to detect anomalies using various techniques. In this paper, we defined anomalies as data that showed significant changes in values within a very short time. For instance, if PM\({}_{2.5}\) concentration, measured every 10 seconds, showed significant drops or jumps during the 10-s period (for instance, observations that fall below Q\({}_{1}\)-1.5 IQR or above Q\({}_{3}\)+1.5 IQR in the box-and-whisker plot), we considered the value to be abnormal, because the amount of change in PM\({}_{2.5}\) concentration is assumed to stay stable or similar within a very short time. However, this rule was not an appropriate criterion for detecting outliers, because too much data fell into this category, and it was hard to think that they were all anomalies, given the data context. Thus, in this research, we defined data as anomalous when the following conditions were met. In reality, the PM\({}_{2.5}\) concentration does not change significantly in most cases. This phenomenon is reflected in the detection of anomalies in our time-series data analysis.  
\[d_{i}=\left|y_{i}-y_{i-1}\right|>th\]  
In other words, if \(d_{i}\) (difference of adjacent \(y_{i}\)s) exceeds a certain threshold, \(th\), then \(y_{i}\) can be an anomaly. Thresholds are chosen by visual inspection according to the characteristics of the PM data at the moment. Details are available in the experimental section.