esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Objective Evaluation
### WNG and Frequency Response  
The algorithm successfully fits the WNG \(A(f_{p})\) to the lower bound \(\gamma(f_{p})\) specified in Section 3, as shown in Figure 7a.  
This ensures a robust beamforming design. For high frequencies \(f_{p}\geq 7031\,\mathrm{Hz}\), the algorithm finds even higher WNG values than the lower bound.  
Figure 7b shows the frequency response of both arrays according to the configuration that is shown in Figure 3. The responses for both arrays were calculated for a sound source emanating from \(\phi=30^{\circ}\) (resulting in a sound source perceived at the location of the left loudspeaker, solid and dashed line)  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Two-Microphone Stereo Systems**} \\ \hline
**Microphone Directivity** & **Angle between the Microphones (\({}^{\circ}\))** & \(DI_{mod}\) \\ \hline Figure of Eight & \(101\) & \(5.95\) \\ Hypercardioid (back attenuation \(=-6\,\mathrm{dB}\)) & \(136\) & \(8.29\) \\ Hypercardioid (back attenuation \(=-10\,\mathrm{dB}\)) & \(156\) & \(8.7\) \\ \hline \multicolumn{3}{c}{**Microphone Array Stereo System**} \\ \hline \(DI_{mod}=11.29\) with \(b_{mic1}(\phi_{m})=\hat{b}_{array1}(\phi_{m})\) and \(b_{mic2}(\phi_{m})=\hat{b}_{array2}(\phi_{m})\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The modified directivity index \(DI_{mod}\) of the state-of-the-art two-microphone stereo systems and the microphone array stereo system described in this study. For the latter one, the desired directivity patterns are used. Only stereo systems with \(\phi_{rec}=\phi_{base}\) are displayed. This angle constraint avoids angular compression or angular expansion in the playback situation.  
Figure 7: (**a**) White Noise Gain (WNG) \(A(f_{p})\), as well as the lower bound for the WNG \(\gamma(f_{p})\) across frequency; (**b**) shown are frequency responses of both arrays for two sound sources emanating from \(\phi=30^{\circ}\) and \(\phi=0^{\circ}\) according to the configuration illustrated in Figure 3.  
according to Figure 3 and \(\phi=0^{\circ}\) (resulting in a phantom source between both speakers, dotted and dash-dotted line). It can be seen that for \(\phi=0^{\circ}\), the responses of both arrays show a high similarity in terms of level differences and have only minor fluctuations of approximately \(\pm 2\,\mathrm{dB}\) above 1000 Hz. Below 1000 Hz, it can be observed that there is a boost of approximately 3 dB, which might be attributed to a violation of a constraint at low frequencies. When the sound source is emanating from \(\phi=30^{\circ}\), a flat frequency response can be observed for Array 1 (on axis) with minor fluctuations of approximately 1 dB across frequency. Array 2 shows a considerably lower level, but larger fluctuations. It can be assumed that these fluctuations will not be perceivable because the location of the sound source will be determined by Array 1.
# Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations
## 2 Contrail Formation and Environmental Impact
### Environmental Impact and Climate Implications  
Contrails, particularly those that persist and evolve into cirrus clouds, contribute significantly to aviation's non-CO\({}_{2}\) climate impacts by trapping longwave radiation and altering the Earth's radiative balance. The extent of their radiative forcing depends on factors such as altitude, atmospheric humidity, and the time of day, with nighttime contrails exerting a stronger warming effect due to the absence of counteracting solar radiation. Studies show that contrails can have an immediate and pronounced impact on regional and global climate, often comparable to or exceeding the radiative forcing of CO\({}_{2}\) emissions on a per-flight basis [28].  
As aviation activity grows, particularly in emerging markets, the frequency and persistence of contrails are expected to increase, exacerbating their short-term warming  
Figure 1: Spatial bounding boxes to highlight the regional air traffic zones.  
effects. Addressing these impacts requires both real-time monitoring and mitigation strategies to minimize contrail formation. By integrating technologies such as CubeSats into contrail monitoring systems, aviation can reduce these non-CO\({}_{2}\) effects and contribute more effectively to global climate goals, including those outlined in the Paris Agreement [29].
## 3 Results and Discussion
### LFIA of Meat Samples  
A series of test strips were prepared, and immunomodtoratographic detection of MG in standard solutions, as well as in extracts obtained from meat samples of various mammals and bird species, were carried out. It is known that matrix components in the tested sample can affect the assay results. The efficiency of MG extraction from tissues is also of great importance because MG is found mainly in the form of complexes with other muscle proteins, which can cause shielding of its epitopes and prevent reliable determination of its content. Because MG is a salt-soluble protein [25], several sample preparation techniques based on MG extraction with PBS were developed and compared. To increase the completeness of the extraction, various concentrations of Triton X-100 were added to the extraction medium. It was demonstrated that the maximum amount of MG was obtained by the extraction with PBST for 15 min, followed by centrifugation of the extract at 5000\(\times\) g for 5 min (Method No. 4 in Table 3). This procedure of sample preparation is simple and rapid because no special expensive equipment is required for its implementation and the total extraction time does not exceed 20 min.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method No.** & **Sample Preparation Procedure** & **MG, mg g\({}^{-1}\) of Park** \\ \hline \multirow{3}{*}{1} & Extraction with PBS, containing 0.1\% Triton X-100 and 0.5 M KCl, followed by an incubation at 100 \({}^{\circ}\)C and centrifugation for 5 min at & 0.03 \(\pm\) 0.01 \\  & 7000\(\times\)\(g\) & \\ \hline \multirow{3}{*}{2} & Extraction with PBS, containing 0.1\% Triton X-100 and 0.5 M KCl, followed by a sonication and centrifugation for 10 min at 5000\(\times\)\(g\) & 0.25 \(\pm\) 0.02 \\ \hline \multirow{3}{*}{3} & Extraction with PBST & 0.13 \(\pm\) 0.03 \\ \hline \multirow{3}{*}{4} & Extraction with PBST followed by a centrifugation for 5 min at 5000\(\times\)\(g\) & 0.40 \(\pm\) 0.03 \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: MG content revealed by the LFIA after different sample preparation procedures.  
Figure 3: Calibration curve for MG in the sandwich-format LFIA.  
Studies on the specificity of the developed test system showed that it could specifically detect MG isolated only from pork samples (Figure 4). There was no cross-reactivity with extracts obtained from meat of other mammals (beef and lamb) or poultry (chicken and turkey).
## 4 Results  
### Simulation Results  
#### 4.1.1 Control Case: Conventional Aperture Imaging  
The conventional aperture image, corresponding to the sequence when the array T1 transmits and receives (one probe) provides the baseline for imaging quality through the different scenarios. Figure 5a,f illustrates the resulting point target and lesion images at a 75 mm depth and without any aberrating layer in the propagation medium. An SOS of 1540 m/s was used to reconstruct these images. The point target (Figure 5a) has an LR of 1.78 mm, and the lesion (Figure 5f) is visible with a contrast of \(-\)16.78 dB and CNR of 0.846. Note that, while the lesion is easily identified from the background, it is difficult to delineate its edges.
# The Use of Attentive Knowledge Graph Perceptual Propagation for Improving Recommendations
## 3 Preliminaries  
This section explains the notation used in the framework and introduces the flow of the recommendation task as a way to better represent our AKGP model.  
This paper adopts the same setup as most knowledge graph-based recommendation system approaches, and uses \(\mathcal{U}=\{u_{1},u_{2},\cdots,u_{m}\}\) to denote the set of users and \(\mathcal{V}=\{v_{1},v_{2},\cdots,v_{n}\}\) to denote the set of items, where \(m\) and \(n\) denote the total number of users and items. In the recommendation scenario, the user-item interaction is recorded in the form of an interaction matrix, denoted as \(Y=\{y_{uv}|u\in\mathcal{U},v\in\mathcal{V}\}\), where \(y_{uv}\) is denoted as the element of the \(u\) row and the \(v\) column of the interaction matrix. When \(y_{uv}=1\), an interaction has been recorded between user \(u\) and item \(v\), such as a click or a purchase. When \(y_{uv}=0\), no interaction has been recorded between the \(u\)th user and the \(v\)th item. The formula is as follows.  
\[y_{uv}=\left\{\begin{array}{ll}1,&\text{User $u$ has interacted with item $v$}\\ 0,&\text{User $u$ has no interaction with item $v$}\end{array}\right. \tag{1}\]  
In addition, this section introduces knowledge graphs as additional auxiliary information in the recommendation system. A knowledge graph is a method of organizing knowledge, such as entities, concepts, relationships and attributes into a graphical structure. It is a semantic network for representing and storing knowledge, similar to human thought patterns, in a collection of triples consisting of \(\langle head,relation,tail\rangle\), representing a knowledge graph in which two entities can be related to each other. The knowledge graph is denoted by \(\mathcal{G}=(\mathcal{E},\mathcal{R})\), and generally, \(h\), \(r\) and \(t\) are used to denote the head entities, relationships and tail entities in the knowledge graph triad, respectively, where \(\mathcal{E}=\{e_{1},e_{2},\cdots\}\) denotes the set of entities and \(\mathcal{R}=\{r_{1},r_{2},\cdots\}\) denotes the set of relationships. The alignment between items and entities is denoted by \(\mathcal{A}=\{(v,e)|v\in\mathcal{V},e\in\mathcal{E}\}\), i.e., every item \(v\) in the item set has an entity element \(e\) with which it is mapped in the entity set.  
In this paper, the user-item interaction matrix \(Y\) and the knowledge graph \(\mathcal{G}\) are used as inputs, and the aim of the recommendation task is to predict whether the user \(u\) is interested in an item \(v\) with which they have not interacted, i.e., the probability \(\hat{y}_{uv}\) of user \(u\) clicking on item \(v\). The prediction value \(\hat{y}_{uv}\) will be a real number between 0 and 1, and a value closer to 1 means that the user is more interested in the item. In the recommendation scenario, after obtaining the predicted value of the user's preference for the item, the item is recommended to the user in descending order according to the predicted value, which completes the recommendation task.
## 3 Results and Discussion
### Proposed Identification of Novel Chemicals in Avocado (P. americana Mill.) Semil 34 Seed  
We also present 23 tentative compounds that, to our knowledge, have not been previously reported in the literature. These compounds belong to various classes, including 10 fatty acyl compounds, five glycerophospholipids, six organooxygen compounds, one glycolipid, and one flavonoid. Among the fatty acyl compounds, pavetannin B1 was provisionally identified with a precursor ion at \(m/z\) 865.1968 [M+H]\({}^{+}\), displaying major fragment ions at \(m/z\) 695, 575, 533, and 287. Another example is lineolaidic acid, detected at \(m/z\) 263.2367 [M+H-H2O]\({}^{+}\) with a mass error of \(-0.7\) ppm. Its fragmentation spectrum exhibited major product ions at \(m/z\) 245, 109, and 95. An example of an organooxygen compound is (1r,3R,4s,5S)-4-((2E)-3-(3,4-dihydroxypropyl))prop-2-enoyl)oxy)-1,3,5 trihydroxycyclohexanecarboxylic acid, with m/z of 353.0878 [M-H]\({}^{-}\) and major ions such as 191, 179, and 134. Other proposed compounds include isomaltulose, with a precursor ion at m/z 289.0917 [M+H-3H2O]\({}^{+}\), D-picose at m/z 161.0446 [M-H2O-H]\({}^{-}\), and LPE 16:0 with \(m/z\) 454.2923 [M-H]\({}^{+}\), among others.
## 2 Optical Access-Metro Networks
### Enabling Optical Technologies  
The resilient packet ring (RPR) [7] architecture and functionality is fully described in the IEEE 802.17 standard. RPR is a packet switched ring-based architecture consisting of two counterdirectional optical fiber ringlets with all nodes performing optical-electronic-optical (OEO) conversion. RPR combines the benefits of SONET/SDH legacy networks, _i.e._, fast recovery and support of legacy time division multiplexing (TDM) traffic with a high level of QoS, with Ethernet's low equipment cost, statistical multiplexing gain, and simplicity. Spatial reuse is deployed in RPR to improve the bandwidth efficiency by means of destination stripping, which enables the destination node to remove packets from the ring, thus allowing simultaneous communications between nodes on different ring segments, as shown in Fig. 1. Unicast traffic is typically sent on the ringlet spanning the shortest path to the destination. Multicast traffic is realized by means of unidirectional or bidirectional flooding. RPR deploys a topology discovery protocol, which allows each node to maintain information about the number and order of nodes as well as the status of each link. A distributed fairness control protocol is deployed which provides fairness among the nodes [8]. Due to its dual-fiber ring topology, RPR is able to recover from a single link or node failure within 50 ms. Failure recovery is performed by means of wrapping and/or steering [9].  
Figure 1: Destination stripping and spatial reuse in RPR.
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
###### Abstract  
In wireless powered communication networks (WPCNs), it is essential to research energy efficiency fairness in order to evaluate the balance of nodes for receiving information and harvesting energy. In this paper, we propose an efficient iterative algorithm for optimal energy efficiency proportional fairness in WPCN. The main idea is to use stochastic geometry to derive the mean proportionally fairness utility function with respect to user association probability and receive threshold. Subsequently, we prove that the relaxed proportionally fairness utility function is a concave function for user association probability and receive threshold, respectively. At the same time, a sub-optimal algorithm by exploiting alternating optimization approach is proposed. Through numerical simulations, we demonstrate that our sub-optimal algorithm can obtain a result close to optimal energy efficiency proportional fairness with significant reduction of computational complexity.  
energy efficiency; fairness; WPCN; energy harvest; wireless power transfer +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Type-1 Fuzzy Controller  
FLC controllers are sterling, as their configuration is based on the designer's experience rather than knowledge of the system's mathematical model. The implemented type-1 fuzzy controller (shown in Figure 3) uses normalized rules in its structure and, thus, scale factors \(K_{e}\) and \(K_{d}\) are used for the inputs, which are the error and its derivative. An extra output scale factor \(K_{b}\) was also used to regulate the control signal in terms of the required performance.  
The fuzzy block includes three consequent modules, which are the fuzzification, inference and defuzzification. The first changes the inputs into fuzzy inputs to designate the degree of the membership; for this case, overlapped triangular and uniformly discretized (between \(-1\) and \(1\)) functions are based on negative big (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM) and positive big (PB); this resulted in 25 adapted rules, which are shown in Table 5. The inference is the mechanism by which linguistic rules are evaluated according to the results obtained in the previous step of an if-then type [42]. Finally, the defuzzification comprises the stage at which the linguistic expressions are translated into numerical values for the output; in this case, equidistant and discretized constant outputs were configured in a range between \(-1\) and \(1\).  
Figure 3: Type-1 FLC Structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{E}\setminus\dot{\mathbf{E}}\) & **NB** & **NS** & **Z** & **PS** & **PB** \\ \hline NB & NB & NM & NM & NS & Z \\ \hline NS & NM & NM & NS & Z & Z \\ \hline Z & NM & NS & Z & PS & PM \\ \hline PS & Z & Z & PS & PM & PM \\ \hline PB & Z & PS & PM & PM & PB \\ \hline \hline \end{tabular}
\end{table}
Table 5: FLC linguistic rules.  
FLC architecture was implemented as in the schematic diagram of Figure 4. The voltage reference estimator will be explained in further sections as it is related to the features gathered from the PV panel.
## 3 Results  
### Validation of the Numerical Models  
The numerical model of the ARCM (in free space) and the numerical models of the exposure scenarios with the ARCM located on or near the forearm, arm, and chest were validated by comparing the S11 parameter (reflection coefficient) frequency characteristics obtained through the simulation with those obtained in laboratory tests using relevant physical models (detailed information on the applied laboratory test methodology is presented in [1; 6; 8]). The analysis of the obtained results showed relevant agreement between the measurement and the simulation results--the differences did not exceed 3.6% in the resonant (matching) frequency of the ARCM and 4.6% in the S11 parameter at 2.45 GHz (Table 4).  
\begin{table}
\begin{tabular}{c c c} \hline
**Part of the Body** & **Outer Dimensions [mm]** & **Tissue (Thickness [mm])** \\ \hline Head & 238 \(\times\) 159 \(\times\) 190 & Skin (4); Fat (4); Bone (8); Brain (Inner part) \\ \hline Arm & \(\mathcal{O}\) 105 \(\times\) 340 & Skin (2); Fat (5); Bone (\(\mathcal{O}\) 25); Muscle (Inner part) \\ \hline Forearm & \(\mathcal{O}\) 56(90) \(\times\) 260 & Skin (2); Fat (2); Bone (\(\mathcal{O}\) 15); Muscle (Inner part) \\ \hline Chest & 320 \(\times\) 188 \(\times\) 200 & Skin (2); Fat (12); Muscle (30); Bone (10); Fat (10); Internal organ (Inner part); Bone (\(\mathcal{O}\) 22) \\ \hline \end{tabular}
\end{table}
Table 3: Dimensions of the simplified, multi-layer models of body parts, developed for this study.
# Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes
## 5 Results
On the one hand, the real ELM demanded more computational effort owing to it being composed of two different ELMs with 10 hidden nodes. On the other hand, as expected, the complex ELM formed by only two hidden neurons took the shortest evaluation time. Consequently, the complex ELM phase error reduction scheme was characterized by not only low complexity, but also high precision. Figure 8: For diverse phase noise reduction techniques, the BER as a function of the decorrelation degree between the mixed tones with the SNR as the parameter and with a 0.5 MHz laser linewidth. This figure is divided into two to enable visibility. **(a)** high SNRs; **(b)** low SNRs.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 4 Numerical Results
### Abstracted Physical Layer
#### 4.1.2 Decoding Region for LDPC Code Ensembles  
For LDPC protograph ensembles, the decoding region was computed slightly differently from (10). Since for structured code ensembles, not only the power and fraction, but also the position of the interferer are important, we introduced a length-\(n_{b}\) protograph noise plus interference vector \(\mathbf{\sigma}_{\text{p}}^{(2)}=\left[\left(\sigma_{n}^{2}+\sigma_{t}^{2}\right) _{0},\ldots,\left(\sigma_{n}^{2}+\sigma_{t}^{2}\right)_{n_{b}-1}\right]\). An element \(\left(\sigma_{n}^{2}+\sigma_{t}^{2}\right)_{j}\) corresponds to the specific signal plus interference level for a VN of type \(j\). Then, the decoding region in (10) can be restated as:  
\[\mathcal{D}^{\prime\prime}=\left\{\mathbf{\sigma}_{\text{p}}^{(2)}\in\mathbb{R}_{ +}^{n_{b}}\ |\ \mathsf{I_{APP}}(j)\to 1\,\forall j\in\{0,\ldots,n_{b}-1\}\right\}.\]  
The signal plus interference level \(\left(\sigma_{n}^{2}+\sigma_{t}^{2}\right)_{j}\) for a VN of type \(j\) is determined as follows. For a certain channel load \(G\), realizations of packet collisions are generated. For each of them, one may determine an instantaneous bit-wise noise plus interference power for each of the \(n\) codeword bits. Then, for each of the \(n_{b}-p_{b}\) unpunctured VN types in the protograph, we determine an average noise plus interference level by averaging over \(n/(n_{b}-p_{b})\) subsequent values of the instantaneous bit-wise noise plus interference powers, yielding \(\mathbf{\sigma}_{\text{p}}^{(2)}\). Decoding is successful only if \(\sigma_{\text{p}}^{(2)}\in\mathcal{D}^{\prime\prime}\).
# W-Band Modular Antenna/Detector Array for the Electron Cyclotron Emission Imaging System in KSTAR
## 5 Conclusions  
The proposed modular antenna/detector array is based on the unit antenna/detector module, which can be independently tuned and installed on the array frame. In case of performance failure due to a specific channel, the corresponding channel module can be easily removed and replaced without interference to the rest of the array, thus minimizing the interrupted time used for maintenance. The unit antenna/detector module consists of an elliptical mini-lens, a dual-dipole antenna with a Schottky diode, an antenna balun, an LNA, and a unit frame. The elliptical mini-lens and dual-dipole antenna with the diode are optimized to have the improved performance in W-band frequencies. For the  
Figure 12: Proposed modular antenna/detector array in an array box.  
ultra-wideband performance, a CPS-to-MSL transition with optimized design parameters is used. To increase the signal-to-noise ratio, an LNA chip with 15 dB gain is installed. The fabricated unit antenna/detector modules are tested in a W-band in-house test setup, and consistently provide a gain increase by 10-20 dB as compared with the conventional antenna/detector array. Additionally, the proposed modular array allows each unit channel to be efficiently updated under a variety of tokamak operating conditions. For example, combinations of new antenna/detector modules in an array with different performance characteristics can be easily placed and measured without interfering with the rest of the array. This would allow us to successfully measure various plasma instabilities (e.g., tearing mode, edge localized mode, and sawtooth) with changes of plasma conditions.  
Conceptualization, G.H.L., H.K.P. and K.W.K.; methodology, G.H.L.; software, J.S.L., D.H.K. and S.H.N.; validation, G.H.L.; formal analysis, G.H.L. and J.S.L.; investigation, S.H.N., B.C.M. and J.S.L.; resources, G.H.L.; data curation, M.J.K., B.T.K. and T.G.K.; writing--original draft preparation, G.H.L. and K.W.K.; writing--review and editing, K.W.K., J.H.L., W.C.L., G.S.Y., H.C.C., H.K.P. and Y.K.I.; visualization, S.H.N. and M.J.K.; supervision, K.W.K. and Y.K.I.; project administration, K.W.K. and Y.K.I.; funding acquisition, K.W.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. NRF-2019M1A7A1A03088464).  
Not applicable.  
The authors declare no conflict of interest.