esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Our analytical approach to estimate EEG phase relies on instantaneous alpha oscillations using the conventional Yule-Walker-based method (AR) and the adaptive method (LMS). The LMS-based AR model serves at least two main purposes. First, it avoids pre-existing knowledge of the exact signal stochastic, which is rarely available in practice when a learning mechanism is used to estimate the required signal statistics. Second, it possesses a tracking mechanism to monitor the variation in the EEG signal. By comparing these two methods, our study showed that the adaptive LMS-based AR model outperforms the AR model in real time for longer prediction lengths. This new approach may raise the possibility of EEG phase prediction with its simplicity and low computational cost. In summary, our proposed technique can help in neurostimulation applications, such as EEG phase prediction aids in versatile brain-machine interface applications.  
Conceptualization, A.S., T.T., and K.K.; methodology, T.T. and K.K.; software, A.S.; validation, A.S. and K.K.; formal analysis, A.S.; investigation, K.K.; resources, K.K.; data curation, A.S. and K.K.; writing--original draft preparation, A.S. and K.K.; writing--review and editing, A.S., T.T., and K.K.; visualization, A.S.; supervision, T.T. and K.K.; project administration, K.K.; funding acquisition, K.K. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by a research grant from Toyota Motor Corporation.  
We thank [PERSON] for helpful discussion and comments.  
A.S. and T.T. declare no conflict of interest. K.K. was supported by a research grant from Toyota Motor Corporation. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The data and code used to support the findings of this study are available from the corresponding author upon reasonable request.
# Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic
showed promising results in better estimation in the PM\({}_{2.5}\) concentration using the data assimilation of the MODIS-observed AOD with optimal interpolation (OI) method in the US [47]. Therefore, in this study, we also attempted to overcome the spatial and temporal limitations in the satellite observation and the low accuracy in the model simulation through the data assimilation based on the OI method. The objective of the study is to estimate realistic levels of surface particulate matter over the Arctic. For this purpose, more accurate AODs are calculated via the data assimilation procedure using the satellite and ground remotely sensed data.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 5 O-RAN Alliance Architecture
### O-Ru  
The O-RU hosts a low physical layer and radio frequency processing functions [58; 63]. Each O-RU can have one or more panels. Each panel will consist of one or more TX-antenna-arrays/RX-antenna-arrays. A TX-antenna-arrays/RX-antenna-array is defined as a logical construct for data routing and is related to the physical antenna described in the O-RU construction. An array element will include one or more radiators, the specification of which is written in IEEE Std 145-1993. As we have explained, the O-FHI and the O1 interface connect O-RU to O-DU and SMO, respectively.  
O-RU supports the beamforming functionality [63]. Beamforming allows a wireless signal to be directed specifically to a receiving device using multiple antennas. Requiring multiple antennas means that this beamforming process can be performed in MIMO. We will discuss the benefits of beamforming in Section 6. O-RAN Alliance architecture specifies that the beamforming commands are implemented between O-DU and O-RU [32; 66]. O-RAN has included the digital, the analog, and the hybrid beamforming technologies in its specifications. O-RAN Alliance also proposed four methods for beamforming, which are predefined-beam, weight-based dynamic, attribute-based dynamic, and channel information-based beamforming method [63; 66].  
In predefined-beam beamforming, the O-RU has predefined a table of beam indexes containing beam weights. The O-DU will send the information on which index should the O-RU use. Unlike the former, weight-based dynamic beamforming allows the O-DU  
Figure 18: Fronthaul data flows.  
to generate the beam weights in real-time for the O-RU to use. Nonetheless, this method requires the O-DU to know the specific antenna characteristics of the O-RU. Sparing O-DU to know the details, attribute-based dynamic beamforming lets the O-DU instruct the O-RU to use a specific beam index associated with definite beam attributes. O-RU is in charge of generating the beamforming weights based on these attributes. Similar to the former, channel information-based beamforming also grants the O-RU to generate the beamforming weights. These weights will be created based on the channel information provided by the O-DU. For the mathematical description of these beamforming methods, please look into [63].  
Since the RU is majorly hardware-based, O-RAN Alliance only focuses to develop the software standards to control this component. Vendors need to follow the specifications provided by O-RAN Alliance in making their RUs to be considered as O-RU.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 2 System Model
### Beam Shaping Model for Massive MIMO Networks
#### 2.1.2 Array Factor for Antenna Array Radiation Pattern  
The array factor \(A_{F}(\cdot)\) can be described as  
\[A_{F}(\theta_{s},\phi_{s},\theta,\phi)=10{\rm log}_{10}\left[1+\rho\cdot\left| \sum_{p=1}^{N_{H}}\sum_{r=1}^{N_{V}}a\cdot w_{p,r}\cdot v_{p,r}\right|^{2}-1 \right], \tag{5}\]  
where \(N_{H}\) and \(N_{V}\) are the number of antenna elements in the horizontal and vertical directions, respectively. Because we consider the practical ULA, we assume eight for both \(N_{H}\) and \(N_{V}\). The parameter, \(\rho\) is a correlation level and it is assumed to be unity. \(a\) equals \(1/\sqrt{N_{H}N_{V}}\), which is a constant amplitude because we assume an equal and fixed amplitude for all antenna elements. \(p\) and are indices of the antenna elements in both directions. Then, we describe the beamforming components \(w_{p,r}\) and \(v_{p,r}\) as  
\[\begin{split} w_{p,r}&=e^{-j2\pi\left((r-1)\frac{d_{V} }{\lambda}\cos\theta_{s}+(p-1)\frac{d_{H}}{\lambda}\sin\theta_{s}\sin\phi_{s} \right)},\\ v_{p,r}&=e^{j2\pi\left((r-1)\frac{d_{V}}{\lambda} \cos\theta+(p-1)\frac{d_{H}}{\lambda}\sin\theta\sin\phi\right)},\end{split} \tag{6}\]  
where \(\lambda\) is a wavelength of the employed band frequency, \(d_{V}\) and \(d_{H}\) are spacing distances between the vertical and horizontal elements of the antenna array, respectively. In our system model, we assume evenly spaced two-dimensional plane antenna. Therefore, both \(d_{V}\) and \(d_{H}\) are \(\lambda/2\).
# Research on Path Planning for Robots with Improved A* Algorithm under Bidirectional JPS Strategy
## 3 Improved A* Algorithm  
### Improvement of the Heuristic Function  
#### 3.1.1 Selection of Heuristic Function  
The heuristic function, as the core of the A* algorithm, significantly impacts the result of the planned path. In response to the issues with the general heuristic function's expression mentioned above, this paper proposes a new distance expression for the heuristic function, namely Octile distance. Octile distance is a combination of Euclidean distance and Manhattan distance that brings the predicted distance closer to the actual distance, thus improving the efficiency of the algorithmic search. Octile distance is illustrated in Figure 5, where the yellow grid indicates the starting point, the purple grid indicates the target point, the gray grid indicates the actual path, the blue straight line indicates the Octile distance,  
Figure 4: Pathfinding process of A* algorithm.  
the red straight line indicates the Euclidean distance, and the green straight line indicates the Manhattan distance. From the figure, it can be seen that Euclidean distance is suitable for multi-neighborhood expansion, Manhattan distance is suitable for four-neighborhood expansion, and Octile distance is more suitable for the eight-neighborhood expansion proposed in this paper.  
The Octile distance formula is as below:  
\[h(n)=(\Big{|}x_{g}-x_{n}\Big{|}+\Big{|}y_{g}-y_{n}\Big{|})+(\sqrt{2}-2)\text{ min}(\Big{|}x_{g}-x_{n}\Big{|},\Big{|}y_{g}-y_{n}\Big{|}) \tag{4}\]  
In Equation (4), (\(x_{n}\),\(y_{n}\)) are the coordinates of the current node and (\(x_{g}\),\(y_{g}\)) are the coordinates of the target point.  
To verify the effect of the heuristic function of the A* algorithm on pathfinding efficiency, a simulation test was performed on a 20 \(\times\) 20 grid map, as shown in Figure 6. Figure 6 represents the result of using Octile distance as the heuristic function, Figure 6 represents the result of using Euclidean distance as the heuristic function, and Figure 6 represents the result of using Manhattan distance as the heuristic function. By comparing the three graphs, it can be seen that the paths obtained in Figure 6,b are equal in length and shorter than the paths obtained in Figure 6 while the number of nodes traversed in Figure 6 is less than the number of nodes traversed in Figure 6. In summary, it can be concluded that Octile distance as a heuristic function is more efficient than the traditional distance formulas when choosing the eight-neighborhood expansion for path planning.  
Figure 5: Octile distance.  
Figure 6: Simulation comparative testing of heuristic functions: (**a**) Octile distance; (**b**) Euclidean distance; (**c**) Manhattan distance.
# Nutrition, Flavor, and Microbial Communities of Two Traditional Bacterial Douchi from Gansu, China
## 3 Results and Analysis
### Analysis of Nutritional Components of Longnan and Qingyang Douchi
#### 3.1.2 Analysis of FFAs in the Two Types of Douchi
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**FFAs**} & \multicolumn{2}{c}{**Content (\%)**} \\ \cline{2-3}  & **LN Douchi** & **QY Douchi** \\ \hline \multicolumn{1}{c}{Saturated Fatty Acids (SFAs)} & & \\ Myristic acid (C14:0) & 0.04 \(\pm\) 0.00 b & 0.07 \(\pm\) 0.01 a \\ Palmitic acid (C16:0) & 10.65 \(\pm\) 0.10 a & 9.63 \(\pm\) 0.14 b \\ Heptadecanoic acid (C17:0) & 0.09 \(\pm\) 0.00 a & 0.08 \(\pm\) 0.00 b \\ Stearic acid (C18:0) & 3.41 \(\pm\) 0.02 a & 3.52 \(\pm\) 0.03 a \\ Arachidic acid (C20:0) & 0.21 \(\pm\) 0.02 b & 0.29 \(\pm\) 0.01 a \\ Behenic acid (C22:0) & 0.28 \(\pm\) 0.02 a & 0.30 \(\pm\) 0.01 a \\ Lignoeceric acid (C24:0) & 0.06 \(\pm\) 0.01 b & 0.11 \(\pm\) 0.01 a \\ Total & 14.74 \(\pm\) 0.14 & 14.00 \(\pm\) 0.21 \\ Monounsaturated Fatty Acids & & \\ (MUFAs) & & \\ Heptacenoic acid (C17:1) & 0.04 \(\pm\) 0.00 a & 0.03 \(\pm\) 0.00 b \\ Oleic acid (C18:In-9) & 18.80 \(\pm\) 0.04 b & 20.01 \(\pm\) 0.13 a \\ _cis_-11-Eicosenoic acid (C20:1) & 0.03 \(\pm\) 0.00 a & 0.03 \(\pm\) 0.01 a \\ Erucic acid (C20:1) & 0.07 \(\pm\) 0.01 a & 0.02 \(\pm\) 0.00 b \\ Tetradecanoic acid (C24:1) & 0.26 \(\pm\) 0.06 & - \\ Total & 19.20 \(\pm\) 0.11 & 20.09 \(\pm\) 0.11 \\ Polyunsaturated Fatty Acids (PUFAs) & & \\ Linoleic acid (C18:2) & 56.79 \(\pm\) 0.08 a & 55.54 \(\pm\) 0.07 b \\ Linolenic acid (C18:3) & 9.25 \(\pm\) 0.03 b & 10.32 \(\pm\) 0.06 a \\ Docosadienoic acid (C22:2) & 0.08 \(\pm\) 0.01 & - \\ _cis_-11,14-Eicosadienoic acid (C20:2) & 0.02 \(\pm\) 0.00 a & 0.02 \(\pm\) 0.00 a \\ Total & 66.14 \(\pm\) 0.12 & 65.88 \(\pm\) 0.13 \\ PUFA/SFA & 4.49 & 4.71 \\ \hline \hline \end{tabular} Note: Letters indicate Duncan’s pairwise differences among different samples (\(p\) < 0.05).  
\end{table} 
Table 2: Composition and content of FFAs in Longnan and Qingyang douchi.
## 3 Results  
An overview of the results by means of specific time points and samples of the demineralization protocol is shown in Figure 2. With increasing exposition time in the acetic buffer solution, i.e., samples representing the specific duration, changes of the enamel translucency were visible in the photographs. The slabs developed a brighter surface with increased scattering and partly adhesion of the dissolved minerals (Figure 2C). The photographs also show irregularly distributed demineralization areas at the enamel surface, causing a marmorizing effect of the slab (Figure 2D). In sound enamel, the PLM images at Figure 1A reveal the ordered, diagonal-aligned structure of the hydroxyapatite crystal that reaches to the surface of the enamel layer. After 15 days of exposition in the acetic buffer solution, the slabs developed characteristic zones of demineralization (Figure 3) [30,31]. This includes the surface zone of high mineral content, similar to sound enamel due to the redeposition of dissolved material--i.e., reprecipitations; the body of the lesion with greatest demineralization and porosity; an intermediate dark zone with altered birefringence and structure of the enamel; and a translucent zone of less large pores and initial demineralization.  
Figure 2: Overview of the results: Photographs of the demineralized enamel slabs, PLM images of the thin cross sections with two different magnifications, degree of polarization (DOP) representations of the PSOCT measurements as en-face projections and B-scans, including samples with a demineralization time of 0 (**A**), 15 (**B**), 32 (**C**), and 49 (**D**) days. EDJ: Enamel dentin junction. Scale bars PLM: 400 (10\(\times\)), 80 (40\(\times\)); Scale bar PSOCT: 400 (m).  
In the further course, the demineralization layer was predominantly defined by the body of the lesion (Figure 2C,D; PLM). The measured thickness of the demineralization layer increased over time (Figure 4A) whereas the maximum lesion depth was 419 \(\upmu\)m at day 49. The general increase of lesion depth over time also showed a high variability with an outlier at day 34 (Figure 4A; lesion depth: 62 \(\upmu\)m). PSOCT en-face representations of DOP values showed no depolarizing effect at the sound enamel layer at day 0, which can be confirmed by the cross-sectional B-scan (Figure 2A; PSOCT). A slightly higher depolarization was observed at day 15 (Figure 2B; PSOCT en-face), which became more pronounced in the corresponding B-scan where a subsurface layer with decreased DOP is present (zoom view of PSOCT B-scan at Figure 2B). With further demineralization, an increased depolarization is visible, indicated by decreasing DOP values in the en-face projections at Figure 2C,D. The corresponding B-scans show a strong depolarization (low DOP values). The linear regression between lesion depth measured with PLM and the mean DOP values at the central region of interest showed a substantial linear correlation (Figure 4B; \(R^{2}=0.7118\)).
# A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks
## 3 System Model
Node \(\mathrm{i}\) evaluates the trust of node \(\mathrm{j}\) during a time window of length \(\Delta\)t, so the updated trust of node \(\mathrm{i}\) toward node \(\mathrm{j}\) is:  
\[\mathrm{T}_{\mathrm{ij}}(\mathrm{t})=\alpha\mathrm{T}_{\mathrm{ij}}(\mathrm{t} -\Delta\mathrm{t})+(1-\alpha)\mathrm{T}_{\mathrm{ij}}^{\mathrm{DIRECT}}( \mathrm{t}), \tag{2}\]  
where \(\mathrm{T}_{\mathrm{ij}}(\mathrm{t}-\Delta\mathrm{t})\) denotes the historical trust value of node \(\mathrm{i}\) toward node \(\mathrm{j}\), and \(\alpha\in[0,1]\) is the weight value of the historical trust value. Actually, the direct observation result is more important and accurate than the historical trust value. Therefore, \(\alpha\) can be defined as \(\mathrm{e}^{-\Delta\mathrm{t}}\). Next, we will describe the calculation of the trust at each protocol layer.
## 12 Simulation Setup
Let us analyze the vulnerability to attack scenarios and the corresponding attack success rate in non-federated learning models:  
Attack ScenariosNon-federated learning models are vulnerable to attacks such as data poisoning attacks, adversarial input attacks, and model extraction attacks. These attacks exploit weaknesses in the centralized storage, data acquisition process, or model sharing mechanisms [47]. Attack Success RateThe attack success rate in non-federated learning models can vary depending on the level of security measures implemented. However, due to the centralized nature of the system, successful attacks can have a more significant impact on the entire model, resulting in a potentially higher attack success rate compared to federated learning models. The comparative analysis of the proposed federated learning model and non-federated learning model, considering attack scenarios and attack success rate, leads to the following observations:  
1. _Attack Scenarios:_ Both federated learning and non-federated learning models are susceptible to various attack scenarios. However, the specific attack vectors and techniques employed differ based on the architecture and characteristics of each model [48]. 2. _Attack Success Rate:_ The attack success rate can vary based on the implementation and security measures in both models. Federated learning models, with their decentralized nature and local data privacy, present inherent challenges to attackers, reducing the attack success rate. Non-federated learning models, though vulnerable to attacks, can experience a higher attack success rate due to the centralized storage and accessibility of data and model parameters. The comparative analysis of attack scenarios and attack success rate in the proposed federated learning model and non-federated learning model highlights the importance of considering security measures in both approaches. Though both models can be vulnerable to different attack scenarios, federated learning models, with their decentralized and privacy-preserving nature, offer potential advantages in reducing the attack success rate. Implementing robust security measures, including secure communication protocols, model aggregation techniques, and attack detection mechanisms, is crucial in enhancing the security and resilience of both federated and non-federated learning models against attacks, which is illustrated through Figure 1:5
# Energy-Efficient Hardware Implementation of an LR-Aided K-Best MIMO Decoder for 5G Networks
## 4 System Architecture Design and Verification
### Software Implementation and Evaluation  
First we have built a software implementation of the proposed detector in MATLAB software provided by Mathworks. The BER performance of the design has been evaluated and compared with conventional MIMO schemes. The Rayleigh fading channel is used as a model of the communication medium. The results are shown in Figure 3. It can be seen that the performance improvement achieved by applying LR to the conventional K-Best is about 3.5 dB at a BER of \(10^{-3}\). The performance of the LR-aided K-Best decoder approaches the ML performance as K increases. Furthermore, it is worth noting that the performance of LR-aided K-Best detector would have better performance at lower SNR compared to the conventional K-best decoder as the number of transmit and receive antennas increases [7].
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### ULDK Service Integration
#### 3.2.3 Data Organization  
Each road was linked to a list of traversed parcels. The file is presented in Figure 7. Additionally, a GeoJSON file with extended road information was created for each road, as can be seen in Figure 8. These included parcel IDs and road nodes located on each parcel. This comprehensive data structure efficiently managed multiple parcels associated with single road segments.  
Figure 6: Data of parcel 226101_1.0109.216_1.geojson located in the area of the Gdansk City.  
Figure 7: Parcel identifiers linked to roads in ’road_parcel_dict.json.
## 4 Cognitive Radio Combined with WBAN
### Tier-3 WBAN Model  
In a Tier-3 WBAN model, the APs communicate with the BNC and server (base stations) [23], Tier-3 delivers an extensive range of medical communications between doctors, emergency health-care services, caretakers, and the medical database in the hospital.
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
###### Abstract  
The inversion of ocean currents is a significant challenge and area of interest in ocean remote sensing. Spaceborne along-track interferometric synthetic aperture radar (ATISAR) has several advantages and benefits, including precise observations, extensive swath coverage, and high resolution. However, a limited number of spaceborne interferometric synthetic aperture radar (InSAR) systems are operating in orbit. Among these, the along-track baseline length is generally suboptimal, resulting in low inversion accuracy and difficulty in achieving operational stability. One of the approaches involves employing lower-frequency bands such as the L band to increase the baseline length to achieve the optimal baseline for a satellite formation. The LuTan-1 mission, the world's first L-band distributed spaceborne InSAR system, was successfully launched on 27 February 2022. L-band distributed formation operation provides insight into the development of future spaceborne ATI systems with application to new exploration regimes and under optimal baseline conditions. There are two novel aspects of this investigation: (1) We described the ocean current inversion process and results based on LuTan-1 SAR data for the first time. (2) A cross-track baseline component phase removal method based on parameterized modeling was proposed for distributed InSAR systems. Both qualitative and quantitative comparisons validated the effectiveness and accuracy of the inversion results.  
Keywords:along-track interferometry (ATI); LuTan-1; sea surface currents; synthetic aperture radar (SAR) +
Footnote †: journal: remote sensing