esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Variational Modal Decomposition of Electrical Load
### Variational Problem Establishment  
In this paper, the alternating direction multiplier method is used to continuously update each mode and its center frequency. The corresponding fundamental frequency band of each mode is gradually demodulated to obtain the center frequency. We assume that the original signal sequence is \(f(t)\), \(K\) is the number of center frequencies, and \(\{u_{k}(t)\}\) represent the components of IMF in the limited bandwidth. Based on the Hilbert transform, the one-sided spectral signal of envelope signal \(u_{k}(t)\) can be rewritten as \(\left[\delta(t)+\frac{j}{\pi t}\right]*u_{k}(t)\). The frequency of each IMF is modeled to the baseband based on the center frequency \(\omega_{k}\). It can be written as \(\left[\left(\left[\delta(t)+\frac{j}{\pi t}\right]\right)*u_{k}(t)\right]e^{- j\omega_{k}t}\).  
The Variational Problem (VP) based on each IMF component signal bandwidth can be written as  
\[\min_{\{v_{k}\},\{\omega_{k}\}}\left\{\sum_{k=1}^{K}\|\partial_{t} \bigg{[}\bigg{(}\delta(t)+\frac{j}{\pi t}\otimes\upsilon_{k}(t)\bigg{)}\bigg{]} e^{-j\omega_{k}t}\|_{2}^{2}\right\} \tag{1}\] \[\text{s.t.}\sum_{k=1}^{K}u_{k}=f \tag{2}\]  
Figure 1: Yearly seasonality load profiles.  
where \(\{u_{k}\}\) is the IMF component, \(\{\omega_{k}\}\) is the center frequency of each IMF component, \(\otimes\) is the convolution operator, \(\delta(t)\) is the Dirac distribution, and \(\partial_{t}\) is the partial derivative function.  
Lagrange Multiplier \(\lambda(t)\) and bandwidth parameters \(\alpha\) are introduced to turn the constrained Variational Problem into an unconstrained Variational Problem, and the extended Lagrangian function for the Variational Problem can be written as:  
\[\begin{split} L(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum \limits_{k}\|\partial_{t}\Big{[}\Big{(}\delta(t)+\frac{j}{\pi t}\Big{)}*u_{k}( t)\Big{]}e^{-j\omega_{k}t}\|^{2}\\ +\|f(t)-\sum\limits_{k}u_{k}(t)\|^{2}_{2}+<\lambda(t),f(t)-\sum \limits_{k}u_{k}(t)>\end{split} \tag{3}\]
## 2 Data
### Buoy  
Buoy surface wind measurements were obtained from the NOAA National Data Buoy Center (NDBC). Buoys consist of two types: either belonging to the Coastal-Marine Automated Network (C-MAN) stations, which use a 2-min data-acquisition period, or are moored buoys, which use an 8-min acquisition period. Anemometer heights at C-MAN stations vary widely, depending on site structure and elevation above sea level. The historical dataset provides unadjusted wind speeds as actually measured by station anemometers. Continuous measurements of wind speed and direction (the direction the wind is coming from, in degrees clockwise from true north) among others, are reported every 6 min, with a precision of 0.1 m s\({}^{-1}\) and an accuracy of 1 m s\({}^{-1}\) and a precision of 1\({}^{\circ}\) and an accuracy of 10\({}^{\circ}\), respectively. 10-m wind speed and direction were acquired for stations EINL1, AMRL1, FRWL1, CAPL1 and 42035 at the closest time of ASCAT-A and -B over the area of study.
# Point Cloud Segmentation from iPhone-Based LiDAR Sensors Using the Tensor Feature
## 3 Methodology  
Suppose the point p in the LiDAR point cloud consists of the coordinate, which can be shown as \(p(x\),\(y\),\(z)\). Based on tensor feature decomposition, the point cloud segmentation method assigns a label to each point \(p\) in difference subsets, and even gives them different geometric or semantic meanings. In this paper, the point cloud segmentation procedure is composed of the following steps:  
1. normal vector computation based on the initial tensor encoding.
2. tensor aggregation from the tensor encoded with normal information.
3. tensor feature decomposition and shape classification by tensor analysis.
4. point cloud segmentation according to the tensor clustering.  
The point cloud is collected from the LiDAR sensor and tensor features are encoded and assembled based on the tensor from the normal vector, then the point cloud dataset is segmented by the tensor feature clustering process, as shown in Figure 1.
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 3 Datasets and Data Preprocessing
### DeepMIMO Dataset  
To train our user resource allocation module, we leveraged a synthetic dataset generated via DeepMIMO [27], a versatile DL dataset specifically designed for millimeter wave and massive Multiple-Input Multiple-Output (MIMO) systems. DeepMIMO offers a diverse range of scenarios enriched with three-dimensional geometries, realistic user distributions, and detailed wireless network demands. DeepMIMO utilizes precise 3D ray-tracing simulations and accommodates a myriad of scenarios tailored to 5G wireless models, thus facilitating the creation of extensive MIMO datasets. For the evaluation of our systems, we employed an outdoor scenario ([[https://www.deepmimo.net/scenarios/ol-scenario/](https://www.deepmimo.net/scenarios/ol-scenario/)]([https://www.deepmimo.net/scenarios/ol-scenario/](https://www.deepmimo.net/scenarios/ol-scenario/)), accessed on 30 March 2025) set within a city block, populated with users as illustrated in Figure 1.  
This scenario includes 18 base stations with a height of 6 m, with each station being an isotropic antenna array element. The main street contains 12 base stations evenly placed on either side of the road. Consecutive stations are separated by 52 m. The remaining base stations are allocated along the secondary street, which runs perpendicular to the main street (as illustrated in Figure 1). The users within the scenario are organized into three uniform grids, culminating in a total user count of 1,184,923. Overall, this dataset was used to perform user resource allocation in 18 stations.  
However, our resource allocation module was trained over different versions of this scenario, meaning that not all users and base stations were selected for each training epoch.  
The ground-truth dataset for our user allocation model based on the DeepMIMO scenarios contains the following information: (i) the spatial position of users, (ii) the spatial position of base stations, (iii) the specific scenario from which the data are derived (that implies a 3D geometry to be \"learned\" by the user allocation model), and (iv) the allocation of each user with a corresponding base station. As for the dataset preprocessing procedure,  
Figure 1: DeepMIMO Outdoor scenario 1. Figure obtained from [27].  
linear normalization was applied along with the implementation of an outlier detection and trimming algorithm in Python (v.3.11.9).
## 3 Results and Discussion  
### Calibration Curves and Figures of Merit  
Matrix-matched calibration standards were generated for both linalool (in Chardonnay juice) and IBMP (in Pinot noir homogenate). These cultivars were selected because they did not have detectable concentrations of linalool or IBMP, respectively, by SPME-GC-MS. Figure 2 shows a representative DART-MS chronogram for IBMP and its internal standard spiked into Pinot noir homogenate, as well as a blank well. A minor interference at the quantifying transition for IBMP can be seen both the well containing only IBMP (Figure 2, left) and the blank well (Figure 2, middle). As with other SPME methods, the use of isotopically labeled standards during SPMESH sheet analyses is important for achieving good precision and accuracy during quantitative analyses to correct for discrimination during the extraction step [19; 23]. Similar results were observed for linalool in Chardonnay (data not shown).  
Previous work by our group on SPMESH sheet-DART-MS used only single stage Orbitrap-MS [18]. Linearity and limits of detection for the IBMP and linalool using MS-MS in comparison to our previous report is shown in Table 1. The limit of detection for IBMP was 10-fold lower in our current work (4 ng/L) as compared to previous work (39 ng/L) [18], an improvement that can credited to the use of MS-MS. Linalool detection limits (24 g/L) were reduced by half to previous work (48 g/L) [18], and were not improved with MS-MS methods (approximately 60 g/L, data not shown).  
Figure 2: DART-Orbitrap-MS chronograms of extracted ions for IBMP (2-isobutyl 3-methoxypyrazine) 167 > 124.0632 _m/z_ and \(d\)3-IBMP 170 > 127.0818 _m/z_ in Pinot noir homogenate (contains no native IBMP); (**left**) spiked with only internal standard, (**middle**) blank well and (**right**) spiked with both standard and internal standard.
## 5 Verification by Field Measurements
These locations were selected as they represent the extremes in terms of HNB to MNB path loss, as location 1 is located at macrocell edge and location 4 is located at the macrocell center with LOS propagation conditions. The measurements were performed by starting a HSUPA data session while the measurement UE was connected to the HNB and then forcing the HUE to transmit at the maximum allowed transmission level (0 dBm during the measurements), by walking away from the HNB, before leaving the femto building, still connected to the HNB, and causing the HUE to generate maximum possible uplink interference in the surrounding macrocell. By accessing radio network controller (RNC) data from the measurement time span, the potential uplink noise rise is found. The femto CPICH transmit power was 10 dBm during the measurements (e.g., case with rather aggressive coverage enhancement for the femtocell). The uplink noise rise measurements where performed at macrocell edge and at macrocell center. Figure 13 shows the measured noise rise and the simulated noise rise. The measurement and simulation results show a good match. The small differences between measured and simulated values are within the measurement accuracy and can be explained by the varying UE transmit power during the measurements due to the fading characteristics of the transmission channel between the HUE and the MNB and between the HUE and the HNB. Figure 12: Measured and simulated femto coverage.
# Computational Analysis of a 200 GHz Phased Array Using Lens-Coupled Annular-Slot Antennas
## 2 Lens-Coupled ASA Array Design  
The single ASA to be employed in the phased array (see Figure 1) was designed based on [PERSON] and [PERSON]'s work [12], as shown in Figure 2a. For operation at \(\sim\)200 GHz on high-resistivity silicon (with relative permittivity of 11.9 and resistivity > 20,000 \(\Omega\)-cm), the radius (_r_) and slot width (_w_) of the ASA are chosen as 106 \(\upmu\)m and 6 \(\upmu\)m, respectively. The far-field E-plane and H-plane radiation patterns in the silicon half-space (without a lens) are simulated using ANSYS HFSS and plotted in Figure 2b. The corresponding antenna return loss is 31.8 dB, and the directivity is 4.3 dB at 200 GHz. The 10 dB factional bandwidth of the return loss is 14%.  
Figure 1: (**a**) The configuration of the designed 200 GHz beam-steering phased array using five lens-coupled annular-slot antennas (the feeding points are shown as red squares), and (**b**) the sideview of the lens-coupled phased array (a 1 \(\times\) 5 array is depicted as the illustrative purpose).  
For realizing a phased array system, multiple such ASAs were parallelly placed with an element spacing (_d_) and attached to an extended hemispherical silicon lens, as shown in Figure 1. Here, \(d\) = 0.55A was chosen for effectively reducing the mutual coupling effect as well as suppressing the grating lobes [13, 14]. In addition, the silicon lens was designed with a radius \(R\) of 5000 \(\upmu\)m. This radius was chosen, so the lens is large enough as compared to the entire antenna array for the high antenna gain. Finally, the extension length \(L\) can be varied to adjust the directivity [15] for obtaining a relatively high gain and a reasonably low sidelobe level (SLL). The above configuration can be fabricated on a high-resistivity silicon substrate (with the desired thickness _L_) using a standard photolithography and lift-off processes [16]. Then, the substrate containing the array and feeding network [17] will be attached to a hemispherical silicon lens for testing.  
While the ASA array (without lens) is fed by an equal magnitude and unique progressive phase difference (the feeding points are shown as red squares in Figure 1a), the far-field radiation patterns (or power density) can be obtained by using the array factor (_AF_) technique [18]:  
\[AF=\sum_{n=1}^{N}e^{j(n-1)\psi}\text{where }\psi=kd\cos(\theta+90^{\circ})+\beta \tag{1}\]  
where \(N\) is the number of elements, \(k\) is the wavenumber in the dielectric half-space, _\(\beta\)_ is the progressive feeding phase difference, and _\(\theta\)_ is the beam angle from boresight. When _\(\psi=0\)_, the \(e^{j(n-1)\psi}=1\); therefore, the entire array will have maximum power density, and thus the beam angle (_\(\theta\)_) of the maximum power density can be obtained using (1). For a lens-coupled phased array, an extended hemispherical silicon lens bends the rays toward the boresight direction. Therefore, a larger gain can be achieved while having a relatively smaller scanning beam angle.
## 2 Modeling and Degradation Simulation
### Elementary Cell Model  
The PV cell model comprises interacting thermal and electro-thermal components. The thermal model assumes uniform heating from irradiance and electrical activity, using R-elements (\(Rx\), \(Ry\), \(Rz\)) for heat distribution and a C-element for volume, defined by material properties (specific heat \(c\), density \(\rho\)) and dimensions (\(l_{x},l_{y},l_{z}\)). The electro-thermal model (Figure 1), validated against one-diode models [16], includes a diode (\(R_{D}\)), parallel resistor (\(RS_{p}\)), and series resistor (\(RS_{s}\)), with thermal effects via RS-elements. Cell current is \(i_{cell}=i_{ph}-i_{D}-i_{RS}\), where \(i_{ph}\) reflects irradiance and temperature effects (see Appendix B for equations).  
Figure 2 illustrates panel construction by connecting cells, adaptable to any size, supporting scalable fault diagnosis.  
Figure 1: Schematic representation of an elementary photovoltaic cell model. (**a**) The bond graph illustrates thermal and electro-thermal interactions within the cell. (**b**) The equivalent block diagram summarizes the key system components for simulation and analysis.  
Figure 2: Diagram showing how elementary PV cells are interconnected to form a full photovoltaic panel. This modular structure enables scalability and fault simulation across various panel sizes.
# Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
## 3 Robust Localization via RTS Smoothing  
### Reliability of Measurement Information  
In the case of a GNSS signal loss, when there are no available GNSS measurements to update, the covariance matrix in Equation (21) tends to be infinite. The Kalman gain \(K\) tends to zero, so that when the measurement information is zero or differs too much from the predicted information, the update equation changes to the following form:  
\[\begin{array}{l}\textit{ffi}\widehat{\mathbf{x}}_{k+1}=\textit{ffi}\widehat{\mathbf{ x}}_{k}\\ \widetilde{\mathbf{P}}_{k+1}=\widetilde{\mathbf{P}}_{k}^{-}\end{array} \tag{29}\]
## 4 Discussion
This concept provides a better starting position with respect to a real-time realization that is planned soon. The analysis of the absorbance sequence after the BLC area analysis (unsmoothed DAS-F, see Figure 3) without further signal processing can be considered very critical in connection with a robust, accurate and sensitive concentration image estimation. Often-cited etalon noise (optical 1/f noise or low-frequency etalon) will also play a central role here [18; 20; 21]. Remote detection is highly prone to etalon phenomena, since the backscattered laser light quickly exhibits optical path differences in the range of a few millimeters and interferes at the detector (camera pixel). As a result, DAS-f and DAS-2f perform better than DAS-F in terms of concentration accuracy (variation or residuals) from a spatiotemporal point of view. This observation could be a result of the spectral filtering (e.g., Gaussian smoothing along the spectral dimension and spectral low-pass filtering) of DAS-f and DAS-2f, which, together with the BLC or the second derivative (spectral high-pass filtering), have a significantly different (spectral) bandpass filter effect than DAS-F. This hypothesis needs to be investigated in a more quantitative manner in the future. In particular, the baseline-free variant of DAS-2f also has the advantage of being used in situations where practically no BLC can be applied in an appropriate manner (e.g., spectral overlap of gas lines). Furthermore, WMS is often preferred to the conventional DAS method due to its higher sensitivity (or robustness against so-called \"1/f noise\"). Practical studies have shown a significantly smaller standard deviation in the concentration determination for WMS-2f compared to conventional DAS signal processing [18; 22]. The DAS-f and DAS-2f concepts introduced in this work might have the potential to reduce this gap between conventional DAS and WMS. However, this is beyond the scope of this work and needs to be addressed in the future. Finally, the DAS-2f approximation or principle also applies to other typical lasers sources in TLS, such as vertical cavity surface emitting lasers (VCSEL), diode lasers (DL) in the near-infrared or quantum cascade lasers (QCL), as the typical tuning behavior (mW/cm\({}^{-1}\)) in relation to the typical laser output power of the individual laser types is very similar.
# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 3 Methodology
### Thresholding for Anomaly Detection
#### 3.4.2 Sliding-Window-Based Dynamic Thresholding  
To address the limitations of static thresholding, sliding-window-based dynamic thresholding adapts the anomaly detection process to local changes in the data distribution. This method leverages a sliding window of size \(w\) to compute a moving average \(\mu_{w}\) and moving standard deviation \(\sigma_{w}\) of the reconstruction errors. The dynamic threshold \(\tau_{w}\) is then defined as follows:  
\[\tau_{w}=\mu_{w}+k\cdot\sigma_{w}, \tag{18}\]  
where \(k\) is a user-defined threshold factor (thFactor) that controls the sensitivity of the anomaly detector. Anomalies are identified by comparing the reconstruction errors \(\mathcal{E}_{\text{test}}\) to the dynamic threshold \(\tau_{w}\):  
\[\text{Anomaly}_{\text{dynamic}}(x)=\begin{cases}1&\text{if }\mathcal{E}_{\text{test}}(x)> \tau_{w}(x),\\ 0&\text{otherwise.}\end{cases} \tag{19}\]  
This approach is particularly effective in environments where the data distribution evolves over time, as it adapts to local variations in the reconstruction errors. However, it requires careful tuning of the window size \(w\) and the threshold factor \(k\) to achieve optimal performance. Additionally, the computational complexity of this method is higher than that of static thresholding due to the need to maintain and update the sliding window statistics.  
The choice of thFactor = 2.0 (\(k\)) is driven by its ability to balance sensitivity and specificity in anomaly detection. A threshold that is too strict may lead to excessive false positives, while a more lenient one might fail to capture real anomalies. Empirical findings from various real-world anomaly detection applications suggest that a factor of 2.0 provides an effective balance based on dataset characteristics. Additionally, this threshold factor is widely used in the anomaly detection literature and industry applications, making it a reliable and comparable choice for detecting deviations in time-series data.
## 5 Experimental Results and Evaluation
### Comparative Evaluation  
Table 2 presents the comparison between our SAUSA and previous Blockchain-based solutions to big data applications. The symbol \(\surd\) indicates that the scheme guarantees the security properties or implements some prototypes to evaluate system performance or other specifications. The symbol \(\times\) indicates the opposite case. Unlike existing solutions, which lack details on the optimal network framework for QoS or evaluations on the impact of applying Blockchain to big data applications, we illustrate a comprehensive system architecture, along with details on the SDN-based service and lightweight data authentication framework. We especially evaluated the performance (e.g., network latency, processing time, and computational overheads) of the Blockchain-enabled security mechanism in the data access authentication and integrity verification process.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Scheme** & **Blockchain** & **Storage** & **Performance** & **Security** & **Privacy** \\ \hline [17] & \(\times\) & DLT & \(\times\) & \(\surd\) & \(\times\) \\ \hline [18] & Green Blockchain & DLT & \(\surd\) & \(\surd\) & \(\times\) \\ \hline [6] & Ethereum & Fog Server & \(\surd\) & \(\surd\) & \(\times\) \\ \hline [7] & \(\times\) & DDS & \(\surd\) & \(\surd\) & \(\times\) \\ \hline [19] & \(\times\) & DLT & \(\times\) & \(\surd\) & \(\times\) \\ \hline [20] & \(\times\) & DDS & \(\surd\) & \(\surd\) & \(\times\) \\ \hline [21] & Ethereum & Storage Server & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline SAUSA & Ethereum & DDS & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison among existing Blockchain-based solutions.  
Figure 8: Processing time of meta-data operations: accessing Swarm and symmetric encryption.  
Regarding storage optimization and privacy preservation for point cloud data sharing, the hybrid on-chain and off-chain data storage structure not only reduces the communication and storage overheads by avoiding directly saving large volumes of raw data or audit proofs in Blockchain transactions, it also protects sensitive information by only exposing the references of encrypted meta-data on the transparent distributed ledger as the fingerprint proof. Unlike existing solutions, which rely on a centralized off-chain storage (e.g., centralized fog server or storage server) to store audit proofs, using a decentralized Swarm network as the off-chain storage is promising to enhance the robustness (availability and recoverability) of point cloud data sharing in multi-domain applications.
## 4 Uncertainty Analysis
### Model Selection and Application
#### 4.2.1 Traditional Machine Learning Models
The random forest model is popular among researchers due to its ease of implementation, high prediction accuracy, and lower tendency to overfit. Additionally, the random forest model can rank the importance of influencing factors, al lowing for the removal of irrelevant factors from the ranking list to achieve better prediction accuracy [107].