esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Multicast transmission is an attractive solution when a large number of users receive the same content in a wide area, for example, as with mobile TV. Ever since the multimedia broadcast multicast service (MBMS) was introduced in the 3 rd Generation Partnership Project (3 GPP), continuing work on the multicast transmission has been done and its importance is growing in the fifth generation (5G) cellular networks. The use cases of multicast transmission have been enlarged from mobile TV and public safety to vehicular-to-everything (V2X) and unmanned aerial vehicles (UAV). Recently, for group communications in public safety networks and for geographical information sharing in automotive, airborne and social networks, multicast transmission has been targeted at fewer users in a relatively small area, which has stimulated extensive research on the single-cell multicast transmission scheme. In the proposed single-cell multicast transmission scheme, a supplementary multicast channel is additionally assigned in a single-cell multicast transmission scheme to exploit channel diversity. The resource allocation is adaptive to the channel variations of the users (responsive to users QoS needs), using channel feedback from the users, in contrast with previous approaches where resources were determined conservatively. An optimal resource allocation problem to minimize the required bandwidth while enabling every user to obtain the target multicast rate is formulated as a convex problem and an iterative algorithm is proposed in a computationally efficient way. Performance is evaluated mathematically and through intensive simulations, where other cell interference is considered using a fluid model. The proposed single-cell multicast transmission scheme provides benefits in comparison to existing multicast schemes in the simulations, under a set of various parameters including the number of multicast users and channel correlation between the multicast channels.  
Keywords:single-cell multicast transmission; radio resource allocation; optimal problem; supplementary multicast channel; public safety networks +
Footnote †: journal:
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 2 Basics of BS Information and LTE Performance Metrics
### BS-Related LTE Physical-Layer Basics  
A public land mobile network (PLMN) [26] is a network that uses Earth-based BSs to provide mobile communication services to terrestrial subscribers. It is identified by the combination of a mobile country code (MCC), which represents a country or geographic area, and a mobile network code (MNC), which is unique to each operator. The PLMN's structure and communication framework are critical for identifying and utilizing the signal data transmitted by the LTE network. The LTE network is organized into tracking areas to optimize mobility management and resource allocation. A tracking area is a logical region that allows a user to move within it without notifying the network. Each tracking area contains multiple BSs, which cooperate to manage the user equipment within the area. The tracking area code (TAC) uniquely identifies each tracking area, and understanding its distribution is important for interpreting signal data from different locations, particularly when assessing the impact of rainfall on signal quality.  
The evolved Node B (eNodeB) is the base station in the LTE network, each of which is assigned a unique eNodeB ID. A single eNodeB typically covers several cells, and each cell is identified by a cell ID. The physical Cell ID (PCI) is used as a physical-layer identifier for a cell, enabling user equipment to decode the transmitted data. The knowledge of which eNodeB and cell a user terminal is connected to is crucial for correlating changes in signal quality to environmental factors (e.g., rainfall). When a smartphone moves between cells, the PCI value changes, and these transitions can be tracked to detect variations in signal parameters caused by weather conditions.
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 7 Conclusions  
This paper presents case study results where a machine learning model was developed to detect ten different conditions of a rotating machine using only acceleration data with the MATLAB Classification Learner Application. In this study, two approaches were explored: (1) programming a Decision Tree model in MATLAB and (2) using MATLAB Classification Learner to identify the best-performing model. The results showed that acceleration data alone is sufficient for detecting machine conditions without the need for additional sensors like tachometers or microphones. The Quadratic Support Vector Machine (SVM) was the top model, achieving over 99.7% accuracy using just 11 key predictors while reducing training time by over 60% compared to models using all 58 predictors.  
However, when evaluating the model on new datasets, a significant drop in test accuracy was observed, indicating several reasons for the drop. Often, this is because the original training data may not have been diverse enough to generalize well to unseen data. Additional models trained on different datasets faced similar declines, highlighting a potential need for a larger and more representative dataset for generalization. But, again, this may be due to the difference in the data frequency as the dataset with similar frequency was initially chosen for training, verification and testing. The following steps will focus on expanding the training dataset to include more diverse records and improving feature selection to better capture the important patterns. But again, it is important to note that one of the key parameters for selection was the rotational frequency. If the rotational frequency changes, this can create tremendous variation in predictor characterization. Therefore, dynamic time warping could be applied to absorb the variation caused by the change in frequency and other regularization techniques to address the potential overfitting.  
Conceptualization, H.H.H. and C.-H.M.; Methodology, H.H.H. and C.-H.M.; Software; H.H.H.; Validation; H.H.H. and C.-H.M.; Writing--original draft preparation, H.H.H.H.; Writing--review and editing, H.H.H. and C.-H.M.; supervision, C.-H.M.; Project Administration, C.-H.M. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the University of St. Thomas research grant and the APC was funded by the University of St. Thomas, Department of Electrical and Computer Engineering.  
Data Availability Statement: Data can be publicly found from Machinery Fault Dataset. Kaggle. Available online: [[https://www.kaggle.com/datasets/uysalserkan/fault-induction-motor-dataset/discussion/361583](https://www.kaggle.com/datasets/uysalserkan/fault-induction-motor-dataset/discussion/361583)]([https://www.kaggle.com/datasets/uysalserkan/fault-induction-motor-dataset/discussion/361583](https://www.kaggle.com/datasets/uysalserkan/fault-induction-motor-dataset/discussion/361583)) accessed on 23 September 2024.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Further Detection of Phase Conversion by XRD  
To further confirm the change of the PVDF crystal phase in the PVDF-HFP membranes, X-ray diffraction was conducted on these membrane samples. Figure 4 shows the amorphous and both the \(\alpha\) and \(\beta\) crystalline phases of PVDF in the PVDF-HFP electrospun membranes with or without post-treatment. It can be clearly seen that the as-spun electrospun membrane has many more regions containing the amorphous phase of PVDF as the peaks representing the \(\alpha\) and \(\beta\) phases have been merged, leading to a much broader and weaker peak. It is worth noting that the amorphous state of the PVDF cannot be detected by FT-IR as discussed in last section. After heat treatment, the regions of the amorphous-state PVDF reduced greatly as two sharp peaks at 18.4\({}^{\circ}\) and 20.8\({}^{\circ}\) appeared, representing \(\alpha\) (020) and \(\beta\) (110), respectively [29; 30]. The sharp increase in the crystallinity of the PVDF-HFP membrane resulted in a great change of the membrane properties, especially the contact angle and LEP, which will be discussed in later sections. Annealing of the heat-pressed membrane can further improve the crystalline structure of the PVDF-HFP membrane in a favorable way. Due to the non-polar structure, the membrane composed of more regions of the \(\alpha\)-phase PVDF had higher hydrophobicity, leading to a higher wetting resistance. While the total crystallinity maintained was nearly unchanged,  
Figure 3: FT-IR (Fourier-transform infrared) spectra of as-spun and thermal -treated electrospun membranes.  
annealing the heat-pressed membrane for one day at 120 \({}^{\circ}\)C could convert the \(\beta\) phase to the \(\alpha\) phase as the relative peaks varied. After the membranes were further annealed for another day, both the \(\alpha\) phase and crystallinity were increased, leading to the increase in the thermal and mechanical stability [30]. More interestingly, a sharp new peak with a high intensity at 28.1\({}^{\circ}\) appeared, which is very close to \(\alpha\) (111) having a two-theta angle of 27.8\({}^{\circ}\). Considering the various new peaks representing the \(\alpha\) phase PVDF crystal appearing in the FT-IR in the A2 membranes, we assumed that the peak at the 2\(\theta\) angle of 28.1\({}^{\circ}\) was \(\alpha\) (111) [30,32]. The high intensity of \(\alpha\) (111) means a big increase in the \(\alpha\) phase PVDF as well as the total crystallinity, so we believe that more amorphous phase of the PVDF along with the \(\beta\) phase was converted to the \(\alpha\) phase. A3 (the membrane annealed for three days) shows identical spectra of XRD results as A2, so it is not shown in Figure 4.
# Global and Local Information Adjustment for Semantic Similarity Evaluation
## 3 Materials and Methods
### Capsule Network  
In this study, a capsule network using CNN is used to extract local features [10; 11; 12; 13]. The capsule network consists of two CNNs, _Conv_1 and _Conv_2. In this study, \(c\) extracted from self-attention is used as the input value of the capsule network. Conv1 proceeds as follows:  
In Equation (9), \(f(\cdot)\) refers to the activation function, \(i\) refers to the index of the word, and \(h\) refers to the kernel size. \(w_{k}\) refers to a learning weight for the convolution filter and has a matrix of \(\mathbb{R}^{h\times U}\), and \(b_{k}\) refers to the bias vector. _Conv_1 is a typical CNN, extracting combined information of adjacent words as much as \(h\) does. Through this, features for _Conv_1 are created. Finally, _Conv_1 has a matrix of \(\mathbb{R}^{(L-h+1)\times N}\) when the length of the sentence is \(L\) and the number of filters that increase the amount of learning is \(N\).  
\[Conv1_{i}=f\Big{(}w_{k}*c_{i-\frac{h-1}{2};i+\frac{h-1}{2}}+b_{k}\Big{)} \tag{9}\]  
_Conv_2 uses the features created in _Conv_1 as an input value. _Conv_2 is processed by Equation (9), like _Conv_1, but it uses the filter size corresponding to the entire size of input values to subdivide the expression of the entire sentence [13; 20]. The features of _Conv_2 extracted in this way have a matrix of \(\mathbb{R}^{1\times N}\) by the number of filters \(N\). After that, PrimaryCaps with \(\mathbb{R}^{D\times\frac{N}{D}}\) dimension is created by reconstructing the number of dimensions of the _Conv_2 function. Here, PrimaryCaps means capsules that have subdivided the entire sentence information into \(D\) pieces. To normalize the size of the vector, PrimaryCaps uses squash, a nonlinear function [10; 11; 12; 13]. Squash is defined as follows:  
In Equation (10), \(P_{j}\) means the one capsule. In this study, PrimaryCaps with squash applied are used as local features.  
\[v_{j}\ =\ \frac{\|P_{j}\|^{2}}{1+\|P_{j}\|^{2}}\frac{P_{j}}{\|P_{j}\|} \tag{10}\]
# Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity
## 4 Numerical Results
### Gradual Addition of Features with Reverse Order of Importance  
Feature importances can be used to perform feature selection. That is, a subset of the most important features can be selected to construct a simpler machine learning model.  
This concept was investigated for both heights using Figure 6. Starting from the most important feature (as calculated from XGBoost) for each height, new features are progressively added according to their importance. After the addition of each new feature, the model's performance is evaluated.  
It is evident that the incorporation of features with the lowest importance does not lead to significant performance improvement.
## 4 Distributed Optimal Dispatching Model and Algorithm of MGs and ESP Based on ADMM
### Distributed Optimal Algorithm  
Algorithm 1 summarizes the distributed optimal algorithm of MGs and ESP in coalition, which is shown as follows.  
Figure 3: The flowchart of the optimization solution for individual MG. BESS: battery energy storage system.  
Algorithm 1, which is a two-level iterative algorithm, has a good performance for minimum total cost including purchasing electricity cost, energy storage cost and power loss cost. Procedures (5)-(12) belong to the inner layer. In Procedures (5)-(12), MGs make optimal scheduling considering the power loss of the last iteration and taking minimum purchasing electricity cost and energy storage cost as the objective. In addition, power loss is updated through the inner layer. If the power loss of the current iteration and the last iteration is eventually the same in the case of limited error, the distributed optimal algorithm converges to the optimal solution.  
More specifically, ADMM is adopted for optimal scheduling of MGs in coalition in Procedures (5)-(12), in which electricity cost (the sum of purchasing electricity cost and energy storage cost) and power loss cost are optimized alternately. First, the scheduling scheme is made by minimizing the local cost of each MG based on the power loss of the last iteration. Second, expected exchange power \(x_{n}^{in}\) and \(x_{gridn}\) can be calculated combined with the power balance constraint, which are informed by the ESP. Third, we have adjusted expected exchange power \(\mathbf{B}\mathbf{w}\) by taking the minimum power loss cost as the objective considering the expected exchange power from MGs. At last, the Lagrange multiplier is updated according to expected exchange power \(x_{n}^{in}\) and \(x_{gridn}\) and adjusted expected exchange power \(\mathbf{B}\mathbf{w}\) until the convergence condition is satisfied.  
In general, on the basis of the satisfaction of power demand, optimal scheduling is achieved with a comprehensive consideration of purchasing cost, energy storage cost and power loss cost. In addition, only expected exchange power is needed in the bidirectional interaction between MGs and ESP, which protects the privacy of MGs and ESP.
## 6 Experimental Results
### Failure Modes  
The system evaluation results in the previous section were presented for the 50 charging attempts, which were selected from the attempts made by the bus drivers during the period of data acquisition in December 2022. The bus depot area contained three chargers located in close vicinity to each other (Figure 7). Since the end-user, for safety reasons, requested a fully automated procedure to support docking to the chargers, without the driver having to interfere with the ADAS system, e.g., by selecting options via buttons, a scenario was assumed whereby the bus drivers should use charger number one, which is located closest to the open space of the bus depot. However, in practice, the drivers also used the other two, for example, if charger number one was just occupied by another bus. Although the resolution of the RTK GNSS localisation system makes it possible to distinguish these three chargers by their location, it is impossible to tell which charger the driver wants to dock to when he/she starts the manoeuvre, as in fact, the initial parts of the trajectories are almost the same for all these chargers located close to each other and in one line. In addition, the driver can change his/her decision about which charger he/she wants to dock during the manoeuvre if an unexpected obstacle arises, such as a person passing through the depot area. Whereas it would be possible to ask the drivers to select the charger they want to dock to using the HMI, e.g., with a touchscreen, a dynamic selection of the charging station was concluded too confusing to the drivers due to a rapid change in the recommended driving cues. Therefore, we had to consider all attempts to dock to another charger than the one selected in the scenario as failures of the ADAS solution. An example of a case when the driver was approaching a different charger than our system planned is presented in Figure 16. This demonstrates a major drawback of the presented approach, as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**xmean** & **xstd** & **ymean** & **ystd** \\ \hline
0.063 m & 0.219 m & \(-\)0.004 m & 0.077 m \\ \hline \hline \end{tabular}
\end{table}
Table 6: The mean and standard deviation values of a localisation error of a bus position under the charger at the end of the docking manoeuvre. The values were calculated based on the data from Table 5.  
Figure 15: The average deviation between the planned and actual path in relation to the distance from the charger. The average value was calculated based on all 50 sequences.  
a completely passive system is not able to tell if the driver wants to approach the left or right charging station if they are mounted on the same pylon.  
As can be noted, regardless of the charger selected by the driver, the beginning of the trajectory starts approximately at the same position. Unfortunately, this makes it impossible for our system to reliably predict which charging station the driver is going to approach.
# Impact of Groundtrack Pattern of a Single Pair Mission on the Gravity Recovery Quality
## 5 Conclusions  
This paper examines the impact of sampling patterns on gravity recovery quality for global and regional studies when the Colombo-Nyquist and modified Colombo-Nyquist rules apply. For the regional study, the groundtrack pattern impact of different satellite constellation scenarios have been studied for a hydrological basin in central Africa. The quality of the gravity products were assessed by different metrics, e.g., by spatial covariance representation. The possible meaning of the sub-cycle concept in terms of global and local impacts was also investigated by different repeat-orbit scenarios with even and odd parities where different solution scenarios in terms of the original and modified Colombo-Nyquist rules have been discussed. Our results emphasize the impact of maximum harmonics of the recovery (both global and regional), the influence of sub-cycle on local gravity recovery, and the mission formation impact on recovery error.  
Conceptualization, S.I.-P.; Methodology, S.I.-P., M.W. and N.S.; Software, S.I.-P., M.W. and N.S.; Analysis, S.I.-P.; Writing-Original Draft Preparation, S.I.-P.; Writing-Review and Editing, S.I.-P., M.W. and A.-S.; Visualization, S.I.-P.  
This research was funded by Iranian National Elites Foundation as a grant for guest researchers at University of Isfahan, Iran.  
The authors declare no conflicts of interest.
## Materials and Methods
### Volatile Compound Analyses  
A headspace solid-phase micro-extraction was used to extract volatile compounds from the wine according to a published method [51]. Briefly, the wine sample (5.0 mL) was mixed with NaCl (1 g) and 1.0018 g/L 4-methyl-2-pentanol (the internal standard, 10 mL) in a 15 mL vial containing a stirring bar. The vial was then capped with a PTFE-silicon septum and heated at 40 \({}^{\circ}\)C for 30 min under agitation. Afterwards, a SPME fiber (50/30 \(\upmu\)m DVB/Carboxen/PDMS, Supelco, Bellefonte, PA, USA) was placed into the head-space of the mixture in the vial to extract the volatile compounds for 30 min under the same agitation and temperature. Finally, the fiber was removed from the head-space of the mixture and immediately inserted into the GC injector for volatile desorption for 8 min. An Agilent 6890 GC interfaced with an Agilent 5975B MS (Agilent Technologies, Santa Clara, CA, USA) was used to analyze the volatile composition in the wine. The volatile compounds were separated on a HP-INNOWAX capillary column (60 m \(\times\) 0.25 mm id, 0.25 \(\upmu\)m film thickness, J&W Scientific, Folsom, CA, USA) under a carrier gas (helium) flow rate at 1 mL/min with a splitless GC inlet mode. The oven temperature program was as follows: 50 \({}^{\circ}\)C held for 1 min and then rose to 220 \({}^{\circ}\)C at 3 \({}^{\circ}\)C /min and held at 220 \({}^{\circ}\)C for 5 min. A voltage of 70 eV was set in the electron impact mode and the mass scan was conducted from _m/z_ 20-450 using a selective ion mode. The analysis for each wine sample was conducted in duplicate. A C7-C24 _n_-alkane series (Supelco) and the same chromatographic procedure was used to calculate the retention indices. The identification of volatile compounds with the available standard was performed by matching their retention time and mass spectrum with their corresponding standard. For the volatile compounds without the available standard, these volatiles were tentatively identified according to their mass spectrum and retention indices with the standard NIST 11 library and NIST Standard Reference Database. The reference standards were dissolved in a synthetic matrix solution containing 9 g/L tartaric acid, 12% (_v/v_) ethanol, and 10 g/L glucose. The matrix was adjusted to pH 3.8 using 5 mol/L NaOH, and then 10 \(\upmu\)L internal standard solution (4-methyl-2-pentanol, 1.0018 g/L) was added.