esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sample Characterisation
It is important to emphasise that heat treatments at higher temperatures of 1173 and 1273 K generate a decrease in the surface area accompanied by the respective loss of microporosity that can be associated with the loss of oxygenated groups on the surface. In the textural parameter determination from (DR) and (DA), models were evidenced that when the energetic heterogeneity described by the n factor of the (DA) equation approached 2, as in the DA model; the pore volumes calculated from both models coincide. With regard to the micropores' energy given by the E parameter of the (DA) equation compared with the adsorption energy Eo of the (DR) equation, it is noted that the values of Eo from the DR model are approximately three times greater than those calculated by the DA model. This comportment is indicative of the increase in the energy of the micropores when chemistry and geometric heterogeneity increase; it was generated by the impregnation process. Figure 2 shows the relationship between these two parameters. The pore size distribution, calculated from the Quenched Solid Density Functional Theory (QSDFT) model, is shown in Figure 3, which indicates the rough surface, the chemical heterogeneity of the activated carbons, and the effect that the impregnations caused on the microporous structure of the original sample G [18]. For samples in the study, the kernel that represented the best settings between 0.028 and 0.130% indicates that the activated carbons have a geometry composed of pores in a cylindrical and slit shape, with a high degree of activation that leads to significant microporosity. Figure 2: The relationship between the Eo parameters of the equation of DR and E of the equation of DA. Figure 3 shows that there are 4 peaks of micropores present in the samples around 4, 5, 6, and 11 A. The greatest amount is around 4 A; it is also noted that the carbonisation to 1273 K generates a loss of the microporosity by around 4 A, while the carbonisation to 1073 K allows access to new microporesity, which is reflected in the increase in micropores around 4 A and 5 to 7 A.
# Melodic Similarity and Applications Using Biologically-Inspired Techniques
## 3 Melodic Sequence Data
### Datasets  
Reliable analysis and modelling of similarity requires first and foremost datasets of unambiguous relationships between music items. [PERSON] [5] among others, makes a strong case regarding the validity of similarity ranking annotations, considering the paradigm differences of the listening experiments that generated them. However, he is more supportive to binary or definite annotations of similarity, such as songs known to be covers, or songs known to be related from musicological studies. Such data can be used to verify a computational model with regard to its retrieval or classification performance, since the distance for music items within a category should be less than the distance of items belonging to different categories. As such, this paper uses two datasets of symbolically represented melodies of varying size and nature, containing melodies that are considered related (e.g., covers of the same song) grouped into definite groups called either families, classes or cliques. Summary statistics for both sets are presented in Table 2.  
The Annotated Corpus of the Meertens Tune Collections [60], or TuneFam-26, is a set of 360 Dutch folk songs grouped into 26 \"tune families\" by Meertens Institute experts. Each contains a group of melody renditions related through an oral transmission process. For this dataset, expert annotators assessed the perceived similarity of every melody over a set of dimensions (contour, rhythm, lyrics, etc.) to a set of 26 prototype \"reference melodies\". In addition, the dataset contains 1426 annotated motif occurrences grouped into 104 classes, where \"motifs\" correspond to recurring patterns inside the melodies of a tune family. The Cover Song Variation dataset [61], or Csv-60, is a set of expert-annotated, symbolically-represented vocal melodies derived from matching structural segments (such as verses and choruses) of different renditions of sixty pop and rock songs. Csv-60 is inherently different from TuneFam-26 in two ways. First, the grouping of melodies into classes is certain: the songs were pre-chosen as known covers of songs of interest. Secondly, cover songs are typically not a by-product of an oral transmission process since cover artists have access to the original version.
## 4 Conclusions  
Our work aimed to develop a novel solution for micro pressure sensors for harsh marine environment applications. A wide-range MEMS piezoresistive micro pressure sensor was presented, which was fabricated with the optimized bulk-micromachining technology and packaged using Si-glass bonding technology. The pressure sensors achieved reliable performance in a harsh environment such as high pressure and high temperature while using a miniature size. To manage the moist and liquid external environment for marine application, a flexible polyimide protection layer was fabricated on the top surface of the micro sensor, which showed reliable performance and stability of protection. Meanwhile, a flexible digital compensation system was developed to calibrate linearity drift in a temperature range, which showed potential for large-scale applications with low cost. However, the sensitivity of the sensor was sacrificed for measurement range and stability to some extent. In future work, we will keep focusing on structure and material optimization of piezoresistive pressure sensors to enhance the performance of pressure sensors.  
Conceptualization, M.W.; methodology, M.J. and M.W.; validation, M.J., B.G. and Y.F.; writing--original draft preparation, M.J.; writing--review and editing, M.W., Y.C., B.J. and G.W.; supervision, M.W.; project administration, G.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China (2018 YFE0120000); National Natural Science Foundation of China (62104056,61771175); the Fundamental Research Funds for the Provincial Universities of Zhejiang (GK199900X001); Zhejiang Provincial Natural Science Foundation of China (LQ21F010010); Zhejiang Provincial Key Research & Development Project (2019C04003); Wenzhou Major Science and Technology Innovation Project (ZG2020016) China; Postdoctoral Science Foundation (2020 TQ0246, 2021M692638); Shanghai Sailing Program (21 YF1451000); and the Fundamental Research Funds for the Central Universities (31020200 QD013).  
Not applicable.  
The authors would like to thank the Institute of Flexible Electronics Technology of THU, Zhejiang. The authors are also grateful to Suzhou Institute of Nana-Tech and Nano-Bionics, Chinese Academy of Science.  
The authors declare that they have no known competing financial interests or personal relationships.  
Figure 9: (**a**) Output without temperature compensation. (**b**) Output with temperature compensation.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 2 Methods
### Performance Comparison  
Since a data imputation technique was applied to extrapolate the missing samples, next, the evaluation of the effectiveness of the proposed data imputation technique was performed. For this purpose, the dataset was trained and tested with a deep learning algorithm first without data imputation. Afterwards, the dataset was trained and tested with the same deep learning model over the imputed dataset. Finally, the comparison of the performance of both the traditional approach and the proposed imputation approach was performed. The results of this experiment are discussed in Section 3.
5G and 6G technologies, RADAR (phased array radar, tracking, and speed measurement RADAR), and wireless communications (front end receiver, transmitters and receivers components, varactor modulators) require a connector or transition that can connect with them and that can couple EM energy through their respective transmitter/receiver antenna systems and then transfer this energy into planar (MMIC or MIC) circuits for further processes, as well as in the reverse direction. The two most promising approaches to these transitions for satellite communication and military applications are mode transformation from the quasi-TEM mode of the microstrip line (MSI) to the rectangular waveguide (RWG) dominant mode (TE\({}_{10}\)), and impedance or filed matching among the two transmission lines [1,2]. Therefore, for the fulfillment of these requirements, many such waveguide-to-microstrip line transitions have been designed and developed based on the use of different coupling methods and techniques [1]. To continue this development, this paper presents a Ka-band rectangular microstrip-to-waveguide (MS-to-WG) transition for sharing power transfer, using a fishtail-shaped microstrip launcher.
## 2 Materials and Methods
### Deep Sequencing Data Analysis  
For each patient sample, the baseline (day 0) and the final (day 20) timepoints were sequenced. The raw read counts were transformed into counts per million (CPM), normalized using the cyclic loess method and grouped by gene (on average 12 shRNAs/gene). The [PERSON]'s rank sum _t_-Test [22] was used to compare the normalized CPM between day 20 and day 0 timepoints and calculate a p-value for each gene, which was corrected for multiple testing by computing FDR-adjusted _p_-values and Q-values based on [PERSON]'s method [23]. Finally, a ratio of Day 20 to Day 0 normalized CPM (referred to as fold change or FC) was then built, log-transformed and used to generate a volcano plot. An shRNA species, i.e., all shRNAs targeting the same gene, was regarded as significantly enriched or depleted when the corresponding Q-value was \(<\)0.05. As a measure of technical screen success, the abundance of the shRNAs directed against control genes _PSMA1, PSMA3, RPS13, RPL6_ and _RPL30_ was evaluated. A screen was regarded as successful if at least one of the control genes was significantly depleted (Q-value \(<0.05\), FC \(<-0.2\)). After the selection of successful screens, the corresponding data were re-analyzed starting from the raw read count. The analysis pipeline was the same as described above, with the difference that the grouping per gene was performed across all successful samples, instead of per patient. This step was implemented to increase the statistical power of the analysis and the probability to identify common trends within the analyzed group of samples. Genes were nominated as candidate dependencies when the depletion criteria (Q-value \(<0.05\) and FC \(<-0.2\)) were met.
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 5 Discussions
### Limitations and Potential Failure Conditions  
While the QP-DETR model demonstrates improvements in detecting QP events, there are potential limitations and conditions under which the model might fail. In regions with high levels of background noise, such as the artificial noise from EFD or internal satellite communication, the model's detection accuracy may decrease. Although we have introduced a proton cyclotron frequency limit to reduce false alarms, interference in the HPM payload data could affect the proton cyclotron frequency constraint, potentially leading to detection failures. The model may also struggle to detect QP events with very low intensities or indistinct texture features. In such cases, the confidence level of detection might be lower, leading to higher rates of missed detection. Additionally, although the QP-DETR model reduces the number of parameters, it still requires significant computational resources for training and inference. In resource-constrained environments, deploying the model might be challenging. By acknowledging these limitations, we aim to provide a clear understanding of the conditions under which the QP-DETR model performs optimally and the scenarios that may require further investigation and optimization.
# CPF-UNet: A Dual-Path U-Net Structure for Semantic Segmentation of Panoramic Surround-View Images
## 1 Introduction
### Motivation  
In recent years, deep learning techniques, especially Convolutional Neural Networks (CNNs) and Transformer architectures, have made significant progress in the field of semantic segmentation. Among them, UNet [11] and its improved versions are widely popular due to their excellent segmentation performance. For instance, UNet++ [12] and Unet3+ [13] retain the advantages of the original UNet's encoder-decoder structure while further enhancing segmentation accuracy by introducing more contextual information and multi-scale feature fusion mechanisms. Nevertheless, issues such as high computational complexity still persist. With the advancements of Transformers in the field of computer vision, more Transformer models have been applied to the UNet model. Recent advancements include models like TransUNet [14], Swin-UNet [15], and Swin-UNet++ [16], which integrate Transformer architectures with UNet structures to enhance segmentation performance. Although Swin-UNet++ has achieved improvements compared to Swin-UNet, its overall architectural complexity and computational complexity are still high, which may limit its deployment in practical applications. Thus, that UNet still plays an important role in various fields, and highlights the necessity for further improvements to the UNet network itself.  
To address the above issues, we propose a dual-path UNet architecture. First, to solve the problem of contextual information fusion in UNet, we fuse the third and last layers of UNet, integrating low-level and high-level information. In this context, we introduce the CAM (Channel Attention Module) and PAM (Position Attention Module) [17] attention modules, which significantly improve the segmentation results by modeling the rich contextual dependencies of local features. Subsequently, we fuse the third and last layers of UNet through FFM (Feature Fusion Module) [18], which utilizes the contextual information from the semantic branch to guide the feature responses of the detail branch. By providing guidance at different scales, we can capture feature representations at different scales, inherently encoding multi-scale information. This guidance method facilitates effective communication between the two branches.
## 2 Principle of WRIM for DLS  
### Regularization Inversion of DLS  
In DLS, PSD can be inverted from the normalized electric field ACF.  
\[g(\tau)\ =\ \int_{0}^{\infty}G(\Gamma)\exp(-\Gamma\tau)d\Gamma \tag{1}\]  
in which \(\Gamma\) is the decay linewidth, \(\tau\) is the delay time, and \(G(\Gamma)\) is the normalized distribution function of the decay linewidth. \(G(\Gamma)\) contains the PSD information and satisfies the conditions \(\int_{0}^{\infty}G(\Gamma)d\Gamma\ =\ 1\). According to the following three formulas, PSD can be solved form the ill-posed Equation (1).  
\[\left\{\begin{array}{l}\Gamma\ =\ Dq^{2}\\ q\ =\ \frac{4\pi l}{\lambda}\sin\left(\frac{\theta}{2}\right)\\ D\ =\ \frac{k_{B}T}{3\pi\eta d}\end{array}\right. \tag{2}\]  
in which \(q\) is the scattered vector, \(D\) is diffusion coefficient, \(\lambda\) is the wavelength of the incident light in vacuum, \(\theta\) is scattering angle, \(l\) is the refractive index of the solvent, \(T\) is the absolute temperature, \(k_{\rm B}\) is the Boltzmann constant, \(d\) is the particle diameter, and \(\eta\) is the solvent viscosity.  
To solve PSD, Equation (1) can be discretized as  
\[g\big{(}\tau_{j}\big{)}\ =\ \sum_{i\ =\ 1}^{N}G(\Gamma_{i})\exp(-\Gamma_{i}\tau_ {j}) \tag{3}\]  
in which \(j\) is the channel number of ACF and \(i\) is the grading number of the inversion particles. Equation (3) can be replaced as following matrix equation  
\[Ax\ =\ b \tag{4}\]  
in which elements of \(A\), \(x\), and \(b\) are \(a_{i,j}\ =\ \exp(-\Gamma_{i}\tau_{j})\), \(x_{i}\ =\ G(\Gamma_{i})\), and \(b_{j}\ =\ g(\tau_{j})\), respectively. \(i\) is the sampling point spaced at the particle size steps along the PSD range [\(d\)min, \(d\)max], and \(j\) is the channel number of the correlator. Theoretically, PSD can be obtained by directly solving Equation (4).  
However, because of the ill-posed nature of Equation (4), its accurate solution cannot be obtained in the practical application. We usually solve its approximate solution using the various optimization algorithms. The optimization solution of Equation (4) can be expressed as the following least square (LS) problem [33]:  
\[\|Ax-b\|_{2}^{2}\ =\ \min\ =\ x_{LS} \tag{5}\]  
In Equation (5), if ACF contains the noises, the solution \(x\) has a large deviation and even instability. The traditional Tikhonov regularization method is an effective way for this kind problem [34; 35]. The solution of Equation 4 can be written as [36]:  
\[M^{\alpha}[x,b]\ =\ \|Ax-b\|_{2}^{2}+\alpha\|x\|_{2}^{2} \tag{6}\]  
in which \(\alpha\) is regularization parameter. The regularization solution of Equation (6) is as follows:  
\[x\ =\ \min\{M^{\alpha}[x,b]\} \tag{7}\]  
In this paper, the regularization parameter is defined by L-curve criterion [37; 38].
## 3 Results and Discussion
### Physical Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Treatment** & **LN** & **AE (\%)** & **FMAP (g) \({}^{*}\)** & **AE (\%)** & **DMAP** & **AE (\%)** & **LAP** & **AE (\%)** & **RL** & **AE (\%)** & **RFM** & **AE (\%)** & **RDM** & **AE (\%)** & **RFM** & **AE (\%)** & **RFM** & **AE (\%)** & **RFM** \\
**(Unit)** & **LN** & **FMAP (g) \({}^{*}\)** & **FMAP** & **(g)** & **DMAP** & **(cm)** & **LAP** & **(cm)** & **RL** & **(g)** & **RFM** & **(g)** & **RDM** \\ \hline
**T1** & 39 & 00 & 104 \(\pm\) 10.08 c & 00 & 15.03 & 00 & 50 & 00 & 13 & 00 & 16 & 00 & 3.0 & 00 \\
**T2** & 97 & 148 & 226 \(\pm\) 8.01 a & 127 & 32.01 & 113 & 87 & 73 & 33 & 154 & 41 & 156 & 8.0 & 167 \\
**T3** & 84 & 115 & 186 \(\pm\) 6.54 b & 79 & 24.65 & 64 & 89 & 78 & 43 & 230 & 42 & 165 & 7.2 & 140 \\
**T4** & 50 & 28 & 109 \(\pm\) 3.51 c & 05 & 16.03 & 07 & 56 & 11 & 29 & 123 & 31 & 94 & 4.4 & 46 \\
**T5** & 42 & 08 & 94 \(\pm\) 5.68 c & \(-\)9 & 15.24 & 02 & 62 & 24 & 37 & 185 & 37 & 134 & 3.8 & 25 \\ \hline \end{tabular} \({}^{*}\) Averages followed by the same lowercase letter in the WMAP column, do not differ from each other by Tukey test at 5% probability. \({}^{**}\) Proper box = 11 kg.  
\end{table} 
Table 3: Number of leaves (LN), fresh mass of the aerial part (FMAP), dry mass of the aerial part (DMAP), length of the aerial part (LAP), root length (RL), root fresh mass (RFM) and root dry mass (RDM), average of 3 repetitions/treatment and the agronomic efficiency index (AEI, %) of each evaluated characteristic of the pepper plants.
## 2 Literature Review
### Feature Engineering  
For time series forecasting research, the existing methods mostly mine the time series data features through decomposition techniques; for example, decomposing the time series data into multiple sets of feature data and then using correlation regression models for forecasting [26]. However, such methods neglect the optimization value of the data density. Studies have shown that enhancing the temporal frequency can improve the prediction accuracy more effectively than traditional data enhancement means [27]. Based on this, this study proposes to convert low-frequency data into high-frequency sequences by means of a quadratic interpolation algorithm, which directly increases the size of the dataset in order to enhance the data. In terms of feature processing, in addition to increasing the dataset, WA has been proven to improve the prediction accuracy of a model [28]. WAstrengthens the impact of the key features and weakens noise interference by quantifying the indicator correlations and setting the weighting coefficients in a hierarchical manner, resulting in higher-quality input data. As for data grouping, correlation analyses, such as Pearson, have commonly been used to detect the magnitude of the correlation between indicators [29]. Meanwhile, a k-means clustering analysis is computationally efficient, works better in low-dimensional grouping problems, and the results can be visualized intuitively [30]. Combining these methods, we first use two correlation indicators, Pearson and Spearman, to check the correlation size between each group of indicators and the container throughput, and then use a k-means to group them together and assign different weights to each group in accordance with the combination of weights. Finally, through the synergistic optimization of data upscaling and feature weighting, a high-quality dataset adapted to this time series prediction task is constructed to provide better input data for the deep learning model.
## 2 Background
### ABAC IN IoT  
ABAC is a process of extracting a set of attributes and defining a policy from it. These policies were used to allow access for particular devices. If the device fails to pass the generated access control policy, it will be restricted from the system. The attributes are defined based on the specifications of the entities and their data [23; 24; 25; 26]. Hence, attributes differ for each data owner device.  
The attributes are defined based on the subject, object, environment and permission. Actually, an attribute consists of a name and its corresponding value. The main categories of attributes are elaborated below.  
* Subject Attributes: The subject attributes define the type of data captured by the IoT device.They include the characteristics of the device. For instance, in an IoT farm field, sensors will capture temperature, pressure or humidity for the specified time interval, but not all the devices can capture only a single datum; hence the subject attribute depend on the application used. Some of the subject attributes are time, location coordinates, position, etc.
* Object Attributes: Object attributes are illustrated as the attributes of the device individuality, i.e., personal constraints belonging only to the particular type of device. These attributes are defined by the manufacturer of the device which are unique for each device. A few object attributes are identity, registration number, model type, model name, and so on.
* Environment Attributes: The environment attributes are the application in which the device is used, counts of data, etc.
* Permission Attributes: This type of attributes defines the permission provided to a particular user that can be read, written, edited, so on.  
The preference of attributes for the access control policy is commonly performed by taking into account all the attributes. At present, IoT devices are popular and used globally for sharing data. The major challenges that exist in ABAC is scalability, resource utilization and heterogeneity. Due to the increase in the number of devices in the IoT, the challenges are critical. Designing an ABAC method, needs to concentrate on these challenges.