esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning Class-Specific Features with Class Regularization for Videos
## 3 Regularization for Convolutional Blocks
### Class Regularization for Visualizations  
As _Class Regularization_ is based on the injection of class-based information inside the feature-extraction process, a direct correlation between classes and features is made at each block in which the method is applied. Being able to represent the class features given a different feature space improves the overall explainability capabilities of the model. Through feature correlation, the method alleviates the curse of dimensionality problem of previous visualization methods that rely on back-propagating from the predictions to a particular layer [28]. Since the classes are represented in the same feature space as the activation maps of the block, we can discover regions in space and time that are informative over multiple network layers. To the best of our knowledge, this is the first method to visualize spatiotemporal class-specific features at each layer of the network.  
By extending the proposed algorithm to include an adaptation of _Saliency Tubes_[29] in each block, we can create visual representations of the features with the highest activations per class. We create two visual examples of the class _bowling_ in Kinetics-400 to demonstrate class activations in different layers of the network (Figure 2). As observed in the clip segment, in both cases, early layer features are significantly less deterministic of the class and mostly target the distinction between foreground and background. In later layers, the focus shifts from the actor to the background in the predictions layer of the first clip. Ball and bowling pins are present at a wallpaper, which demonstrates that a strong still-frame visual signal is favored in case the action or objects within the action are occluded. In the second clip, the main field of focus of the network in the final layers is towards the area between the actor's hand and the ball. We note that, by design, all network architectures used fuse all temporal activations to a single frame at their final convolution block, which only allows the visualization of spatial extension of the activations.  
The amplification of layer features can also be visualized as in Figure 3. The top-3 kernels to be amplified for a baseball hit example correspond to spatiotemporal features such as the appearance of the bat, the field, and the movement of the bat during a swing. In addition, these amplifications are also propagated to deeper layers in the network through the connections of the most informative kernels.
## 2 Materials and Methods
### Synthesis  
All silver(I) complexes were synthesized by employing a self-assembly approach. To a solution of AgNO\({}_{3}\) (0.17 g, 1.0 mmol) in H\({}_{2}\)O (10 MI, deionized), then, an ethanolic solution of pyridine-3-carboxaldhyde (0.19 mL, \(\sim\)2.0 mmol), 2-pyridone (0.095 g, 1.0 mmol) or 3-hydroxypyridine (0.19 g, 2.0 mmol) added drop by drop with stirring. The solution was kept at ambient for few days to give colorless crystals of the target complexes. The crystals were filtered off, washed with small amount of ethanol, and dried in air. FTIR and NMR spectra of the three complexes are given in Figures S1 and Figure S2 (Supplementary data), respectively.  
[Ag(pyridine-3-carboxaldhyde)\({}_{2}\)NO\({}_{3}\)], **1**[26], Yield: 0.15 g, 80% with respect to the ligand. (%); Calc.: C, 37.52; H, 2.62; N, 10.94; Ag, 28.08. Found: C, 37.49; H, 2.63; N, 10.94; Ag, 28.10. \({}^{1}\)H NMR (DMSO-\(d_{6}\), 500 MHz): 8 7.64 (t, 1H at C-5), 8.24 (d, 1H at C-4), 8.28 (d, 1H at C-6), 9.06 (s, 1H at C-2) and 10.08 (s, 1H at C-7). \({}^{13}\)C NMR (DMSO-\(d_{6}\), 125 MHz): 8 125.16, 125.18 (C-5), 131.90, 131.95 (C-3), 136.89, 137.14 (C-4), 152.10, 152.22 (C-2), 155.40, 155.40 (C-6), 192.23, 192.24, 193.17, 193.82 (C-7 of aldehyde).  
[Ag\({}_{3}\)(2-pyridone)\({}_{3}\)(NO\({}_{3}\))\({}_{3}\)], **2**[27], Yield: 0.196 g, 74% with respect to the ligand. (%): Calc.: C, 22.66; H, 1.90; N, 10.57; Ag, 40.71. Found: C, 22.67; H, 1.91; N, 10.57; Ag, 40.70.  
\({}^{1}\)H NMR (DMSO-\(d_{6}\), 500 MHz): 8 6.20 (t, 1 H, H-4), 6.33 (d, 1 H, H-3), 7.39-7.41 (m, 2 H, H-5, H-6), 11.61 (s, 1 H, NH, exchangeable with D\({}_{2}\)O); \({}^{13}\)C NMR (DMSO-\(d_{6}\), 125 MHz): 8 106.57, 106.81 (C-5), 119.59, 119.83 (C-3), 137.04, 137.33 (C-6), 141.65, 141.89(C-4), 163.16 (C=O).  
[Ag(3-hydroxypyridine)\({}_{2}\)]NO\({}_{3}\), 3 [28], Yield: 0.31 g, 86% with respect to the ligand. (%): Calc.: C, 33.36; H, 2.80; N, 11.67; Ag, 29.96. Found: C, 33.36; H, 2.81; N, 11.66; Ag, 29.97. \({}_{1}\)H NMR (DMSO-\(d_{6}\), 500 MHz): 8 7.2-7.4 (m, 2 H, H-4, H-5), 8.01 (d, 1 H, H-5), 8.12 (d, H, H-2), 10.05 (s, 1 H, OH, exchangeable with D\({}_{2}\)O); \({}^{13}\)C NMR (DMSO-\(d_{6}\), 125 MHz): 8 123.26, 123.68 (C-4), 125.00, 125.37 (C-5), 139.82, 139.09 (C-2), 141.25, 141.49 (C-6), 154.50 (C-3).
## 3 Results and Discussion
### Economic Analysis  
According to the data provided by the Logistics Support Department of the university, the price of electricity at this university is 0.505 Yuan/kWh. Thus, the annual electricity costs can be reduced by about 146 thousand Yuan after the reformation according to the results presented in Section 3.1. The payback period refers to the number of years required to recover the investment. The shorter the payback period, the more favorable the project. It is defined as Equation (3):  
\[payback\ period=initial\ investment/cost\ saving\ per\ year \tag{3}\]  
The investment in energy efficiency improvement and the operation optimization of a solar water heating system included the design cost, technology development cost, equipment cost, material cost, transformation project cost, automatic control system development cost, operation commissioning cost, etc. The total investment for the reformation of each student apartment would be about 200 thousand Yuan, and the annual electricity saving would be about 146 thousand Yuan. Thus, the static investment recovery period would be about 16 months. The lifetime of the air source heat pump unit used in this system is 15 years.
## 5 Packet Length Control for Sparse Signal Transmission
### Relationship between Communication Parameter and Mutual Coherence
#### 5.2.2 Threshold of PRR for Reliable Signal Reconstruction  
Therefore, given the required packet reception rate \(p_{threshold}\), appropriate parameter selection can achieve accurate sparse signal transmission as long as the following condition is satisfied:  
\[P_{prr}=(1-Q(\sqrt{2\beta}))^{L}\geq p_{threshold} \tag{20}\]  
where:  
\[p_{threshold}=\frac{M}{N}=\frac{cK\log{(N/K)}}{N} \tag{21}\]  
with \(K\) being the signal sparsity and \(c\) a constant.  
Figure 9 shows the packet length effect on the relationship between \(BER\)and mutual coherence using RCS projection, which shows that mutual coherence increases when the channel condition becomes worse and the packet length becomes longer. Considering specific applications (specific signal sparsity) and the relationship between mutual coherence and recovery error, as shown in Figure 7, the application's requirements can be met through appropriate parameter selection.  
Figure 9: Packet length effect on the relationship between \(BER\) and mutual coherence using RCS projection.
## 2 Materials and Methods
### Methodology  
After the pre-processing of the UAV-LiDAR point cloud data, CHM and normalized point clouds were generated (Section 2.3.1). Then, the accuracy of ITD and the seed point selection for all three methods (local maxima, local minima, and mean shift algorithm) were evaluated based on CHM or normalized point clouds in the leaf-off season (Section 2.3.2, Figure 3). The method with the highest accuracy was applied to extract two seed point types that could be used for the ITS in the leaf-on season (Section 2.3.3). After re-screening the seed points, three seed-based ITS methods (Seeds + Kmeans, Seeds + Dalponte2016,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Plot ID**} & **Tree Number** & **Planting** & **Point Cloud Density pts-m\({}^{-2}\)** & \multicolumn{2}{c}{**Tree Height (m) Leaf-Off**} \\  & **Leaf-Off/Leaf-On** & **Spacing (m)** & **Leaf-Off/Leaf-On** & **Max** & **Min** & **Mean** \\ \hline Y8-1 & 57/57 & 4 \(\times\) 6 & 12.61/29.91 & 22.23 & 19.76 & 21.17 \\ Y11-1 & 42/42 & 4 \(\times\) 8 & 22.21/55.7 & 26.29 & 21.95 & 24.69 \\ Y12-1 & 23/22 & 3 \(\times\) 5 & 9.49/45.67 & 26.63 & 23.57 & 25.39 \\ Y12-2 & 25/25 & 3 \(\times\) 5 & 9.14/45.75 & 26.13 & 23.90 & 25.17 \\ Y14-2 & 53/50 & 3 \(\times\) 8 & 18.49/35.27 & 24.63 & 21.97 & 23.55 \\ Y14-4 & 39/39 & 3 \(\times\) 8 & 17.91/34.83 & 27.09 & 23.24 & 25.55 \\ Y16-1 & 18/18 & 6 \(\times\) 5 & 10.99/41.94 & 28.90 & 25.40 & 27.56 \\ Y16-2 & 31/30 & 6 \(\times\) 5 & 27.67/55.06 & 30.95 & 27.05 & 29.15 \\ Y16-3 & 29/29 & 6 \(\times\) 5 & 27.31/56.54 & 30.17 & 26.48 & 28.59 \\ Y17-1 & 48/48 & 6 \(\times\) 5 & 22.45/73.92 & 30.58 & 25.95 & 28.18 \\ Y17-2 & 40/39 & 6 \(\times\) 5 & 17.48/39.83 & 27.38 & 24.36 & 25.92 \\ Y20-3 & 24/22 & 5 \(\times\) 6 & 36.47/101.65 & 33.71 & 31.98 & 32.74 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of poplar plot per site locations.  
Figure 2: Examples of UAV-LiDAR data in leaf-off and leaf-on conditions. (**a**) Eleven stand age poplar plantation plots from the leaf-off season; (**b**) seventeen stand age poplar plantation plots from the leaf-off season; (**c**) eleven stand age poplar plantation plots from the leaf-on season; (**d**) seventeen stand age poplar plantation plots from the leaf-on season.  
Seeds + Silva2016) were conducted (Section 2.3.3), comparing four direct ITS methods (Dalponte2016, Li2012, PTrees, LayerStacking). A new segmentation refining method was used to improve the accuracy of crown delineation and ITS in all sample plots (Figure 3).
## 4 Results and Discussion
### Performance Results
#### 4.2.3 Training Speed  
The value of batch size and the compute capability of the processor affect the training speed. We first compare the training time (per epoch) when using different batch sizes on the same device. We choose the Titan RTX as the platform because it is fast enough and has enough memory. Figure 9 shows that the training speed increases with the increase in batch size, but the growth rate gradually slows down.  
Meanwhile, we compare the training speed of the CPU and GPUs in Figure 10. Due to the limitation of the memory space of the RTX 2080 Ti and RTX 2060 Super, we use a batch size of 8 and 16 on them, respectively. Meanwhile, we use a batch size of 32 on the Titan RTX. The training time per epoch is 480 min, 250 min, 170 min, 22 min, 18 min, and 11 min for the Intel i7-7500U CPU (Laptop), Intel i7-7700K CPU (Desktop), Intel Xeon Platinum 9242 CPU (Xeon server), Titan RTX, RTX 2080 Ti (RTX2080), and RTX 2060 Super (RTX2060). For the Xeon CPU sever, even when adopting a batch size of 96, the training time is around  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Beam Size** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
**BLEU** & 26.06 & 26.71 & 26.88 & 26.92 & 26.94 & 26.96 \\
**Time (s)** & 1700 & 2497 & 2587 & 3070 & 4384 & 5308 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The test BLEU and test time based on different beam sizes.  
Figure 8: The test BLEU and relative test time (relative to beam size=1).  
Figure 9: The training time (minutes) per epoch on Titan RTX based on different batch sizes.  
15\(\times\) as long as that on the Titan RTX GPU. That is, the GPU has absolute superiority in training neural networks.
## 4 Results and Discussion
### C-SAR/02  
(1) QPSI mode localization accuracy analysis  
The C-SAR/02 satellite QPSI model contains data of October 14 and November 14, and the positioning accuracy obtained is shown in Figure 12.  
As can be seen from Figure 12, the image on October 14 contained only 5-point targets because some of the equipment was used to perform the imaging experiment on the following day. The image on November 14 contained 14-point targets. In the case of the  
Figure 12: Localization accuracy of C\(-\)SAR/02 QPSI mode data.  
Figure 11: Localizaion accuracy of C\(-\)SAR/01 FSII mode data.  
QPSI mode images of C-SAR/02, the positioning accuracy ranges from \(-12\) to \(2\) pixels in the range direction and from \(-19\) to \(-8\) pixels in the azimuth direction.
2. QPSII mode localization accuracy analysis  
The positioning accuracy of the QPSII mode data of C-SAR/02 satellite obtained using this method is shown in Figure 13.  
Figure 13 indicates that the image from November 12 contains 14-point targets. In the case of the QPSII mode image of C-SAR/02, the positioning accuracy ranges from \(-7.25\) to \(-5.75\) pixels in the range direction and between \(-12\) and \(-5.5\) pixels in the azimuth direction.
3. FSI mode localization accuracy analysis  
The C-SAR/02 satellite FSI mode data contained data of October 16 and October 18. The positioning accuracy obtained is shown in Figure 14.  
Figure 14 illustrates that the images of October 16 and 18 contain 11- and 14-point targets, respectively. The positioning errors of the two FSI mode images of C-SAR/02 exhibit significant differences. The positioning errors of the CRs in the image of October  
Figure 14: Localization accuracy of C\(-\)SAR/02 FSI mode data.  
Figure 13: Localization accuracy of C\(-\)SAR/02 QPSII mode data.  
16 are approximately between \(-40\) and \(-37\) pixels in the distance direction and \(-46\) and \(-42\) pixels in the azimuth direction. The positioning errors of the CRs on the image of October 18 are approximately between 2 and 6 pixels in the distance direction and approximately 14 and 17 pixels in the azimuth direction.
# S2p: Provenance Research for Stream Processing System
## 4 Online Provenance
### IDs Generation in Source Operators  
We hijack each source data before being ingested by source operators, generate a unique ID, and attach the ID to this source data, similar to the method in [23]. Maintaining a unique ID for every data is for the reaccess purpose when answering provenance queries in the future. The enriched stream data carries the IDs of the source data contributing to it. Based on the set of ID information, the provenance graph can be built as a tree rooted at sink results with each leaf representing one source data.  
DSP systems read stream data from files, sockets, and other third-party systems (e.g., Apache Kafka, RabbitMQ). We generate source data IDs basing on where they are ingesting data from accordingly. For instance, we attach file offset to source data as its ID if this DSP application reads data from filesystems (e.g., Hadoop FileSystem ([[https://ci.apache.org/projects/flink/flink-docs-stable/ops/deployment/hadoop.html](https://ci.apache.org/projects/flink/flink-docs-stable/ops/deployment/hadoop.html)]([https://ci.apache.org/projects/flink/flink-docs-stable/ops/deployment/hadoop.html](https://ci.apache.org/projects/flink/flink-docs-stable/ops/deployment/hadoop.html)))), as these systems internally manage all source data. In these systems, we can then leverage their data management features without further manually storing source data.  
However, for other data sources, e.g., sockets, we have to generate unique IDs on our own. For these, we adopt Snowflake [24] to generate IDs for each stream data. We also save IDs and their corresponding data for reverse querying, which happens in the provenance analyzing phase. Reverse querying, in this paper, refers to querying the source data according to its ID. Section VI will discuss how to store these kinds of data at a lower storage cost.
# Analysis of Research Hotspots and Trends in Confined-Space Work Safety Based on CiteSpace
## 3 Results
### Research Hotspots
#### 3.6.2 Keyword Cluster Analysis  
The keyword clustering function in CiteSpace software helps identify frequently occurring keywords in the literature and group them according to relevance. To clarify the relationships between keywords, the clustering function in CiteSpace was applied for keyword clustering analysis (Figures 8 and 9). The keywords were further divided based on K-clustering under the log-likelihood ratio (LLR) algorithm, which tends to produce clusters that are unique, less repetitive, and more aligned with reality [47]. This clustering helps reveal the thematic structure, research hotspots, and development trends within the research field. For this study, 10 clustering tags were selected for subsequent analysis. The Q value of the keyword clustering map is greater than 0.3, and the S value is greater than 0.5, indicating that the clustering is significant and reasonable, with keywords being both related and independent [48]. Excluding \"limited space\", \"confined space\", and \"enclosed space\", the largest clusters in the Chinese and English literature are \"#2 Safety Management\" and \"#0 Risk Assessment\", respectively. It is worth noting that the overlap between keyword clusters in the Chinese and English literature is minimal, suggesting that the relationship between research hotspots is not closely aligned.  
CiteSpace was used to create a keyword clustering timeline diagram to analyze the occurrence time and duration of each cluster. In Figures 10 and 11, diamond nodes represent keywords, and connecting lines indicate the co-occurrence time of the keywords. The research on accidents and asphyxiation in confined spaces in the Chinese literature is a long-standing research hotspot. In contrast, in the English-language literature, the accident mechanism is the research hotspot with the longest time, followed by the allowable exposure range and risk assessment.
# Prediction of Walnut Deterioration Using Kernel Oxidative Stability
## 3 Results and Discussion
### Relations between Kernel OSI and Quality of Kernel and Extracted Oil  
Many methodologies have been developed and implemented for the determination of both primary and secondary oxidation products, including sensory and chemical analysis, such as peroxide value (PV), UV absorbances and volatile compounds [23]. PV, UV absorption and sensory methods determine the oxidation of a sample in real-time, while oxidative stability (Rancimat induction time) indicate oxidative susceptibility and stability. We evaluated the OSI operational parameters on walnut kernels by establishing the relation between traditional methods for measuring primary and secondary oxidation products and kernel OSI. Since we observed small coefficient of variance when analyses were conducted at 110 and 120 \({}^{\circ}\)C and 20 and 25 L h\({}^{-1}\) (Table 1), these parameters were selected to further study the relation of the kernel OSI and other kernel quality and oxidative markers currently used by industry and researchers. Samples at harvest, four and six months after harvest were used for correlation analysis. While correlations do not prove the equivalence between the results, it provides a measure of the strength of a relationship between two methods. Correlations between kernel OSI and kernel moisture, fat content, hexanal and rancidity, and oil OSI, PV, UV absorbances at 232 (K232), and 268 (K268), were studied (Table 3). Moisture had no significant linear correlation with kernel oxidative stability using any of the studied parameters. Temperature at 120 \({}^{\circ}\)C and Airflow 20 L h\({}^{-1}\) did not show significant values of Pearson correlations with rancidity. This lack of significant relationship may be due to a lower range of values obtained in the Rancimat, using those operational parameters (2.7 to 6.8 h), compared to the range of rancidity (0 to 6.7) detected in the samples. On the other hand, OSI measured at 110 \({}^{\circ}\)C with 20 and 25 L h\({}^{-1}\), and 120 \({}^{\circ}\)C with 25 L h\({}^{-1}\) had a high and significant negative correlation (\(-0.7\), \(-0.8\) and \(-0.7\), respectively) with rancidity detected in the samples. A higher OSI corresponded to less rancidity perception. The Pearson correlation coefficient was higher (\(r>0.7\)), mostly in the measurements at 110 \({}^{\circ}\)C with both 20 and 25 L h\({}^{-1}\) airflows. A study on potato chips using 110 \({}^{\circ}\)C and 20 L h\({}^{-1}\), concluded that using those operational parameters, OSI could be measured directly from the oil-based food samples rather than from the extracted oil [24]. However, that study did not include a comparison of operational parameters.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 4 Hansel Services
### Wi-Fi(tm)Access Point Location Service (WALS)  
In order to exploit Wi-Fi routers for navigation it is necessary to know their location. Wi-Fi Access Point Location Service (WALS) is the service that ingests data points from an external application (described in further sections) and routinely computes those positions. Then, a rover receiver wishing to use a certain Wi-Fi Access Point (WAP) to navigate will query this service to know the location of this WAP. A system-level description of WALS is shown in Figure 2.  
This service can be understood as the equivalent of a GNSS orbits and clocks (ODTS) provider, but for Wi-Fi(tm).  
The service has three main components:  
* DB which stores: (a) The WAP positions and (b) both GNSS and Wi-Fi(tm) measurements;
* wage, a task that is routinely executed and whose sole purpose is to select a random WAP from the DB, fetch the most recent measurements associated to this WAP, and compute its position (that will be later stored in the DB);
* API the entry point of the service that will interface the external world.  
The service is conceived to support indoor navigation of smartphones and mobile devices by relying on a pre-configured Wi-Fi(tm) network.
# Human Activity Recognition Using CSI Information with Nexmon
## 1 Introduction
In that work, the CSI information was used to classifying human activities by preprocessing the data with the use of discrete wavelet transform (DWT), principal component analysis (PCA), power spectral density (PSD), SVM, and LSTM recurrent neural networks implementation [12]. In our work which is mainly based on the master theses of some of the authors, we explore CSI data acquisition possibilities using Nexmon with a Raspberry Pi 3 B+, Pi 4B [13], and Asus RT-AC86U based platform [14; 15] and human activities classification using machine learning algorithms with a focus on practical deployment. We conducted three different types of experiments not only on different hardware but also in different environmental contexts and using different algorithms. The paper's main contribution is to solve the HAR problem by using inexpensive hardware without the necessity of humans wearing any devices whilst achieving accuracy on par with or better to published methods. The approach is first acquiring CSI data from inexpensive networking hardware and then applying machine learning algorithms to distinguish between different activities. Even if there are differences in the used hardware, the environmental contexts, and the algorithms, all approaches solve the HAR problem with similar and good accuracy. This is encouraging for practical deployments.