esa-sceva/satcom-chunk-collection huggingface dataset:
# RCA-PixelCNN: Residual Causal Attention PixelCNN for Pulsar Candidate Image Lossless Compression
## 4 Experiments
### Datasets  
1. The HTRU1 (High Time Resolution Universe Survey) dataset originates from observations conducted by the Parkes Telescope in Australia using multiple beams (13 beams). The central observing frequency is 1352 MHz, and each beam records a bandwidth of 400 MHz, with the actual data usage being in the middle 340 MHz bandwidth. This dataset comprises 1196 known pulsars (positive samples) from 512 distinct sources and 89,996 non-pulsar candidates (negative samples). Within the HTRU1 dataset's HTRUS subset, there are 60,000 binary classification images sized 32 \(\times\) 32. These images include both known pulsars and non-pulsar candidates. Each image consists of two channels, analogous to the RGB channels in natural images, although the content of each channel differs from that of natural images. Channel 1 represents the period-dispersion subgraph, channel 2 corresponds to the frequency-phase subgraph, and channel 3 represents the time-phase subgraph. The left four columns in Figure 7 depict the positive samples from the HTRU1 dataset, where the first row shows the period-dispersion graph, the second row displays the frequency-phase graph, and the third row depicts the time-phase graph. In this study, the HTRU1 dataset is utilized for training, testing, and validating the models. The baseline models, including the GMM and STM, require the data to be in the form of 1D tensors. Therefore, the 2D images need to be reshaped into 1D sequences before being used with these models. On the other hand, both the PixelCNN baseline model and the RCA-PixelCNN model are capable of directly processing structured image data without the need for reshaping.
2. FAST Pulse Candidate Data: The FAST pulse candidate images are obtained from 19-beam observations using the PRESTO software processing. These image-formatted pulse candidate data files are sourced from the early data center of FAST and are intended for internal use. In practical applications, the model is initially trained using the HTRU1 dataset. Subsequently, transfer learning is applied to fine-tune the model parameters on the FAST data, ensuring the model's better suitability for the specific application scenarios of FAST.
# Building Reliable Massive Capacity SSDs through a Flash Aware RAID-Like Protection +
## 2 Background and Related Work
### Basics of Flash Memory and SSDs  
Most of thee SSDs in the market today provide high performance and large capacity by connecting a large number of NAND flash memories to channels and parallelizing them with a SSD controller [19; 20; 21; 22; 23].  
NAND flash memory chips are composed of multiple dies and planes, which has a large number of blocks, and each block has a fixed number of pages [19; 21]. Read and write operations, which are basic operations of flash memory, are performed in units of pages. IO requests that are issued from the host are interleaved at the multiple channel, die, and plane levels [19; 21; 22; 24].  
One of the unique characteristics of flash memory is that, once data have been written, pages cannot be overwritten [25]. In order to write data to the page, the block containing the page must be erased first. This erase operation is another order of magnitude slower than a page write operation. Furthermore, the number of erasures after writing, generally termed the Program/Erasure (P/E) cycle, is limited, depending on the manufacturing technology. Today, four types of technologies, namely, single-level cell (SLC), multi-level cell (MLC), triple-level cell (TLC), and qual-level cell (QLC), are widely used [2].  
Empty pages in flash memory are eventually exhausted due to the continuous service of write requests. To get rid of invalid pages, which hold old data that were logically overwritten, and turn them back to clean pages, a process that is called garbage collection (GC) is performed. Because the erase operation is only performed in block units, GC starts by finding the target block (also called victim block) to be erased [19; 20; 21; 26]. However, in many cases, the victim block may hold a mixture of valid and invalid pages, so, before the erase operation is done on this block, valid data that are contained in the block must be transferred to a page of another block that has already been erased [20; 21; 27]. This movement of valid page data is called write amplification (WA), which is the main overhead of GC [19; 21; 26; 27].
## 4 Discussion
On the other hand, setting the fragmentation size to 0.3 increases the number of analyzed features with a relatively small number of samples, reducing the prediction accuracy of Model_II. Table 5 summarizes the results of EX09. Additionally, the graph in Figure 17 demonstrates these results. Furthermore, the execution time of the three studied models is compared in EX10. The three models processed the same dataset (Dataset_VI), and the execution times were monitored accordingly. The obtained results were 97, 75, 600, and 370 min for Model_0, Model_I, Model_II, and Model_II (with fragmented Dataset_VI with size (0.2)), respectively. This denotes that Model_I has the lowest execution time at 22.4% less than Model_0 and 87.5% less than Model_II. These results can be clearly explained because Model_I analyzed smaller file sizes with the traditional predictive model. Model_0 demonstrated a higher execution time since the entire sequence was being examined. These results imply that Model_I retained the high accuracy scores of the detection of diseases' biomarkers seen in Model_0 with 22.4% less execution time. On the other hand, Model_II had the relatively highest value of execution time considering the file sizes being analyzed with this model. Additionally, the fragmented Dataset_VI displayed a lower execution time since the LSTM network analyzed smaller fragments. Figure 18 summarizes the execution time scores obtained by EX10. Finally, the work in the literature that analyzed Dataset_II by applying conventional univariate/multivariate modeling for feature extraction and RF as a predictive model reported average accuracy scores of 0.77 and 0.91 as the highest reported accuracy scores [12; 18]. Hence, the reported prediction accuracy scores of Model_0 that was published earlier and Model_I introduced in this study outperform the results obtained by the work in the literature on the same dataset (EX06). On the other hand, Model_II reported relatively less prediction accuracy than the work in the literature, as explained earlier; this can be justified by the data size being studied using the deep learning model (EX10). Figure 19 summarizes the comparison of the average accuracy scores reported by Model_0, Model_I, Model_II, and the literature work.
## 5 Packet Length Control for Sparse Signal Transmission
### Performance Improvement with Data Interleaving  
As mentioned in the above part, a longer packet length has a bad effect on signal recovery. In this part, we use the traditional communication technique to improve it. We first introduce the interleaving technique and then show its effect on sparse signal transmission by analyzing mutual coherence, the relationship between mutual coherence and recovery error and the relationship between mutual coherence and bit error rate (BER). At last, considering that interleaving length is limited by memory size and the application's timeliness, the relationship between mutual coherence and interleaving length is investigated.
# Effect of Radial Height of Helical Static Blade on the Cavitation Performance of Inducer
## 5 Conclusions  
1. In a certain range, with the increase in the radial height of helical static blade, the head increases, the efficiency decreases, and the critical cavitation number increases. There is an optimal radial height from 0.05 to 0.125 to improve the head and the cavitation performance of inducer at the same time.
2. The backflow and vortex universally exist in the channel of the helical static blade and will gradually strengthen with the radial height. The backflow causes the fluid to have a smaller attack angle because of the opposite turning direction of the blades and helps to improve the head. However, the enhanced flow will increase the low-pressure area at inlet as well.
3. The helical static blade can delay the time when bubbles block the channel of inducer, but the effectiveness is limited because the cavitation area prominently enlarges with the radial height of the helical static blade.  
Conceptualization, J.L.; methodology, J.L.; software, C.B.; validation, C.B.; formal analysis, J.L.; investigation, C.B.; resources, J.L.; data curation, C.B.; writing--original draft preparation, C.B.; writing--review and editing, J.L.; visualization, C.B.; supervision, J.L.; project administration, C.B.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Stationary Localization Algorithms
### Distributed Localization Algorithms
#### 3.2.2 Large-Scale Hierarchical Localization (LSHL) Approach  
In [21, 22], the authors proposed a Large-Scale Hierarchical Localization (LSHL) approach. Surface buoys drift on water surface and get their locations from GPS. Anchor nodes can directly communicate with the surface buoys to get their absolute positions. Unknown nodes cannot directly communicate with the surface buoys but can communicate with anchor nodes to localize themselves. The whole localization process is divided into two sub-processes: anchor node localization and unknown node localization.  
Anchor node localization is not discussed in [21, 22]. During the unknown node localization process, there are two types of nodes: reference nodes and non-localized nodes. Each node (including reference nodes and non-localized nodes) periodically broadcasts a beacon message, containing its ID. All the neighboring nodes which receive this beacon message can estimate their distances to this node using measurement techniques, such as ToA. Besides, each non-localized node keeps a counter, \(m\), of the reference nodes to which it knows the distances. Once the localization process starts, each non-localized node keeps checking \(m\). If \(m<4\), the non-localized node broadcasts a localization message which contains all its received reference nodes' locations and its estimated distances. Besides, the non-localized node uses the 3-dimensional Euclidean distance estimation approach to estimate its distances to more non-neighboring reference nodes. After this step, the set of its known reference nodes is updated. Correspondingly, \(m\) is updated and the node returns to the \(m\)-checking procedure. Until \(m\geq 4\), the non-localized node selects 4 reference nodes with the highest confidence values to localize itself. After the non-localized node is localized, it computes confidence value \(\eta\). If \(\eta\) is larger than or equal to the confidence threshold \(\lambda\), the non-localized node labels itself as a new reference node. Simulation results show that localization coverage and average communication overhead are not affected much by the  
Figure 4: The Node Discovery and Localization Protocol.  
mobility of sensor nodes, while localization error increases noticeably with the moving speed of sensor nodes. Thus, LSHL cannot provide accurate localization accuracy in mobile UWSNs. Moreover, LSHL has inherent problem of confidence threshold \(\lambda\), which can affect its performance.
## 5 Experiment Results and Discussions
### Robustness of Semi-Supervised Machine Learning  
This section investigates the variational autoencoder robustness against the noise of the training dataset. The variational autoencoder assumes that all the training examples belong to a unique class, in our case, benign examples. However, during the initial setup of the saturation attack detection systems, some machines are connected to the SDN network, which may have abnormal behavior or, during data collection, some attacks are already perpetrated. Therefore, we conducted multiple experiments using the variational autoencoder with a training dataset that includes a small percentage of attack observation (i.e., noisy samples), which are very close to the benign ones. The attack examples are selected by using the k-nearest neighbor data structure, which was trained using benign examples. Figures 7 and 8 show the detection performance results of the variational autoencoder in terms of precision, recall, and F1-score with different noise percentages, for both physical and simulated SDN environments. We can observe that the variational autoencoder is robust and maintains comparable high performance, even with a high noise ratio. We also observe that the physical experiments demonstrate better robustness than the simulated experiment. In fact, with 15% noise, the F1-score and recall are smaller than 0.8, while in the simulated experiments they are always above 0.8. Therefore, we chose to utilize the variational autoencoder in our hybrid approach.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**Physical Environment**} & \multicolumn{4}{c}{**Simulated Environment**} \\ \hline
**Attack** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline UDP & 100\% & 22\% & 55\% & 100\% & 84\% & 85\% \\ \hline TCP-SYN & 100\% & 2\% & 4\% & 100\% & 80\% & 89\% \\ \hline TCP-SARFU & 100\% & 23\% & 37\% & 100\% & 82\% & 90\% \\ \hline ICMP & 100\% & 16\% & 28\% & 95\% & 83\% & 91\% \\ \hline IP-Spoofing & 99\% & 11\% & 20\% & 97\% & 61\% & 75\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: [PERSON] 1 min trained with only one attack and tested on all the attacks.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**Physical Environment**} & \multicolumn{4}{c}{**Simulated Environment**} \\ \hline
**Algorithm** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline Isolation Forest & 56\% & 92\% & 69\% & 38\% & 100\% & 56\% \\ \hline One-Class SVM & 22\% & 97\% & 35\% & 11\% & 100\% & 20\% \\ \hline Basic Autoencoder & 99\% & 24\% & 38\% & 96\% & 80\% & 86\% \\ \hline variational Autoencoder & 86\% & 93\% & 90\% & 85\% & 97\% & 91\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Semi-supervised classification algorithm tested on all the attacks (1 min).
## 2 Materials and Methods
### Formation of Ag Nanoparticles on the TiO\({}_{2}\) Surface  
The Ag thin films (\(h_{Ag}\) = 5; 7.5; 10 nm) were deposited on the surface of TiO\({}_{2}\) (Ag/TiO\({}_{2}\)) by using a magnetron sputtering technique. After creating a high vacuum (up to 4\(\times\)10\({}^{-3}\) Pa), the chamber was filled with a high purity Ar gas until a working pressure of \(\sim\)2.4 Pa was reached. One Ag target (99.995% purity) with a diameter of 50.8 mm was used for the deposition of Ag thin films. A growth rate of 2.5 nm/min for Ag thin films was reached using a DC power supply at 31 W sputtering power. AgNPs on the TiO\({}_{2}\) surface (AgNP/TiO\({}_{2}\)) were formed using the solid-state dewetting (_SSD_) process. Thus, Ag/TiO\({}_{2}\) structures were annealed immediately after deposition in a vacuum chamber (2.4 Pa Ar gas pressure) at different temperatures (\(T_{a}\)) and times (\(t_{a}\)). Since the formation of AgNPs was carried out in a vacuum, the oxidation of silver nanoparticles should be negligibly low. Furthermore, previous research suggests that the concentration of Ag\({}^{+1}\) and Ag\({}^{+3}\) oxidation states were not higher than 17%, even though the calcination was done in a furnace under constant Ar gas flow [31].
## 4 Experimental Results
### Cross-Validation Experiments
Finally, the highest average predictive accuracy of the rules was reached by ensembles of DTs trained by GB (3, 50) and RF-3 (97.3%). Note that for the latter model, a few more rules were produced (3.8 versus 5.1). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Acc.** & **Fid.** & **Acc. R. (a)** & **Acc. R. (b)** & **Nb. R.** & **Nb. Ant.** \\ \hline DIMLP (25) & **98.1** (0.1) & 98.6 (0.7) & 96.7 (0.7) & 98.1 (0.1) & 5.5 (0.4) & 1.9 (0.1) \\ DIMLP (50) & **98.1** (0.1) & 98.2 (0.8) & 96.6 (1.0) & 98.2 (0.3) & 5.5 (0.6) & 1.9 (0.1) \\ DIMLP (100) & **98.1** (0.1) & 98.3 (0.6) & 96.5 (0.8) & 98.1 (0.1) & 5.3 (0.4) & 1.9 (0.1) \\ DIMLP (150) & **98.1** (0.1) & 98.5 (0.8) & 97.0 (0.8) & **98.3** (0.3) & 5.3 (0.4) & 1.9 (0.1) \\ \hline RF (25) & 97.6 (0.4) & 98.7 (1.2) & 96.7 (1.3) & 97.8 (0.4) & 8.1 (0.5) & 2.3 (0.1) \\ RF (50) & 97.5 (0.2) & 98.8 (0.6) & 96.8 (0.7) & 97.7 (0.4) & 9.4 (0.7) & 2.4 (0.1) \\ RF (100) & 97.5 (1.5) & 98.7 (0.8) & 97.1 (0.5) & 98.0 (0.5) & 10.2 (0.5) & 2.4 (0.1) \\ RF (150) & 97.5 (1.5) & 98.7 (1.0) & 96.6 (1.2) & 97.7 (0.4) & 10.2 (0.6) & 2.4 (0.1) \\ \hline RF-3 (25) & 97.7 (0.3) & 99.0 (0.9) & 96.8 (0.7) & 97.7 (0.3) & 4.6 (0.5) & 1.8 (0.1) \\ RF-3 (50) & 97.5 (0.2) & 98.7 (0.6) & **97.3** (1.1) & 98.0 (0.7) & 5.1 (0.6) & 1.8 (0.1) \\ RF-3 (100) & 97.5 (0.2) & 99.2 (0.7) & 97.1 (0.5) & 97.7 (0.3) & 4.8 (0.8) & 1.8 (0.1) \\ RF-3 (150) & 97.5 (0.2) & 99.0 (0.6) & 96.9 (0.4) & 97.7 (0.4) & 4.8 (0.5) & 1.8 (0.1) \\ \hline GB (1,25) & 97.4 (0.3) & 99.1 (0.8) & 96.5 (0.7) & 97.4 (0.3) & **3.6** (0.1) & **1.5** (0.1) \\ GB (1,50) & 97.2 (0.3) & 99.4 (0.6) & 96.9 (0.5) & 97.4 (0.4) & 5.4 (0.2) & 2.0 (0.1) \\ GB (1,100) & 96.9 (0.3) & 98.5 (0.7) & 96.7 (0.8) & 97.6 (0.2) & 6.3 (0.3) & 2.1 (0.1) \\ GB (1,150) & 97.1 (0.4) & 98.1 (0.9) & 96.0 (0.9) & 97.5 (0.2) & 6.5 (0.4) & 2.1 (0.1) \\ \hline GB (2,25) & 96.4 (0.8) & 99.2 (0.5) & 96.9 (0.7) & 97.0 (0.8) & 3.9 (0.2) & **1.5** (0.0) \\ GB (2,50) & 96.5 (0.6) & 99.3 (0.6) & 96.2 (0.6) & 96.6 (0.6) & 4.1 (0.3) & 1.5 (0.1) \\ GB (2,100) & 96.4 (0.9) & 99.4 (0.5) & 96.2 (0.8) & 96.6 (0.8) & 4.3 (0.4) & 1.6 (0.1) \\ GB (2,150) & 96.4 (0.9) & 99.4 (0.5) & 96.2 (0.8) & 96.6 (0.8) & 4.3 (0.4) & 1.6 (0.1) \\ \hline GB (3,25) & 97.4 (0.8) & 99.4 (0.3) & 97.2 (0.9) & 97.6 (0.7) & 3.8 (0.0) & **1.5** (0.0) \\ GB (3,50) & 97.6 (1.1) & **99.5** (0.3) & **97.3** (1.0) & 97.7 (1.0) & 3.8 (0.0) & **1.5** (0.0) \\ GB (3,100) & 97.8 (0.8) & 98.9 (0.7) & 97.1 (0.8) & 98.0 (0.6) & 3.8 (0.0) & **1.5** (0.0) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average results obtained on the “Divorce” dataset. For each column, the highest average accuracy or average fidelity is represented in bold, along with the lowest average number of rules or average number of antecedents. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Acc. ** & **Fid. ** & **Acc. R. (a)** & **Acc. R. (b)** & **Nb. R.** & **Nb. Ant.
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 5 Energy Harvested and Cooperative-Enabled Routing Protocol (EHCRP) Scheme
### Topology Formation Phase  
Once all the nodes are deployed, and path cost estimation is done, the SNs then exchange hello and reply packets. They exchange messages comprising different factors, such as node location, node ID, total energy and distance to/from the sink and other neighboring nodes. After exchange of a hello packet (HP), each node retains a neighbor table (NT) that further helps in updating the routing table of each node. Hello packets are broadcast during the topology formation phase by the CNC to all neighboring nodes. The nodes receive the packet and give a reply as acknowledgment to the CNC. The CNC also updates its routing table with the BSNs information received in the form of feedback messages. The header included in a normal hello packet is shown in Figure 4[43; 46]. These headers are included in a normal hello packet as well as in normal data packets, but in emergency data transmission, an emergency label is added to the start of the packet. By reading the first label as an emergency, the data packet is given the highest priority and the data is sent directly to the CNC. If the hello packet is sent to the neighbor which does not send it in a specific time, then the link is considered as broken. Information about CNC and all possible routes are also exchanged during this process. The total energy (TE) used during the phenomena comprises of residual energy and harvested energy.
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
## Nomenclature  
\(P_{SI}\): Power of direct component  
\(P_{SL,lim}\): Power of image component  
\(P_{IMD}\): Power of intermediate distortion component  
\(P_{IMD,\;in}\): Power of image component of IMD  
\(P_{Ds}\): Power of desired Signal  
\(P_{TRs}\): Total power at the receiver input  
\(P_{Kautz}\): Power after applying analog/RF cancellation using Kautz filter  
\(P_{GALL}\): Power after applying analog/RF cancellation using GALL filter  
\(SINR\) and \(SINR_{WC}\): SINR without any cancellation technique  
\(SINR_{30 dB}\): SINR with 30 dB of antenna cancellation  
\(SINR_{40 dB}\): SINR with 40 dB of antenna cancellation  
\(SINR_{CS1,30 dB}\): SINR with CSI and 30 dB antenna cancellation  
\(SINR_{CS1,40 dB}\): SINR with CSI and 40 dB antenna cancellation  
\(SINR_{Kautz}\): SINR after Kautz filter implementation  
\(SINR_{GALL}\): SINR after GALL filter implementation  
\(SINR_{Kalman}(Kautz)\): SINR after Kautz filter with Kalman filter implementation  
\(SINR_{Kalman}(GALL)\): SINR after GALL filter with Kalman filter implementation  
\(SINR\): SINR after CSI and 30 dB antenna cancellation with Kalman filter  
\(SINR_{Kalman}(CS130 dB)\): implementation  
\(SINR_{Kalman}(CS140 dB)\): SINR after CSI and 40 dB antenna cancellation with Kalman filter  
\(O_{WL}\): The output of a non-linear amplifier  
\(O_{PD}\): The output of PA after PD technique  
\(O_{FF}\): The output of PA after FF technique  
\(O_{NFB}\): The output of PA after NFB technique  
\(BER_{R,lim}\): BER in the presence of receiver front end impairments  
\(BER\): BER after applying linearization techniques to receiver front end components