esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Effect of Vibration on the Apparent Viscosity
#### 4.3.4 Effect of Different Vibration Amplitude  
Figure 15 compares the analytical and simulated effective value of the apparent viscosity without and with different vibration applied, whose amplitude is, separately, 0.25, 0.3 and 0.35 g. It is shown that the predictions agree well with the simulations. When vibration is applied, the apparent viscosity is reduced. The value is further reduced when the amplitude of applied vibration is growing. For example, when the amplitude of applied vibration is 0.3 and 0.35 g at 500 Hz, the effective value of the analytical apparent viscosity is 962 and 697 Pa-s, respectively. Compared with that under the no-vibration field (3601 Pa-s), the apparent viscosity is decreased separately by 73.3% and 80.6%. The reason is that the apparent viscosity is reduced with vibration applied, and it is further reduced when the vibration amplitude is increased [27; 28]. This can also be seen from the relationship between apparent viscosity and shear stress in Equation (A8). Therefore, increasing the amplitude of applied vibration can reduce the apparent viscosity inside the extrusion liquefier of ME, which helps promote the forming quality of ME products.
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 6 Conclusions  
ArduSiPM technology has become a significant application in the SoC (system-on-chip) industry for all-in-one detectors, renowned for integrating processors, memory, and peripherals on a single chip. Our recent evaluation of the Cosmo ArduSiPM SoC's second generation of ArduSiPM technology has affirmed its exceptional versatility and robust performance.  
We emphasize the importance of firmware for its flexibility, allowing us to upgrade whenever we find better algorithms. This ensures continuous improvement of the system, including peripherals, interrupts, memory, and external communication. This approach consistently enhances detector performance without changing the hardware. Despite its complexity, these improvements are essential for the ongoing development and efficiency of the device.  
Our key objective is to enable the Cosmo ArduSiPM SoC to autonomously generate histograms and perform reduction, thus reducing reliance on external computing. This move makes processing more efficient and positions the Cosmo ArduSiPM at the forefront of detectors with edge computing capabilities.  
Conceptualization V.B. and D.K.; data curation, V.B., B.A., G.C., F.I., L.M. and L.R.; formal analysis, V.B., G.C., F.I., L.M. and L.R.; funding acquisition, V.B. and D.K.; investigation, V.B., G.C., F.I., L.M. and L.R.; methodology: V.B., B.A., G.C., F.I., L.M. and L.R.; project administration, V.B., D.K. and F.I.; resources, V.B., G.C., F.I., D.K. and L.R.; software, V.B., B.A., G.C., F.I., L.M. and L.R.; supervision, V.B. and D.K.; validation, V.B., B.A., G.C., F.I., L.M. and L.R.; visualization, V.B., B.A., G.C., F.I., L.M. and L.R.; writing-original draft, V.B., B.A., F.I. and D.K.; writing-review and editing, V.B., B.A., F.I., L.M. and D.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by CSN5 (National Scientific Committee 5) at the Italian National Institute of Nuclear Physics (INFN), within the framework of the MICRO (Compact-electronics SOC Particle-detectors and Bioluminescence) experiment. Additional funding was provided by Microchip Technology Inc., which offered a internship for graduates that was awarded to [PERSON].  
Not applicable.  
Figure 19: \({}^{60}\)Co gamma spectrum obtained with Cosmo ArduSiPM. The characteristic peaks at 1173 keV and 1333 keV are easily detectable.  
Data Availability Statement: Data are contained within the article.  
We would like to extend our gratitude to the Technology Transfer Office of INFN (National Institute for Nuclear Physics) and Microchip Technology for enabling this work.  
[PERSON] was employed by the company Microchip Technology. [PERSON] was a Politecnico di Milano student who completed an internship at Microchip Technology during his thesis period. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Smart Gateway for Healthcare Networks Based on Beam Steering Technology
## 6 Conclusions  
This paper presents a smart gateway system that includes an AOA estimation and a beam steering function in a small circular antenna array. The fabricated antenna was assessed for its estimation accuracy of incoming wave using OTA testing in a Rice propagation environment. An estimation error within \(\pm 15^{\circ}\) is found regardless of the K-factor, indicating that the AOA estimation using the MIQ method achieves sufficient accuracy for a small number of elements, even though the K-factor is very small. Moreover, the ability of the beam steering in the proposed antenna was evaluated through propagation measurements using a human phantom in an indoor environment. The CDF characteristic in the case when all directions are divided into \(45^{\circ}\) intervals is improved by about 6 dB compared to that of the conventional dipole antenna, indicating great effectiveness. The proposed antenna can either realize energy saving by reducing the radiated power at the sensor device or expand the coverage area of the proposed smart gateway. These outcomes show that the proposed antenna has great potential as a smart gateway system that can provide a high estimation accuracy and an enhanced received signal.  
Future work may include investigations of low-profile construction of the proposed antenna for more practical use, like that used for the ESPER antenna. We have previously proposed a disk-loaded monopole stacked with a patch antenna, which is capable of radiating in all three orthogonal polarizations [44]. By using this antenna, we expect to achieve both a low profile for the gateway antenna and the reception of vertical and horizontal polarizations at the same time.  
Conceptualization, K.H.; investigation, K.H.; methodology, K.H.; validation, K.T. and Y.O.; writing--original draft, K.H. All authors provided critical feedback and helped shape the research, analysis, and manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# An Experimental Study on the Formation of Spatial Cognitive Maps in Humans
## 2 Materials and Methods
### Learning and Test Trials  
In the **Maze condition**, each learning trial showed a simulated movement through a virtual maze, similar to the practice trials. However, the maze now featured a greater number of intersections arranged across two dimensions on a horizontal plane. Movement paused for 2.5 s at each intersection, while a novel geometric shape was displayed. At the beginning of each trial, participants were shown a schematic map of the maze route, annotated with red arrows and text (see Figure 1c for an example).  
In the **Control condition**, the learning trials displayed a schematic maze map resembling Figure 1c, but without red annotations. Geometric shapes were presented sequentially, each superimposed on a different blue disc. The location and timing of shape presentations matched those in the Maze condition.  
In **both conditions**, each test trial began with the same schematic map shown during learning (but without annotations). The geometric shapes from the preceding learning trial were presented in a new random serial order next to the map. Participants were instructed to click on the corresponding blue disc on the map. If responses were incorrect or did not occur within 12 s, an error message was displayed for 3 s and then the next shape appeared. Thus, unlike the learning trials, the test trials did not allow response corrections.
## 4 Model-in-the-Loop Simulation Test and Analysis
### Variance Analysis of MIL Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Origin** & **DF** & **Adj SS** & **Adj MS** & **F Value** & **\(p\) Value** & **Eminence** \\ \hline Factor A & 1 & 66.83 & 66.835 & 0.96 & 0.349 & \\ Factor B & 1 & 2.78 & 2.776 & 0.04 & 0.846 & \\ Factor C & 1 & 380.77 & 380.767 & 5.45 & 0.040 & ** \\ Factor D & 1 & 228.00 & 228.002 & 3.26 & 0.098 & * \\ Error e & 11 & 768.17 & 69.833 & & & \\ \hline \end{tabular}
\end{table} 
Table 10: Analysis of impact strength using variance method. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Origin** & **DF** & **Adj SS** & **Adj MS** & **F Value** & **\(p\) Value** & **Eminence** \\ \hline Factor A & 1 & 63.083 & 63.083 & 12.87 & 0.004 & *** \\ Factor B & 1 & 2.878 & 2.878 & 0.59 & 0.460 & \\ Factor C & 1 & 2.232 & 2.232 & 0.46 & 0.514 & \\ Factor D & 1 & 50.368 & 50.368 & 10.27 & 0.008 & *** \\ Error e & 11 & 53.931 & 4.903 & & & \\ \hline \end{tabular}
\end{table} 
Table 9: Analysis of dynamic load coefficient using variance method. \begin{table}
\begin{tabular}{c c c c c c} \hline
**CA** & **EI** & **Speed Drop** & **Dynamic Load Factor** & **Impact Strength** & **Switching Time** \\ \hline \multirow{4}{*}{SA} & BS & A\({}_{1}\)D\({}_{1}\)C\({}_{2}\)B\({}_{1}\) & A\({}_{2}\)D\({}_{2}\)C\({}_{1}\)B\({}_{2}\) & C\({}_{1}\)D\({}_{2}\)A\({}_{2}\)B\({}_{2}\) & A\({}_{1}\)D\({}_{1}\)C\({}_{1}\)B\({}_{1}\) \\ \cline{2-6}  & \multirow{4}{*}{FP} & AD major & AD major & CD major & AD major \\  & & C mid & C mid & A mid & C mid \\ \cline{1-1}  & & B minor & B minor & B minor \\ \hline \end{tabular}
\end{table} 
Table 12: Comparative analysis of simulation results and MIL test results. The simulation results indicate that clutches C1 and C5 exert the most significant influence on the output shaft's velocity drop amplitude. The test results demonstrate a high level of significance for clutch C1 and a moderate level for clutch C5; the simulation results show that both C1 and C5 have the most substantial impact on the output shaft's dynamic load coefficient, and the test results confirm this with a high level of significance for both clutches. The simulation results indicate that clutches C4 and C5 exert the most significant impact on the output shaft impact strength. However, the test results show that the significance of these clutches is normal. The simulations suggest that clutches C1 and C5 have the greatest influence on switching time. The test results show that clutch C1 has a moderate level of significance, while clutch C5 shows no significant influence. The test results indicate that the switching time is largely concentrated around 1.5 s, with minimal variation between individual tests. Consequently, the significance of each factor on the switching time is low. While some discrepancies exist between the simulation and test results, the overall trends are generally consistent. These discrepancies could stem from the omission of certain secondary factors during the simulation modeling process, as well as inherent errors present in the testing procedures.
# Nitrogen-Related Defects in Crystalline Silicon
## 2 Background Information of N-Related Defects in Si
### The N\({}_{\rm i}\)N\({}_{\rm i}\)Si\({}_{\rm f}\) Defect  
It is interesting at this point, for the sake of completion, to examine complexes when self-interstitials are added to the N\({}_{\rm i}\)N\({}_{\rm i}\) defect. The first member of the family is the (N\({}_{\rm i}\)N\({}_{\rm i}\))Si\({}_{\rm f}\) complex, which is also label as (N\({}_{\rm i}\))\({}_{2}\)Si\({}_{\rm f}\) (refer to Figure 5) [50]. This structure can be thought of as a tri-self-interstitial structure [50; 78] similar to that assigned to the W-photoluminescence center [79], where two of the self-interstitials were displaced by nitrogen atoms. A number of LVMs were calculated [50] (566.3, 570.4, 574.8, 810.4, 834.8, 936.2, 947.6 cm\({}^{-1}\)). Experimentally, two bands at 930 and 953 cm\({}^{-1}\) have been associated [45; 79] with N\({}_{\rm i}\)N\({}_{\rm i}\)Si\({}_{\rm f}\). The values of these bands compare well with the latter two theoretical values [50] at 936.2 and 747 cm\({}^{-1}\). It was predicted that the structure does not possess an acceptor level, but possess a single-donor level at around E\({}_{\rm v}\) +0.2 eV. Theoretical results [50] show that N pairs act as shallow traps for self-interstitials. Furthermore, since nitrogen affects [12; 18; 21] the diffusion of self-interstitials in Si, any experimental information about the N\({}_{\rm i}\)N\({}_{\rm i}\)Si\({}_{\rm f}\) complex will deepen the understanding of the role of self-interstitials in various processes in the material.  
Figure 5: A schematic representation of (**a**) the Si lattice and (**b**) the N\({}_{\rm i}\)N\({}_{\rm i}\)Si\({}_{\rm f}\) defect. Black and gray circles represent the N and Si atoms, respectively [50].
# CNN for User Activity Detection Using Encrypted In-App Mobile Data
## 5 Conclusions  
In this paper, a novel approach was introduced for encrypted Internet traffic classification, both for identifying known and unknown traffic and categorizing in-app activity type, based only on frame's size and time related information. User actions identified through analysing network traffic can be used in forensic investigations and security incident analysis, to improve correlation of events. Profiling users based on their in-app activities is also useful for marketing or intelligence purposes. Deep Learning obviates the need to select features by a domain expert as it automatically selects features through training, making it a desirable approach when new classes constantly emerge, and patterns of old classes evolve. Performance of the proposed CNN based method that learns traffic as images was compared with DNN that uses statistical features. The results demonstrate that the proposed CNN model has outperformed DNN when overall performance is considered. Moreover, a windowing approach was used to perform classification by observing a short time window of a flow instead of the entire session. Even though this is significantly a harder task as there is less information in partial encrypted traffic flow compared to the entire flow, our model was able to identify in-app activities with an accuracy of 92% even by observing the traffic only for 0.2 s. The novel approach of using a threshold on the confidence values exploits the model's output layer to identify in-app activities while removing noise traffic generated by untrained in-app activities with an average accuracy of 88%. Conceptualization, M.H.P., Y.R., S.D. and A.K. ; methodology, M.H.P., Y.R. and S.D. ; software, M.H.P. ; validation, M.H.P. ; formal analysis, M.H.P. ; investigation, M.H.P., Y.R. and S.D. ; data curation M.H.P. ; writing--original draft preparation M.H.P. ; writing--review and editing, Y.R. and S.D. ; visualization, M.H.P. ; supervision, Y.R., S.D. and A.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This data set is shared openly with the research community to foster new studies and allow reproduction of the results presented. ([[https://www.dropbox.com/s/9](https://www.dropbox.com/s/9) tihc9 wx2 si1t/Dataset.7z2d=0]([https://www.dropbox.com/s/9](https://www.dropbox.com/s/9) tihc9 wx2 si1t/Dataset.7z2d=0) (accessed on 10 December 2021)). [PERSON] would like to express her thanks to the Loughborough University, UK, for supporting her research. [PERSON] would like to acknowledge the Engineering and Physical Sciences Research Council (EPSRC), UK, for their support of his work (EP/W00366X/1). The authors declare no conflict of interest. Figure 9: Noise detection comparison of the CNN and DNN models.
# An Efficient QC-LDPC Decoder Architecture for 5G-NR Wireless Communication Standards Targeting FPGA
## 4 Proposed 5G-NR Decoder Architecture
### MINU Processing Unit  
The MINU processing unit receives Z sequences of dcmax values. Thus, it covers Z check node (CN) units, where each one processes 19 values to find the two minimums.  
The CN unit structure is presented (see Figure 5). Each of the 384 CNs within the MINU is constructed from the SOLL unit and an output selector that organizes the two minimums in their positions according to the index of Min1. Every CN unit input is regulated by a \"2:1 Mux\", which supplies a maximum stored value to complete its 19 inputs for the layers of a weight smaller than dcmax.  
The SOLL unit performs parallel computations of the first and second minimums (Min1 and Min2), as well as the index of the first minimum, while also indicating the original sign of each LLR and its product. Moreover, the process involves only (2 \(\times\) dcmax-3) comparison blocks. With BG1 and the maximum codeword length, the SOLL approach  
Figure 5: Check node unit structure and its modeling on Simulink.  
requires only 35 comparison blocks for every CN unit. This method involves conducting two types of comparisons. Initially, the MMB evaluates and sorts two inputs, assigning the lesser and greater values to the corresponding 'S' and 'B' outputs, respectively. Subsequently, MB determines the minimum value among the 'B' outputs from the MMB blocks, with the MB having a single output 'S' which represents the second minimum output of the SOLL unit after the comparison is complete.  
After the MMB blocks have completed their comparison operation, the SOLL unit assigns the smallest value between the 'S' outputs of MMB as the first minimum output. Additionally, each MMB block indicates the position of the smaller values, with the 'L' output being assigned binary '0' if the left input is smaller and '1' if the right input is smaller. A tree of \"2:1 Mux\" is then used to generate an address, with the 'L' outputs of the MMB blocks being used to address the tree and identify the min1-indexed position (see Figure 6).
# An Improved Evolutionary Multi-Objective Clustering Algorithm Based on Autoencoder
## 2 Proposed Algorithm  
Figure 1 shows the architecture of the proposed AE-IEMOKC, which is established by combining an autoencoder with IEMO-KC. First, the original dataset \(X\) is mapped to the low-dimensional embedding layer as the feature representation \(H\) of \(X\) by the encoder of the autoencoder. Then, \(H\) is transformed as the reconstructed data \(X^{\prime}\) by the decoder of the autoencoder. This process is repeated iteratively to minimize the loss (see Equation (4)). Then, the final \(H\) obtained from the embedding layer is divided into different clusters by IEMO-KC. The clusters are constantly adjusted during the continuous iterations of IEMO-KC to minimize the two objectives \(f_{1}\) and \(f_{2}\) (see Equation (5)) for better clustering solutions. A set of optimal non-dominated clustering solutions with different \(k\) (Pareto front) can be obtained after the number of generations (_gen_) reaches the maximum (_maxgen_). The following subsections provide a detailed introduction to the autoencoder and IEMO-KC.
# Characterizing Micro-Displacements on Active Faults in the Gobi Desert with Time-Series InSAR
## 1 Introduction
Further, [PERSON] [9] mapped and deduced the velocity field along the North Anatolian Fault (NAF) at Ismetpas in Turkey and found a maximum creep rate of ca 20 mm a\({}^{-1}\) in 2014; subsequently, [PERSON] [10] revealed there were systematic patterns of both vertical and horizontal ground displacement across London, UK, which appeared to be fault-constrained and in agreement with the predicted tectonic structures in Southern Britain. Finally, [PERSON] [11] located and quantified the activity of Hengchun Fault through the superimposition of the structural sketch map with PSI results and confirmed the Hengchun Fault as an active left lateral transpresive fault; meanwhile, in 2019, [PERSON] [12] reported that East Beijing is subsiding with a maximum rate of 140 mm a\({}^{-1}\) due to the overextraction of groundwater, but this subsidence is spatially controlled by the geological faults, such as Nankou-Sunhe (F4) and Shunyi-Liangxiang (F7), to some degree, showing a correlation between uneven subsidence and fault distribution. Aside from tectonic activity, the PSI method has also been widely applied to investigate human-induced surface deformation, such as the surface subsidence caused by the construction of Lee Tunnel in London [13], the urban subsidence in Yanggon, Myanmar due to groundwater extraction and overlaying building loads [14], and the surface deformation in Ha Long and Cam Pha, Vietnam triggered by the underground coal mining exploitation [15]. Based on previous studies, this research focuses on the Jiujing region as a case study and aims to develop a new application approach to identify, map, and characterise the micro-displacements along active faults through the combination of vertical (D\({}_{\text{U}}\)) and horizontal (D\({}_{\text{E}}\)) deformation data reprocessed by the PS method. This paper first presents an investigation of known faults and tracing of unmapped faults in Section 4.2, then discusses the kinematics of convergent wrench fault zone deformation in northwest China in Section 4.3, and finally assesses the factors that may affect Sentinel-1 data in Section 4.4. Overall, this study develops a new approach for future geological mapping work in geologically quiescent intraplate regions.
## 4 Methodology  
To cope with the issues of low resource utilization and high system latency in satellite networks, this study proposes a collaborative optimization framework based on cooperative dynamic programming. It aims to ensure fairness and efficiency in resource allocation within dynamic network environments. To address the network's significant temporal variability and intricate constraints, we have proposed a multi-objective optimization model and introduced Gradient Surgery methods to manage the interactions between various objectives. This approach helps to ensure the successful convergence of the optimization process.  
Figure 1 illustrates the overall framework of the method proposed in this paper. This framework comprises several key modules that work collaboratively to achieve the optimization of objectives and the handling of constraints. Firstly, the input to the model consists of raw data from the data collection module. The data preprocessing module normalizes the raw data to ensure data quality and accuracy. Subsequently, the data are transferred to the optimization module, which employs multi-objective optimization methods to solve for the optimal decision and then introduces distributed gradient descent and Gradient Surgery techniques to enhance optimization efficiency and solution precision. The optimization results generate the Pareto frontier, showcasing trade-off solutions among multiple objectives.