esa-sceva/satcom-chunk-collection huggingface dataset:
# Dolphin Sounds-Inspired Covert Underwater Acoustic Communication and Micro-Modem
## 5 Experiment Result  
To verify the functionality and performance of the PBC micro-modem, indoor experiments were conducted in a channel pool as shown in Figure 14. The pool length, width and depth are 45 m, 6 m, and 5 m respectively. For convenience, the transducers are only submerged in the water and the rest of micro-modem is placed on the desk in front of the water pool. The transducer and electronic part of the modem are connected by a cable. Also, each modem is connected to a corresponding computer via a RS-232 interface for control and monitoring.  
According to the frequency of the click signal, we use the 2-8 kHz transducer and BRUEL &KJAER's 8105 hydrophone to receive the signal. As the demodulation is mainly carried out by signal correlation, in order to reduce the bit error rate, we take the channel and noise effects into account. We removed the signal from the call library if the correlation coefficient between them is more than 0.30, retaining only the number c1, c2, c3, c4, c6 signals.  
Figure 13: Software flowchart.  
To evaluate the similarity between the dolphin-call samples and the received signals through the pool channel, we collected the received signals from the receiver, and the signal and call samples were compared in the spectrogram as follows. A comparison of the spectrogram of the call samples and the received signals is shown in Figure 15. From the spectrum of the received signal, it can be seen that there is multipath superposition on the signal, and there is some noise interference. After the signal passed through the pool channel, its spectrum changed; however, a certain extent of the spectrogram characteristics of dolphin calls are still maintained.  
The time-frequency analysis and demodulation results are shown in Figure 16. Among them, the two signals were received at different distances (7.2 m and 10 m) and we reduced the transmit power by half at 10 m to further test the modem performance; the same information is carried for the purpose of contrast. During the two hours of testing, no error occurred and a data rate of 27.1 bits per second was achieved in the indoor trial.  
The purpose of this experiment is to verify the algorithm and the performance of the modem. According to the specific needs, we can increase the distance between the clicks and the number of click signals.  
Figure 14: Schematic of the test environment.  
Figure 15: Comparison of the spectrogram of the call sample and the received signals; (**a**) The spectrogram of the dolphin calls sample; (**b**) The spectrogram of the received signals.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 5 Design of \(Less\) Protocol
### Routing Path Update  
Sink node calculates the best routing path for each node after reconstructing the link quality matrix with sparse sampling. The changes of routing paths are embedded in the update command packets. We design an efficient method to disseminate update commands in the following.  
Figure 7(a) is an illustration with six nodes including a sink. The value labeled on each link represents the transmission cost and the directed arrows indicate data transmission paths. Figure 7(b) shows two typical reasons of routing path changes: (1) the previous connections are invalid (\(link_{BC}\)); and (2) an alternative connection with less cost (the cost of \(link_{AB}\) is increased from 1 to 3) appears. Note that update commands are necessary for the first case.  
Sending update packets with the routing path information node by node is energy consuming, especially in a dynamic network. Figure 7(b) is a rough dissemination method which carries same information during the forwarding phase with redundancy. To overcome this, we decrease the cost of updating routing path by two steps. (1) Decreasing packet length gradually. Sink node disseminates update packets to target nodes with full routing path information at the beginning. When update packets pass through relay nodes, the information for the relay node is removed from the update packet (Figure 7(c)). (2) Compressing redundancy. To further improve update efficiency, we compress update packets with identical paths as shown in Figure 7(d). Finally, the overhead of delivering routing path update commands can be alleviated.
###### Abstract  
With rapid urbanization, maintaining a high quality of life (QoL) for city residents has become a critical challenge for policy-makers and urban planners. Smart cities, leveraging advanced technologies and data analytics, present a promising pathway to enhance urban services and promote sustainability. This paper introduces an innovative adaptation of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, integrating a Belief Structure (BTOPSIS) to improve the evaluation and interpretation of survey data. Our approach effectively addresses the distribution of responses across categories and the uncertainty often present in such data, including missing or ambiguous answers. Additionally, we perform a sensitivity analysis to assess the stability of the BTOPSIS rankings under varying utility function parameters, further validating the robustness of our method. We apply this framework to the 2023 'Quality of Life in European Cities' survey, analyzing diverse urban factors such as public transport, healthcare, cultural facilities, green spaces, education, air quality, noise levels, and cleanliness. Additionally, our study offers a comparative analysis of BTOPSIS against other multi-criteria methods used for evaluation data from this report, showcasing its strengths and limitations in addressing the dataset's complexity. Our findings reveal significant variations in residents' perceived QoL across European cities, both between cities and within countries. Zurich and Groningen rank highest in satisfaction, while Tirana, Skopje, and Palermo are ranked lowest. Notably, residents of cities with populations under 500,000 report higher satisfaction levels than those in larger cities, and satisfaction levels are generally higher in EU and EFTA cities compared to those in the Western Balkans, with the highest satisfaction observed in northern and western Member States. To aid urban planners and policy-makers, we propose a ranking tool using the BTOPSIS method, capturing nuanced resident perceptions of living conditions across cities. These insights provide valuable guidance for strategic urban development and advancing the smart city agenda across Europe.  
quality of life; smart cities; TOPSIS; belief structure; BTOPSIS; survey data Article
# Key Barriers to Personalized Learning in Times of Artificial Intelligence: A Literature Review
## 2 Method
### Formulation of Research Questions  
To analyze personalized learning experiences revealed by the literature through empirical research findings, the researchers formulated four questions, following the SMART method criteria for the formulation of research questions [38], which are presented below:  
Figure 1: Phases of the Systematic Literature Review Process.  
**RQ1.**: _What research methods are predominantly used in studies published between 2018 and 2024, and how do they contribute to the outcomes of personalized learning?_
**RQ2.**: _Which technologies are most frequently utilized in studies published between 2018 and 2024 to achieve personalized learning, and how are they applied across different educational contexts?_
**RQ3.**: _What instructional modalities (e.g., online, blended, face-to-face) are most commonly reported in studies focused on personalized learning between 2018 and 2024, and what patterns or trends can be identified?_
**RQ4.**: _What barriers to the implementation of personalized learning are reported in studies published between 2018 and 2024, and how do these barriers vary across different educational levels or regions?_
## 2 Case Studies  
In this study, three distinct case studies (A, B, and C) are analyzed to evaluate the performance of the implemented optimization algorithms [16]. In the following sections, the term design variables (DVs) includes all the continuous parameters optimized by the algorithms, such as outer diameters, thicknesses, and nodal coordinates. In addition, a special variable named discrete topological variable (DT) is introduced (case C study). Although treated as a continuous DV in the range [0, 1], the DT is internally mapped to a discrete set of five predefined structural configurations, enabling topology optimization.
# Silicon Integrated Nanophotonic Devices for On-Chip Multi-Mode Interconnets
## 2 Silicon Multi-Dimensional (De)Multiplexers  
The mode (de)multiplexer is one of the most important elements in the MDM system, which is used to realize mode conversion and combine/separate signals carried by different modes. Over the recent years, various mode (de)multiplexers have been proposed and demonstrated on the SOI platform, which will be reviewed in Section 2.1. The link capacity can be further enhanced for multi-mode data communications by introducing some other dimensions, such as wavelengths and polarizations. The hybrid (de)multiplexer enables the simultaneous combination/separation of signals carried by multiple degrees of freedom, which is crucial in WDM/PDM/MDM hybrid systems. A review on silicon hybrid (de)multiplexers will be given in Section 2.2. The mode capacity can also be improved by employing spatially separated super-modes in the densely packed waveguide array (DPWA). Silicon-DPWA-based mode (de)multiplexers will be reviewed in Section 2.3.
## 2 Materials and Methods
### Image Acquisition and Processing
#### 2.2.2 Sentinel-2 Satellite Imagery  
Sentinel-2 is a wide-range, high-resolution, multispectral imaging mission created by the European Space Agency (ESA). It consists of two polar-orbiting satellites placed in the same orbit, Sentinel-2A and Sentinel-2B. Sentinel-2 is free to use and the revisit cycle is 10 days (or 5 days when two satellites are operating simultaneously). Sentinel-2 images cover 13 bands in the visible, NIR, and shortwave infrared (SWIR) wavelengths with three different spatial resolutions [24].  
In this study, the Sentinel-2 multispectral image acquired on 11 July 2016 was downloaded from [[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)). Atmospheric correction of the image was performed using the Sen2 cor toolbox (provided by ESA). Four bands (R, G, B, and NIR) with 10 m spatial resolution and six bands (3 red-edge, Narrow NIR, 2 SWIR) with 20 m spatial resolution were selected as the data source. For the purpose of image analysis, the bands with 20 m spatial resolution were resampled to a pixel size of 10 m. Then we used the resized 10 m airborne image to correct the Sentinel-2A dataset with registration module in ENVI 5.3. The Sentinel-2A CIR composite image for the study area is shown in Figure 2.
## 3 Experiment and Analysis
### Robot Sorting Experiment  
With the VS-MF method proposed in this paper, the sorting position, pose and order of stacked parcels within the view of the camera can be quickly and accurately obtained. We conducted comprehensive testing on the effectiveness and stability of this method in a robot system. We constructed the robotic sorting system shown in Figure 9, simulating a real sorting scenario in a logistics environment. After performing \"hand-eye calibration\" on the robot and RGB-D camera [30], the target sorting position and pose information were converted into the base coordinate system of the robot. Then, the robot was guided to sort the stacked parcels in the optimal order planned in our method.  
Finally, 50 rounds of sorting experiments were carried out, and the number of express parcels in each round of sorting scenarios was not less than six; that is, the sorting test experiment was carried out on not less than 300 express parcels. The stacking parcel sorting process is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MAP** & **Sorting Success Rate** \\ \hline RSDNN & 73.4 & 79.5 \\ VSMT & 76.2 & 89.7 \\ VS-MF (\(k_{OD}=1,k_{NV}=1,k_{H}=1\)) & 89.2 & 92.0 \\ VS-MF (\(k_{OD}=1,k_{NV}=1,k_{H}=2\)) & 87.1 & 90.3 \\ VS-MF (\(k_{OD}=1,k_{NV}=2,k_{H}=1\)) & 91.8 & 93.7 \\ VS-MF (\(k_{OD}=2,k_{NV}=1,k_{H}=1\)) & 93.3 & 94.3 \\ VS-MF (\(k_{OD}=2,k_{NV}=2,k_{H}=1\)) & 91.7 & 93.7 \\ VS-MF (\(k_{OD}=1,k_{NV}=2,k_{H}=2\)) & 90.0 & 92.3 \\ VS-MF (\(k_{OD}=2,k_{NV}=1,k_{H}=2\)) & 88.1 & 91.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performances of different strategies for sorting order in sorting success rate and system stability.  
In the individual experiments, our solution achieved more accurate results in detecting the sorting position sand poses of parcels compared with solutions proposed in [19,20]. The sorting point was closer to the central area of the parcel, and the pose was also better guaranteed to be perpendicular to the sorting plane. In the process of sorting, other methods were more likely to preferentially sort parcels in obscured or low positions due to unreasonable sorting order. This could lead to the failure of sorting and the destruction of the sorting environment due to excessive force between parcels. From the results of sorting experiments, we found that the method proposed in this paper could achieve a sorting success rate of 94.3%, which was a 4.6% improvement compared to the sorting experiment in [20], showing the robustness and stability of the system. Our solution could perform better than other methods because multi-modal information was fused in our method to detect sorting positions and poses reasonably, and a more suitable strategy for the sorting order of stacked parcels is included. We proved that VS-MF can be effectively applied to the task of robot sorting of stacked parcels in complex logistics scenarios.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 3 Spatiotemporal Feature Enhancement Methods
### Spatiotemporal Feature Enhancement Based on Spatial Features
#### 3.1.4 Other Spatial Feature Enhancement Methods  
Beyond the above spatiotemporal feature enhancement methods previously mentioned, additional research works have exhibited remarkable qualities in their capacity to further amplify these features. For example, [PERSON] et al. [84] proposed HLR-Net, a hybrid convolutional neural network that integrates various convolutional structures, including Inception modules, gradient protection layers, and bidirectional GRU layers, to enhance spatiotemporal features. The three-layer Inception module efficiently extracts and refines spatial features, while the gradient protection layer utilizes two residual network blocks to mitigate the vanishing gradient problem. The bidirectional GRU layer further strengthens temporal features by facilitating information flow in both the forward and backward directions. Figure 8 shows the schematic diagram of the other spatial feature enhancement methods.  
Figure 8: Other spatial feature enhancement methods. (**a**) The architecture of Ref. [84]. (**b**) The architecture of Ref. [85]. (**c**) The architecture of Ref. [86].  
The collaboration between global spatial information and local fine-grained spatial information plays a crucial role in enhancing spatial features. For this reason, [PERSON] et al. [85] combined global and local spatial information to enhance feature extraction, which contains two branches, dealing with global features and local features, respectively. The local feature branch divides the whole feature into three parts according to the real spatial distribution of the lip: the left corner, right corner, and the middle of the lip, and these three parts are successively entered into the local branch. These branches collaborate through a joint loss function, effectively leveraging both global and fine-grained spatial information.  
[PERSON] et al. [86] proposed a pure visual lip-reading method that extracts spatial features using the distances between 20 key points on the lips. The symmetric Euclidean distance (SED), central Euclidean distance (CED), and three-point angle (TPA) are computed to form a feature vector, which is then processed by a CNN for spatial feature extraction. Due to limited temporal feature capability, only 26 characters and 10 digits can be recognized.  
To address the limitation of conventional 3D convolution in fully extracting spatial information, [PERSON] et al. [87] employed deformable 3D convolution, which adaptively adjusts sampling positions based on lip shape structure, thereby making more effective use of spatial information.  
It is worth noting that the efforts of spatial features are significant in the enhancement of spatial characteristics, but this ability is very limited in some cases, even lacking specific capability to describe temporal characteristics.
###### Abstract  
Unmanned aerial vehicles (UAVs) equipped with multispectral sensors present an opportunity to monitor vegetation with on-demand high spatial and temporal resolution. In this study we use multispectral imagery from quadcopter UAVs to monitor the progression of a water manipulation experiment on a common shrub, _Baccharis pillularis_ (coyote brush) at the Blue Oak Ranch Reserve (BORR) \(\sim\)20 km east of San Jose, California. We recorded multispectral imagery at several altitudes with nearly hourly intervals to explore the relationship between two common spectral indices, NDVI (normalized difference vegetation index) and NDRE (normalized difference red edge index), leaf water content and water potential as physiological metrics of plant water status, across a gradient of water deficit. An examination of the spatial and temporal thresholds at which water limitations were most detectable revealed that the best separation between levels of water deficit were at higher resolution (lower flying height), and in the morning (NDVI) and early morning (NDRE). We found that both measures were able to identify moisture deficit across treatments; however, NDVI was better able to distinguish between treatments than NDRE and was more positively correlated with field measurements of leaf water content. Finally, we explored how relationships between spectral indices and water status changed when the imagery was scaled to courser resolutions provided by satellite-based imagery (PlanetScope).We found that PlanetScope data was able to capture the overall trend in treatments but unable to capture subtle changes in water content. These kinds of experiments that evaluate the relationship between direct field measurements and UAV camera sensitivity are needed to enable translation of field-based physiology measurements to landscape or regional scales.  
unmanned aerial vehicles; vegetation water status; drought; NDVI; NDRE; _Baccharis pillularis_2019; 11, 1853; doi:10.3390/rs11161853
# A Neural Network Approach for the Analysis of Reproducible Ribo-Seq Profiles
## 3 Materials and Methods
### Statistictial Analysis on the Nucleotide Composition of the Subsequences  
Inferential statistics can provide insights on the specific patterns and characteristics of the data and highlight relationships between variables. The \"SubsequencesDataset\", obtained with the procedure described in Section 3.2, is analyzed to explore the nucleotide composition of the mRNA sub-sequences, relating to the assigned labels (\(+1\) and \(-1\)).  
First, the relative frequency of each nucleotide (A, T, G, C) has been computed. To assess the significance of our results and to find out whether the obtained frequencies are typical of a certain speed or the correspondence is just obtained by chance, we built a statistical test. The aim of the test is to assign a probability value (\(p\)-value) to the following null hypothesis: the slow and fast sub-sequences are characterized by a random nucleotide composition. In order to test this hypothesis, \(10^{4}\) new profiles are generated by randomizing the nucleotide sequence, keeping fixed the \(+1\) and \(-1\) labels and therefore the length of the original sub-sequences. In each of the obtained sequence, the relative frequencies of the four nucleotides within the fast and slow sub-sequences can be calculated. This procedure leads to eight null distributions of relative frequencies, one for each nucleotide both for slow and fast translation. Comparing the null distributions against the original relative frequencies allows to calculate the \(p\)-values. In particular, if the original frequency is lower than the mean of the null distribution, we define the \(p\)-value as the probability of a random sub-sequence to show a relative frequency smaller than that observed in the original sub-sequence. On the contrary, if the original frequency is higher than the mean of the null distribution, the \(p\)-value is calculated as the probability of finding by chance a higher relative frequency value. If the \(p\)-value lies under the significance threshold of 0.05, the null hypothesis of completely random nucleotide frequencies is rejected. This indicates a statistically significant difference in the nucleotide composition between slow/fast and randomly generated sub-sequences.
# Dynamic Modeling of Motorized Spindle System with Unbalanced Mass and Spindle Inclination
###### Abstract  
The high-speed motorized spindle has been wildly used in the field of aerospace processing, due to its advantages such as high speed, high precision, and high efficiency. CNC machine tools used for processing aerospace products require high machining accuracy, and once the spindle fails, it will seriously affect the quality of product processing. Thus, it is important to study the faults of the spindle, especially the faults caused by subtle errors. In this work, a dynamic model of a spindle with unbalanced mass fault and spindle inclination fault is established, and the natural frequencies and mode shapes of the motorized spindle are calculated by using the whole transfer matrix method (WTMM). The deflections of the spindle initial end in the different situations are discussed when the two faults happen independently. The results show that the spindle end deflection of the same fault has different sensitivity at different speeds. At the third order of natural frequencies, the deflection of a motorized spindle is greatest regardless of the fault that occurs. Although the motorized spindle rotates at the same speed, different faults could cause different mode shapes. At the lower speed, when the unbalanced mass fault happens, the mode shape is in an arched shape, and while the spindle inclination fault happens, the mode shape is in a concave shape.  
motorized spindle; whole transfer matrix method; unbalanced mass; spindle inclination; dynamic modeling +
Footnote †: journal: Applied