esa-sceva/satcom-chunk-collection huggingface dataset:
In this framework, advanced multi-temporal differential interferometric SAR (A-DInSAR) approaches were extensively and successfully exploited to investigate coastal ground deformation [17; 18]. These techniques, such as Permanent Scatterers (PS) [19], small baseline subsets (SBAS) [20], and interferometric point target analysis (IPTA) [21] use a medium-to-large dataset of SAR images acquired at different times over the observed area to follow the temporal evolution of a deformation phenomenon, retrieving the mean deformation rate and the time series for each point target. Moreover, they can take benefits from the massive availability of free-of-charge and commercial SAR data archives collected and provided by space agencies, e.g., the European Space Agency (ESA), Italian Space Agency (ASI), Japan Aerospace Exploration Agency (JAXA), Canadian Space Agency (CSA), and German Aerospace Center (DLR). This paper is meant as a demonstrator to show the capability of InSAR data to provide a detailed understanding of the coastal hazards constraining ground deformation phenomena occurring along coastal areas. To these purposes, we present and discuss some meaningful InSAR-based case studies along the Italian coastline, where multi-temporal techniques were applied to X-band COSMO-SkyMed (CSK) and C-band ENVISAT SAR data to evaluate local natural and anthropogenic phenomena. We analyze the advantages and limits of the adopted sensors and techniques regarding spatial and temporal coverage, the density of targets, lack of data, etc. Finally, we discuss the potentialities of Sentinel-1 (S1) missions to improve the coastal monitoring and sustainability. Indeed, S1 SAR data show some peculiarities that may be suitably exploited for performing a free-accessible near real-time surveillance service of coastal areas with SMART features (i.e., Sustainable, Measurable, Automatic, Realistic, and Time-oriented) to support decisions from public administration, government institutions, and civil protection entities regarding risk mitigation and prevention.
###### Abstract  
The cochlear neuromodulator provides substantial auditory perception to those with impaired hearing. The accurate insertion of electrodes into the cochlea is an important factor, as misplaced may lead to further damage. The impedance measurement may be used as a marker of the electrode insertion guidance. It is feasible to investigate the impact of the dielectric properties of the cochlea tissue layers on the electrode insertion guidance using sophisticated bio-computational methods that are impractical or impossible to perform in cochlear implant (CI) patients. Although previous modeling approaches of the cochlea argued that the capacitive impact of the tissue layer can be neglected using the quasi-static (QS) approximation method, it is widely accepted that tissue acts as a frequency filter. Thus, the QS method may not always be appropriate due to short-duration pulses. This study aimed to investigate the impact of the frequency-dependent dielectric properties of the cochlea tissue layers on the impedance variation by following a systematic approach. The volume conductor model of the cochlea layers was developed, the dielectric properties of each tissue layer were attained, and the cochlea neuromodulator settings were applied to obtain the results based on both QS and transient solution (TS) methods. The results based on the QS and TS methods were compared to define to what extent these parameters affect the outcome. It was suggested that the capacitive impact of the cochlea layers should be considered after a certain frequency level.  
bio-impedance; capacitive effect; cochlea implant; computational modeling; dielectric properties; quasi-static simulation; transient simulation +
Footnote †: journal: Applied sciences
# Spatiotemporal Variations in Vegetation Canopy Interception in China Based on a Revised Gash Model
## 2 Materials and Methods
### Data Preparation  
The NDVI data were reconstructed from the moderate resolution imaging spectroradiometer (MODIS) products to estimate _FVC_. In brief, the spatial resolution of the MODIS NDVI product is 500 m and the temporal resolution is 16 days, with the criteria of low  
Figure 1: Vegetation biomes and associated normalized difference vegetation index (NDVI) in China.  
clouds, low view angle, and highest NDVI value. The leaf area index (_LAI_) data-set with a resolution of 500 m in spatial and 8 days in temporal, respectively, were collected from the MODIS LAI product (MYD15A2) to estimate the _VAI_ based on Equation (4):  
\[VA1=LAI_{g}+L_{s} \tag{4}\]  
where \(LAI_{g}\) is the leaf area index provided by MODIS LAI product; \(L_{s}\) is the area index of dead leaves, branches, stem, and trunk. In addition, \(L_{s}\) was estimated according to its relationship with the LAI as described in [PERSON] et al. (2002) [47].  
The landcover product (MCD12Q1), which contains 17 landcover types based on the International Geosphere Biosphere Programme (IGBP) classification, was collected to separate different vegetation types. As these vary, the input vegetation parameters changed during the model simulation [32; 46].  
In the present study, 3-h precipitation intensity data sourced from China's meteorological forcing dataset, which was supported by the National Tibetan Plateau Scientific Data Center, were used to run the revised Gash model. The dataset was based on tropical rainfall measuring mission (TRMM) precipitation data and reanalysis data, such as Princeton reanalysis data, global land data assimilation system (GLDAS) data, and radiation data from global energy and water exchanges project-surface radiation budget (GEWEXSRB), and it integrated the conventional meteorological observation data of the China Meteorological Administration, which has a better accuracy of 0.1\({}^{\circ}\)[48; 49]. A monthly potential evapotranspiration dataset from the National Earth System Science Data Center ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)) accessed on 21 January 2022) was also used to run the model. The data were calculated using the Hargreaves potential evapotranspiration equation with a temporal resolution of 1 mon and a spatial resolution of 1 km [50].  
The canopy interception evaporation data (Ei) used for validation were derived from the PML_V2 global evapotranspiration (ET) and gross primary production, which is obtained from the National Qinghai-Tibet Plateau Science Data Center ([[https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)]([https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)) accessed on 24 January 2022) [51]. Based on the Penman-Monteith-Leuning (PML) model, PML_V2 coupled the GPP process according to the stomatal conductance theory, which greatly improved the accuracy of the ET simulation. The resolution in spatial and temporal scale were 0.05\({}^{\circ}\) and 8 days, respectively. Data from 2003 to 2018 were used for this validation.
# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
## 5 Conclusions  
Remote sensing technology has become more and more mature with the development of satellite technology. Obtaining high-quality remote sensing images is one of the applications of remote sensing technology. By analyzing remote sensing images of buildings and obtaining information to better formulate strategies, it is of great significance to better solve the problem of sustainable development [38]. In the past, it mainly relied on manual methods to extract feature information from remote sensing images of buildings. This method requires a lot of labor costs and is inefficient. Therefore, we are looking for a new high-efficiency and low-cost way to complete remote sensing images. The extraction of buildings has become a top priority.  
This paper first introduces the structure of the proposed FRA-Net, including the designed SRM, FPP module, and MFA module. It mainly starts from two aspects: simplifying the model structure and improving the feature extraction ability of the model. Then related ablation experiments and model comparison experiments are carried out on the LandCover dataset. Compared with other methods (FCN, SegNet, UNet, PSPNet, and FPN_MSFF), the proposed method has advantages in accuracy and computational efficiency. After that, the two models proposed in this paper, SR-SegNet and FRA-Net, are verified on the Massachusetts building dataset, and the generalization performance and effectiveness of the model are proved once again.  
Conceptualization, Y.M. and S.J.; methodology, Y.M. and S.J.; software, Y.M., Y.X. and S.J.; validation, Y.M. and S.J.; formal analysis, Y.M. and S.J.; investigation, Y.M., Y.X. and S.J.; resources, S.J.; data curation, S.J.; writing--original draft preparation, Y.M. and. Y.X.; writing--review and editing, S.J. and D.W.; visualization, Y.M. and Y.X.; supervision, S.J.; project administration, S.J.; funding acquisition, S.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data and the code of this study are available from the corresponding author upon request ([EMAIL_ADDRESS]).  
The authors declare no conflict of interest.
## 6 Results
### Equalization by the Least Mean Square Algorithm  
The LMS equalization was performed on the distorted data obtained from the OFDM receiver. Figures 9 and 10 show the constellation diagrams of QPSK and 16 QAM obtained before and after the equalization. After equalization, the EVM of the equalized constellations was computed using Equation (15).  
The implementation of the proposed LMS algorithm is examined with different fiber lengths in the presence of dispersion. It was observed that for QPSK modulation in OFDM subcarriers and a maximum fiber length of 100 km, we obtained an EVM of 17% after equalization, while for 16 QAM modulation in OFDM subcarriers and a maximum fiber length of 50 km we obtained an EVM of 12% after equalization, both of which are below the 3 GPP threshold. We obtained an EVM improvement of 1.7% for QPSK and 3.1% for 16 QAM after LMS equalization for the same fiber length.  
Figures 11 and 12 present the EVM results versus the fiber length both for QPSK and 16 QAM, revealing that an LMS equalizer can be applied effectively to provide dispersion in a converged mm-wave A-RoF system at 60 GHz.  
Figure 10: EVM measurements for 16 QAM modulation using LMS equalization (50 km SMF link). (**a**) Constellation diagram for 16 QAM before equalization with EVM 15.1%. (**b**) Constellation diagram for 16 QAM after LMS equalization with EVM 12%.  
Figure 9: EVM measurements for the QPSK modulation using LMS equalization (100 km SMF link). (**a**) Constellation diagram for QPSK before equalization with EVM 18.7%. (**b**) Constellation diagram for QPSK after LMS equalization with EVM 17%.
## 2 Mainshock-Aftershock Sequences
Figure 2 illustrates the \(M_{\mathrm{w}}\) and \(PGA\) distributions for mainshock-aftershock pairs. Two key observations emerge: (1) The mainshocks exhibit significantly higher \(M_{\mathrm{w}}\) compared to their associated primary aftershocks, with limited overlap in the range of 5.8-6.3. Further statistical analysis shows that the mean magnitude difference between mainshocks and primary aftershocks is about 0.9. (2) Despite differing \(PGA\) selection thresholds, both mainshocks and aftershocks exhibit comparable \(PGA\) ranges. To explore the relation between the mainshock and its aftershock \(PGA\), the relative intensity ratio \(\kappa\) is firstly defined as follows:  
\[\kappa=PGA_{\mathrm{as}}/PGA_{\mathrm{ms}} \tag{1}\]  
where subscripts'ms' and 'as' denote the mainshock and aftershock, respectively. Figure 3 displays the distribution of relative intensity ratios \(\kappa\) for the selected seismic sequences. The histogram reveals that the \(\kappa\) values span a broad range from 0.18 to 1.89, reflecting substantial aftershock variability relative to mainshocks. Overall, 83% of seismic sequences exhibit \(\kappa<1\), confirming the predominance of mainshock-dominant intensity pat  
Figure 1: Samples of as-recorded mainshock–aftershock sequences: (a) 1972 Managua earthquake; (b) 1983 Coalinga earthquake; (c) 1994 Northridge earthquake. Figure 2: Distribution of \(M_{\mathrm{w}}\) and \(PGA\) for mainshocks and aftershocks. terns. However, a non-negligible subset (17%, \(n=24/144\)) demonstrates \(\kappa\geq 1\), suggesting potential aftershock amplification phenomena in specific tectonic settings. Figure 4 compares the mean pseudo-acceleration response spectra of mainshocks only, aftershocks only, and mainshock-aftershock sequences. Note that the acceleration time histories are respectively normalized by their \(PGA\) to anchor the zero-period spectral ordinate for a standard spectral shape. It can be seen that the mainshock spectra are higher than the aftershock spectra for the period \(T>0.1\) s, attributable to the significant difference in magnitude between mainshocks and aftershocks [30]. In addition, the sequence spectra demonstrate near-complete overlap with mainshock spectra, indicating negligible aftershock-induced spectral modifications in elastic systems.
## 5 Experimental Results and Discussion
### Performance Evaluation and Analysis  
This section presents and analyzes the experimental results. In this context, the decoding-complexity and rate-controlling performances are analyzed first. Thereafter, the decoding-complexity reductions achieved by the proposed as well as state-of-the-art algorithms and their quality impacts are discussed with respect to the experimental setup discussed in Section 5.1. Here, the proposed algorithm considers generating bit streams with a 40% decoding-complexity reduction target over HM 16.0 (i.e., complexity level L2).
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 3 Problem Statement and Modeling
### VNF Placement Problem Statement  
The entities involved in the VNF placement problem are: (i) the users requesting new SFCs; (ii) the SFCs; (iii) the VNF instances; and (iv) the edge nodes.  
The number of entities and their high resource heterogeneity and the dynamism of their availability make this problem even more challenging. Moreover, an edge node can execute multiple instances of one or more VNFs, and a single VNF instance can be shared by multiple applications. Additionally, every application request needs to traverse the VNF instances of the corresponding SFC.  
Each user is associated with one or more SFC requests. An SFC request represents the request for a service, issued by a user and represents a chain of VNFs to be placed and executed in an edge environment.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Description** \\ \hline \(d_{p}(l_{ij})\) & Propagation delay over link \(l_{ij}\) \\ \(d_{tm}(l_{ij})\) & Transmission delay over link \(l_{ij}\) \\ \(d_{prc}(v_{inst},h)\) & Processing delay of VNF instance \(v_{i}\) in edge node \(h\in H\) \\ \(d_{q}(v_{inst},h)\) & Queuing delay in node \(h\in H\) + queuing delay for a VNF instance \(v_{i}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Types of delay.  
For simplicity, in the remainder of the article, we use the term VNF to explicitly refer to a VNF type (e.g., firewall, proxy, etc.), and the term instance is always referred to the VNF type instantiated and running on an edge node.
# Effect of Soil Reinforcement on Tunnel Deformation as a Result of Stress Relief
## 3 Results and Analysis
### Effect of Reinforcement Depth on Tunnel Heave  
Figure 8 compares the measured and computed normalized tunnel heave and maximum tunnel heave variation with different reinforcement depths, where \(H_{\text{e}}\) is the final excavation depth. When none of the soil elements were reinforced, the computed maximum tunnel heave was 0.116% \(H_{\text{e}}\). When the soil area S1 was reinforced, the computed tunnel heave decreased from 0.116% \(H_{\text{e}}\) to 0.099% \(H_{\text{e}}\) with a constant reinforcement Young's modulus. However, significant changes were not observed when the reinforcement depth increased further. Thus, it is obvious that the soil reinforcement depth underneath the basement reduced the tunnel heave, even for a thin layer. The further increase of the reinforcement depth did not have an effect and was uneconomical.  
Figure 7: Measured and computed changes in tunnel diameter with different soil reinforcement moduli.
## 3 Stochastic FIR Filter with Adaptive Scaling
### Adaptive Scaling Algorithm  
To improve the calculation accuracy of SFIR filters, a scaling method has been utilized on the filter coefficients [13; 14], which demonstrates improvements in accuracy. However, the scaling is quasi-static coefficient-only processing and cannot be applied to the real-time input signals directly. Based on the noise analysis in Section 3.1, a new adaptive scaling algorithm is proposed in this section, where both the input signal and the filter coefficients are adaptively scaled to the high accuracy region of stochastic computing.  
As shown in (6), SNR improves with the increasing numerical value \(P\). Thus, a scaling factor \(\alpha\) is set to scale up the numerical value \(P^{\prime}=\alpha\cdot P\) before stochastic bit stream generation. The SNR can be re-written as (7). To reduce the hardware complexity of the scaling operation, \(\alpha\) is selected in \(\alpha=2^{\beta}\) manner, where \(\beta\) is shown as (8). As a result, the scaling operation can be implemented with shift registers. After scaling operation, the scaled numerical value \(P^{\prime}\) can be transformed into a stochastic bit stream.  
\[\text{SNR}=10\cdot\log_{10}\frac{N\cdot P^{\prime}}{1-P^{\prime}}=10\cdot\log _{10}\frac{N\cdot\alpha P}{1-\alpha P} \tag{7}\]  
\[P^{\prime}=2^{\beta}\cdot P\leq 1\Rightarrow\beta=\left\lfloor\log_{2} \frac{1}{P}\right\rfloor \tag{8}\]  
After the bit-stream generation and stochastic computing-based operation, the output numerical value has to be re-scaled, which can also be implemented with shift registers.  
Combing the scaling and re-scaling operation, the adaptive scaling algorithm (ASA) is applied to an \"AND\" logic to implement multiplication \(z=x\cdot c\), shown as Algorithm 1. Note that the \"Find scaling factor\" step can also be implemented by shift registers, which would be presented in the next section.  
```
0:\(x,c\in[0,1]\)
0:\(z=x\cdot c\)
1: Find scaling factor: \(\beta_{x}=\lfloor\log_{2}1/x\rfloor\),\(\beta_{c}=\lfloor\log_{2}1/c\rfloor\)
2: Scaling: \(x<<\beta_{x},c<<\beta_{c}\)
3: Bit Stream Generation: \(x,c\Rightarrow X_{i},C_{i},i=1,2,...,N\)
4: AND Logic: \(Z_{i}^{\prime}=X_{i}\cap C_{i}\)
5: Bit-wise Re-scaling: \(Z_{i}=Z_{i}^{\prime}>>(\beta_{x}+\beta_{c})\)
6: Transform Back: \(\hat{z}^{\prime}=\sum_{i=1}^{N}Z_{i}/N\)
```  
**Algorithm 1** Adaptive Scaling Algorithm (ASA)  
Figure 4: (**a**) MSE performance of stochastic bit stream representation with different bit stream length. (**b**) SNR performance of the stochastic bit stream representation for \(P=0.1,0.2,0.3,0.4\) with scaling as (8).
# Deep Learning for Information Triage on Twitter
## 3 Preliminary Study: Types of Information Found on Twitter  
### Analysis of Tweets from the Great Earthquake  
In this section, we perform the study of tweets from the time of the Great East Japan Earthquake and describe the results of classification of those tweets.  
We used the data provided by Twitter Japan in the Big Data Project ([[https://sites.google.com/site/prj311/](https://sites.google.com/site/prj311/)]([https://sites.google.com/site/prj311/](https://sites.google.com/site/prj311/)), accessed on 23 June 2021). The tweets represent a time period of one week from 11 March to 17 March 2011, from the time of the Great East Japan Earthquake.  
After omitting 151 tweets from before the earthquake, we randomly extracted 6000 tweets and analyzed them manually by six project members (expert annotators, each annotator analyzed 100 tweets). The manual analysis revealed that many of the tweets were actually retweets or contained other second-hand information. This led us to divide the information into primary information and secondary information.  
Primary information refers to the kind of information that a person directly saw, heard or personally did. Secondary information refers to indirect information, such as re-posting or re-telling what was described by someone else, or describing a friend's opinion about books, or what someone saw on TV. It is indirect information described on the Internet by the third party.  
The primary information was represented by 1539 tweets (26%), secondary information was represented by 2083 tweets (36%), and other kinds of information not coming under the definition of either primary or secondary information was represented by 2227 tweets (38%) (Figure 1). Examples of each type of information were represented in Table 1.  
Primary information was represented by tweets in which users directly describe their own state, such as _samui_ (\"I'm cold\") or _tsurai_ (\"It's so hard...\"). There were many tweets of this kind. In such tweets, the speaker refers to themselves using first-person expressions, such as_savatashi_ (\"I\" [general]), or _boku_ (\"I\", or \"me\" [masculine]). Apart from this, a frequent expression appearing in primary information tweets was _nau_ (\"now\"). Additionally, an expression indicating that the user is describing their own present state was the use of a rhetorical figure called _taigen-dome_ (ending a sentence with a noun or noun phrase often used in Japanese poems, such as \"shining stars\" instead of \"stars are shining\").  
Compared to primary information, there was a larger amount of secondary information. However, much of this information was directed outside of the affected areas. Apart from that, there were numerous retweets containing information about the lifeline between the outside areas and the areas affected by the disaster, or in-tweet citations such as _...to no koto_ (\"it is said that/they say that...\"), or _...rashii_ (\"apparently...\").  
Figure 1: Ratio of primary, secondary and other information in tweets from the time of the Great East Japan Earthquake.