esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Discussion  
### Shear Behaviours with Different Water Contents under Low Confining Pressures  
The test results when the water content is 16% and the confining pressure is 50 kPa can be observed in Figure 3, which representatively illustrates the shear behaviours of the soil samples with different water contents during the triaxial test. Figure 3a shows the deviatoric stress; Figure 3b shows the volume strain; Figure 3c shows the variation of the stress ratio \(q/p\) in the shearing process with the dilatancy rate \(\mathrm{d}\varepsilon_{\mathrm{v}}/\mathrm{d}\varepsilon_{\mathrm{d}}\). The following parameters are indicated in the figure: \(w\) is the water content of the sample (%), \(\sigma_{3}\) is the confining pressure that is applied during the triaxial test, \(\varepsilon_{1}\) is the axial strain during shearing, \(q\) is the deviatoric stress, \(p\) is the average net stress (the pore-air pressure is \(u_{\mathrm{a}}=0\) during the test), \(\varepsilon_{\mathrm{v}}\) is the volume strain, and \(\varepsilon_{\mathrm{d}}\) is the deviatoric strain. During the triaxial test, the sample shows volume shrinkage in the beginning (point I in Figure 3 indicates the state point of maximum volume shrinkage), and then it transitions to dilatancy (point II corresponds to the peak stress state point). After passing the peak stress, the sample displays softening until the shearing stabilises. When the water content is high, sample softening is not evident. In a saturated state, the sample primarily shows shrinkage, shown  
Figure 2: Soil-water characteristic curve.  
in Figure 4[22]. In this study, the development and change process of the stress and deformation during shearing are discussed.
# CubeSat Imaging Payload Design for Environmental Monitoring of Greenland
## 3 Modular Design
They scored each configuration independently, and then we summarized those results to present them in Table 6. As a result, Design 2 was assessed to be very complex, and it did not fulfill the hardware limitations. Design 3 failed to provide redundancy, and each SoM did not handle all the cameras. Furthermore, Design 5 was assessed to take longer than the required development time, and it could require extra knowledge in hardware design. As a result, Design 1 is the most straightforward configuration, and the team agreed to move forward with
# Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues
## 3 CVAE for Knowledge-Grounded Dialogue  
The task of knowledge-grounded dialogue generation can be framed as follows: Given a dialogue context \(C=(C_{1},C_{2},\ldots,C_{|c|})\) comprising \(|c|\) turns of conversation, and a segment of knowledge text \(k=(k_{1},k_{2},\ldots,k_{|k|})\) containing \(|k|\) words, the goal is to generate a response \(y=(y_{1},y_{2},\ldots,y_{|y|})\) that aligns with the provided context and is informed by the knowledge text. This is achieved by maximizing the probability \(p(y|C,k)\). A practical approach for solving this problem involves using the Conditional Variational Autoencoder (CVAE) framework [27]. This framework approximates the distribution of the random variable \(\mathbf{y}\) (representing the response) conditioned on \(\mathbf{c}\) (representing the context) and \(\mathbf{k}\) (representing the knowledge). This is accomplished by introducing a latent variable \(\mathbf{z}\).  
The training objective of CVAE can be formulated as maximizing the Evidence Lower Bound (ELBO), as detailed below:  
\[\mathcal{L}_{\text{CVAE}} = -\text{KL}(q_{\phi}(\mathbf{z}|\mathbf{y},\mathbf{c},\mathbf{k})||p_{\theta}(\mathbf{ z}|\mathbf{y},\mathbf{c},\mathbf{k})) \tag{1}\] \[+\mathbb{E}_{\mathbf{z}\sim q_{\phi}(\mathbf{z}|\mathbf{c},\mathbf{k})}\log p_{ \theta}(\mathbf{y}|\mathbf{c},\mathbf{k},\mathbf{z})\]  
In the above formula, KL represents the Kullback-Leibler divergence. \(p_{\theta}(\mathbf{y}|\mathbf{z},\mathbf{c},\mathbf{k})\) acts as the decoder, reconstructing the response \(\mathbf{y}\) using the latent variable \(\mathbf{z}\), as well as context \(\mathbf{c}\) and knowledge \(\mathbf{k}\); \(q_{\phi}(\mathbf{z}|\mathbf{y},\mathbf{c},\mathbf{k})\) serves as the inference model, approximating the true posterior; \(p_{\theta}(\mathbf{z}|\mathbf{c},\mathbf{k})\) is the prior model, which samples the latent variable from the prior distribution. In these models, \(\theta,\phi\) are parameters that respectively pertain to the inference and decoder models.  
The CVAE model mentioned above employs a single latent variable to encode information from context and knowledge sources. A typical implementation of this CVAE involves concatenating \(c\) and \(k\) as input to the encoder, subsequently sampling the latent variable \(z\sim q_{\phi}(z||c,k)\)([PERSON], 2016; [PERSON], 2016), as illustrated in Figure 1a. However, it has been noted that, in the absence of explicit supervision, the solitary latent variable generated by the standard CVAE fails to learn disentangled representations that accurately reflect the distinct latent structures of different sources ([PERSON] et al., 2016). This limitation significantly impedes the model's performance enhancement and interpretability potential by exploiting variational latent variables.
## 3 Data and Methodology
### Urban LULC Modeling
#### 3.4.2 Multi-Layer Perceptron-Markov Chain (MLP-MC)
The MLP constructs a network of neurons between two example classes and driving factors, together with a web of connections that consist of sets of weights. Then, the sample cells are divided into two groups. The first 50% of the sample cells are used for training and the second 50% for validation [31; 45; 46]. This process is to develop the relationship between explanatory factors and activation levels of the transition potential. Thus, the weight has been adjusted to minimize the error to improve accuracy [46; 47]. When the accuracy rate is more than 80% or reaches the maximum iteration (1000 epochs), the transition potential map of each sub-model, which is an output of the MLP, yields the suitability of LULC classes for the future period. * LULC Change Prediction  
The LCM uses a change prediction process to determine the quality of the future LULC in specific time modeled by MC. It produces two types of results. The first is a hard prediction model which results from a projected LULC map through a land allocation function [36]. The second is a soft prediction which is a comprehensive vulnerability map based on the set of transition. Generally, the values of the soft prediction outputs are between 0 and 1 [28].
###### Abstract  
The detection of early stages of caries is still one of the major challenges in preservative dentistry. Since it is known from polarized light microscopy (PLM) that intrinsic enamel birefringence is affected by demineralization, polarization-sensitive optical coherence tomography (PSOCT) could facilitate the noninvasive detection and assessment of early carious lesions. The present study aims to correlate enamel lesion progression and depolarization measurements based on PSOCT in an artificial demineralization model. A total of 18 enamel slabs were prepared from bovine incisor teeth and demineralized in an acetic buffer solution for up to 49 days. The degree of polarization (DOP)--indicating depolarization and thus, demineralization--was calculated from PSOCT measurements and compared to lesion depth which was measured from PLM images. Artificial lesions showed characteristic zones of natural enamel demineralization in PLM images. DOP representations showed no depolarization for sound, nondemineralized enamel, whereas significant changes were found after 15 days of acid-exposition. The linear regression analysis of the DOP and the measured lesion depth showed a substantial correlation (\(R^{2}=0.71\)). The results indicate that PSOCT-based depolarization imaging provides an unambiguous contrast for initial enamel demineralization which is correlated to the lesion progression.  
c 2020 10 2971 2020 24 April 2020 10.3390/app10082971
# eSCorpius-m: A Massive Multilingual Crawling Corpus with a Focus on Spanish
## 3 esCorpius-m at a Glance  
A total of 39,502 compressed WARC (Web Archive) files were processed which represent two months of Common Crawl (see Section 4.3 for more details). The compressed information occupied about 180 TB and the size of the processed decompressed information is estimated to be more than 0.8 PB. Prior to content deduplication, the downloaded corpus was composed of 549,887,283,621 words. The deduplicated and cleaned corpus size is 2,710,379,754,463 (2.5 TB), with 645,772,362 paragraphs and 242,248,582,193 tokens. esCorpius-m supports 34 languages, with Spanish being the most dominant, covering nearly 20% of the entire corpus (see Figure 1). While languages like French, German, Spanish, and Portuguese make up a significant portion, it also features low-resource languages like Basque, Galician, Occitan, and Maltese. Table 1 shows the figures per language. The Data Availability Section contains the link to the resource. As argued in ref. [42], corpora for language modeling should not only be compared in terms of size, but also in the quality and traceability of their data. Table 2 shows a comparison of esCorpius-m with the main state-of-the-art multilingual datasets. For our comparison, we focused on massive crawled datasets consisting of hundreds of billions of tokens, similar in size to our corpus with 242 billion tokens. The only deviation from this criterion was the ParaCrawl corpus (we include ParaCrawl as a reference, but regarding the size, note that the comparison is apples-to-oranges because unlike the other datasetsshown in the table, Paracrawl is a parallel dataset, focusing on machine translation, rather than language modeling or similar tasks), given its exceptional processing quality. The comparison has been performed not only in terms of size, but also encompassing quality factors related to language identification, parsing, cleaning and deduplication that will be further explained in the following section. For multilanguage datasets, the numbers reported in the table correspond to non-English samples only.
###### Abstract  
For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction for rolling bearing is established by integrating multi-domain mixed features and temporal convolutional network (TCN). The variational mode decomposition method based on the dung beetle optimization algorithm is developed to reduce signal noise by determining the optimal parameters adaptively. To construct a health indicator of rolling bearing effectively, an isometric feature mapping algorithm is introduced to reduce the dimensionality of multi-domain mixed features, integrating time-domain, frequency-domain, and entropy features of vibration signals under non-stationary and nonlinear conditions. By considering the advantages of a multi-head attention mechanism (MA) and bidirectional gated recurrent unit (BiGRU), a TCN-based multi-head attention and bidirectional gate (TCNMABG) is developed to predict the RUL of rolling bearing accurately, whose detailed implementation process of TCNMABG is described based on XJTU-SY dataset. To verify the performance of TCNMABG, the FEMTO-ST dataset is introduced to perform the numerical experiments, and the results show that prediction error is reduced by 65.96% on average.  
multi-domain mixed features; remaining useful life prediction; temporal convolutional networks; feature dimensionality reduction; rolling bearing +
Footnote †: journal: _Applied_  
_aciences_
# On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks
## 1 Introduction
Therefore, the impulsive noise should be taken into consideration while analyzing the performance of WSNs for industrial applications. To this regard, a more practical model is the [PERSON]'s Class-A (MCA) model, which has shown to provide excellent fits to a variety of noise and interference measurements [26; 27; 28]. The advantage of this model lies in its generality to represent a number of interference signals with arbitrary impulsive effects. By varying model parameters, we can model a wide class of interferences ranging from pure AWGN to highly impulsive noise [29; 30]. In light of this, we present the performance analysis of multi-hop DF relaying system over Nakagami-_m_ fading channels in the presence of impulsive noise. The justification for the choice of Nakagami-_m_ distribution as the small-scale fading in our analysis is threefold. Firstly, a large number of field measurements show that the small-scale fading in indoor environments follows Nakagami-_m_ fading [31; 32; 33]. Secondly, Nakagami-_m_ distribution describes via the \(m\) parameter a wide range of fading distributions. For instance, it converges to one-sided Gaussian distribution with \(m=1/2\), to Rayleigh with \(m=1\), and to purely Gaussian as \(m\) approaches infinity. Given appropriate bounds on the parameters, the lognormal and Weibull distributions can also be well approximated by the Nakagami-_m_ distribution in some ranges ([34], pp. 284-288). Furthermore, it can also closely approximate the Nakagami-_n_ ([PERSON]) and Nakagami-_q_ ([PERSON]) distributions with appropriate parameter mappings ([35], p. 25). Thus, our results can be readily extended to other fading scenarios by simply varying the model parameters. Last but not least, to the best of our knowledge, it is still an open research question on the capacity performance of multi-hop DF relaying systems over Nakagami-_m_ fading channels. In this paper, we intend to fill this gap. The remainder of this paper is organized as follows. In Section 2, we introduce the cooperative transmission system under investigation as well as the fading channel and noise model. In Section 3, closed-form expressions for the end-to-end average BER of the relaying system under the considered channel condition are derived. The analytical expression for the end-to-end average capacity and the statistics of the end-to-end instantaneous capacity are derived in Section 4. Analytical and simulation results are presented and discussed in Section 5. Section 6 concludes the paper and discusses about future work.
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 2 IberSpeech-RTVE 2022 Evaluation Databases
### RTVE2022-SoS Database  
An excerpt of the whole RTVE database presented in Table 1 has been used as development data for the search on speech challenge. Specifically, the search on speech challenge provided two different development datasets. The dev1 dataset consists of about 60 h of speech with human-revised word transcriptions without time alignment. The dev2 dataset, which was the one actually used as development dataset for the search on speech challenge, consists of 15 h of speech data. For the challenge, the timestamps for the roughly 2500 occurrences of the queries used in the development (dev2) and test evaluation datasets were also provided.  
A summary of the main database features such as the development (dev2)/test dataset division, the number of word occurrences, the duration, and the number of speakers is presented in Table 6.  
The number of terms and the number of total occurrences both for STD and Qbe STD tasks for the RTVE2022-SoS database are presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**File ID** & **Data Type** & **\#occ.** & **dur. (min)** & **\#spk.** \\ \hline \multirow{2}{*}{Mavir-02} & train & 13,432 & 74.51 & 7 (7 males) \\ \hline \multirow{2}{*}{Mavir-06} & train & 4332 & 29.15 & 3 (2 males, 1 female) \\ \hline \multirow{2}{*}{Mavir-08} & train & 3356 & 18.90 & 1 (1 male) \\ \hline \multirow{2}{*}{Mavir-09} & train & 11,179 & 70.05 & 1 (1 male) \\ \hline \multirow{2}{*}{Mavir-12} & train & 11,168 & 67.66 & 1 (1 male) \\ \hline \multirow{2}{*}{Mavir-03} & dev & 6681 & 38.18 & 2 (1 male, 1 female) \\ \hline \multirow{2}{*}{Mavir-07} & dev & 3831 & 21.78 & 2 (2 males) \\ \hline \multirow{2}{*}{Mavir-04} & test & 9310 & 57.36 & 4 (3 males, 1 female) \\ \hline \multirow{2}{*}{Mavir-11} & test & 3130 & 20.33 & 1 (1 male) \\ \hline \multirow{2}{*}{Mavir-13} & test & 7837 & 43.61 & 1 (1 male) \\ \hline \multirow{2}{*}{ALL} & train & 43,467 & 260.67 & 13 (12 males, 1 female) \\ \hline \multirow{2}{*}{ALL} & dev & 10,512 & 59.96 & 4 (3 males, 1 female) \\ \hline \multirow{2}{*}{ALL} & test & 20,277 & 121.3 & 6 (5 males, 1 female) \\ \hline \end{tabular}
\end{table}
Table 4: MAVIR database: number of word occurrences (#occ.), duration (dur.) in minutes (min.), and number of speakers (#spk.) for training (train), development (dev) and testing (test) datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Task** & **\#dev. Terms** & **\#dev. Occurrences** & **\#test Terms** & **\#test Occurrences** \\ \hline STD & 374 & 1014 & 223 & 2121 \\ \hline Qbe STD & 102 & 425 & 106 & 1192 \\ \hline \end{tabular}
\end{table}
Table 5: MAVIR database query information: number of terms and number of occurrences for STD and Qbe STD tasks both for development and test datasets (‘dev.’ stands for development). The term length of the development query lists varies between 4 and 27 graphemes. The term length of the MAVIR test query lists varies between 4 and 28 graphemes.
# On Vulnerability of Selected IoT Systems to Radio Jamming--A Proposal of Deployment Practices
## 3 The Problem Statement  
In this article a verification will be provided regarding the immunity of a Weightless BS and SigFox to intentional interference originating from an intentional jammer disturbing their operation. This verification will be performed in two steps: Firstly, the packet error rate (PER) response will be obtained by means of performing PER measurements in the anechoic chamber located in the Laboratory of Electromagnetic Compatibility (LEC) at Wroclaw University of Science and Technology (WUST), for all possible combinations of operational settings available in SigFox and Weightless. Secondly, knowing PER dependence on CNIR, results will be transformed into a, so called, interference margin (\(M_{int}\)) providing a measure of a system's resistance to jamming (Section 6.2 will provide an in-depth discussion of \(M_{int}\)).  
Figure 1: An Internet of Things (IoT) network exposed to intentional jamming.  
The said assumption of a jammer transmitting at \"legal\" EIRP (Equivalent Isotropic Radiated Power) levels can be considered to be a limiting factor of the finding arrived at with the use of this model, since it appears to be natural for a jamming device to radiate at the highest power available. However, as also noted in Section 8.1, the fact that an IoT network can be threatened by a relatively low-power culprit, makes the situation even more dangerous as such weak signals may be difficult to detect by means of typical search methods such as angular spectrum scanning.
## 3 Study Area, Data and Workflow
### Data  
In carrying out its flood emergency mapping service for Myanmar, the NDRCC activated a data emergency acquisition mechanism to access as much available data as possible through different approaches. One approach involved collecting and processing satellite images from its database. Another approach used involved coordinating Chinese satellites for disaster observations. Exposure, auxiliary and post-disaster data were also accessed through a global open sourced approach. By 14 August 2015, 73 images from four satellites of the flooded area had been accessed, including 25 pre-disaster images drawn from HJ-A/B satellites covering the entire territory of Myanmar (Figure 3) and 48 post-disaster images drawn from HJ-A/B/C and SJ-9A satellites covering the entire country with coverage shown in Figure 4. The dates and quantities of images used are outlined in Table 1. Satellite images were used for water body information extraction.  
The HJ-1A/B/C and Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting (SSCEDMF) manage two optical satellites and one radar satellite. The HJ-A/B satellites were launched in September 2008 and both were fitted with CCD cameras to observe the Earth over 700 km areas at a ground pixel resolution of 30 m and across four spectrum bands. HJ-1C was launched in November 2012 with an S-wave band synthetic aperture radar (SAR) and with two working band modes and scanning image swath widths of 40 km and 100 km, respectively. The SJ-9A satellite was launched in October 2012 and was fitted with a multispectral scanner with a spatial resolution of panchromatic 2.5 m/10 m multispectrum and a swath width of 40 km.  
GlobalLandCover30 data were also accessed from an information service platform as exposure information. Baseline geographic data including township points, village points, provincial borders, and national borders were accessed from the Myanmar Information Management Unit (MIMU) [48], a service of the UN Country Team and Humanitarian Country Team based in Myanmar and managed by the UN Resident and Humanitarian Coordinator. These data were used for affected administrative  
unit estimations and referencing. Disaster information was collected from relief web for estimation validation and condition referencing.  
Figure 3: Pre-flood HJ-1A/B Images of Myanmar.
###### Abstract  
We analysed 10 years (2008-2017) of continuous eddy covariance (EC) CO\({}_{2}\) flux measurements of net ecosystem exchange (_NEE_) in a young pedunculate oak forest in Croatia. Measured _NEE_ was gap-filled and partitioned into gross primary productivity (_GPP_) and ecosystem reparation (_RECO_) using the online tool by Max Planck Institute for Biogeochemistry in Jena, Germany. Annual _NEE_, _GPP_, and _RECO_ were correlated with main environmental drivers. Net primary productivity was estimated from EC (_NPPEC_), as a sum of \(-\)_NEE_ and \(R_{h}\) obtained using a constant \(R_{h}\):\(R_{ECO}\) ratio, and from independent periodic biometric measurements (_NPPBM_). For comparing the _NPP_ at the seasonal level, we propose a simple model that aimed at accounting for late-summer and autumn carbon storage in the non-structural carbohydrate pool. Over the study period, Jastrebarsko forest acted as a carbon sink, with an average (\(\pm\)std. dev.) annual _NEE_ of \(-\)319 (\(\pm\)94) gC m\({}^{-2}\) year\({}^{-1}\), _GPP_ of 1594 (\(\pm\)109) gC m\({}^{-2}\) year\({}^{-1}\), and _RECO_ of 1275 (\(\pm\)94) gC m\({}^{-2}\) year\({}^{-1}\). Annual _NEE_ showed high inter-annual variability and poor correlation with annual average global radiation, air temperature, and total precipitation, but significant (\(R^{2}\) = 0.501, \(p\) = 0.02) correlation with the change in soil water content between May and September. Comparison of annual _NPPEC_ and _NPPBM_ showed a good overall agreement (\(R^{2}\) = 0.463, \(p\) = 0.03), although in all years _NPPBM_ was lower than _NPPEC_, with averages of 680 (\(\pm\)88) gC m\({}^{-2}\) year\({}^{-1}\) and 819 (\(\pm\)89) gC m\({}^{-2}\) year\({}^{-1}\), respectively. Lower values of _NPPBM_ indicate that fine roots and grasses contributions to _NPP_, which were not measured in the study period, could have an important contribution to the overall ecosystem _NPP_. At a seasonal level, two _NPP_ estimates showed differences in their dynamic, but the application of the proposed model greatly improved the agreement in the second part of the growing season. Further research is needed on the respiration partitioning and mechanisms of carbon allocation.  
carbon flux; carbon balance; net ecosystem exchange; dendrometers; tree biomass; litterfall; logistic model