esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Wireless Body Area Network
### WBAN Positioning  
In the spectrum of short range RF technologies, WBANs share the space with a number of short range protocols. IEEE 802.15 Task Group 6 describes a WBAN as: \"_low power devices operating in or around the human body (but not limited to humans) to serve a variety of applications including medical, consumer electronics /personal entertainment and other_\" [5]. A WBAN operates close to 1-2 m range or within the human body and is concerned with the connectivity of nodes operating within this space. A Wireless Personal Area Network (WPAN) is termed as a network operating up to 10 m around the human body and is normally associated with non-medical based communications such as MP3 or sports/fitness based applications. A Wireless Local Area Network (WLAN) typically has a range of up to 30 m depending on protocol type and has fixed power requirements.  
A WBAN can be described as a specific type of Wireless Sensor Network (WSN) [6]. Figure 1 shows the context of a WBAN in relation to WPAN, WLAN and Wide Area Network (WAN). WBANs are normally associated with the human body, but not exclusively humans, where comparatively fewer nodes are deployed than traditional WSNs. With the small set of nodes, even limited data loss in WBANs could be significant, whereas data loss in a WSN is less of an issue as nodes may yield redundant information and can potentially source data from adjacent nodes. Therefore, each WBAN node needs to provide certain QoS guaranties. This may require additional measurements to ensure successful real-time data delivery, since reliability of measurements is vital in the medical domain. Both WSNs and WBANs are concerned with small footprint, low power sensing and have low data rate requirements. However a WSN is not designed for the specific set of challenges that are inherent in communications around and within the human body. This has led to a specific research focus on the medical application side of sensor networks and in particular the suitable communication protocols that are specific to WBANs.
# Special Issue on IberSPEECH 2022: Speech and Language Technologies for Iberian Languages
## Contributions  
### Natural Language Processing  
Natural language processing (NLP) is a very dynamic and diverse field. The special issue showcases the breadth of research within NLP with papers covering important topics including summarizing, topic classification, chatbots, and datasets.  
In the age of information overload, the ability to identify key points in vast textual sources is crucial. The article by [PERSON] et al. (contribution 1) focuses on extractive summarization, which involves selecting and extracting the most important information or key sentences from a given document or set of documents to create a concise summary. The authors present the _Attentional Extractive Summarization_ framework, which exploits the attention mechanisms of Siamese hierarchical networks to learn the relations between documents and summary sentences. Following their innovative approach, it is no longer required to perform sentence labeling and oracle extraction, a laborious process involved in state-of-the-art approaches. The experimentation with two corpora of newspaper summarization shows it is possible to obtain high quality summaries.  
Natural language understanding is also a key aspect in the interaction with chatbots. In particular, open-domain chatbots have the ability to maintain a conversation with users in a wide range of topics. This entails multiple challenges, as the chatbot must be able to manage an interaction in which topics vary over time, but the information provided must be consistent with the history of the dialogue and the conversation topic. In (contribution 8) [PERSON] et al. present novel Zero-shot approaches to accurately identify the topic and subtopic of the conversation at every moment, and the automatic estimation of inconsistent responses as a first step to endow chatbots with the ability to avoid contradictory responses to variations of semantically similar user inputs. The evaluation results show a good efficacy in both tasks with several dialogue datasets with up to 18 distinct topics.  
Underlying the wide range of NLP topics, current advances in the area would not be possible without large high-quality resources for training and testing language models. [PERSON] et al. (contribution 10) present the massive multilingual crawling corpus _esCorpus-m_. esCorpius-m contains data in 34 languages different from English, with a strong focus on Spanish. One of the most prominent characteristics of the dataset is that it is cleaner than other state-of-the art corpora thanks to a novel cleaning and de-duplication pipeline, and that it maintains document and paragraph boundaries, and incorporates traceability to the source documents. The deduplicated and cleaned corpus, shared in HuggingFace, has a size of 2.5 TB, with 645,772,362 paragraphs and 242,248,582,193 tokens.
###### Abstract  
In recent years, the development of new constellations including Galileo, BeiDou Navigation Satellite System (BDS) and Quasi-Zenith Satellite System (QZSS) have undergone dramatic changes. Since January 2018, about 30 satellites of the new constellations have been launched and most of the new satellites have been included in the precise orbit and clock products provided by the Multi Global Navigation Satellite System (Multi-GNSS) Experiment (MGEX). Meanwhile, critical issues including antenna parameters, yaw-attitude models and solar radiation pressure models have been continuously refined for these new constellations and updated into precise MGEX orbit determination and precise clock estimation solutions. In this context, MGEX products since 2018 are herein assessed by orbit and clock comparisons among individual analysis centers (ACs), satellite laser ranging (SLR) validation and precise point positioning (PPP) solutions. Orbit comparisons showed 3D agreements of 3-5 cm for Galileo, 8-9 cm for BDS-2 inclined geosynchronous orbit (IGSO), 12-18 cm for BDS-2 medium earth orbit (MEO) satellites, 24 cm for BDS-3 MEO and 11-16 cm for QZSS IGSO satellites. SLR validations demonstrated an orbit accuracy of about 3-4 cm for Galileo and BDS-2 MEO, 5-6 cm for BDS-2 IGSO, 4-6 cm for BDS-3 MEO and 5-10 cm for QZSS IGSO satellites. Clock products from different ACs generally had a consistency of 0.1-0.3 ns for Galileo, 0.2-0.5 ns for BDS IGSO/MEO and 0.2-0.4 ns for QZSS satellites. The positioning errors of kinematic PPP in Galileo-only mode were about 17-19 mm in the north, 13-16 mm in the east and 74-81 mm in the up direction, respectively. As for BDS-only PPP, positioning accuracies of about 14, 14 and 49 mm could be achieved in kinematic mode with products from Wuhan University applied.  
multi-GNSS experiment (MGEX); orbit and clock comparison; satellite laser ranging (SLR) validation; precise point positioning (PPP) 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Vol., pp. 1-10, 2010-2010, 2010.
## 6 Cyber-Defense and Countermeasures Against Attack Paths  
This paper provides theoretical and computational results that prove that attack paths are a valuable working tool for studying possible cyber threats related to intruders' penetration into CPSs. Extracting the shortest attack path, as achieved in this work, allows defenders to detect the most vulnerable assets and analyze different types of prepared countermeasures in case of an attack, to thwart the attackers' intention.  
Countermeasures against cyber-attacks include corrective actions, procedures, special devices, and methods aimed at reducing threats, neutralizing vulnerabilities, and eliminating or preventing damage to the CPSs caused by an adversary's malicious actions. These countermeasures are generally divided into three main categories. The first category is preventive measures, which are designed to stop an attack before an intruder can exploit a vulnerability. Early identification of the shortest attack path allows defenders to better understand which critical components of the system should be defended first or which devices should be regularly patched and updated.  
The second category is detection measures, which include various actions related to identifying and reporting threats as they occur. For example, machine learning and big data techniques can detect unusual patterns by scanning the network. Knowing the most likely attack paths allows defenders to understand which network segments to focus on and where to place detection systems, such as intrusion detection systems. The third category is corrective actions. In addition to preventive and detective measures, which are usually performed proactively, corrective actions are taken after an attack is detected to mitigate its impact or recover from the damage caused. Attack path analysis may help isolate affected systems or segments, to prevent an attacker from moving further along the attack path.  
The fast and easy-to-use path extraction algorithm proposed in this paper may serve as an assistant tool with other countermeasures. The schematic representation of a defender's actions using different types of countermeasures in response to malicious actions along an attack path is shown in Figure 9.
## 2 Materials and Methods  
### TROPOMI SIF  
The single payload of TROPOMI mounted on the Sentinel 5 Precursor (S-5P) satellite has a near-polar, sun-synchronous orbit. TROPOMI SIF has a repeat cycle of 16 days in the nadir direction, and its equatorial overpass time is \(\sim\)13:30 local time [54], similar to that of OCO-2. Compared to OCO-2, the wider swath (\(\sim\)2600 km) and higher number of observations per second of TROPOMI make it feasible to generate near-daily gridded SIF products at relatively fine temporal and spatial resolution [25]. Recently, TROPOMI SIF has successfully been retrieved using a data-driven approach based on principal component analysis in the atmospheric windows of 735-758 nm and 743-758 nm for the far-red SIF [53].  
In this study, we used the unigrided TROPOMI far-red SIF (743-758 nm) from February 2018 to December 2020 ([[https://s5P-troposif.noveltis.fr/data-access/](https://s5P-troposif.noveltis.fr/data-access/)]([https://s5P-troposif.noveltis.fr/data-access/](https://s5P-troposif.noveltis.fr/data-access/)), assessed on 1 December 2021). In order to eliminate the influences of different solar illumination geometry on the SIF magnitude, by applying a day-length correction factor, the instantaneous TROPOMI SIF was converted to a daily average [58]. In addition, SIF observations with a cloud fraction higher than 0.2, view zenith angle (VZA) over 15\({}^{\circ}\), and solar zenith angle (SZA) larger than 70\({}^{\circ}\) were excluded in order to reduce the effects of clouds and the observational geometry [19; 59; 60]. Finally, the filtered SIFs were averaged at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) spatial resolution and 8-day temporal resolution (Figure 1b).
## 2 Sampling and Methods
### Analytical Methods
#### 2.2.4 LTA-XRD and SEM-EDS  
Minerals in the coal were determined using five selected LTA (low-temperature ash) samples on a Rigaku SmartLab X-ray diffractometer (XRD) machine (Rigaku, Tokyo, Japan) with a Ni-filtered Cu K\(\alpha\) X-ray source (energy 8.04 keV and X-ray wavelength 1.5406 A) and the XRD pattern was recorded with a step size of 0.01\({}^{\circ}\), over a 20 interval of 2.6-70\({}^{\circ}\)[33]. The XRD evaluation of the LTA was conducted at room temperature. The X-ray diffractograms were subjected to quantitative mineralogical analysis using Crystal impact software (Match), based on methods established in the literature [34].  
Nine selected polished blocks were coated with carbon, and these samples were investigated under a scanning electron microscope in conjunction with energy-dispersive X-ray spectroscopy (SEM-EDS, Jeol JSM-IT200 LA, Jeol, Tokyo, Japan) to observe minor mineral characteristics and to determine elemental distribution in the coal samples. The operating parameters were set to 20 kV acceleration voltage and high vacuum mode.
# Nanotechnology, Nanomedicine, and the Kidney
## 5 Nanotechnology for Renal Therapy
### Tissue Engineering
#### 5.2.2 Kidney Regeneration  
Rental transplantation is the treatment of choice for patients with end-stage kidney disease. However, its implementation is strongly limited by the chronic shortage of organ donors. To overcome this limitation, one solution is to regenerate the damaged kidney. In recent years, nanotechnologies have been used, combined with tissue engineering and material sciences, to develop promising applications in the field of organ regeneration.  
Different nanotechnological tools are being investigated for kidney regeneration, such as carbon nanotubes [89; 90], exosomes [91; 92], and nanofibers [93]. This field of nanomedicine is still in its infancy but is very promising. Nanoparticles and nanomaterials could be used to either deliver biomolecules into the kidney to promote regeneration or be used as a substrate to support kidney regrowth and regeneration. For example, exosomes isolated from liver stem cells injected in AKI murine models decreased urea and creatinine levels [91]. In addition, to improve AKI kidney through functional and structural improvement, [PERSON] et al., showed that endothelial progenitor cell-derived exosomes can stimulate pro-angiogenesis and protect the kidney from ischemia-reperfusion injury in glomerulosclerosis and tubulointerstitial fibrosis [92]. Toward the development of a supportive nanostructure for renal regeneration, biomimicking nanofibers have been developed to support the culture of human kidney-2 epithelial cells (HK-2). Cells cultured on the electrospun polymer functionalized with bioactive peptides and ureidopyrimidinone (UPy) formed a polarized cell barrier, a parallel gene expression profile for the proteins involved in the membrane transporter system, and the metabolic function of brush border enzymes [93].
# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 5 Performance Tests--Results and Analysis
### Insert Operation  
For each database structure, the insert operations are presented in Table 3. The insert operation will perform the insertion of a new client. In the case of relational MySQL, we need to look for the user before inserting the client, in order to make sure that it exists in the database and is not marked as deleted; otherwise, an exception may occur. If the user is found, we create the client object with the help of a constructor that has all the fields from the client, except the id field that we set to auto-increment, as parameters. In the case of using document-based MySQL, no previous verification is needed, we build the client object, and, with the help of the _add()_ method, we save the element in the database.  
In order to obtain a clean code, when creating objects, we can use constructors, or we can use the Builder annotation (i.e., @Builder) available in the _lombok_ package. If we annotate a class with _@Builder_, a constructor with all the respective class fields becomes available, with the help of which we can create objects such as _Client.builder().build()_, the necessary fields being added after the builder. This way, the code is much cleaner than if we had used setters. Figure 3 presents the execution time of the INSERT operation on relational MySQL and document-based MySQL.  
For the insert operation, the differences between the execution times before and after the optimization are not very big in the case of both databases, as we can see in Figure 3. Since, in the case of relational MySQL, for inserting a client, a search operation is performed before, the user is searched after the _id_ field that already has an index created, being the primary key. The same goes for the actual insertion when looking for the next id.  
In the case of document-based MySQL, the insertion is faster than in the case of relational MySQL because no validation of the fields is conducted, saving the object as it was built. In relational MySQL, it is checked to respect the type of each specified field and its length. We observe that the optimization methods do not have a great impact on the insertion operation, in the case of both databases, with the improvement in the response times being insignificant.
## 3 Results and Discussion  
### Chemical Composition of Oregona Essential Oil  
Figure 3 reports the graphical representation of the volatile profile of OEOs. Globally, thirty-two volatile compounds belonging to six phytochemical groups (monoterpenes, monoterpenoids, sesquiterpenenes, ethers, ketones, and alcohols) were found.  
Figure 3: Volatile organic compounds emitted from oregano essential oil. Results indicate mean percentage values \(\pm\) standard deviation (S.D.) of three measurements and are expressed as relative peak areas (peak area of each compound/total area of the significant peaks to all samples) \(\times\) l00.  
Most of the compounds identified belong to the group of monoterenes; among these, the most abundant chemicals were p-cymene, \(\gamma\)-terpinene, and myrcene. However, the most abundant volatile compound detected was carvacrol (79.9 \(\pm\) 3.5%), determining the monoterpneoids groups as the most abundant class of VOCs in OEOs analysed in this study. Carvacrol, p-cymene, and \(\gamma\)-terpinene are also the main compounds detected in other oregano plants grown in the Mediterranean area [43; 44; 45]. Although the volatile composition of OEOs may be affected by many factors (environmental conditions, season of collection, age of plants, and geographical origin) [44; 46; 47], several studies have reported the possibility of classifying oregano chemotypes according to their essential oil composition in carvacrol type, thymol type, and both of them (carvacrol and thymol in almost equal amounts) [43; 46; 48]. According to [PERSON]'s [48] classification, our results show that because of the higher content of carvacrol (\(\sim\)80%), the OEOs studied corresponded to the carvacrol oregano type, thus determining the smell and use of the condiment oregano. Besides characterizing OEOs' aroma, carvacrol exhibits a plethora of bioactivities, including antioxidative properties, inhibition of antibiotic-resistant bacteria, inhibition of microbial and fungal toxins, and anti-carcinogenic activity [49; 50; 51; 52]. These aspects highlight the potential of using OEOs belonging to the carvacrol oregano type as a multifunctional food ingredient.
# Entropy-Based Dynamic Rescoring with Language Model in E2E ASR Systems
## 1 Introduction  
In conventional ASR systems [1] that introduce deep learning or not, LM is still an essential part of the whole system. In end-to-end models, LM is not necessary since an end-to-end model itself can decode the intermediate representations of input features into a word sequence.  
However, even for an end-to-end model, there are still benefits to introducing LM into it. LM is useful for introducing extra corpora information. For example, LM can be pretrained on a huge mount of text data, such as the Wikipedia corpus, with a much higher training speed compared to an end-to-end model trained on same mount of text data with audio signal data to be processed. In another common situation, that speech corpus may not be sufficient. In this case, LM is even more useful. However, what should be noticed is that transcripts of speech corpora and pure text corpora may not come from the same data distribution. For example, speech corpora contain speech expressions, such as ah, em, etc., while text corpora contain more written expressions with words that are hardly used in speech.  
With the development of machine learning and deep learning, language modeling has made great progress in natural language processing applications. LM has evolved from the conventional n-gram model to a transformer-based [2] end-to-end system [3; 4; 5] capable of training on tremendous corpora with much higher prediction accuracy. In recent years, efforts have been made to leverage the advantages of novel LM to ASR. Some of them focus on integrating novel LM into the ASR system by techniques such as CTC [6; 7], while others focus on adapting LM to the task domain.  
Because shallow fusion can be easily implemented by combining logits from LM and ASR while obtaining significant improvement, it is still the most common method for applying LM integration in practice. Our method is an extension of shallow fusion. With the estimation of model confidence, this method can automatically decide and dynamically change an LM weight of shallow fusion during decoding. Experiments on two corpora,Librispeech and TEDLIUM-2, validate that our method can reach similar performance compared to tuned LM weights.  
This article will start with introducing conventional and end-to-end (E2E) [3] frameworks of ASR and how LM can be integrated into end-to-end systems, then explain our proposals and show the results of previous proposals and our proposals on LM integration. Finally, we will summarize the experiments' results and introduce several future plans on LM integration.
## 2 Discriminative Bayesian--Active Shape Model (DB-ASM)
### Feature Detector  
As mentioned above, the local feature detector of the present DB-ASM is a MOSSE filter. It is known that it is very fast and robust against rotation and partial occlusion. Equation (2) represents the MOSSE filter.  
\[\text{G}=\text{F}\odot\text{H}^{*} \tag{2}\]  
Since the input image is given as \(f\); filter as \(h\); 2D Gaussian Map as \(g\) in the Equation (2), each variable is to produce F, H, G, respectively, through 2D Fourier transform. Here, F is acquired by convoluting an input image using the 2D Fast Fourier Transform (FFT). \(\odot\) refers to the element-wise multiplication, whereas * refers to complex conjugate. The filter in Equation (2) can be derived by using Equation (3) on the local feature image at each location.  
\[\text{H}^{*}=\frac{\sum_{j=1}^{\text{N}}\text{G}_{\text{j}}\odot\text{F}_{ \text{j}}^{*}}{\sum_{j=1}^{\text{N}}\text{F}_{\text{j}}\odot\text{F}_{\text{ j}}^{*}} \tag{3}\]  
In Equation (3), each image and Gaussian map are to be treated with FFT and then an element-wise convolution to obtain the cumulative data. The cumulative data is to be divided to obtain the filter H*. The created filter H and input image I are to be treated in the element-wise way and Equation (4) is used to calculate a response map (or correlation map).  
\[\text{D}_{i}^{\text{MOSSE}}\left(\text{I}\left(y_{i}\right)\right)=\ F^{-1} \left\{\text{F}\left\{\text{I}\left(y_{i}\right)\right\}\odot\text{H}_{i}^{*}\right\} \tag{4}\]The response map calculated by Equation (4) is to predict the weighted peak with likelihood. Then, an isotropic Gaussian is be obtained through this likelihood. Equation (5) gives the isotropic Gaussian of \(y_{i}^{WP}\).  
\(y_{i}^{WP}\) in Equation (5) refers to a location having the highest correlation in the response map as shown in Figure 1, whereas \(\sum_{yi}^{WP}\) refers to a Gaussian covariance matrix in \(y_{i}^{WP}\) and the response map.  
\[y_{i}^{WP}=\max_{z\in\Omega}\left(p_{i}\left(z_{i}\right)\right),\,\sum_{i} \begin{array}{c}wp\\ y_{i}\end{array}=\text{diag}\left(p_{i}\left(y_{i}^{WP}\right)^{-1}\right) \tag{5}\]  
where \(p_{i}()=\)_Response Map_.  
Figure 1 illustrates the visual appearance of the PDM, the MOSSE filter and the response maps, respectively, at five sampled landmarks among 56 on the given face.