esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Ferment Processing Parameters on the Quality of White Pan Bread
## 3 Results and Discussion
### Ferment Storage Time
#### 3.2.3 Effect of Ferment Storage Time on Bread Quality
The highest scores for crumb softness were for breads containing ferment at an inclusion level of 70% that was fresh (0 h stored) and stored for 48 h and 72 h. A slight decrease in crumb resilience was observed for breads made with fresh ferment (0 h stored) and added at an inclusion level of 70% and for breads made with ferment stored for 48 h and added at 50% and 70% inclusion levels. The breads containing ferment scored slightly lower than, or equal to the control, for crumb resilience. Crumb strength scores slightly decreased for breads made with ferment that was stored for 48 h and 72 h regardless of the ferment inclusion level. The other breads containing ferment scored equal to, or higher than the control, for crumb strength. [PERSON] reported that breads produced with flour ferments had total bread scores which were comparable to the control bread, processed using a sponge- and dough-baking process [32]. The scores were comparable for external characteristics (volume, loaf symmetry, crust colour, break and shred) and internal characteristics (crumb grain, texture, aroma, mouthfeel) with the exception for flavour which was less enhanced [32]. Overall, the scoring results of this study showed that the most noticeable improvement in the ferment containing breads was seen in crumb softness. Bread processed with fresh ferment (0 h stored) at a 70% inclusion level and breads processed with ferments stored for 48 h and 72 h, at inclusion levels of 50% and 70%, exhibited a decrease in bread crumb characteristics. This trend was observed for crumb colour, crumb strength and texture.
# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
###### Abstract  
Crowd anomaly detection is crucial in enhancing surveillance and crowd management. This paper proposes an efficient approach that combines spatial and temporal visual descriptors, sparse feature tracking, and neural networks for efficient crowd anomaly detection. The proposed approach utilises diverse local feature extraction methods, including SIFT, FAST, and AKAZE, with a sparse feature tracking technique to ensure accurate and consistent tracking. Delaunay triangulation is employed to represent the spatial distribution of features in an efficient way. Visual descriptors are categorised into individual behaviour descriptors and interactive descriptors to capture the temporal and spatial characteristics of crowd dynamics and behaviour, respectively. Neural networks are then utilised to classify these descriptors and pinpoint anomalies, making use of their strong learning capabilities. A significant component of our study is the assessment of how dimensionality reduction methods, particularly autoencoders and PCA, affect the feature set's performance. This assessment aims to balance computational efficiency and detection accuracy. Tests conducted on benchmark crowd datasets highlight the effectiveness of our method in identifying anomalies. Our approach offers a nuanced understanding of crowd movement and patterns by emphasising both individual and collective characteristics. The visual and local descriptors facilitate high-level analysis by closely relating to semantic information and crowd behaviour. The analysis observed shows that this approach offers an efficient framework for crowd anomaly detection, contributing to improved crowd management and public safety. The proposed model achieves accuracy of 99.5 %, 96.1%, 99.0% and 88.5% in the UMN scenes 1, 2, and 3 and violence in crowds datasets, respectively.  
crowd anomaly detection; visual descriptor; sparse feature tracking; neural networks +
Footnote †: journal: Applied  
Article
# Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
## 2 GNSS Use in Geodesy and Jamming
### Localisation of a Moving Jammer  
To characterise the signal and jammer power relationship, the following can be used: (a) the carrier-to-noise density power ratio C/N\({}_{0}\), (b) the jammer-to-noise density power ratio J/N\({}_{0}\), (c) the jammer-to-signal power ratio J/S, or (d) the jammer-to-noise power ratio J/N. An impact of GNSS jammers on consumer grade receivers is well established in [PERSON] et al. [35], while this paper deals with an impact on surveyors' professional receivers. For passing-by jammers, which emit intermediate power, a significant decrease in receiver performance may potentially cause even a professional GNSS receiver to lose its lock, which may at least extend the surveyors open field work. With information that such a disturbance from a steady velocity source is on the way, and from estimations of the distance or angle of arrival of the jammer-receiver, it is possible to estimate how long a degradation of functionality may last. The benefits of multi-constellation and multi-frequency receivers [35] for object positioning tracking in the presence of narrow band interfering sources are obvious.  
The localisation of jammers is based on measurements of the effective carrier-to-noise density power ratio, C/N\({}_{0\,\mathit{eff}}\)[40], with an array of sensors at prior known locations. The approach of the moving interferer localisation is constrained by the relatively poor sensitivity of the averaged C/N\({}_{0}\) of the satellites in view to the jammer-receiver distance \(d\) and also a relatively low sampling rate (1 s) due to the fast-moving jammer with velocities from 8.3 to 25.0 m/s. The relation can be modelled as, for example [35]:  
\[\frac{C_{avg}}{N_{0}}|_{eff}=\overline{\beta}+10\alpha\ \log_{10}(d). \tag{7}\]However, the approach of the moving interferer localization, e.g., Betz [41], was enhanced by the known velocity of the jammer due to an in-lined located set of sensors. Assuming that at the lowest C/N\({}_{0}\), the jammer was at the shortest distance to the receiver, the calibration of \(\alpha\) and \(\overline{\beta}\) is possible. Based on experimental data, these two parameters were established for each receiver for all drives enabling the localisation through bi-lateration with the pairs of synchronised receivers.
## 3 Results and Discussion
### Distribution of Water Vapor  
Figure 10 shows the concentration of water vapor in the gas diffusion layer under the rib on the cathode side, for which similar studies have been carried out previously [22; 30; 31]. It can be clearly seen from Figure 10 that the concentration of water vapor under the rib of the serpentine flow field in the conventional design was significantly higher than that in the rib groove designs, and the water vapor concentration under the rib was higher in the middle and lower on both sides. This was because the water vapour generated by the electrochemical reaction in the conventional design of serpentine flow field PEM fuel cell could only be discharged along the direction of the flow field, where the flow path was long and the resistance was large. However, the water vapor under the rib of the serpentine flow field with a rib groove design could also be transported through the grooves, reducing drainage  
Figure 8: Oxygen concentration of the cathode flow field when the cell voltage was 0.6 V. (**a**) Conventional design, (**b**) 1/3 of the rib groove, (**c**) 2/3 of the rib groove, (**d**) full rib groove.  
Figure 9: Oxygen concentration along the cathode flow field.  
resistance and therefore enhancing the drainage capacity of the fuel cell, thereby simplifying the water management system of the PEM fuel cell.
## 3 Materials and Methods
### Model Validation
#### 3.4.2 Results of Model Validation  
1. First validation experiment  
Figure 3a shows the x-direction air velocity profiles at the chamber's central location (X = 0.4 m). The velocity profile produced by the numerical model from the current study is compared to the numerical model developed by [PERSON], 2011 [37] and the experimental  
Figure 1: Geometric parameters of the first physical experiment.  
Figure 2: Locations of velocity measuring poles and settle plates (second validation experiment). (**a**) Locations of velocity measuring poles. (**b**) Locations of settle plates on the floor (plan view).  
data (measured by [PERSON], 2006) [35]. The experimental data are well predicted by the model developed in this study.  
Figure 3b shows the simulated particle concentration's comparisons by the current CFD model, the CFD model by [PERSON] et al. [38], and the experimental data [35]. The developed model (in this study) reasonably agrees well with the experimental data. In the original data measurements [35], the range of error is only given for the concentration measurements, and the accuracy of the velocity measurements is not mentioned. However, it is expected that the range of errors in measuring the point air velocity are quite small compared to the corresponding errors in the concentration measurements.
## 3 Results
### Bacterial Indicator Species Analysis  
Indicator species analysis was conducted separately for cultivars and rootstocks at the genus level. Twenty-four bacterial genera were associated with one or more cultivars (Figure 4a), while only thirteen were found to be specifically associated with the rootstocks (Figure 4b), and only one genus (_Eloraea_) was associated with both rootstock and cultivars. The number of genera that associated with a single cultivar or rootstock were low-_Marmoricola_ was specifically associated with one cultivar (Reberger) while the genera _Escherichia/Shigella_ and _Enterobacter_ were exclusively associated with the rootstock 125 AA. Instead, the vast majority of bacterial genera were indicative of multiple groups, with the majority associated to 3 (out of the 4) groups for both cultivars (Figure 4a) and rootstocks (Figure 4b).  
The genus _Eloraea_, which was an indicator species in both rootstocks and cultivars, is relatively unknown, with few known members; the genus is thought to belong to the aerobic anoxygenic photosynthetic bacteria [49], which are obligate aerobes that can capture energy from light through photosynthesis [50]. Species in the genus _Marmoricola_ are all mesophilic, non-pathogenic and often found in soil environments [51]. The genera _Escherichia/Shigella_ and _Enterobacter_ all belong to the Enterobacteriaceae, and have members which are known human, animal and plant pathogens, as well as plant-associated growth-promoting species, and lignin degraders [52]. The genera _Desulfacinum_, _Desulfaculus_ and _Desulurivibrio_, which were indicator species for all but the cultivar Felicia, are known to be anaerobic sulfate reducers [53].  
Figure 4: Indicator species analysis of bacterial genera per cultivars (**a**) or rootstocks (**b**). Color and bubble size relate to sequence abundance per genus, bubbles without color indicate that the genus was not an indicator for that particular cultivar or rootstock. \"\" \(p\) < 0.001, \"\" \(p\) < 0.01, \"\" \(p\) < 0.05. Genera that are indicator species in both datasets are shown in bold.
## 3 Results and Discussion
### Advancing GGI: Interrelationship Among DDL, LCC, and Resilience  
Advancing the resilience of GGI systems requires a nuanced understanding of the interrelations among \(DDL\), LCC, and resilience. Figure 6a presents the Pareto nondominated solutions, illustrating GGI configurations across various \(DDL\). Four distinct design alternatives emerge, each balancing diverse management objectives with differing LCC, Oper-R, and Tech-R values (Table 3). This study focuses on solutions meeting the criteria of Tech-R \(\geq\) 95%, Oper-R \(\geq\) 70%, and minimal LCC.  
Figure 6a presents Pareto-optimal solutions categorized by management priorities. Four optimal design types are highlighted: Design A (balanced), Design B (Oper-R priority), Design C (Tech-R priority), and Design D (LCC priority). These offer tailored options based on stakeholder goals. Design A is identified as the optimal solution, achieving a Tech-R of 99.2%, Oper-R of 70.4%, and an LCC of USD 69.8M at a \(DDL\) of 60%. This configuration represents a balanced trade-off between cost efficiency and resilience (Figure 6a). When an LCC cap is imposed (e.g., USD 70.0M) (Figure 6c,d), Designs B (\(DDL\) = 60%) and C (\(DDL\) = 80%) emerge as alternatives optimized for Oper-R and Tech-R, respectively. Design B demonstrates robust resilience against significant GREI failures and GI degradation, while Design C excels in managing extreme weather conditions. Thus, the selection of  
Figure 6: Distribution of optimized GGI configurations in relation to LCC, Tech-R, and Oper-R across various \(DDL\) scenarios. The four non-dominated solutions (Designs A, B, C, and D) are highlighted, showing (**a**) three–dimensional relationships between LCC, Tech-R, and Oper-R; (**b**) trade–offs between Tech-R and Oper-R; (**c**) LCC variations with Oper-R; and (**d**) LCC variations with Tech-R.  
a specific design should depend on budget constraints, local conditions, and desired GGI configurations.  
The findings reveal that Oper-R remains relatively stable across varying _DDL_ for a fixed LCC. Decentralized systems (green points in Figure 6c) outperform centralized systems (blue points) marginally, reflecting better adaptability to diverse stressors. Furthermore, the distribution of Tech-R highlights the superior performance of decentralized designs (Figure 6d), attributed to their inherent flexibility and enhanced resilience to extreme storm events, even in the face of significant GREI damage [31].  
Design D, representing a fully decentralized network, achieves the lowest LCC. While its resilience is slightly lower than those of Designs A, B, or C, it reliably prevents overflow under a 5-year design rainfall scenario. These findings align with the work of [PERSON] et al. [44] and emphasize the importance of multi-objective optimization in infrastructure design. They underscore the necessity of data-driven decision-making, balancing cost minimization, functionality, and resilience in the development of sustainable and robust GGI systems.
# Review of Satellite Interferometry for Landslide Detection in Italy
## 2 Data Collection  
In this review work, we exploited the Google Scholar (GS) engine to collect all the possible scientific contributions, where the authors exploited the InSAR technique to study landslides. We collected original articles, book chapters, conference proceedings, and extended abstracts written in English and published by international journals after peer review.  
The data collection is based on the following searching criteria. The base level is constituted by the region name in Italian and in English for those regions with a commonly used translation, e.g., Toscana/Tuscany. Each regional name is connected to a double level of keywords. The first level comprises the type of application, such as \"Iandslide monitoring,\" \"landslide mapping,\" and \"landslide detection.\" The second level includes the keywords related to the technique or algorithm of analysis, such as \"satellite interferometry,\" \"InSAR\" (and other derived acronyms such as A-DInSAR or D-InSAR), \"SBAS\" (Small Baseline Subset), \"PSI\" (Persistent Scatterers Interferometry), and other widely recognized acronyms as \"SqueeSAR,\" \"IPTA\" (Interferometric Point Target Analysis), or \"CPT\" (Coherent Pixels Technique).  
Google Scholar has been chosen because it is considered a \"superset of Web of Science (WOS) and Scopus, with substantial extra coverage\" [23]. [PERSON] et al. [23] report that the database of articles included in the category _Earth Sciences and Technology_ has a correspondence of 95% between GS and WOS. The 5% difference is related to the fact that GS includes non-journal sources, such as theses. GS is a powerful tool to analyze the existing literature in one specific field, as in our case, but it can overestimate the number of citations of a single author. Thus, it should be used with caution when the aim is to quantify authors/journals metrics [24]. GS certainly is less automatized than WOS, but is a free source of information as complete as other databases [24].  
The articles are stored in different folders connected to a summary table with different fields, such as:  
* The title of the document;
* Region;
* Toponym and municipality (for small scale works, we focused on the province or region);
* Affiliation of the first author;
* Year of publication;
* Digital object identifier (DOI), when available;
* Type of InSAR approach and algorithm;
* Satellite and band;* Type of landslide monitored;
* Type of application. (We defined 5 classes: (1) back-analysis, (2) characterization, (3) landslide inventory update, (4) mapping, and (5) monitoring. An article can be assigned to multiple applications. A description of each class is given in Section 5.)
# Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning
## 5 Architecture and Training  
In this section, the architecture and training procedure are presented. The goal of our neural network was to implement a strategy for occlusion avoidance, which would reduce the problem of occluded object detection to an occlusion-free scenario. This step is necessary, since the direct gradient-based optimization method is only utilizable in the original, synthetic environment. However, a CNN-based method does not require environment-calculated gradients, and only requires the input images for its functionality. This allows the below-described approach to be usable in real-world environments, given the right configuration. The Convolutional Neural Network (CNN) presented in this section was also trained to use the RGB-D images for predicting the occlusion map and gradient directions, as we predicted that these would aid the network in learning useful representations of the input.
###### Abstract  
Owing to the hasty growth of communication technologies in the Underwater Internet of Things (UIoT), many researchers and industries focus on enhancing the existing technologies of UIoT systems for developing numerous applications such as oceanography, diver networks monitoring, deep-sea exploration and early warning systems. In a constrained UIoT environment, communication media such as acoustic, infrared (IR), visible light, radiofrequency (RF) and magnet induction (MI) are generally used to transmit information via digitally linked underwater devices. However, each medium has its technical limitations: for example, the acoustic medium has challenges such as narrow-channel bandwidth, low data rate, high cost, etc., and optical medium has challenges such as high absorption, scattering, long-distance data transmission, etc. Moreover, the malicious node can steal the underwater data by employing blackhole attacks, routing attacks, Sybil attacks, etc. Furthermore, due to heavyweight, the existing privacy and security mechanism of the terrestrial internet of things (IoT) cannot be applied directly to UIoT environment. Hence, this paper aims to provide a systematic review of recent trends, applications, communication technologies, challenges, security threats and privacy issues of UIoT system. Additionally, this paper highlights the methods of preventing the technical challenges and security attacks of the UIoT environment. Finally, this systematic review contributes much to the profit of researchers to analyze and improve the performance of services in UIoT applications.  
Underwater Internet of Things (UIoT); trends; challenges; security and privacy 2021  
Dept 2021  
10 December 2021
## 2 Materials and Methods
### Theoretical Principal
#### 2.3.5 Mathematical Models  
Statistical parameters such as the coefficient of determination (\(R^{2}\)), reduced chi-square (\(\chi^{2}\)), and root mean square error (RMSE) were calculated to select the most appropriate model. The \(\chi^{2}\), RMSE, and \(R^{2}\) were calculated according to the following Equations (2)-(4).  
Modeling the drying process is an important key aspect of drying technology. In this study, the experimental drying data of _Arbutus unedo_ L. fruits were fitted into nine commonly used thin-layer drying models, listed in Table 1. Constant and comparative indices for each model were determined using the Curve Expert Professional 2.3 software. The selection of the most suitable model describing the process was based on the coefficient of determination (\(R^{2}\)) and chi-square (\(\chi^{2}\)). The best performing model has the highest values of \(R^{2}\) and lowest values of \(\chi^{2}\).
## 1 Introduction
### Previous Literature
The research also emphasizes the difficulty of implementing real translators, mainly due to the great diversity of devices and few resources available. This survey underscores the importance of IPv6 for enhancing IoT security, scalability, and connectivity, marking a critical step towards the future of Internet protocols and their application within the IoT ecosystem. These examples represent the most current, closest approaches in the endeavor to implement a gateway or Relay. While each project bears unique features, they collectively underscore the research domain's focus on creating and implementing communication gateways between these two pivotal technologies, highlighting the field's ongoing relevance and the imperative for advanced solutions in promoting IoT interconnectivity. It is worth noting that all of the projects share a common goal: to facilitate and optimize communication between different network protocols, evidencing the evolution of research in this area, although the NB-IoT seems to be the least-represented research in this field so far. Hence, the device presented in this study contributes to the growing body of literature on IoT interoperability by providing a simpler, easier, yet reliable solution to a pressing problem. Previous research in this area focused on various aspects of IoT communications, but the use of future-proof NB-IoT as the main communications layer to cross the bridge adds another dimension to the discourse--as a new, unique approach to aid the integration of 6 LoWPAN devices into IPv4 networks. As the IoT landscape continues to evolve, the significance of developing interoperable solutions that can adapt to the changing technological environment cannot be underestimated. In conclusion, we not only present a novel solution to a critical challenge in IoT interoperability, but also highlight the importance of simplicity and efficacy in the design of communication relays--or gateways. The findings of this study are expected to have significant implications on the development of future IoT networks, paving the way for more integrated, versatile, user-friendly IoT ecosystems.