esa-sceva/satcom-chunk-collection huggingface dataset:
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 4 Application of _Trichoderma_ Fungi in the Control of Fungal Plant Pathogens
### Competition for Nutrients and Space
Fungi of the genius _Trichoderma_ have developed a mechanism responsible for the biosynthesis of substances, which allows them to extract this element from the soil much faster than other microbes. Siderophores are low-molecular compounds that chelate irons by forming a complex with them. Once the ferric-siderophore complex is formed, the Fe\({}^{3+}\) cations are converted to soluble Fe\({}^{2+}\), which are recognized by the membrane receptors of _Trichoderma_ sp. and then taken up by them [55; 113]. In addition to iron, siderophores also chelate and convert manganese, nickel, molybdenum, and cobalt ions into soluble forms [117; 118]. The mechanism by which _Trichoderma_ makes Fe available to plants is still under investigation. However, there are studies confirming that the levels of iron in cucumber, soybean, or in wheat after treatment with the _Trichoderma_ sp. strain were significantly higher than those in unvaccinated controls [119; 120; 121]. [PERSON] et al. [122] documented that _T. asperellum_, _T. harzianum_, _T. longibrachiatum_, and _T. viride_ showed the ability to produce ferric ion chelators belonging to the carboxylate and hydroxamate groups. [PERSON] et al. [123] proved that _T. atroviride_, _T. kontigii_, and _T. paraviridescens_ produced siderophores of the catecholate type, whereas _T. virens_, _T. atrobrunneumum_, _T. citrinovidide_, _T. crissum_, _T. guizhouense_, _T. hamatum_, _T. simmonsii_, and _T. tomentosum_ produced compounds belonging to hydroxamate group. In addition, _T. asperellum_ showed the ability to produce both types of ferric chelators.
# Error Mitigation Methods for FSM Using Triple Modular Redundancy
## 2 Theoretical Background  
In this section, definitions and concepts related to FSMs will be presented. Later, the basics of triple modular redundancy will be presented and sources of radiation errors described.
# Accuracy of 3D-Printed Master Cast Workflow Using a Digital Light Processing Primer
Horizontally oriented parts require reduced printing time compared to vertically oriented parts. A higher layer thickness results in reduced printing time, too, but impairs surface quality [16]. If it is necessary to obtain a real cast from an intraoral digital impression for a prosthodonic work, the accuracy of the fabrication process is crucial. Accuracy is defined by the subterms trueness and precision [17]. For trueness evaluation, a test object, which represents a copy of the original object is compared with this original object to evaluate deviations between the test object and the original [17; 18]. Precision defines the reproducibility [17]. For precision analysis test objects (copies) from the same original reference, objects are produced with the same device and method [17]. These copies are compared with each other [17; 18]. In digital dentistry, distance measurements or 3D surface comparisons can be accomplished to compare test and original objects [2]. To enable virtual 3D surface measurements, it is necessary to digitize real objects with a high-resolution industrial scanner. For jaw-sized scans, industrial scanners, such as the ATOS III Triple Scan, have a trueness of 3 um and precision of 2 um [19]. Once all objects are converted into 3D data, they can be superimposed by a registration process. During the registration process, the data sets are aligned as close as possible in one coordinate system. Applying a best-fit algorithm, even supposedly identical data sets show deviations meaning that the surface of one data set can be partly located relatively above the other and in some areas below. Therefore, from the measurement direction of one data set, values of <0, 0, and >0 are obtained in relation to the aligned data set. In conclusion, the output of the deviation values has to be differentiated between minus or plus values or the values have to be processed as absolute numbers. Three-dimensional evaluations can focus on specific areas of interest like full-arch accuracy, interproximal areas, occlusal surfaces, or preparation margins. In the present study, the full-arch accuracy and local accuracy of casts obtained by a conventional and a digital workflow were investigated. In the conventional workflow, silicone impressions were applied to produce gypsum casts. In the digital workflow, intraoral optical impressions were used to generate data for 3D printing. The null hypothesis was that the cast obtained from the conventional and digital workflow did not differ significantly at \(p\leq 0.05\). Additionally, the deviations within the digital workflow were evaluated. Therefore, the deviations between the reference cast and digital intraoral impression and the differences between digital intraoral impression and the printed cast were analyzed. For the local accuracy of the preparations which were integrated into the cast, the null hypothesis stated that there were no statistically significant differences between the digital and conventional workflow.
## 4 Experiments and Results
### Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Bad Weather (\(a\))** & **Specific Meaning** & **Sector Capacity (Operations/Hour)** \\ \hline
0 & Normal weather & 27 \\
1 & High echo intensity and high cloud base (including cloudy weather) & 22 \\
2 & High cloud base and low–high echo intensity (including cloudy weather) & 23 \\
3 & High echo intensity and middle cloud base (including cloudy weather) & 20 \\
4 & Low cloud base and high echo intensity (including cloudy weather) & 18 \\
5 & High visibility and low echo intensity (including high blowing and floating dust weather) & 24 \\
6 & Echo intensity in high visibility (including high blowing and floating dust weather) & 22 \\
7 & Echo intensity in moderate visibility (including high blowing and floating dust weather) & 23 \\
8 & Moderate visibility and high echo intensity (including high blowing and floating dust weather) & 23 \\
9 & Low visibility and high echo intensity (including high blowing and floating dust weather) & 16 \\
10 & Low precipitation and low echo intensity (including thunderstorms) & 16 \\
11 & Echo intensity in moderate precipitation (including thunderstorms) & 14 \\
12 & High precipitation and high echo intensity (including thunderstorms and thunderstorms) & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Sector capacities under different types of bad weather.
# An Analysis of the Short Utterance Problem for Speaker Characterization +
## 5 Experiments & Results
### Baseline  
Our first experiment sets a benchmark based on the SRE10 \"coreext-coreext det5 female\" experiment. This experiment only includes utterances with approximately 300 s of audio. Hence, in order to illustrate the degradation of short utterances we have considered two datasets obtained from the same utterances.  
* **Long**. The original utterances provided by the organizers for the evaluation, with approximately 5 min of audio per utterance. These utterances play the role of long reference utterances.
* **Short Random**. An alternative version of the original SRE10 dataset with restricted information. Each utterance of the original dataset is chopped restricting its audio speech to be in the range 3-60 s. The chop marks, starting point and initial position were randomly chosen. Utterance chopping was done after Voice Activity Detection (VAD). These utterances can suffer from degradation due to errors in the phoneme estimations \(G_{i}(\mathbf{A})\) and mismatch in the phoneme distribution \(\boldsymbol{\alpha}\).  
Thanks to these two datasets we have available a version of the utterances with full information and a version with partial knowledge. Now we must define the scenarios for the evaluation, assigning the roles of enrollment and test. We are interested in three particular scenarios:  
* **Long-Long**. The official NIST SRE10 experiment. The Long dataset plays both roles, enrollment and test, in each trial. This experiment represents the case in which we have complete information for both speakers.
* **Long-Short**. In this scenario the Long dataset also plays the role of enrollment, while the shortened dataset is used for test. In this scenario we study the scenario where the reference speaker, the enrollment, is perfectly characterized while the candidate (the test) speaker is unreliably represented.
* **Short-Short**. The Short Random dataset plays both roles, enrollment and test. This scenario reveals the performance with very limited information.  
The three scenarios are evaluated by means of the same trial list, which defines the comparisons to evaluate. The only difference among scenarios is the particular audio within the utterance to model the speakers. The results with these three configurations can be seen in Table 1.  
These results confirm that short utterances degrade performance. Besides, as long as more and more data are represented by means of short utterances, we suffer more degradation. This degradation affects both evaluation metrics EER and minDCF.
## 8 Real Time Validation  
The proposed algorithm was tested on a real-time basis for the practical transmission network of Rajasthan state in India. This network consists of conventional generators such as thermal power plants, nuclear and hydro power plants. Renewable energy was also integrated to the network in a significant amount. The transmission network in Bhadla region of Rajasthan state is detailed in Figure 16 where the large capacity of solar PV power is integrated into the grid. The 400 kV, 220 kV and 132 kV grid substation (GSS) and lines are described in detail. The installed capacity (IC) of solar PV plants is also mentioned. Power flow in the lines, transformers and from solar PV generation are also mentioned in terms of active power and reactive power, which were obtained through load flow study (LFS) using the MiPower software by using a database of the complete grid of Rajasthan state. Phase-A to ground fault was created on the 220 kV Bhadla-Kanasar line and the current was recorded on the Bhadla end. This current was processed using the proposed algorithm in the MATLAB software and FI is illustrated in Figure 17. It is observed that the FI has higher values than the threshold for phase-A and has lower values than the threshold for phases-B & C. Hence, the proposed algorithm can effectively be used for the protection of the grid with a high penetration of solar energy in the utility grid.  
Figure 14: Effect of switching transients associated with capacitive load on the performance of the algorithm.  
Figure 15: Effect of switching transients associated with inductive load on the performance of the algorithm.
# Study of Subsidence and Earthquake Swarms in the Western Pakistan
###### Abstract  
In recent years, the Quetta Valley and surrounding areas have experienced unprecedented levels of subsidence, which has been attributed mainly to groundwater withdrawal. However, this region is also tectonically active and is home to several regional strike-slip faults, including the north-south striking left-lateral Chaman Fault System. Several large earthquakes have occurred recently in this area, including one deadly Mw 6.4 earthquake that struck on 28 October 2008. This study integrated Interferometric Synthetic Aperture Radar (InSAR) results with GPS, gravity, seismic reflection profiles, and earthquake centroid-moment-tensor (CMT) data to identify the impact of tectonic and anthropogenic processes on subsidence and earthquake patterns in this region. To detect and map the spatial-temporal features of the processes that led to the surface deformation, this study used two Synthetic Aperture Radar (SAR) time series, i.e., 15 Phased Array L-band Synthetic Aperture Radar (PALSAR) images acquired by an Advanced Land Observing Satellite (ALOS) from 2006-2011 and 40 Environmental Satellite (ENVISAT) Advanced Synthetic Aperture Radar (ASAR) images spanning 2003-2010. A Small Baseline Subset (SBAS) technique was used to investigate surface deformation. Five seismic lines totaling \(\sim\)60 km, acquired in 2003, were used to map the blind thrust faults beneath a Quaternary alluvium layer. The median filtered SBAS-InSAR average velocity profile supports groundwater withdrawal as the dominant source of subsidence, with some contribution from tectonic subsidence in the Quetta Valley. Results of SBAS-InSAR multi-temporal analysis provide a better explanation for the pre-, co-, and post-seismic displacement pattern caused by the 2008 earthquake swarms across two strike-slip faults.  
InSAR; SBAS; subsidence; seismic profiles; faulting; earthquake; Western Himalayas +
Footnote †: journal: Remote Sensing
# Natural Photosensitizers in Clinical Trials
## 3 Natural Photosensitizers in Clinical Trials
### Phycocyanin  
Phycocyanin is an important antenna protein of the light-harvesting pigment in cyanobacteria, rhodophytes, cryptophytes, and glaucophytes, with various biological properties [66]. It localizes primarily in mitochondria, whose disruption of the membrane potential is a key signal to activate the apoptosis cascade. Damaged mitochondria release cytochrome c into the cytoplasm, which is a key step in activating the caspase cascade, particularly caspase-3, ultimately leading to cell death. Although the main mechanism of cell death following PDT with phycocyanin is apoptosis, necrosis has also been observed in some cases, especially at higher doses of phycocyanin and more intense irradiation [67]. Data from various studies suggest therapeutic uses for phycocyanin, such as anticancer, anti-inflammatory, anti-angiogenic activities and the ability to treat certain autoimmune disorders [68]. In earlier work, the compound has shown promise as a photosensitizer in the treatment of several cancers, such as breast and liver cancers, and in dentistry [69; 70; 71; 72; 73].  
Phycocyanin in Clinical Trials of Photodynamic Therapy  
We located one clinical trial with phycocyanin as a photosensitizer. [PERSON] et al. studied the effect of PDT with phycocyanin on the bond strength of the resin composite to healthy dentin. The therapeutic regimen they used involved applying a 1000 ug/mL phycocyanin solution to the surface of healthy dentin, leaving it for 5 min, and subsequent irradiation with a 220 mW diode laser at a wavelength of 635 nm and an energy density of 61.2 J/cm\({}^{2}\) for 3 min in continuous rinse with water and air drying. This study showed that photodynamic therapy with phycocyanin can be recommended as an antimicrobial method and has no negative effect on bonding to healthy dentin using a universal adhesive in a self-etching protocol [74].
## 2 Materials and Methods
### System Description
#### 2.3.3 Validation with Healthy Individuals  
Before the testing of the system with patients, the safety of use was analyzed with the development team as well as with a significant number of healthy individuals. A total of 62 participants from diverse backgrounds and different age groups completed a research survey and tested the system's prototype, offering comprehensive insights and valuable information regarding the participants' anxiety levels, pain management strategies, previous rehabilitation experiences, and their openness to using VR for therapeutic purposes. The diversity of people's age groups, occupations, and technological and VR expertise, as well as previous physiotherapy and psychological experiences, provided a rich, valuable, and truthful dataset that reflects a wide range of backgrounds and perspectives, enabling us to design a VR application that meets the needs of a broad and generalized user base.  
We have thus gained valuable insights into the specific requirements and expectations of users, which are essential for ensuring the creation and optimization of a practical and user-friendly application. By understanding healthy users' needs and preferences, we were able to validate the safety of use and tailor the application to provide effective and engaging therapeutic experiences, all while relying on VR to transpose these qualities in an immersive environment.
# Ras Multimers on the Membrane: Many Ways for a Heart-to-Heart Conversation
## 4 Membrane Interactions Regulate Ras Enrichment, Orientation, and Dimerization
This likely means less flexibility in KRas orientations on PIP2-containing membranes and slower transitions between the different orientations. At present, it is unknown whether the membrane distal conformation remains a significant population on PIP2 membranes, or whether Ras dimerization differs significantly on PIP2- vs PS-containing membranes. It is clear, however, that anionic lipids have a major impact on Ras dimerization. Indeed, a recent NMR study reported that the presence of PS shifts KRas G12D dimers from symmetric \(\alpha\)4-\(\alpha\)5: \(\alpha\)4-\(\alpha\)5 dimers to asymmetric \(\alpha\)4-\(\alpha\)5: \(\beta\)2-\(\beta\)3 dimers [71]. This highlights the sensitivity of Ras dimerization to its surroundings and the importance of anionic lipids (among many other factors) in this process. GTP-binding and isoform differences also impact Ras-membrane interactions and likely dimerization. The conformational dynamics of KRas [72; 151; 152], HRas [72; 140], and NRas [153; 154] are all GTP-dependent (and different from each other). Among the major Ras isoforms, dimers of NRas appear to be the easiest to reconstitute _in vitro_, needing only a simple lipid bilayer comprising 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) in multiple studies [84; 85]. By contrast, in vitro reconstitution of HRas and KRas dimers needed addition of the Ras-binding domain (RBD) of Raf [115] or the use of nanodiscs (likely as a confinement mechanism) [92]. The origins of these isoform differences in'readiness to dimerize' are yet to be determined, but a reasonable guess would be the differences in how they interact with the membrane.
## 4 Design of the Equalizers  
An equalizer is a filter placed at the receiver layer in the transmission chain, whose main task is to adjust, as much as possible, the signal at its output to match that which had been transmitted.  
This is possible by applying a model that controls the relationship between the input and the output of the filter.  
There are two architectures of equalizers: linear and non-linear [15].
# A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data
## 2 Materials and Methods
### Methods
#### 2.2.3 Air Pollution Perception Index, APPI  
Considering the air pollution complaint density or emotional intensity alone cannot fully characterize the public perception of air pollution. For example, in areas with a high complaint density of air pollution, public complaint emotion's intensity may be low. On the contrary, in areas with low complaint density, the intensity of public complaint emotion may be high. Therefore, this paper proposed the air pollution perception index, APPI.  
APPI can be used to evaluate the public's perception of air pollution more reasonably through the piecewise function processing of complaint density \(Air_{d}\) (Formula (4)) and complaint emotional intensity \(Air_{s}\) (Formula (5)). The specific calculation process of the index is as follows:  
**(1) Normalization**  
We first used the min-max normalization method [29] (Formulae (6) and (7)) to linearly transform the complaint density \(Air_{d}\) and complaint emotional intensity \(Air_{s}\) to unified \(Air_{d}^{*}\) and \(Air_{s}^{*}\), which ranges between 0 and 1.  
\[Air_{d}^{*}=1-\frac{Air_{d}-Min(Air_{d})}{Max(Air_{d})-Min(Air_{d})} \tag{6}\]  
\[Air_{s}^{*}=\frac{Air_{s}-Min(Air_{s})}{Max(Air_{s})-Min(Air_{s})} \tag{7}\]  
**(2) Piecewise function model**  
Then, we used Formula (8) to construct a piecewise function model \(f\) for APPI, according to the normalized complaint density \(Air_{d}^{*}\) and complaint emotional intensity \(Air_{s}^{*}\).  
\[f(Air_{d}^{*},Air_{s}^{*})=\left\{\begin{array}{l}(Air_{s}^{*}+1)^{\frac{Air_{s }^{*}}{Air_{s}^{*}}},Air_{s}^{*}\geq Air_{d}^{*}\\ \\ (Air_{d}^{*}+1)^{\frac{Air_{s}^{*}}{Air_{d}^{*}}},Air_{d}^{*}>Air_{s}^{*}\end{array}\right. \tag{8}\]  
The advantage of using the piecewise function model [30] to measure the public's perception of air pollution is that if there is a large difference between the complaint density and complaint emotional intensity, it can reduce the deviation value's impact on the results.  
**(3) APPI**  
Finally, the final air pollution perception index (APPI) is obtained by normalizing the piecewise function \(f\), as shown in Formula (9).  
\[APPI=\frac{f\big{(}Air_{d}^{*},Air_{s}^{*}\big{)}-Min}{Max-Min} \tag{9}\]  
The APPI ranges between 0 and 1. The closer the APPI value is to 1, the stronger the public perception of air pollution.
# Finsformer: A Novel Approach to Detecting Financial Attacks Using Transformer and Cluster-Attention
## 3 Materials and Methods
### Proposed Method
#### 3.3.2 Transformer-Based Attack Behavior Detection Framework  
In the Finsformer model, an improved Transformer architecture is adopted, as depicted in Figure 3. Here, the traditional multi-head attention mechanism has been innovatively replaced with a cluster-attention mechanism to more effectively handle the task of detectingattack behaviors in financial systems. The following sections detail the design features of the network, its input-output characteristics, and the intricacies of the intermediate blocks, along with a mathematical explanation of the advantages of this design.  
Model design. The Finsformer model incorporates a cluster-attention mechanism in place of the conventional multi-head attention mechanism. This mechanism initially clusters the input data based on feature similarity, then calculates attention weights based on these clusters. This approach more accurately captures complex patterns and relationships in financial transaction data. The mathematical expression for the cluster-attention mechanism is  
\[\text{ClusterAttention}(Q,K,V)=\text{Softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{ k}}}+M\bigg{)}V \tag{7}\]  
where \(M\) is a mask matrix derived from data clustering, and \(d_{k}\) is the dimensions of the key vectors.  
Additionally, the Finsformer model employs a structure of multiple encoders and decoders, each comprising a cluster-attention module and a feed-forward neural network. These layers are stacked sequentially to extract and process features layer by layer. Following each cluster-attention module is a feed-forward neural network, which includes two linear transformations and an activation function.  
\[\text{FFN}(x)=\text{max}(0,xW_{1}+b_{1})W_{2}+b_{2} \tag{8}\]  
The input-output dimensions of the model depend on the number of features in the financial data and the requirements of the classification task. Generally, after processing through the embedding layer, the dimensionality of the input data matches the size of the model's internal hidden layers.  
Performance analysis. The cluster-attention mechanism enhances the model's understanding of financial transaction patterns through cluster analysis. This method allows the attention mechanism to focus more on groups of transactions with similar features, thereby improving the accuracy in identifying anomalous behaviors. By incorporating the cluster-attention mechanism, the Finsformer model retains the advantages of the Transformer architecture while being more suited to handle the characteristics of financial data, especially in identifying complex and covert attack patterns.