esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Biofilm Agents to Overcome _Pseudomonas aeruginosa Antibiotic Resistance_
## 2 The Biofilm Life Cycle
### Biofilm Maturation  
During the maturation phase, QS and c-di-GMP regulate the production of matrix components so that the biofilm reaches a stable tridimensional structure. In this advantageous niche, bacteria are protected against biocides, the host immune system, antibiotics or other deleterious environmental conditions. The rhamnpids create channels so that nutrients can circulate through the biofilm. The species diversity in the biomass favors horizontal gene transfer, which is a way for micro-organisms to acquire fitness genes. Hence, various phenotypes emerge, creating small colony variants that contribute to tissue damage and resistance. They notably possess more pili and produce more polysaccharides and c-di-GMP, which is why they better adhere to abiotic surfaces [38]. Interestingly, the advantageous microenvironment created by the extracellular matrix enables the establishment of QS-mediated bacterial interactions, in the form of collaboration or competition between various species. Bacteria collaborate via the exchange and share of public goods, secreted enzymes or chelators that help the digestion of nutrients. However, the presence of cheaters (i.e., bacteria that benefit from public goods without producing any) can be a threat to the biofilm community. The different bacterial species can also directly or indirectly compete with other pathogens. They can steal nutrients such as iron from their rivals, ensure the degradation of their signaling molecules, enhance biofilm dispersion or manipulate the host immune system to eradicate them. Furthermore, bacteria also secrete antimicrobials such as bacteriocines or respiratory chain inhibitors. For example, _P. aeruginosa_ and _Staphylococcus aureus_ often form mixed biofilms in infected lungs of CF patients [39]. _P. aeruginosa_ synthetizes then the 4-hydroxy-2-heptylquinoline-\(N\)-oxide (HQNO) that inhibits _S. aureus_ growth. However, it also protects _S. aureus_ from antibiotics such as to bramycin or vancomycin, because of cell metabolism reduction. Interestingly, the increased alginate production in mucoid strains of _P. aeruginosa_ reduces the secretion of anti-staphylococcal molecules and enables the cohabitation of the two species within the biofilm [35]. These interactions also result in the overproduction of virulence factors that cause severe damage to host tissues and stimulate the inflammatory reaction [40].
# HarmoDehazeDiffusion: Multi-Scale Spatial Fusion Network for Dense Haze Removal
## 3 The Method
### Multi-Scale Feature Extraction  
As shown in Figure 3, in addition to spatial features, we also introduce multi-scale feature extraction layers to replace the traditional bottleneck layers in the denoising U-Net network of the diffusion model.  
Specifically, to address the limitation of the traditional U-Net, which typically uses plain convolutional layers or bottleneck layers between the encoder and the decoder, resulting in a limited performance in capturing long-range contextual information, our proposed multi-level dilated layer expands the receptive field by leveraging dilated convolutions with different dilation rates, thereby capturing contextual information at various distances in the image. The multi-level dilated layer first employs three dilated convolutional layers with dilation rates of 2, 4, and 8, respectively. These dilated convolutional layers can progressively enlarge the receptive field while maintaining the resolution of the feature maps, thus capturing multi-scale features from the image contexts.  
Next, the features extracted from these three dilated layers, along with the global features, are fed into a squeeze-and-excitation block [39]. The squeeze-and-excitation block adaptively learns the importance of different channels in the feature map through a channel attention  
Figure 3: Overview of the proposed multi-scale feature extraction.  
mechanism, further enhancing the discriminative power of the features. Specifically, the squeeze-and-excitation block first performs global average pooling on the input features, compressing the spatial dimensions of each channel and thereby capturing the global information for each channel. This step not only reduces the computational complexity but also effectively mitigates overfitting. Subsequently, channel weights are generated through two fully connected layers and a nonlinear activation function. Finally, channel-wise feature enhancement is achieved by multiplying the generated channel weights with the original features. This mechanism adaptively adjusts the contribution of each channel based on the requirements of the task, allowing the network to focus more on task-critical feature channels, thereby significantly improving the overall performance of the model.
## 3 Results and Discussion
### Analysis of the Optimal Extracts
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Phenolic**} & \multicolumn{2}{c}{**Pulp**} & \multicolumn{2}{c}{**Peel**} \\ \cline{2-5}  & **UAE** & **STE** & **UAE** & **STE** \\ \hline \(\beta\)-Resoricylic acid & 0.29 \(\pm\) 0.02 \({}^{\text{b}}\) & 0.32 \(\pm\) 0.02 \({}^{\text{a,b}}\) & 0.32 \(\pm\) 0.01 \({}^{\text{a,b}}\) & 0.34 \(\pm\) 0.02 \({}^{\text{a}}\) \\
4-Hydrooxybenzoic acid & 0.26 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.26 \(\pm\) 0.02 \({}^{\text{a}}\) & 0.25 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.25 \(\pm\) 0.01 \({}^{\text{a}}\) \\ Pyrogallol & 0.65 \(\pm\) 0.03 \({}^{\text{b}}\) & 0.68 \(\pm\) 0.05 \({}^{\text{b}}\) & 0.88 \(\pm\) 0.02 \({}^{\text{a}}\) & 0.66 \(\pm\) 0.02 \({}^{\text{b}}\) \\ Pyrocatechuic acid & 0.46 \(\pm\) 0.03 \({}^{\text{b}}\) & 0.48 \(\pm\) 0.02 \({}^{\text{b}}\) & 0.49 \(\pm\) 0.02 \({}^{\text{a,b}}\) & 0.53 \(\pm\) 0.02 \({}^{\text{a}}\) \\ Caffe acid & 0.58 \(\pm\) 0.03 \({}^{\text{a}}\) & 0.48 \(\pm\) 0.02 \({}^{\text{b}}\) & 0.  
and the antioxidant capacity of OM pulp and peels. In particular, it is possible to examine which technique is most efficient for each part of the fruit. For example, it was observed that for total polyphenol recovery from OM, it is more efficient to use UPAE than STE, whereas the peels would be preferable as a raw material to pulp. On the contrary, for the most efficient ferric-reducing power, STE in OM pulp is required. Finally, the use of STE in OM peel is more efficient for the extraction of total betalains. Such a clear delineation helps in accurately classifying and understanding the underlying data. However, in the discriminant analysis presented in Figure 5, the AAC lacks scoring coefficients. This could indicate multicollinearity, where AAC is highly correlated with other variables, such as DPPH. In such cases, its unique contribution to the discriminant function is minimal.
###### Abstract  
For a partially coherent Laguerre-Gaussian (PCLG) vortex beam, information regarding the topological charge (TC) is concealed in the cross-spectral density (CSD) function phase. Herein, a flexible method for the simultaneous determination of the sign and magnitude of the TC for a PCLG vortex beam is proposed based on the measured CSD amplitude and phase after the beam propagates through a dual cylindrical lens with adjustable angles. Both the stripes in the CSD amplitude and phase exhibit quantitative relationships with the value of TC, that is, \(N=2|l|+1\). Meanwhile, the angle adjustability of cylindrical lenses allows flexible measurements, thus rendering the scheme effective for both high and extreme coherences.  
vortex beam; topological charge; partially coherent; cylindrical lens +
Footnote †: journal: _Photonics_
## 2 Materials and Methods
### Molecular Descriptors  
Recently, the 2171 compounds and their repellent activity (0 or 1) listed in Table S1 were successfully modeled from a three-layer perceptron (TLP) [27] including 20 molecular descriptors as input neurons [25]. These descriptors encoded topological information (e.g., size, branching), the presence of specific atoms, functional groups, structural features, and physicochemical properties. The 1-octanol/water partition coefficient (log \(P\)), calculated according to the regression equation of [PERSON] et al. [28; 29] (MlogP), belonged to the last category of the selected molecular descriptors. All these previous descriptors were considered in the current study. MHlogP based on the atom/fragment contribution method of [PERSON] [30] was also added to the set of descriptors. The reason for the choice of MHlogP was threefold. First, MHlogP was ranked among the first descriptors with MlogP during the feature selection process that led to the design of a classification model for predicting repellents for clothing application [31]. Secondly, the models allowing the calculation of the MHlogP and MlogP descriptors are based on different methodologies. Last, the atom/fragment contribution method of [PERSON] and [PERSON] [30] is widely used to compute the 1-octanol/water partition coefficients of pesticides and chemicals of environmental concern [32; 33; 34; 35; 23]. Besides MlogP or MHlogP descriptors, 13 topological indices [36] and descriptors encoding specific functional groups and structural fragments in the molecules were also considered in addition to the 19 other descriptors selected in [PERSON] et al. [25]. Selected on the basis of our previous results [25; 31], their goal was to find the same optimal set of descriptors for the classification models built either with MlogP or MHlogP descriptors.  
The QSPR (Quantitative Structure-Property Relationship) models for the calculation of the MlogP and MHlogP descriptors, which are implemented in VEGA 1.1.4 ([[https://www.vegahub.eu/portfolio-item/vega-qsar/](https://www.vegahub.eu/portfolio-item/vega-qsar/)]([https://www.vegahub.eu/portfolio-item/vega-qsar/](https://www.vegahub.eu/portfolio-item/vega-qsar/)) (accessed on 10 January 2024)), were selected because both QSPR models give an estimate of the reliability of the predicted log \(P\) results. Indeed, the reliability of the predicted value is identified as low, moderate, or good depending on the degree of belonging of the predicted molecule to the applicability domain of the model. Similar compounds with predicted and experimental log \(P\) values are displayed and various indices are calculated. Consequently, the VEGA platform appeared particularly suited to performing this kind of comparative study. The MlogP and MHlogP descriptor values calculated for the 2171 molecules are listed in Table S2.  
It is worth noting that for the calculation of all the other descriptors, the RDKit software ([[http://www.rdkit.org/](http://www.rdkit.org/)]([http://www.rdkit.org/](http://www.rdkit.org/))) version 2021-03-4 was used.
## 4 Security  
In addition to the previously considered quantitative QoS parameters, security guarantees play an important role within industrial WSNs. Indeed, without any protection mechanism, the network could suffer from attacks or malfunctions that degrade the desired QoS by introducing additional delays, or not delivering correctly and timely the needed information. For example, these malfunctions can perturb the production chain, as one of the machines would move at an unexpected time or in the wrong direction. Such perturbations can have important consequences going from delayed and damaged production to broken equipment. Additional costs are not the only consequence; employee's lives can be endangered in the worst case; for example in case of explosions due to false temperature measurements in chemical industries. A threat analysis is conducted in this section followed by an evaluation of the selected standards in order to determine whether the WSNs are protected against the identified threats. Within the scope of this survey, only attackers located within the range of the WSNs and taking advantage of the wireless characteristics of the industrial networks are considered, as many methods like firewalls [12] are efficient to protect the networks against attacks coming from the outside. Moreover, attacks requiring physical capture of sensor platforms are excluded from this analysis.
## 2 Occurrence of Patulin in Apple Juice and OTA in Coffee, Beer and Win
### Occurrence of OTa to coffee
\begin{table}
\begin{tabular}{l l l} \hline \multirow{2}{*}{**Country**} & \multirow{2}{*}{**Type of coffee**} & **OTA level** \\  & & **(range ng or ng/kg)** \\ \hline Africa-various & Robusta & 2.4-23.3 \\ countries & & \\ \hline Arabia & Green & 0.73-34.08 \\ \hline Australia & Soluble coffee & 0.2-4.0 \\ \hline Brazil & Green, Instant, Roasted & 0.1-6.5 \\ \hline Cameroon & Robusta & Traces-2.2 \\ \hline Canada & Roasted & 0.1-2.3 \\ \hline Colombia & Arabica & 0-3.3 \\ \hline Denmark & Roasted & 0.1-3.2 \\ \hline East Africa & Not specified & 0.2-62.0 \\ \hline Ethiopia & Arabica & 100] \\ \hline Germany & Roasted & 0.2-11-2.1 \\ \hline Hungary & Roasted & 0.1-2.3 \\ \hline India & Green & 0.2-13.5 \\ \hline Indonesia & Robusta & 0.2-1.0 \\ \hline Japan & Raw and Instant & 0.16-1.1 \\ \hline Kenya & Arabica & \textless{}0.01 \\ \hline Mexico & Arabica & 1.4 \\ \hline Spain & Green & 0.67 \\ \hline Switzerland & Green & 0.4-7.8 \\ \hline Taiwan & Green & 0.1-0.5 \\ \hline Taiwan & Nep specified & 2.2 \\ \hline CSA & Roasted & 0.1-1.2 \\ \hline \end{tabular}
\end{table} 
Table 2: OTA levels reported in coffee in different countries around the world. In summary, OTA is found at different stages of coffee production and processing, including cherry, green coffee and roasted coffee [94; 101; 105; 122; 123] and in many countries, as presented in Table 2.  
Nine countries have specific regulations for OTA, with legislative limits ranging from 5 to 50 \(\mu\)g/kg. If a maximum limit for OTA in coffee were to be established, it could affect international trade for producing countries which do not control this parameter. Without such limits (uncontrolled) this could have an impact on human health in coffee importing countries. The occurrence of OTA in coffee beans can be due to both environmental conditions (climate, length of storage and transportation) and processing conditions (wet, mechanical or dry processes) [100]. The occurrence and the formation of OTA in the dry process have been studied by several authors [96; 112]. OTA was present before storage, indicating the possibility that harvesting and post-harvest handling of coffee chemicals could be the critical steps leading to contamination [96]. There is currently little information available on the presence of OTA producing fungi in coffee beans used in the wet and mechanical processes and the impact of these processes on the production or presence of OTA.
## 3 Experimental Details and Data Acquisition
#### Data Set Description  
The data set was obtained from a real semiconductor ashing process. The process consisted of seven steps, including pumping, gas flow, pressure stabilization, plasma ignition, ashing, over-ashing, and the vent of the chamber. Table 1 presents the process values for the experiment condition.  
The state variable identification (SVID) data collected from the process chamber were as follows: gas1 monitor, gas2 monitor, source power1, source power2, pressure, APC position, chuck Temp.1, chuck Temp.2, and chamber body temp. In this study, only the APC position and chamber body temperature were used. The reason for this will be covered in Sections 4 and 5. Two data sets were collected; the first data set included 472 normal and 48 leaks and the second data set included 795 normal and 44 leaks. These two data sets were used to study the temperature and APC position relationship in Section 5 and to evaluate the performance of the proposed method in Section 7.
## 3 Proposed Methodology
### Weighting Schemes
#### 3.3.3 Dynamic Weighting Scheme  
Dynamic weighting involves updating the weights during the training process based on the model's performance on each task. This scheme allocates more resources to the task with a higher error rate or lower performance. The joint loss function remains the same but \(\alpha\) is updated at each training iteration or epoch based on a defined criterion, such as performance on the validation set. By dynamically updating the weights, the model can adapt to the difficulty of each task, potentially leading to improved overall performance. One approach to implementing dynamic weighting is to monitor the validation loss for each task and adjust \(\alpha\) accordingly. If the validation loss for the discrete task increases, \(\alpha\backslash\)alphac is increased to allocate more weight to the categorical loss, and vice versa. This ensures that the model focuses on the task that requires more attention, balancing the overall learning process.
## 2 Framework and Methodologies
### Channel State Information  
Channel state information (CSI) characterizes the properties of a communication link and falls under the physical layer information of wireless communication protocols. It is employed to describe the fading factor of the signal across each transmission path [39]. When the receiver converting the signal \(Y\) transmitted from the sender, the system model in the frequency domain can be expressed as:  
\[Y=HX+W \tag{1}\]  
where \(X\) is the set of transmit signals, \(W\) is the additional Gaussian white noise matrix, and \(H\) is the channel transfer matrix, i.e., the CSI matrix:  
\[H=[H_{1},H_{2},\cdots,H_{m}] \tag{2}\]  
According to the IEEE 802.11n/ac standards, when operating at a 20 MHz bandwidth, the Intel 5300 NIC can extract data from 30 subcarriers for each transmit-receive link. Each received \(H\) is a complex value, which can be expressed as:  
\[H_{i}=|H_{i}|e^{j\sin(\angle H_{i})} \tag{3}\]  
where \(|H_{i}|\) and \(\angle H_{i}\) represent the amplitude and phase information of the \(i\)-th subcarrier, respectively. The equipment used in this paper has 2 transmitters and 3 receiver segments, so the data contain 180 subcarriers.
# Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems
## 2 Related Work
It provides additional information, such as database objects, in order to solve data convergence and data divergence problems; however, the major drawback of an object-centric approach is that it forces the representation of complex systems by means of a novel log format, which is not widely accepted in the industry when compared with the IEEE XES standard. This necessitates new process mining tools and existing process mining techniques cannot be used. Domain experts cannot easily interpret OCBC models discovered from XOC logs as they have constructs that are different than conventional process modeling notations such as the BPMN or Petri net notations [15]. Our aim is to enable the building of an event log that could be managed by the existing tools used in process mining. Our work also relates to artifact-centric approaches [11; 16; 25]. These approaches identify data artifacts, i.e., documents like a purchase order, in order to discover the life cycles of objects with one-to-many and many-to-many relations between them. For each data artifact, its instances and all related events are extracted and transformed into a separate event log. As a result, multiple event logs are obtained. The benefit of applying an artifact-centric approach is that it addresses the data convergence and data divergence problems explicitly by defining an instance notion in each artifact; however, more support should be provided for domain experts to identify the essential artifacts for data extraction from ERP systems. Our approach attempts to address this issue. Unlike existing approaches, we propose a metamodel for describing the semantics of transactional data from an ERP system. Inspired by artifact-centric approaches, the data structure of the process is conceptually represented by multiple related documents and their transactions. As far as we know, this is the first work that uses domain-specific language for obtaining event logs from object-centric information systems such as ERP systems. The benefit of designing a specific language to support data extraction is that it allows the domain experts to localize the correct data for the process mining analysis in different tables and data structures of ERP system at the higher level of abstraction, thus avoiding implementation details. This consequently guides a domain expert towards converting data from a database to a suitable event log without the need for programming.
## 1 Introduction
Section 3 presents the experimental findings from sequential machine trials, first validating the method on the THERA(tm) system before extending it to the ZERO(tm) MT, which was tested under more demanding conditions. The recorded temperature variations exhibited a direct correlation with the thermal errors, which were analysed using both a first-order analytical model, following [PERSON] et al. [35], and a finite element method (FEM) simulation [11; 36]. These analyses confirmed that the observed error evolution was primarily driven by ambient temperature fluctuations. Finally, Section 4 summarises the key findings, underscoring the potential of the MIIM approach for large-scale MT calibration in industrial environments. This study highlights its effectiveness in characterising environmental thermal errors, offering a scalable and automated solution for improving MT precision and stability in real-world machining applications.
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 3 Results
### On the Remarkably Large and Stable Characteristics of the Copolar Correlation Coefficient
#### 3.5.1 Central Location of the Copolar Correlation Coefficient: Location Parameters  
As it can be seen from Table 7, the copolar correlation coefficient values for the Cimetta tower are remarkably large: the median (average) value of the 1440 samples (5-min temporal resolution over 5 days) is 0.9972 (0.9962). These values are typical of small spherical drops. As expected, the probability distribution functions are skewed to the left and the average value is smaller than the median for all of the five selected days.