esa-sceva/satcom-chunk-collection huggingface dataset:
# The Importance of Gram-Negative Rods in Chronic Rhinosinusitis
## 4 Discussion
oxytoca_ (n = 1). In contrast, _E. coli_ and _Citrobacter_ spp. exhibited a lower capacity for biofilm formation. However, bacteria in biofilm form are also present in healthy sinuses [32; 73]. The presence of biofilm in a group of patients without signs of chronic inflammation suggests that biofilm alone is not responsible for the manifestation of chronic sinusitis. A parallel correlation with other etiopathogenic factors is necessary. However, biofilm in CRS patient's sinuses is associated with more severe symptoms and poorer outcomes. Clinicians often emphasize the role of Gram-positive bacteria in CRS; however, our findings also highlight a significant contribution of Gram-negative bacteria, such as _Escherichia coli_, _Klebisella oxytoca_, and _Citrobacter_ spp. The presence of these microorganisms may indicate an atypical course of infection, for example, following prior antibiotic therapy, surgical interventions, or immunocompromised patients. All Gram-negative strains identified in our study demonstrated biofilm-forming capacity, which has important clinical implications: biofilms can lead to recurrent infections and reduced efficacy of standard antibiotic treatments. Such cases often require prolonged therapy and consideration of combination treatment strategies. The study revealed varying susceptibility of Gram-negative rods to ampicillin, tigecycline, and amoxicillin-clavulanic acid. This underscores the need for (1) individualized antibiotic therapy guided by susceptibility testing (antibiogram), (2) microbiological diagnostics in cases of treatment-resistant CRS, and (3) consideration of combination therapies or localized treatments aimed at biofilm eradication. The treatment of CRS involving Gram-negative bacteria may necessitate interdisciplinary collaboration between otolaryngologists, microbiologists, and infectious disease specialists. It should be tailored to each patient based on medical history, microbiological findings, and clinical presentation.
## 3 Intelligent Congestion Control Model Based on DRL in Heterogeneous V2X  
For heterogeneous V2X, due to the main features such as the complexity and diversity of network types, the high-speed mobility of nodes, and the dynamic nature of network topology, frequent switching may lead to poor user experience when nodes are connected to the network. In addition, the diversification and redundancy of transmission paths will cause changes of QoS parameters such as throughput, delay and packet loss in different IoV services, and these changes may degrade the transmission quality and eventually lead to network congestion. QoS parameters are closely related to network services. Moreover, the actual sampling value of QoS can be used to describe the actual operation status of the network more objectively, and to reflect the support degree of the network for the service from a global perspective.  
Therefore, for a specific service cost in V2X, DRL is used to realize the congestion control of vehicle communications, combining the perception ability of DL with the decision-making ability of RL. RL is used to define the optimization goal, and DL provides the strategy operation mechanism, so as to design and implement intelligent congestion control based on QoS that is on-demand-driven, maximizes network operation efficiency, and ensures the requirements of low latency and high reliability. The overall framework of the ICCDRL algorithm proposed in the paper is shown in Figure 2.  
In the network environment of vehicle communications, the ICCDRL takes the observed QoS parameters, including the number of ACK packets, round-trip delay RTT, throughput and packet loss rate, as the state space. By observing the state information in the environment, the policy function required by the Agent is constructed and the optimal control action is generated at the same time.  
Obviously, the Agent contains information about the current state of the network, the policy function based on the neural network, and the obtained action space. The Agent adjusts the size of the congestion window based on the actions output by the policy function and optimizes the performance of V2X. After generating actions and interacting with the  
Figure 2: The Overall Framework of The ICCDRL Algorithm.  
environment, the Agent gains rewards from the environment. The Agent judges the merit of the selected action based on the reward value and updates the parameters of the policy function so that the policy function can generate actions that harvest more reward values.  
According to the definition of the reward function, the optimal congestion control action is selected to adjust the size of the congestion window, so as to maximize the long-term reward received and realize intelligent congestion control. Moreover, agent and environment continuously interact with each other and try to find a balance between throughput, latency and packet loss, exploring the optimal policy and giving feedback by taking successive Actions (e.g., changing _cvand_) to achieve their desired goals, i.e., large throughput and low latency.
# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 3 Audio Datasets
### Data Analysis  
During this stage of the pre-training corpora evaluation, the acquired data must be analysed for a posterior cleaning thereof. To this end, the first required step is the forced alignment of the audio and text files. The Montreal Forced Aligner (MFA) was chosen for this purpose since both the tool and the models are publicly available. The model spanish_mfa v2.0.0a [49] was employed for aligning the corpora _XRey_ and _Tux_, whereas the English dataset _Hi-Fi TTS 92_ was aligned using the english_mfa v2.0.0a [50] model. These two models implement a Gaussian Mixture Model (GMM)-HMM architecture that uses Mel Frequency Cepstral Coefficients (MFCCs) and pitch as the acoustic features and phones as the text input. The spanish_mfa model was trained on 1769.56 h of audio content, while the english_mfa model used 3686.98 h for training. The alignment configuration had a beam width of 1 and a retry beam of 2. The pipeline discarded any audio files that were not appropriately aligned during this process, resulting in a total of 51,397 (98.07%) for _Tux_ and 35,007 files (99.18%) for _Hi-Fi TTS_. This process did not discard any files from _XRey_ since they had been already force-aligned in the dataset preparation stage, as described in our previous work [3].  
As the next step of the evaluation pipeline, using the information gathered from the forced alignments--mainly time marks of individual phones and words--different measurements were performed on the audio datasets, more specifically: phonetic frequency, SNR, and uttering speed.
## 3 Results  
### Impact of Data Assimilation  
In order to assess how the proposed DA techniques impact the updated variables, Figure 2 shows the percentage changes between DA and OL for the set of variables of interest in this study: LAI, ET, NEE, SSM, and RZSM. First, all three DA approaches have an impact on the model estimation of these variables. Specifically, the GLASS LAI DA and the joint (GLASS LAI and SMAP soil moisture) DA show higher values of LAI in most areas of CONUS when compared to OL, whereas SMAP DA presents lower values of LAI than the original OL, especially over the central part of CONUS, mostly characterized by short-rooted plants (grassland and cropland). This also translates into larger regions where the joint DA decreases the OL-simulated LAI with larger (negative) differences between the joint DA and the OL (darker red areas in the top right panel of Figure 2).  
ET and NEE also show larger differences with the original OL run after the application of GLASS LAI DA and the joint DA (when compared to the SMAP DA). This is due to the fact that these two vegetation variables are directly related to LAI in Noah-MP (and only indirectly related to surface soil moisture). Overall, both ET and NEE decrease after the application of DA, except for NEE in the western portion of CONUS in the SMAP DA experiment and for ET in all three DA experiments. This may be correlated to the land cover type and should be further investigated in the future.  
For SSM, in the southwestern region of CONUS (often characterized by severe droughs), the LAI DA shows lower SSM values with respect to the original OL simulation, with stronger differences in the mid-western region. SMAP DA presents larger SSM values in the western US, but slightly smaller SSM values in the Midwest. Once again, the joint DA SSM map looks like a combination of the previous two, with large negative differences in the southwest and moderate positive differences in the central and eastern regions of CONUS.  
The model OL produces higher RZSM values than the GLASS LAI DA and the joint DA, especially in the central and eastern US, which is consistent with what was shown for SSM. The impact of SMAP DA on RZSM is limited, as also found in previous works [61; 62]. [PERSON] et al. [61] showed that SMAP DA improves the estimation of top soil layer up to 7%, but the improvement in the deeper soil layer is not evident. It is important to reiterate that SMAP can only detect water content in the top few centimeters of the soil column; thus, RZSM is not directly assimilated. Nevertheless, the assimilation of LAI and SMAP observations at the surface is transferred to lower layers, as RZSM is controlled by transpiration, root water uptake, and water content of the above soil layer in the Noah-MP model [24; 63].
# Shock Vibration Control of SDOF Systems with Tubular Linear Eddy Current Dampers
## 5 Design of the TLECD
### Design Example  
Aimed at the force-velocity relationship of the TLECD and given the practical factors, a simplified design flow chart for the TLECD can be seen in Figure 13, where the design begins with the input of the dimensionless maximum damping force, the dimensionless critical relative velocity, and the parameters of the structure and the loading, and it ends if the error of the maximum damping force is less than 1%.  
Based on the flowchart in Figure 13, a design example is given. The parameters of the structure and the loading are taken to be the same as the structural traits in Section 4.1 and the half-cycle sine shock in Section 4.3, respectively. The dimensionless maximum damping force is considered to be 0.1, and the dimensionless critical relative velocity is set to 2.0. As a result, according to Equations (1) and (2), the target maximum damping force is 1.15 MN, and the target critical relative velocity is 7.35 m/s. After proceeding with the design flow chart in Figure 13, the actual maximum damping force and the actual critical relative velocity are obtained as 1.14 MN and 7.39 m/s, respectively. Some corresponding physical design parameters of the TLECD-HA are shown in Table 3.  
Furthermore, the corresponding force-velocity relationship can be seen in Figure 14.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(d_{in}\) & 200 mm & \(h_{d}\) & 2 mm \\ \(h_{n}\) & 100 mm & \(B_{r}\) & 1.4 T \\ \(l_{arr}\) & 100 mm & \(n_{mpp}\) & 7 \\ \(l_{ml}\) & 100 mm & \(\sigma_{c}\) & 57.4 MS-m\({}^{-1}\) \\ \(h_{g}\) & 2 mm & \(h_{bi}\) & 10 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical parameters of a design case.  
Figure 13: Design flow chart for the TLECD.
## 2 Model, Loads, and Boundary Conditions of Gear System in Drum Shearer
### Gear Pairs Misalignments in Ranging Arm  
Since the mechanical structure is elastic, all parts of the ranging arm will elastically deform after being loaded. At this time, the gears will be misaligned meshing due to the deformation of the shafts and housing. The gear misalignment refers to the component of the axis non-parallelism of a gear pair in the plane of action, and a misaligned gear pair cannot meet the ideal meshing state with full tooth surface contact, but will suffer from unbalanced force and stress concentration.  
Through static analysis, the misalignment results of all gear pairs in the ranging arm under the maximum load condition (input torque 2000 Nm) can be obtained, as shown in Figure 5, where the red curve represents the three-direction forces exerted by the coal seam on the drum, and the blue curve does not include the effect of external forces. It can be seen that the impact of external forces on the gear misalignment is relatively significant, with some misalignment increasing and some decreasing. The maximum absolute change in the misalignment is 12.1 \(\upmu\)m, and the maximum relative change is about 50%.  
Figure 4: The external load (forces and torque) and boundary condition on the ranging arm.  
Figure 3: Force diagram of a cutter drum.  
Figure 2: Force diagram for a cutting tooth (red line) of a cutter drum.  
Different gear misalignments have completely different tooth surface load distribution. On this basis, the tooth surface flash temperature and friction power loss will be different. Therefore, accurate load conditions are the prerequisite for calculating tooth surface flash temperature distribution and friction power loss.
## 4 Discussion
### Compression Resistance  
Compression resistance values ranged from 18.45 to 37.85 N mm\({}^{-1}\), similar to the range 17.09-41.85 N mm\({}^{-1}\) reported by [PERSON] et al. [24] when briquettes were produced from mixtures of _Ceiba pentandra_ sawdust and oil palm mesocarp fibers. On the other hand, values were above those reported by [PERSON] et al. [23] in briquettes made from mango seed shells and cashew shells. This is probably because they used bigger particle sizes (3 to 11 mm), although, as the present research confirms, the smaller particle size, the higher the compression resistance values. [PERSON] et al. [45] noted that improving the strength of briquettes during pressing is based on the presumption that high density produced during the pressing allows efficient compaction.  
All treatments showed higher compression resistance values than treatment T1 (100% large particles). The same relationship between large proportions of small particle fractions and compression resistance was presented by [PERSON] et al. [13] when 100% of the particles used were smaller than 1 mm. These authors also found high compaction values when the two smallest fractions (f1 < 1 mm and f2 from 1 to 4 mm) were used in proportions of 75% to 25%, respectively. [PERSON] et al. [46] reported a relationship between compression values and the proportion of large particles different from that observed here, assuming that compression resistance increased with larger particle sizes. They attributed this to the production of briquettes at low temperature, such that the formation of a solid bridge resulting from natural bonding of the chemical materials may be minimal or absent. Therefore, the main factors that contributed to the bond formed during this densification could have been the mechanical interlocking of biomass fibers and the adhesive force between the particles.  
Compression resistance is one of the most important properties in the evaluation of the quality of briquettes, as it indicates the stacking capacity and impact caused by friction during transport, which can cause abrasion and crumbling [47]. According to the response surface and contour graphs (Figure 6), the highest compression resistance values (34 N mm\({}^{-1}\)) were found when the percentage of small particle sizes approached 100%, and large and small particles ranged from 0 to 5%. However, high resistance compression values (>30 N mm\({}^{-1}\)) also were found when the percentage of small particles ranged from 30 to 40%, medium particles 5 to 30%, and large particles 40 to 60%.
## 3 Results and Discussion
### Machine Learning Classification
#### 3.2.2 Baseline Model  
The baseline model for EEG data interpretation does not focus on intricate practical applications, but serves as a foundation for comparing more complex models. The dataset for training this model, as shown in Figure 6, consists of flattened vector representations of the brain heatmaps. Each vector, representing a unique pattern of brain activity, is labeled with the corresponding cognitive state. A machine learning classifier, such as a Support Vector Machine (SVM), is trained to categorize these vectors, distinguishing between different cognitive states based on the heatmap patterns. SVMs have been used in different works for EEG signal classification [34, 35].
# Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models
## 3 Results and Analysis  
### Main Volatiles in Pesticide Samples  
A total of 128 volatile compounds were detected in the pesticide samples vis gas chromatography-mass spectrometry, which are shown in Table 2. According to the results, there are two obvious characteristics of the pesticide volatiles. First, there are many types of pesticide volatiles, including alcohol, aromatic hydrocarbon, phenol, halogenated hydrocarbon, and soon. The complexity of pesticide volatiles has been a difficult problem in the detection of pesticide gases. Over the years, many scholars have been committed to trying to break through the limitations of sample preparation procedures, separation and detection techniques [33], but there is still no optimal solution. Therefore, the electronic nose system designed in this paper contains more gas sensors so that it can detect more volatiles, thus improving the accuracy of its pesticide detection. The second significant characteristic of the pesticide volatiles is that different pesticides have both the same and different volatiles. This is the key premise of using an electronic nose to detect pesticides in soil. The same volatile matter between pesticides allows electronic noses to quickly distinguish healthy soil from contaminated soil, with the distinguishing markers being the same compounds emitted by these pesticides. Then, the different volatiles between pesticides make it possible to distinguish the different types and brands of pesticides in the contaminated soil.  
The chromatographic results of the pesticide samples further proved the feasibility of using an electronic nose to detect pesticide residues in soil and the rationality of selecting the core components of an electronic nose (sensor array).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Compound** & **Glyphosate** & **Chlorpyrifos** & **Cylithrin** & **Deltamethrin** & **Mancozeb** & **Dithane** \\ \hline Alcohol & 10 & 0 & 0 & 1 & 3 & 4 \\ \hline Aromatic & & & & & & \\ hydrocarbon & 2 & 23 & 35 & 18 & 3 & 8 \\ \hline Phenol & 0 & 0 & 0 & 1 & 0 & 0 \\ \hline Halogenated & & & & & & \\ hydrocarbon & 0 & 1 & 1 & 0 & 0 & 0 \\ \hline Nitrile & 0 & 0 & 0 & 0 & 0 & 1 \\ \hline Ether & 2 & 0 & 0 & 0 & 0 & 2 \\ \hline Aldehyde & 1 & 0 & 0 & 0 & 3 & 1 \\ \hline Ketone & 0 & 0 & 0 & 0 & 1 & 2 \\ \hline Alkane & 8 & 0 & 0 & 0 & 11 & 10 \\ \hline Olefin & 0 & 0 & 0 & 0 & 0 & 3 \\ \hline Amide & 0 & 0 & 1 & 0 & 0 & 0 \\ \hline Ester & 2 & 0 & 0 & 0 & 0 & 6 \\ \hline Other & 3 & 1 & 0 & 1 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The detected volatiles in pesticides.
# Performance Analysis of Multihop Underground Magnetic Induction Communication
## 3 Performance Analysis  
In this section, we propose an MI-WUSN evaluation methodology for single-hop and multihop systems. Table 1 shows a summary of how MI-WUSNs have been studied most recently in comparison to this work to analyse the system performance in dynamic channel conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Comparison** & [14] & [15] & [26] & **Proposed Work** \\ \hline Environment & Underground, soil medium & Underground, underwater & Underwater & Underground, soil medium \\ \hline Communication medium & Homogeneous & Homogeneous and non-homogeneous & Non-homogeneous & Non-homogeneous \\ \hline Number of hops & Multihop & Single hop & Multihop & Single hop and multihop \\ \hline System parameters & Path loss, BER, Bandwidth & \begin{tabular}{c} Multipath Fading, \\ Penetration efficiency, \\ Attenuation loss \\ \end{tabular} & \
\begin{tabular}{c} Number of relays, \\ Throughput \\ \end{tabular} &
\begin{tabular}{c} SNR, Channel capacity, \\ BER \\ \end{tabular} \\ \hline Communication range & \(\approx\)10 m & 0.3 m & \(\approx\)25 m & 25 m \\ \hline \end{tabular}
\end{table}
Table 1: A summary of comparison between this work and other MI-WUSNs.  
Figure 3: Proposed multihop MI communication system.  
The literature presented in Table 1 gives an insight into different approaches towards MI-WUSN solutions for dynamic and extreme channel conditions like underground and underwater. Table 1 clearly indicates that the proposed work is unique and comprehensive, and a higher communication range can be achieved using the given methodology compared to the other work in literature. We present a novel system performance evaluation methodology in terms of three key performance indicators (KPIs), i.e., signal-to-noise ratio (SNR), channel capacity and bit error rate (BER). Following the performance evaluation of point-to-point or single hop communication using MI-WUSN, the impact of static and varying channel state between multiple hops will be evaluated. With varying channel state, the deviation in system performance metrics will be studied compared to the asymptotic bounds offered by statis channel conditions. The associated trade-offs will also be analysed and discussed.
# Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed
## 2 Methodology and Data Source  
### Data Sources  
In this study, the proposed model was implemented only for short-term forecasting of wind speed in order to avoid the high computational time of making dynamical downscaling by using NWP models such as Weather Research and Forecasting Model (WRF). Wind speed data from the Halifax Dockyard station in Nova Scotia, which is located: Latitude 44.66\({}^{\circ}\) N, Longitude 63.58\({}^{\circ}\) W. Wind speed was measured at a height of 3.80 m, and it was used as a source for two different seasons, spring (March 2015) and summer (July 2015), as shown in Figure 1. For both seasons, the data recorded at each hour, (576 reads/24 days) as observations and (168 readings/7 days), respectively, as the training and testing groups. The proposed LSTM implementation fits well with the time series dataset, which can improve the convergence accuracy of the training process.
# Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach
## 2 Materials and Methods
### Test Site and Measurement Campaign
#### 2.1.2 Met Mast  
The 100 m met mast is located 1000 m east of the escarpment and is equipped with numerous measurement instruments, which are mounted at several heights. It is a met tower that meets the guidelines of the IEC 61400-12-1 (2005) standard for power performance measurements [22]. These include various first class cup anemometers (at 10 m, 25 m and 100 m), first class wind vanes (at 25 m, 50 m, 75 m and 92 m), barometers (at 5 m and 98 m), thermometers (at 5 m, 50 m, 75 m and 98 m), hygrometers (at 5 m, 50 m, 75 m and 98 m) and three three-dimensional (3-D) ultra sonic anemometers (_USA_) mounted at 50 m, 75 m and 98 m. In north-south direction the mast is equipped with booms of a length of 5 m to reduce the influence and the flow interaction with the met mast main body. The sampling frequency of the met mast sensors has been set to 20 Hz. In this context, first class means that the sensor has an accuracy class of 0.5 and meets all requirements of the IEC 61400-12-1 (2005) standard [22] for wind turbine power performance testing. The met mast therefore meets or exceeds wind industry best practices for wind measurements.