esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The odor abatement efficiency using two different abatement technologies (a dry filter and a wet acid scrubber) was evaluated with dynamic olfactometry methodology. The results show that the wet acid scrubber prototype presents an average odor removal efficiency of 16%, whereas dry filter has from limited to no effect, representing a promising achievement for the prototype and for its installation in naturally ventilated pig farms. The above-mentioned odor abatement efficiency could be considered as a good result for a prototype, even if further analysis with longer sampling periods is needed.  
The aim of the present study was to determine the odor removal efficiency inside the barns, but in the future, it could be certainly interesting to evaluate the effects of abatement systems also in neighboring areas through the use of air dispersion models, in particular considering the well-known problem of conflicts with citizens living nearby livestock activities. In addition to air dispersion models, also field inspection could be applied to provide the characterization of odor exposure in a defined assessment area.  
Since dynamic olfactometry is time-consuming, a discontinuous measurement method and it does not provide any information on quality, origin, offensiveness or intensity of a smell, a combined analytical-olfactory approach could be useful. Indeed, both gas chromatography and electronic nose could be applied, for this purpose, to identify which odorous compounds characterize pig facilities and how the abatement technologies can affect them.  
In conclusion, there is not one best method for measuring odors. Each situation should be individually evaluated by considering the applicability and limitations of each method and the aim that needs to be reached. In most cases, an integrated approach combining different methods and techniques is the best solution to depict exhaustively the problem and define a good strategy for its management.  
C.C. writing--original draft preparation, formal analysis, investigation, data curation; E.T. writing--review and editing, formal analysis, data curation; J.B. supervision; M.G. writing--review and editing, funding acquisition. All authors have contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
The APPROACH project \"Sistemi filtration per la riduzione di polveri, odori e ammoniaca e per migliorare il benessere di animali e operatori all'interno delle porcliaie\" was financed by Regione Lombardia in the context of Sub-Measure 16.1 of the Rural Development Program 2014-2020.  
**Institutional Review Board Statement:** Not applicable, this study was conducted in a pig farm, sampling air without handling or stressing the animals.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
As shown in Supplementary Figure S2a,b, a larger horizontal shift of the InGaN peak with respect to the GaN peak is observed for the sample after transfer to Si, indicating a higher strain relaxation in the MQW layer. This result is identical to the strain relaxation in Raman spectra after transfer. Figure 4 shows the PL spectra, measured at room temperature, of the InGaN/GaN MQW LED structures before and after transfer onto various substrates. In the spectrum obtained before transfer, the LED emission peak at about 441 nm typically corresponds to the MQWs. However, in the spectrum obtained after transfer, the PL peak of GaN LEDs on Si, glass, and PET substrates is red-shifted to 442.4, 443.5, and 443.2 nm, respectively, because the tensile stress in the top GaN was reduced by the transfer via stress redistribution, as shown in Figure 3. The compressive strain of the InGaN QW material was also reduced in this regard. Two main effects could play important roles in the PL emission wavelength of the MQWs: One was the quantum-confined Stark effect (QCSE), which is related to the piezoelectric field, and the other was the bandgap shrinkage of InGaN within the MQWs. The first effect caused a blue shift while the latter caused a red shift in the PL emission when the compressive strain was reduced in the MQWs; their compensations led to the small red shift observed in the PL emissions. The small red shift of wavelength of LEDs on the glass and PET substrates resulted from an increase in joule heating when compared to the LEDs on the Si substrate. However, the reduced QCES would increase the spatial overlaps between the wave functions of electrons and holes within the QWs which could, in turn, significantly enhance the electron-hole recombination efficiency of the InGaN/GaN MQWs LEDs [16]. Figure 3: Raman spectra of GaN LEDs before and after transfer onto various substrates. Figure 5a,b display the current-voltage (I-V) curves of GaN LEDs before and after transfer onto various substrates. To confirm that the reflection at the interface between the GaN LED and substrate was negligible, we compared the LEDs before and after transfer onto the Si substrate. Figure 5a shows that the electrical properties of the LEDs are not changed after transfer. This result shows that CLO is a damage-free process without degradation in the contact metal or formation of structural defects and cracks. Figure 5b displays the light output power of the InGaN/GaN LEDs before and after transfer. The light output intensity (at an injection current of 20 mA) of the LED after transfer was 33% higher than the output intensity of the LED before transfer. This improvement can be attributed to the enhancement of the electron-hole recombination efficiency with the decrease in QCSE by strain relaxation. Figure 4: PL spectra of GaN LEDs before and after transfer onto various substrates. Figure 5: (a) I–V curves and (b) optical output power of GaN LEDs before and after transfer onto the Si substrate.
## 6 Conclusions  
The effects of the slip parameters, Soret and Dufour parameters, Brownian motion, and thermophoresis on the skin friction, heat transfer, and mass transfer coefficients in stagnation boundary-layer flow over a stretching/shrinking surface of nanofluid were investigated numerically. A dual solution was found, and the effects of the selected parameters were presented graphically. The results revealed that:  
* The skin friction coefficient decreased as the first-order slip parameter and the magnitude of the second-order slip parameter (\(\sigma\) and \(|\delta|\)) increased, whereas the heat transfer rate increased.
* The range of solutions widely expanded with the increment of the first-order slip parameter, while the expansion of the range of solutions was very small when the second-order slip parameter was considered.
* The smallest Soret number \(Sr\) was required to increase the heat transfer rate at the surface.
* The heat transfer rate at the surface increased rapidly as the Brownian motion parameter \(Nb\) decreased, while the largest value of \(Nb\) was required to increase the mass transfer rate at the surface.
* The largest value of the thermophoresis parameter \(Nt\) was required to increase the heat transfer rate, while, for the mass transfer rate, the smallest value of \(Nt\) was needed.
* The first solution was a stable solution and, hence, its physical properties could be realized, whereas the second solution was an unstable solution and, hence, not physically realizable.
## 3 Results
### CLEF 2018 eHealth TAR  
The evaluation of the BERT- and MeSH-based methods with the established CLEF 2018 eHealth TAR dataset was performed on all 30 test reviews. Only the evaluation on an abstract relevance level took place since screening and searching for relevant full-text articles was out of scope. The results of the BERT and MeSH-based method runs are visualized in Figure 10.  
The best-performing _AutoTar_ method by the University of Waterloo achieves the overall highest recall. Following as the second-best method when it comes to overall recall, our MeSH-based method achieved a recall of 86.4% and outperformed our BERT-based method with a recall of 71.8%, despite the initially better performance of the BERT-based method at lower document ranks.  
Figure 10: Recall performance comparison of the developed BERT- and MeSH-based methods on the CLEF 2018 eHealth TAR subtask 1: “No Boolean Search” with existing methods [44]. The line graph depicts the averaged recall at different ranks across all 30 test topics. Only the existing CLEF runs with the highest overall recall per participating team are displayed.  
Figure 9: The content similarity is illustrated as a Venn diagram indicating the overlap of the unique MeSH terms occurring in the top 500 retrieved publications from the MeSH-based and BERT-based method with the SME-rated relevant terms for both SME use cases.
# Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
## 3 Results
### Substrates
#### 3.2.1 Influence of Molecular Weight  
Figure 7 summarizes the variability of all molecules present in the DUD-E dataset, taking into account the molecular weight. The results showed that from the total of 22,321 active ligands considered for all 101 DUD-E targets, 7990 have a molecular weight below 400 Da, while 8833 have a molecular weight in the range of 400-500 Da, with 5498 with a molecular weight over 500 Da. The distribution of decoys across these ranges was the same, as they were generated automatically from the known ligands included.  
Table 5 decomposes the number of ligands identified in the top 1% of compounds ranked, according to the molecular weight. AutoDock identified a total of 1935 actives in the top 1% of ligands, while in Vina, this number was of 2002. The results showed that Vina was, on average, better than AutoDock in identifying actives in the top 1% of small ligands (<400 MW) (536 versus 395 actives)  
Figure 6: Heat map correlation for metalloenzymes between Vina and AutoDock scores and ligand properties, such as fraction of hydrophobic accessible surface area (FASA_H), fraction of polar accessible surface area (FASA_P), fraction of positive and negative accessible surface areas (FASA+ and FASA-), molecular weight (MW), and number of rotational bonds (Rot. B).  
Figure 7: Molecular weight of all active molecules present in the DUD-E database.  
and for large-sized ligands (>500 MW) (581 versus 497 actives). However, AutoDock was able to rank more medium-sized actives (400-500 MW) among the top 1% of the results (1043 versus 885).  
Regarding each family of proteins, all exhibited the same tendency--smaller ligands were more difficult to discriminate and appeared at worst ranking positions for both Vina and AutoDock.  
Figure 8 shows the influence of molecular weight on the average ranking distribution of the molecules within the full-ranked list determined for each protein target. The results showed that there was a similar tendency for both GPCR and kinase protein families, where the smaller ligands were ranked worst and the medium ligands were ranked better. For both GPCRs and kinases, AutoDock could rank smaller ligands better than Vina, even though their ranking position was relatively high. As for the medium-sized active molecules (300-400), these two families exhibited opposite results--while Vina provided better recognition for kinases, AutoDock was more effective in discriminating actives and decoys for GPCRs.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
###### Abstract  
Deformable lung CT registration plays a crucial role in clinical applications such as respiratory motion tracking, disease progression analysis, and radiotherapy planning. While voxel-based registration has traditionally dominated this domain, it often suffers from high computational costs and sensitivity to intensity variations. In this work, we propose a novel point-based deformable registration framework tailored to the unique challenges of lung CT alignment. Our approach combines geometric keypoint attention at coarse resolutions to enhance the global correspondence with attention-based refinement modules at finer scales to accurately model subtle anatomical deformations. Furthermore, we adopt a bi-directional training strategy that enforces forward and backward consistency through cycle supervision, promoting anatomically coherent transformations. We evaluate our method on the large-scale Lung250M benchmark and achieve state-of-the-art results, significantly surpassing the existing voxel-based and point-based baselines in the target registration accuracy. These findings highlight the potential of sparse geometric modeling for complex respiratory motion and establish a strong foundation for future point-based deformable registration in thoracic imaging.  
deformable lung CT registration; bi-directional flow estimation; multi-scale attention 2025
# Trajectory Tracking Control of a Manipulator Based on an Adaptive Neuro-Fuzzy Inference System
###### Abstract  
Taking an intelligent trimming device hydraulic manipulator as the research object, aiming at the uncertainty, nonlinearity and complexity of its system, a trajectory tracking control scheme is studied in this paper. In light of the virtual work principle, a coupling dynamic model of the hydraulic system and manipulator system is established. In order to improve the anti-interference and adaptive abilities of the manipulator system, a compound control strategy combining the adaptive neuro-fuzzy inference system (ANFIS) and proportional integral derivative (PID) controller is proposed. The neural adaptive learning algorithm is utilized to train the given input and output data to adjust the membership functions of the fuzzy inference system, then the PID parameters can be adjusted adaptively to accomplish trajectory tracking. Based on MATLAB/Simulink, the simulation model is established. In addition, to prove the effectiveness of the ANFIS-based PID controller (ANFIS-PID), its performance is compared with PID and fuzzy PID (FPID) controllers. The simulation results indicate that the ANFIS-PID controller is superior to the other controllers in control effect and control precision, and provides a more accurate and effective method for the control of agriculture.  
hydraulic manipulator; ANFIS-PID; trajectory tracking; MATLAB/Simulink
## 6 Discussion
Our goal is to offer new LED arrays that will increase the communication potential of V2V VLC systems in terms of SNR and communication distance, in addition to existing headlights and taillights that meet the standards set by the automotive industry. Therefore, rather than relying on existing light output schemes, our study investigates how these schemes can be optimized for communication. In addition, LED arrays are designed in this paper to increase the received power rather than reduce the background noise. In arrays with better performance, the received power is larger and the background noise is higher, as expected from the shot noise formula given in Equation (13). However, since the received power is higher, the SNR values of these arrays are also better. The literature comprehensively examines different transmitter LED configurations in indoor VLC systems. However, in indoor studies, the LED arrays are usually used for the purpose of yielding a uniform SNR throughout the room. No literature for indoor studies aims to increase the communication range, as this is unnecessary for indoor applications. In addition, existing outdoor studies for increasing the SNR and communication range in V2V VLC focus on optimizing the receiver side. This paper aims to increase the SNR value and communication range of V2V VLC systems by changing the geometry of the LED arrays on the transmitter side. In addition, in indoor studies, the LED configurations are varied only through the arrangement of the locations of the LEDs. However, we adjust the orientations of the LEDs as well to provide the same effective FOV throughout different geometries. We establish a reference configuration that has an FOV of 60\({}^{\circ}\), and we design LED arrays where each LED has a 30\({}^{\circ}\) FOV, yet we manage to ensure that every configuration illuminates approximately the same area. This approach allows us to more accurately reflect the actual performance of different LED configurations and exploit the smaller FOV of these LEDs to increase the communication range and SNR. Therefore, this paper gives an insight into how to optimize system performance by increasing the SNR and communication range of V2V VLC systems by changing the geometry of the LED array transmitters and the orientations of each LED. In addition, while existing studies in the literature generally deal with a limited number of LED configurations, our paper uses six transmitter configurations, bringing a new perspective to the literature by developing a unique analysis methodology with a wide range of LED configurations with different orientations of LEDs, revealing the potential of new structures that can be used especially in outdoor V2V VLC systems. In particular, the honeycomb-shaped hexagonal array and the octagonal array configurations do not exist in the literature, and we examine the effects of these structures on the SNR and communication distance in detail. In fact, our newly used honeycomb-shaped hexagonal configuration gave the best SNR and communication distance. In this context, our study makes a significant contribution to the existing literature.
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 7 Future Directions  
Even though the existing solutions in the literature review have established the road to developing trustworthy malware detection and classification models, evasive malware detection is still challenging. To detect evasive malware, several approaches have been taken such as: generating API-based evasive malware signatures, discovering evasion behaviors using multiple execution environments, and using the known evasion techniques to detect evasive malware. To the best of our knowledge, each evasive malware detection solution has its own weaknesses. For example, the distinction between evasion techniques that have been used in legitimate behavior and malicious-related evasion techniques is still a challenge. Additionally, it's quite difficult for the developed models, which are learned based on the known evasion techniques, to detect and recognize the unknown ones. Moreover, using several execution environments without high complexity in terms of time and resources is another challenge.  
Despite several studies having been done to enhance the evasive malware detection rate, there is no available dataset from which the evasive behaviors are represented. Therefore, creating an evasive behavior dataset would contribute to the efforts of researchers to produce robust solutions. For evasive malware detection purposes, efficient feature extraction and representation techniques are required to extract and represent a feature set that represents evasion techniques related to only malicious behaviors  
On the other hand, zero-day malware and unknown malware variants' daily production has been greatly increased since the availability of online tools by which to create new malware or reformat the existing ones using obfuscation techniques to introduce new variants. Therefore, efficient updating learning mechanisms are required to render the developed models to adaptively learn the coming new behaviors. For this end, deep learning techniques in conjunction with unsupervised machine learning techniques can be designed and implemented for updating learning and developing models which are adaptively learning new malicious behaviors.
## 3 Evaluation Settings
### Databases  
To evaluate the performances of QoE models, we will consider a large number of open databases proposed by different research groups. In this study, we will employ the following twelve databases: _W-SQoE-I_[16], _W-SQoE-II_[17], _W-SQoE-III_[18], _W-SQoE-IV_ (_full_) [19], _TRDB_[31], _VLDB_[31], _TR04 (full)_[21], _VL04_[21], _TR06 (full)_[21], _VL13_[21], _TRCQ_[22] and _VLCQ_[22]. Among these databases, only _W-SQoE-IV (full)_ has both H.264 and HEVC video formats while all the other databases have just H.264 video format. Most databases are tested by PC monitors, except that _W-SQoE-IV (full)_, _TR06 (full)_, and _TR06 (full)_ are tested on both PC monitors and smartphones. In addition, _TR06 (full)_, _VL13_, _TRCQ_, and _VLCQ_ are the only databases that contain long sessions of 3 to 6 min. There are some other databases like _LIVE-NFLX-II_[20], _LIVE-NFLX_[32], _LIVE-Stall_[24], and _LIVE-TVSQ_[33]. However, they are not considered in our study because (1) they are quite simple in terms of influence factors and/or (2) the segment parameters such as bitstream-level parameters or MOS values are not fully provided.  
In order to have a better understanding the performances of the models, the _W-SQoE-IV (full)_, _TR04 (full)_, and _TR06 (full)_ databases are divided into sub-databases; each corresponds to a pair of a viewing device and a codec as shown in Table 2. In addition because subjective segment quality values are not available for some databases (i.e., _W-SQoE-III_, _W-SQoE-IV (full)_, _TR04 (full)_, _VL04_, _TR06 (full)_), and _VL13_), we use the _P.1203_model to estimate these values from the corresponding bitstream-level parameters. This helps to evaluate the models using as many databases as possible.
# MCST Scheme for UAV Systems over LoRa Networks
## 3 System Model  
In the LoRaWAN network architecture, we consider the LoRa network, network among the end devices and gateway, as illustrated in Figure 1. We only focus on a single relaying transmission flow in which one end device as a source node (\(S\)) connects to the other end device, which is assumed as a destination node (\(D\)) through the gateway in the network. We assume the gateway as a relay node (\(R\)) without considering the relaying strategies, i.e., decode-and-forward, amplify-and-forward, and so on, for simplicity. We simply define the transmission as a basic relaying flow (BRF) transmission, which is the combination of a source-relay (\(SR\)) transmission and a relay-destination (\(RD\)) transmission, by neglecting the time synchronization problem. Besides, there is no direct transmission between \(S\) and \(D\) since LoRaWAN does not support end-device to end-device communication [24]. We also assume that the end device and gateway are identical for theoretical purposes. It means that they have the same transmit power and channel bandwidth, and are operating with the same frequency channel in the network. Therefore, the term nodes in this paper simply represent the end device and gateway. Let \(\mathrm{N}=\{1,\ldots,n,\ldots,N\}\) be the set of nodes where \(N\) is the total number of nodes. Each BRF transmission can be selected at most \(F=N/3\) from the set of BRF transmissions, \(\mathbb{F}=\{1,\ldots,f,\ldots,F\}\) in one network topology, where \(F\) is the total number of BRF transmissions. Besides, the selection of node combinations in a single BRF transmission is randomly performed in the network topology.