esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A TDLAT sensor was developed for the simultaneous tomographic imaging of temperature and species concentration. The influences of the number of beams, initial beam angle, and flame distribution on the reconstructed results were analyzed in numerical simulations. Four various flame profiles including symmetrical and asymmetrical distributions were reconstructed using four different beam distributions. The simulation results indicate that the larger numbers of views and laser beams provide a significant contribution in improving the accuracy of the tomographic reconstruction. If working within a limited number of beams, an acceptable error range could still be achieved by keeping the direction of the laser beams vertical and parallel to the symmetry axis. However, this method has some limitations, such as the symmetry of the combustion field needs to be known in advance, and it is impossible to divide into a very dense mesh.  
To validate the system performance, the temperature and H\({}_{2}\)O mole fraction distribution of the symmetric premixed flames including one and two burners are reconstructed with an 8 \(\times\) 8 beam distribution. The distribution images of temperature and H\({}_{2}\)O mole fraction are carried out during three combustion states, and the time resolution is less than 1 s. The results show that the TDLAT sensor is a new effective method for reconstruction of the temperature and H\({}_{2}\)O concentration distribution. Furthermore, it exhibits a good potential for combustion flame monitoring. The dynamic flame shape diagnosis can be used for combustion feedback control in order to maintain combustion efficiency and  
Figure 14: Double flat flame burner reconstruction images. (**a**) Temperature distribution and (**b**) H\({}_{2}\)O concentration distribution.  
Figure 13: The geometric description of two burners to generate a double peaks flame shape.  
minimize pollutant emissions during their operation life cycle. The spatial and temporal resolutions can be increased by improving the scan frequency of DFB laser and data-processing algorithms in the future.  
This work has the support of the National Natural Science Foundation of China (Grant Nos.: 41405034, 41775128, 51504186, 11204320), the External Cooperation Program of Chinese Academy of Sciences (Grant No.: GJHZ1726), the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No.: 2014 BAC17B03), and the Special Fund for Basic Research on Scientific Instruments of the Chinese Academy of Science (Grant No.: YZ201315).  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed materials; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Estimation of Impact Loads Transmitted to Vibro-Ripper Housing Using Transfer Path Analysis
## 2 Vibration Experiments with a Vibro-Ripper  
### Theoretical Background of Transfer Path Analysis  
The TPA method is employed to investigate the main transfer paths of mechanical systems of acoustic and acceleration energy generated by an excitation source. The sensitivity of each component to the transfer path can be quantified through experiments [12; 21]. This approach takes account of air- and structure-borne noises [12; 22]. The latter is caused by structure vibration, whereas the former is transmitted through the air. A microphone or acoustic receiver is used to measure these noise components. The governing equation of the TPA method for each system component is given in Equation (1). The respective noise and vibration can be defined as the product of the transfer functions about the excitation source and transfer path as [12]:  
\[P_{i}(\omega)=\sum_{i=1}^{n}VTF_{i}(\omega)\cdot F_{i}(\omega)+\sum_{j=1}^{r} NTF_{j}(\omega)\cdot Q_{j}(\omega) \tag{1}\]  
where \(F_{i}\) (\(i=1,\ldots,n\)) represents the interfacial forces arising from structural vibration loads, \(Q_{j}\) (\(j=1,\ldots,r\)) denotes the volume velocities related to acoustic loads, \(NTF_{j}\) is the transfer function for noise, and \(VTF_{i}\) is the transfer function for structural vibrations. The TPA method is divided into a passive subsystem (the transfer path and receiver positions) and a global subsystem (noise and vibration sources) (Figure 3). At the interface between the two subsystems, acoustic or structural loads can be defined according to the type of coupling. The excitations are propagated to receiver points along the paths. The loads are typically interface forces (\(F\)) or volume velocities (\(Q\)) (e.g., in an engine mount, air intake and exhaust system), and the receiver responses are sound pressure (\(P_{i}\)) or vibration, (e.g., in-vehicle noise at the driver's ear, and structural vibration) [12; 23].  
The paths are represented by their corresponding frequency response functions (FRFs), which are often called noise transfer functions. Each of them describes the relationship between one input Degree of Freedom (DoF) and one response DoF. The model also includes a passive subsystem of FRFs, denoted by (\(H_{pi}\)) in Figure 3, generally referred to as local FRFs. These FRFs express the relationship among the responses at the input degrees of freedom (\(a_{pn},p_{j}\)) and also at additional response locations called overdetermination points (\(a_{pk}\)). This model assumes a causal load-response relationship and that all the FRFs are characteristics of the global subsystem [12; 24].  
Figure 3: Schematic diagram of the TPA method showing an active subsystem generating structural and acoustic loads, and a passive subsystem responding to these loads (modified from [PERSON] et al. [12]).
## 2 Materials and Methods
### Gas Chromatography-Mass Spectroscopy  
The GC/MS method was used to identify volatile compounds found in the coffee samples and their relative abundance to be used as targets to further develop the ML model. For this purpose, the same samples measured using the e-nose were assessed for volatile aromatic compounds using a gas chromatograph with mass selective detector 597/B (GC/MSD; Agilent Technologies, Inc., Santa Clara, CA, USA; detection limit 1.5 fg). This was coupled with a PAL3 autosampler system (CTC Analytics AG, Zwingen, Switzerland) to measure the entire batch of samples in a single run. A total of 5 mL of each coffee sample replicate was placed in a 20 mL using an 18 mm magnetic screw cap with polytetrafluoroethylene and silicone septum. To ensure no carryover effects, two blank samples were included, one at the start and one at the end of the batch of samples. The method used was as described by [PERSON] et al. (2019). An HP-5 MS (Agilent Technologies, Inc., Santa Clara, CA, USA) column and headspace method with solid-phase microextraction (SPME) divinylbenzene-carboxen-polydimethylsiloxane (DVB-CAR-PDMS) fiber (Agilent Technologies, Inc., Santa Clara, CA, USA) were used. Furthermore, helium was used as the carrier gas at a flow rate of 1 mL min\({}^{-1}\). The inlet was set to splitless mode.  
For volatile compounds identification, the National Institute of Standards and Technology library (NIST; National Institute of Standards and Technology, Gaithersburg, MD, USA) was used. For this, only the compounds identified with a certainty \(>\) 80% were reported. Compounds with a very low relative abundance and that were only present in one or two samples were removed for the purposes of this study.
## 3 Results and Discussion
### Dynamic Detection of Rhizosphere Pathogens by Real-Time Quantitative Polymerase Chain Reaction  
The standard curve of y = -3.1199x + 39.913, \(R^{2}\) = 0.9977 was used for the dynamic detection of rhizosphere _P. nicotianae_ by the RT-qPCR method. There was a significant difference between each treatment and the control (Table 3). Results suggested that DNA copies of _P. nicotianae_ treated with a 500-times dilution of DADS were greater than the DNA copies treated with a 500-times dilution of garlic essential oil, which indicated that garlic essential oil diluted 500 times had a stronger inhibitory activity against _P. nicotianae_ than DADS diluted 500-times in the root irrigation test. Furthermore, there were more DNA copies of _P. nicotianae_ treated with a 500-times dilution of garlic than DNA copies treated with a 500-times dilution of DADS in the fumigation test. All drugs had a concentration effect, and DNA copies decreased significantly relative to the control.  
Values are the means of three replicates \(\pm\) SD. Values within the same column followed by different lowercase letters are significantly different (\(p<0.05\)).  
The results of the pot experiment showed that DADS treatment could significantly reduce the incidence of tobacco black shank and the number of _P. nicotianae_ pathogens in rhizosphere soil. Studies have shown that DADS, as a biomimetic organic sulfur soil fumigant, has strong penetrability, rapid volatilization in the soil, and low requirements for soil water content. Therefore, the toxicity to plants is relatively low, and the detoxification period after application can be shortened accordingly [17].
## 2 Optimization Design Method
### Central Composite Design Methodology for Vibration Optimization  
To determine the interaction between the vibration factors caused by the controllable mechanism welding robot and to study the comprehensive effect of each factor on the dynamic performance of the controllable mechanism welding robot, the CCD method is used to optimize the welding accuracy of the welding torch at the end of the controllable mechanism welding robot. The CCD and BBD depicted in Figure 7 show that many points of the CCD method will exceed the original level, which enables the CCD method to better fit the corresponding surface [15]. A surface model of the vibration response of each factor and the welding torch at the end of controlled mechanism welding robot is established by a design simulation test. The optimum target and the optimum ascending direction are determined to solve the optimum parameters of the vibration attenuation target of the welding end, so as to realize the vibration optimization of the welding end of the controlled mechanism welding robot. The design steps of the CCD methodology of the end of the controllable mechanism welding robot are as follows:  
Step 1: Determine the experimental design method. At present, the most frequently used experimental design methods are the CCD method and the Box-Behnken experimental design (BBD) method. Compared with the Box-Behnken experimental design method, the CCD method can better fit the corresponding surface, and it is more suitable for multi-factor and multi-level experiments with continuous variables. Based on the assumption that there are many factors involved in the vibration problem of the welding robot mechanism, the CCD method was selected for the experimental design in this paper.  
Step 2: Design the test data sheet and carry out simulation calculations. According to the relationship diagram between the factors and sub-factors of the controllable mechanism welding robot, the central composite design data table is designed, the vibration harmonic response calculation of the actuator, and nonlinear data fitting are carried out. On this basis, the surface equation under the data is obtained. The obtained surface equation is subjected to error analysis to check the fitting effect and the feasibility of the equation.  
Step 3: Parameter optimization design. Using Minitab software, the factors, sub-factors intervals, and optimization target values are set at the front end, and the 3D model diagram of the output under constraints is calculated and drawn, and the corresponding equations and extreme values are obtained to complete the optimization of the test results.  
Figure 8 shows the flow chart of the experimental design:  
Figure 7: Central composite design (CCD) and Box-Behnken design (BBD).
## 5 Conclusions  
In this paper, an innovative method, originally introduced by [PERSON] et al. [25] to compute error magnitude values in an RS decoder, was implemented to evaluate its realistic performance. Two platforms, an ARM based MCU and an Artix-7 FPGA, were considered to assess different implementation strategies, fully sequential and fully combinational, respectively. As a benchmark for the analysis, we selected [PERSON]'s algorithm because it is widely used and, to date, it is considered the standard method for error magnitude evaluation in Reed-Solomon code. For both implementations, the two algorithms were designed considering six practical configurations, normally used in specific applications. All the solutions were evaluated in terms of output delay, that is, time to get a valid result when the system is fed with valid inputs. The FPGA based solutions were also evaluated in terms of resources' (LUTs') usage. The results show that, when implemented with a parallel strategy, [PERSON]'s method performs better in respect to [PERSON]'s one in terms of occupied LUTs, while suffering from the strongly recursive approach in terms of output time. However, [PERSON]'s method performs definitively better in case of MCU, when it can take advantage of the minor number of operations required to produce the output, with an output delay up to three times lower in the configurations examined in this paper. From these results, we can conclude that, if an FPGA platform has been considered for the implementation of the whole decoder, the designer should select [PERSON]'s method if there are stringent time constraints, while [PERSON]'s method is eligible when the area occupation is critical. Instead, when the decoder has to be implemented on an MCU platform, [PERSON]'s method should be preferred owing to the reduced execution time.  
Figure 13: Graphical representation of the results of Table 5 regarding the MCU implementations’ code execution time.  
**Author Contributions:** Conceptualization, V.B. and I.D.M.; Methodology, V.B., M.B., and I.D.M.; Validation, V.B., M.B., and I.D.M.; Formal Analysis, V.B. and M.B.; Investigation, V.B., M.B., and I.D.M.; Data Curation, V.B. and M.B.; Writing--Original Draft Preparation, V.B., M.B., and I.D.M.; Writing--Review & Editing, V.B., M.B., and I.D.M.; Visualization, V.B., M.B., and I.D.M.; Supervision, I.D.M.; Project Administration, I.D.M.; Funding Acquisition, I.D.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### PLSR Model Using Full Spectra  
PLSR is an efficient modelling method for spectral analysis due to its capability to deal with the dataset with more variables for \(\mathbf{X}\) than \(\mathbf{Y}\). The PLSR model was built using leave-one-out cross-validation. The optimal PLSR model was obtained with 15 LVs. The results of the PLSR model using the full spectrum are shown in Figure 2. The PLSR model using the full spectrum obtained good performance with \(r_{c}\) and \(r_{p}\) over 0.9. The results indicated that it was feasible to use HSI to detect SO\({}_{2}\) residual content in sulfur-fumigated _Fritillaria thunbergi_ Bulbus.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Set** & **Number** & **Range (g/kg)** & **Means (g/kg)** & **Standard Deviation (g/kg)** \\ \hline Calibration set & 111 & 0–6736 & 2.502 & 1.950 \\ Prediction set & 37 & 0–6.536 & 2.472 & 1.963 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical analysis of SO\({}_{2}\) residual content in the calibration set and the prediction set.  
Figure 1: **(a)** Full spectra and **(b)** average spectra of the samples under 4 different treatments; **(c)** Average spectrum, average spectrum minus standard deviation spectrum, and average spectrum plus standard deviation spectrum of a randomly selected sample.  
Figure 2: Prediction results of **(a)** the calibration set and **(b)** the prediction set of the PLSR model using the full spectrum.
# Evaluating the Physicochemical Properties of Some Kosovo's and Imported Honey Samples
## 3 Results and Discussion
### Moisture Content  
The moisture content (%) in Kosovo's honey samples varied from 14.0 to 19.0 (mean value \(\pm\) SD = 16.53 \(\pm\) 1.35%), while the moisture content values in imported honey samples ranged between 15.23 to 17.45 (mean value \(\pm\) SD = 16.60 \(\pm\) 0.60%). All of the moisture values in both local and imported honey samples were below 20%, which is the maximum value allowed by Council Directive 2001/110/EC [10]. The moisture content values in this study are close to those obtained in other studies [16; 27]. No significant differences were observed when comparing the water content of Kosovo's and imported honey samples (_p_-value \(>\) 0.05). The water content values in Kosovo's honey samples are also close to the results obtained by other researchers from EU countries. Several studies showed similar results regarding the water content in honey samples from Croatia, Romania, and Italy, ranging between 15.40 to 18.03% [28; 29; 30; 31; 32]. The water content is a very important parameter and depends on numerous factors, such as the botanical origin, harvesting season, level of maturity in the hive as well as processing techniques, packaging, and storage conditions [33]. The water content is also an important parameter in showing both the viscosity and moistness of honey.
# A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes
## 3 The Proposed Method  
Figure 1 shows the overall structure of APFI. The model structure of APFI includes a training phase and a verification phase. The model structure proposed in this paper is divided into a training phase and a verification phase. The training phase is seen as the process of training the classifier, indicated by blue arrows in Figure 1. In the training phase, we train the proposed feature extraction network. We optimize the network with an additive angular margin loss (Arc-Loss) so that the network can learn discriminative large-margin features. During verification phase, compare the input fingerprint to each identity in the database. The distance between the feature vector of the input image and the feature vector of the image in the database is calculated to judge the similarity degree of the two images. To further refine the fingerprint features extracted using the training network, the SE module is embedded in the feature extraction network. At the same time, the Arc-Loss is improved. The improved loss function is more suitable for the identification of partial fingerprints. The feature extraction network framework proposed in this paper is analyzed in Section 3.1. The loss function is introduced in detail in Section 3.2.  
At the verification phase, as shown by the red arrow in Figure 1, the fingerprint image was input into the feature extraction network to obtain the feature, and the distance between it and each fingerprint feature in the database was calculated. The smaller the feature distance, the greater the similarity between the two images. We find that cosine distance [40] is more suitable to represent the differences between fingerprint features than Euclidean distance commonly used in the past. For the fingerprints of the same label, their feature angle is close to 0 degrees, and the cosine of the feature angle is close to 1. For fingerprints of different labels, their characteristic angle is close to 90 degrees, and the cosine of the characteristic angle is close to 0. The cosine distance calculation formula is as follows, where \(x_{1}\) and \(x_{2}\) represent the feature vectors of the two fingerprints to be verified, respectively.  
\[\cos\theta=\frac{x_{1}\cdot x_{2}}{\parallel x_{1}\parallel\cdot\parallel x_{ 1}\parallel} \tag{1}\]
# Efficient Tree Policy with Attention-Based State Representation for Interactive Recommendation
## 4 Results and Discussion
### Experimental Setting
#### 4.1.3 Compared Methods  
We compare our method with state-of-the-art IRS methods of different types, as listed below:  
* Popularity: Ranks the top k frequent items according to their popularity measured by a number of ratings, a simple but widely adopted baseline method.
* SVD: Suggests recommendations based on singular value decomposition (SVD). For the IRS setting, the model is trained after each user interaction and gives recommendations with the predicted highest rating.
* DDPG-KNN: A DDPG-based method that maps the discrete action space to a continuous one, then selects \(K\) nearest items in the continuous space with the max Q-value obtained by the critic network [23]. In our experiment, the \(K\) value is set to \(\{1,0.1N\}\).
* DQN-R: A DQN-based method that adopts a refined DQN to evaluate the Q-values of the items and chooses the item with the max Q-value [24].
* TPGR: Adopts a tree-structured policy and uses the policy gradient to optimize the tree-structured policy [5]. This is the state-of-the-art IRS approach and is similar to our proposed method.  
For the deep RL methods DDPG-KNN, DQN-R, and TPGR, we use Tensorflow version 1.4 for implementation. We will open-source our code after acceptance. The experimental details can also be found in Appendix A.
# Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI
## 4 Discussion
### Contribution and Limitations
Furthermore, SAMURAI's computational performance under high-resolution inputs or complex scenes was not evaluated in this study. Future research should assess whether SAMURAI can maintain its detection accuracy advantage in high-resolution video streams (e.g., 4K) and evaluate latency across different hardware configurations to determine practical deployment feasibility. From a practical implementation perspective, SAMURAI's higher computational requirements present challenges for deployment in resource-constrained environments, potentially necessitating more substantial hardware investments compared to YOLOv8-based systems. The trade-off between detection accuracy and computational efficiency must be carefully considered within the context of available infrastructure and monitoring objectives. Therefore, further testing in diverse environmental conditions and various computational setups is necessary to validate the generalizability and practical feasibility of the findings. While YOLOv8 is expected to be advantageous in real-time safety monitoring applications where immediate detection is critical, SAMURAI is anticipated to offer superior accuracy in occlusion-heavy environments where long-term worker tracking is required. Based on the current technological capabilities, YOLOv8 may be more suitable for open construction sites with minimal occlusion and where rapid detection is prioritized, whereas SAMURAI would be better suited for complex, multi-level construction environments with frequent occlusions where tracking continuity is essential. Understanding the strengthsand limitations of each model allows for the development of more adaptable detection systems, potentially leveraging a hybrid approach that balances speed and accuracy to optimize detection performance in construction environments.