esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Autonomous Mowers Weed Control Effect in Globe Artichoke Field
## 5 Conclusions  
In the present study, the use of autonomous mower with random trajectories for weed management in globe arichoke showed encouraging results. All three autonomous mowers showed satisfactory performances, suggesting their suitability in a horticultural crop with a plant spacing as large as that of the globe arichoke considered in the research. The autonomous mower weed management resulted in an overall higher weed control, obtaining a lower average height, cover and biomass of weeds, compared to the conventional system. The primary energy consumption estimated for the autonomous mower weed management was lower than the conventional system consumption, with a difference of 704.63 kWh\(\cdot\)ha\({}^{-1}\cdot\)year\({}^{-1}\). Furthermore, the comparison of the estimated annual cost of the two weed management systems highlighted that the autonomous mower management was cheaper compared to the conventional system, with a saving of EUR 1059.96 ha\({}^{-1}\cdot\)year\({}^{-1}\).  
Such findings help to corroborate favorable results obtained by other authors regarding the possibility to employ autonomous mowers with random trajectories in an agricultural context [25; 26]. Further studies would be useful to investigate the effects of these autonomous mowers' weed management on weed biodiversity, to better understand the consequences of their use on the environment. Furthermore, another interesting future prospective for this technology could be the improvement of the autonomous mowers for working in a fleet with a RTK positioning system in order to remove the physical boundary wire and provide each machine with a working area and a specific working path. The interactions between the autonomous mowers and data sharing could lead to improve or modify the working areas and the working patterns according to more efficient weed management.  
**Author Contributions:** Conceptualization, M.F. and C.F.; methodology, M.F., L.G., C.F. and M.S.; software, M.F. and L.G.; validation, M.F., M.S. and L.G.; formal analysis, M.F.; investigation, L.G.; resources, M.F.; data curation, M.F., C.F. and L.G.; writing--original draft preparation, L.G.; writing--review and editing, M.S., M.P., M.R. and A.P.; visualization, M.R.; supervision, M.F.; project administration, M.F. and M.R.; funding acquisition, M.R. and A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to acknowledge the Husqvarma group for providing the three autonomous mowers and technical support; the Agriculture and Environmental Research Centre \"Enrico Avanzi\" for hosting the trials, and for the technical support.  
The authors declare no conflict of interest.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 2 Related Work
Synchronization in multimedia systems actually refers to the temporal relations between media objects in a multimedia system [6]. In multimedia applications, when packets are transmitted from the sender, they undergo different delays on their way to the destination and hence lose their timing relations. Such a misalignment in temporal relation is one of the reasons to cause synchronization error. To handle these errors, three kinds of synchronization have been presented in [6,13] and are discussed below.
###### Abstract  
The lack of supported versions of the tris[(1-benzyl-1\(H\)-1,2,3-triazol-4-yl)methyl]amine (TBTA) ligand, suitable for flow-chemistry applications at scale, prompted us to develop a new route for the immobilization of such tris-triazole chelating units on highly cross-linked polystyrene resins. With this aim, the preparation of the known TBTA-type monomer **3** was optimized to develop a high-yield synthetic sequence, devoid of chromatographic purifications at any stage. Then, bead-type (**P7**) and monolithic (**M7**) functional resins were obtained by the easy and scalable suspension- or mold-copolymerization of **3** with divinylbenzene. Both types of materials were found to possess a highly porous morphology and specific surface area in the dry state and could be charged with substantial amounts of Cu(I) or Cu(II) salts. After treatment of the latter with a proper reducing agent, the corresponding supported Cu(I) complexes were tested in the copper-catalyzed alkyne-azide cycloaddition reaction (CuAAC). The immobilized catalysts proved active at room temperature and, in batch and with catalyst loadings as low as 0.6 mol%, afforded quantitative conversions within 20 h. Independent of the alkyne structure, extended use of the supported catalyst in flow was also possible. In the reaction of benzylazide and propargyl alcohol, this allowed a total turnover number larger than 400 to be reached.  
supported catalysts; click chemistry; flow chemistry; 1,2,3-triazoles; monoliths; nitrogen ligands +
Footnote †: journal: materials
## 2 Materials and Methods
### Proposed Assessment Method
#### 2.1.4 HDR-Derived Features  
A direct assessment using the previously mentioned features obtained from Equations (1)-(6) may lead to erroneous evaluation results that do not correlate well with the subjective evaluations, as previously demonstrated in Figure 1. To overcome this problem, our proposed assessment method uses HDR-derived features as standards when evaluating the features of a test image. The HDR-derived features are extracted by using the same equations (Equations (1)-(6)) applied to many HDR images. Since the HDR images are visually preferred by HVS as shown in Figure 2, HDR-derived features can be used as references, according to which the features of test images are evaluated to determine the final quality scores. Our study demonstrates that this method correlates well with the subjective evaluations.
## 4 Analysis Example and Methods
### Numerical Solution and Experimental Methods  
#### 4.2.1 Experimental System  
The schematic drawing of an experimental system for the aircraft hydraulic pipe is shown in Figure 3. The test pipe is connected with rubber hoses and fixed on the test bench at each end, and it is installed on the pipe bracket through an elastic clamp in the middle position. Hence, the boundary conditions at both ends of the pipe are simplified as fixed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Quantity** & **Symbol** & **Value** & **Unit** & **Quantity** & **Symbol** & **Value** & **Unit** \\ \hline pipe length & \(L_{1}\) & 534.261 & mm & Bending angle & \(\Phi\) & 1.649 & rad \\ pipe length & \(L_{2}\) & 72.996 & mm & Pipe density & \(\rho_{P}\) & 7760 & kg/m\({}^{3}\) \\ pipe length & \(L_{31}\), \(L_{32}\) & 61.630 & mm & [PERSON]’s modulus & \(E\) & 190 & GPa \\  & \(L_{33}\) & 123.260 & mm & Poisson’s ratio & \(v\) & 0.27 & - \\  & \(L_{4}\) & 72.996 & mm & Oil density & \(\rho_{f}\) & 872 & kg/m\({}^{3}\) \\  & \(L_{51}\), \(L_{52}\) & 267.131 & mm & Bulk modulus & \(K_{f}\) & 1.95 & GPa \\ outer diameter & \(D\) & 9.525 & mm & Kinematic viscosity & \(v\) & 19.7 & mm\({}^{2}\)/s \\ pipe wall thickness & \(e\) & 0.889 & mm & Transducer mass & \(m_{s}\) & 0.006 & kg \\ bending radius & \(R\) & 38.100 & mm & Oil brand & & 10\# aircraft hydraulic oil \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical and material properties of the aircraft hydraulic pipe and fluid.  
Figure 2: Schematic drawing of the ARJ21-700 aircraft hydraulic pipe.  
constraints, and the middle constraint of the pipe is the elastic support. The 7 piston swash plate type variable axial piston pump, with a displacement of 92 L/min, with a maximum working pressure that can approach 35 MPa, is adopted to regulate the system flow rate and meet the requirements of wide pressure and the Reynolds number range working conditions. The throttle valve is used to control the fluid pressure of the hydraulic pipeline system by adjusting the valve opening. The pressure sensor and flow sensor with high resolution are placed at both ends of the pipe to monitor fluid signals, and two lightweight acceleration sensors are placed at the pipe to collect vibration signals. The output signals are acquired and processed by a dynamic analyzer associated with a computer.  
Figure 4 shows the experimental system and test bench layout, which mainly consists of five parts: the hydraulic pump station, test pipe and its component; sensor assembly and NI measurement system and data acquisition computer. The hydraulic pump station and measurement system are shown in Figure A1 (Appendix C). The detailed specification of the experimental equipment and measurement systems are listed in Table A2 (Appendix C).  
Figure 4: Photograph of experimental platform.  
Figure 3: Schematic drawing of experimental system.
## 3 A Rule-of-Thumb Model of Road Lighting Illuminance Distributions
### Illuminance Construction of Simulated Road Lighting Scenes  
We reconstructed the illuminances of above-mentioned simulated road lighting scenes (Figure 2), the single-type, staggered-type, and double-type road lighting setups as shown in Figure 10. The results of simulated and reconstructed road lighting formed by 10 \(\times\) 7 points are shown in Table 1. The differences between reconstructed illuminances, referring to simulated illuminances, are calculated from the 10 \(\times\) 7 points. The mean difference, calculated from the 70 points, is from 10.5 to 19%. The differences in minimum illuminance and the average illuminance and uniformity tend to be smaller.  
Figure 8: Illuminance distributions in the transverse direction fitted with linear function.  
Figure 10: _Cont._  
Figure 9: Illuminance modelling using logistic/linear functions with feature points in the transverse direction of single type, staggered type, and double type. (**a**) Logistic/linear fittings are applied for single- and staggered-type luminaires setup between two luminaires. (**b**) Linear fittings are applied for double-type luminaires setup between two luminaires. TD1: Transverse Direction No. 1, TD2: Transverse Direction No. 2, TD3: Transverse Direction No. 3. The feature points with the same rank numbers correspond to those in Figure 4. TD1 and TD3 are perpendicular to the luminaires direction. TD2 is in the middle for the single and double types, and perpendicular to the luminaires for the staggered type.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Road Types** & **Minimum Illuminance (lx)** & **Average Illuminance (lx)** & **U\({}_{0}\)** & **Mean Difference** \\ \hline S-single & 5.5 & 18.0 & 0.31 & / \\ R-single & 5.8 & 19.6 & 0.30 & 15.2\% \\ Difference & 5.1\% & 8.9\% & 3.2\% & / \\ \hline S-staggered & 16.0 & 27.9 & 0.57 & / \\ R-staggered & 16.0 & 29.3 & 0.55 & 10.5\% \\ Difference & 0.0\% & 5.0\% & 3.5\% & / \\ \hline S-double & 10.0 & 22.4 & 0.45 & / \\ R-double & 10.3 & 26.4 & 0.39 & 19.0\% \\ Difference & 3.0\% & 17.8\% & 11.3\% & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Illuminance parameters of simulated and reconstructed road lightings. S represents simulated road lighting, and R represents reconstructed road lighting. The ‘difference’ is the difference of reconstructed and simulated illuminance. The ‘mean difference’ is the mean of difference of reconstructed and simulated illuminance calculated point to point. U\({}_{0}\) is the overall uniformity and is calculated by dividing the minimum and average illuminance.  
Figure 10: Illuminance reconstructions of simulated road lighting scenes of (**a**) single type (**b**) staggered type, and (**c**) double type in Section 2.1. The measured illuminance points for these road lighting scenes are 10 \(\times\) 7. The reconstructed illuminances are based on nine feature points.
## 4 A Transmission Strategy for Minimizing Energy Consumption
### Application to Real Hardware
Finally, the corrections for the transmission power and the packet transmission rate can be calculated similarly to the ideal case. In conclusion, the general receipt to apply the proposed method for a specific device is:  
1. Characterize the energy consumption of the device as a function of the transmission power
2. Approximate this behavior with an analytical expression for \(g(p_{tx})=p_{T}/p_{tx}\)
3. Introduce this expression in Equation (21) and solve for \(x\)
4. Calculate the corresponding values of \(snr^{*}\) (using Equation (8)) and \(T^{*}\) (using Equation (11))
5. Calculate correction factors numerically as explained in Section 4.2
# Feature Transformation-Based Few-Shot Class-Incremental Learning
## 5 Conclusions  
This paper investigates the few-shot class-incremental learning and proposes a feature transformation method tailored for two-stage learning frameworks. Under the constraint of a fixed feature extractor, we sample various few-shot tasks from the base dataset to train a feature transformation module. Classification is then performed by comparing the transformed features of test samples with a maintained set of class prototypes. Experimental results demonstrate that the proposed method achieves strong performance across three benchmark datasets. It effectively improves the distribution of class prototypes, aligning them more closely with the ideal structure described by the neural collapse phenomenon. In addition, we explore the performance of other commonly used feature transformation methods under the two-stage training constraint. The results show that the proposed method is better suited for this framework and achieves the best performance among all evaluated transformation techniques.  
Recent lightweight adaptation techniques, such as LoRA and Adapters, have shown strong performance in few-shot and incremental learning tasks by enabling efficient low-rank or bottleneck updates within frozen backbones. While our current design focuses on a standalone linear transformation layer, exploring the integration of these approaches with fixed feature extractors could be a promising direction for future work.  
**Author Contributions:** Conceptualization, X.Z. and Y.L.; methodology, X.Z. and Y.L.; software, X.Z.; validation, X.Z. and Y.L.; formal analysis, X.Z. and Y.L.; investigation, X.Z. and Y.L.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z. and Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Life-Cycle Assessment (LCA)
### Results of LCA  
Based on the above analysis, the total CO\({}_{2}\) emissions and energy consumption during different processes is summarised in Table 12. Three overall processes are considered which are tunnel construction, rail construction, and maintenance. For the tunnel and rail construction processes, the total CO\({}_{2}\) emissions and energy consumption account for the environmental impact of both material production and transportation. In terms of the maintenance process, the annual CO\({}_{2}\) emissions and energy consumption estimated in Table 10 are assumed to be the same for each operating year. As mentioned before, the design life of this tunnel is 100 years. The total CO\({}_{2}\) emissions and energy consumption are also graphically illustrated in Figure 3.  
As shown in Figure 3a, the tunnel construction process contributes about 94.2% of the overall life-cycle CO\({}_{2}\) emissions. This is due to a large amount of concrete consumed, which also has a much higher CO\({}_{2}\) emission coefficient than the other materials. The CO\({}_{2}\) emissions induced by the rail construction process are comparable with those produced by the maintenance process. The construction process (i.e., both tunnel and rail construction) contributes approximately 97.1% of the life cycle's CO\({}_{2}\) emissions and the CO\({}_{2}\) emissions caused by the maintenance process appear to be negligible compared with the construction process. However, Figure 3b shows that about 55.2% of life-cycle energy consumption can be attributed to the maintenance process. The energy consumption of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Transport Type, Distance,** & **CO\({}_{2}\) Emissions** & **Energy Consumption** \\  & **and Fuel** & **(Ton)** & **(\(\times\)10\({}^{6}\) MJ)** \\ \hline Concrete & 5 Trucks; 300 km; 0.08 L/km & 0.0074 & 0.0057 \\ Accelerator & Truck; 400 km; 0.08 L/km & 0.0020 & 0.0015 \\ Waterproof rubber belt & Truck; 300 km; 0.08 L/km & 0.0015 & 0.0011 \\ Rebar & 2 Trucks; 300 km; 0.08 L/km & 0.0030 & 0.0023 \\ Ballast & Train; 50 km; 7.9 L/km & 0.024 & 0.019 \\ Sleepers & Train; 80 km; 7.9 L/km & 0.039 & 0.03 \\ Rails & Train; 400 km; 7.9 L/km & 0.194 & 0.15 \\ Fastening system & Train; 400 km; 7.9 L/km & 0.194 & 0.15 \\ Elastomeric pads & 3 Trucks; 300 km; 0.08 L/km & 0.004 & 0.0034 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Transportation characteristics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**CO\({}_{2}\) Emissions**} & **Energy Consumption** \\
**Process** & **(Ton)** & **(\(\times\)10\({}^{6}\) MJ)** \\ \hline \multirow{2}{*}{Construction} & Tunnel (material production and transport) & 48,038 & 859 \\ \cline{2-3}  & Rail (material production and transport) & 1472 & 10 \\ \hline Maintenance & 1472 & 1069 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Life-cycle CO\({}_{2}\) emissions (ton) and energy consumption (\(\times\)10\({}^{6}\) MJ).  
tunnel construction process is approximately 44.3% and the portion from the rail construction is much smaller (less than 1%) and can be ignored.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 4 Experiment and Evaluation
### Data Pre-Processing
#### 4.2.1 Features of Bi-gram and Tri-gram Clusters  
Bi-gram is a cluster of two adjacent characters extracted from a string. For example, the domain name \"example\" (top level domain \".com\" was removed) includes the following bi-grams: _ex_, _xa_, _am_, _mp_, _pl_ and _le_. A domain name can contain characters in 26 alphabetical characters (a-z), numeric characters (0-9), \".\" and \"-.\" characters. Therefore, the total number of possible bi-grams, _TS_(bi-gram) is 38 \(\times\) 38 = 1444. From the set of benign domain names, we extract a list of \(N\) most frequently encountered bi-grams, denoted as _DS_(bi-gram). _DS_(bi-gram) is used for calculating 8 bi-gram related features of each domain name.  
Tri-gram is a cluster of three adjacent characters extracted from a string. For example, the domain name \"example\" consists of the following tri-grams: _exa_, _xam_, _amp_, _mpl_ and _ple_. Similar to the calculation of the total number of bi-grams, the total number of possible tri-grams, _TS_(tri-gram) is 38 \(\times\) 38 \(\times\) 38 = 54,872. From the set of benign domain names, we extract the list of \(M\) most frequently occurring tri-grams, denoted as _DS_(tri-gram). _DS_(tri-gram) is used for calculating 8 tri-gram related features of each domain name.  
The features of bi-grams and tri-grams (we call both of them as _n-gram_) for each domain name \(d\) include:  
* _count(d)_ is the number of n-grams of the domain name \(d\) that are also found in _DS_(_n-gram_).
* \(s(d)\) is the weight of n-grams of the domain name \(d\), calculated by the following formula: \[s(d)=\frac{\sum_{i=1}^{count(d)}f(i)\times vt(i)}{count(d)}\] (4) where \(vt(i)\) is the rank of n-gram \(i\) in \(DS(n\)-gram).
- \(ma(d)\) is the average of the general frequency distribution of the n-grams of the domain name \(d\), which is calculated by the following formula: \[ma(d)=m(d)/len(d)\] (5) where \(len(d)\) is total number of n-grams in the domain name \(d\).
- \(sa(d)\) is the average of the weight of n-grams of the domain name \(d\), calculated by the following formula: \[sa(d)=s(d)/len(d)\] (6)
- \(tan(d)\) is the average number of popular n-grams of the domain name \(d\), calculated by the following formula: \[tan(d)=count(d)/len(d)\] (7)
- \(taf(d)\) is the average frequency of popular n-grams of the domain name \(d\), calculated by the following formula: \[taf(d)=\frac{\sum_{i=1}^{count(d)}f(i)}{len(d)}\] (8)
- \(entropy(d)\) is the entropy of the domain name \(d\), calculated by the following formula: \[entropy(d)=-\sum_{i=1}^{count(d)}\frac{vt(i)}{L}\times log(\frac{vt(i)}{L})\] (9) where \(L\) is the number of popular n-grams in the set of benign domain names, \(L\) = \(N\) for bi-grams and \(L\) = \(M\) for tri-grams.
## 2 Materials and Methods
### Techno-Functional Blend Characteristics  
Moisture content was determined gravimetrically after drying the sample at 135 \({}^{\circ}\)C in the oven (SML, Zalmed, Lomianki, Poland), according to Official Method AAC44-19.01, AACC 2000. The water absorption capacity (WAC), oil absorption capacity (OAC), and hydrophilic/lipophilic index (HLI) of _T. catappa_ powder blends were measured following the protocol outlined elsewhere [21], with slight modifications. Briefly, the WAC was determined by mixing 3 g of the sample with 30 mL of distilled water, while the OAC was assessed by combining the sample with 30 mL of rapeseed oil in centrifuge tubes. The dispersions were vortexed (Heidolph Reax, Schwabach, Germany) for 30 s, allowed to rest, then centrifuged at 3000\(\times\) g (Thermo Fisher Scientific, Waltham, MA, USA). The supernatants were removed, and tubes were placed in an oven (Vindon Scientific, Rochdale, UK) at 50 \({}^{\circ}\)C to eliminate residual liquid. WAC and OAC values (g of liquid/g of dry sample) were calculated as described previously [21]. The hydrophilic/lipophilic index (HLI) was then determined by calculating the WAC/OAC ratio.  
The water absorption index (WAI), reflecting sample gelatinization and entrapment of small molecules within the matrix or their release into the supernatant, as well as the water solubility index (WSI), which indicates the quantity of compounds released from the gel, and swelling power (SP), were evaluated based on the method by [PERSON] et al. (2020) [2], with minor adjustments. Each 3 g sample was dispersed in 30 mL distilled water within pre-weighed centrifuge tubes and heated to 90 \({}^{\circ}\)C for 10 min in a water bath (MLL147, AJL Electronics, Krakow, Poland). After cooling to room temperature, samples were centrifuged at 3000\(\times\) g for 10 min, after which the sediment was weighed and the supernatant transferred to pre-weighed Petri dishes for drying at 110 \({}^{\circ}\)C for 24 h in an oven (SML, Zalmed, Lomianki, Poland) to determine solid content. WAI (g water/g dry solid), WSI (g water/100 g dry solid), and SP (g water/g dry solid) were calculated as described by [PERSON] et al. (2020) [22].  
Foaming capacity and stability (FC, FS) were evaluated according to [PERSON] et al. (2012) [23] with slight modifications. Briefly, the flour/blend (1 g) was dispersed in distilled water (50 mL) in a graduated cylinder, tapped, and shaken vertically by hand for 5 min. The initial and final volumes were noted, and the difference was expressed as FC [mL/g DM]. Then the sample was left for 1 h, and the suspension volume was recorded. The foam stability was calculated as [%] according to the following equation:  
\[\text{FS}\left[\%\right]=\left(V1\text{H}/V_{\text{initial}}\right)\times 100 \tag{1}\]  
Emulsifying activity and stability (EA, ES) were measured as described by [PERSON] et al. (2024) [24] by combining the sample with water and oil, homogenizing the mixture, and assessing the emulsion volume after centrifugation. Stability was further confirmed by reheating and allowing the emulsion to settle.