esa-sceva/satcom-chunk-collection huggingface dataset:
# Eco Valorization of _Eucalyptus globulus_ Bark and Branches through Liquefaction
###### Abstract
During liquefaction, the polymeric components of the cell wall are converted into smaller fragments, which can be used as macromonomers in the manufacture of polymers such as polyurethane foams [52], epoxy resins [53], and polyurethane adhesives [54]. [PERSON] et al. [55] studied the liquefaction of _Eucalyptus globulus_ sawdust using 2-ethylhexanol (2-EH) as a solvent, using \(p\)-toluenesulfonic acid (PTSA) (2.44%) as catalyst with a solvent:biomass ratio of 1:5. The optimal liquefaction results were found at 160 \({}^{\circ}\)C for 180 min. Furthermore, [PERSON] et al. [56] studied the liquefaction of Eucalyptus globulus bark using PTSA (3%) as catalyst and a mixture of 2-EH and diethylene glycol (DEG) (1:3) as solvent with a biomass:solvent ratio of 1:10. In this study, the best yield of liquefied products was achieved at 150 \({}^{\circ}\)C for 180 min. In another work, [PERSON] et al. [57] liquefied the _Eucalyptus globulus_ bark to prepare the polyurethane foams. In this work, the mixture of DEG and 2-EH was used as a solvent, in a mass proportion of 1:1:3 (biomass: DEG:2-EH), using PTSA (3%) as a catalyst. The best liquefaction yield was achieved at 160 \({}^{\circ}\)C for 120 min. The objective of this study was to determine the optimal conditions for the liquefaction of the bark and branches of _Eucalyptus globulus_, in terms of reaction time, temperature, particle size, and type of catalyst (acid or base). These forest residues were also characterized as to their chemical composition in order to identify the possible ways of this biomass transformation to obtain value-added products (foams, polymers, and adhesives) while liquefaction is carried out at low pressure and temperature with an appropriate catalyst. The advantage of using these wastes over wood would bring higher revenue for Forest management companies and make polymer companies less dependent on oil prices fluctuations.
## 3 Findings
### Diagnostics for Unit Systems  
Present manufacturing systems are outfitted with different sensors that provide continuous checking and diagnosis, but sensors cannot be equipped across all the parts in the manufacturing system due to big data challenges. These outcomes in non-observable parts limit our capacity to help successful and continuous real-time monitoring and fault diagnosis activities. The exact diagnosis is the most significant step because the fault is the primary cause of a manufacturing system's failure in the fault treatment. Among a wide range of possible faults in a manufacturing system, operative faults occur most often (about 70%). [PERSON] et al. [42] solved maintenance optimization issues in manufacturing systems by considering the diagnosis and suggested a hybrid neural network technique focusing on developing a diagnosis system. [PERSON] et al. [43] proposed that a fault identification, as well as a diagnostic module, is depicted dependent on an internal programmable logical controller. Generally, manufacturing industries have a large number of machines with different old programmable logic controllers that can benefit from an upgrade to new technology. The literature related to the upgradation of manufacturing equipment is mentioned below in Table 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Literature Review on Upgradation in the Context of Flexible Machine Systems**} \\ \hline \hline
**SI. No.** & **References** & **Findings** \\ \hline
1 & [44] & Introduces a plan for usage of a data preparing kit that will upgrade a manufacturing machine allowing it to coordinate into an industry 4.0 environment. \\
2 & [45] & Explains that the traditional manufacturing industry upgrading is partially important in this trend. \\
3 & [46] & Explores the situation of a system upgrade, both electronics and mechanical, which requires extensive software modifications. \\
4 & [47] & Considers the problems of selecting and upgrading equipment for creating and upgrading production systems on facilities with discrete manufacturing. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Literature review on upgradation related to flexible unit systems.
# Insider Threat Detection Using Machine Learning Approach
## 4 Experimental Results
### Classification  
Finding a suitable classifier that can identify the class of a testing instance based on the provided features related to that instance is the main goal of the classification task. We used four ML classification models to classify the users into normal and abnormal: NN, SVM, AdaBoost, and random forest. We aimed to reduce the imbalanced dataset problem and avoid the ML models' bias; we used the SMOTE balancing technique only in the training set to ensure no data leakage and received a balanced dataset of 1552 instances. Table 6 shows the results of supervised machine learning algorithms after DFS, PCA, and SMOTE on the unseen test data part of CERT r4.2. All experiments were performed using the grid search optimisation algorithm with 10-fold cross-validation. Additionally, Table 7 displays the results without utilising any balancing techniques.
# GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
## 5 Results
### Examine the Impact of Languages on Inference Results  
In this experiment, we investigated how different inputs affect GeoLocator's reasoning procedure and subsequent inference results. We provided GeoLocator with images containing text prompts in various languages and observed how these inputs influenced its geographical location references. Specifically, we tested inputs in Chinese and English to determine whether there were significant differences in the model's inference results. Our findings indicated that while there were no substantial differences in the overall inference results between the two languages, the model's reasoning processes differed. For instance, when the input text was in English, the model focused on natural geography, flora, architecture, and infrastructure in its analysis. In contrast, when the input text was in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Images** & **Data Type** & **Google Search Engine** & **GPT-4** & **GeoLocator** & **Distance * (miles)** \\ \hline Nighttime Image & Unknown ** & Country & **City/Town** & 3.28 \\ \hline \hline \end{tabular}  
* Distance means the distance difference between the real location and the inferred location. ** This means that the Google Search Engine, GPT-4, or GeoLocator is not able to infer the address. Geographical categories: country, state, city/town, and street. A deeper background color indicates that a more specific address is inferred in terms of the geographical categories mentioned above.  
\end{table}
Table 3: _Cont._Chinese, the model emphasized country-specific, regional, and zoning clues, concentrating on more specific details. Moreover, when queried in English, the model identified the image as being from a park, a classification that did not occur with queries in Chinese. This variation may be attributed to several factors. First, the model's training data could contain language-specific biases, leading it to associate images with geographical locations more commonly referenced by speakers of a particular language. Second, different languages may convey distinct cultural and geographic context clues, which the model may use to inform its location predictions.
## 2 Materials and Methods
### Characterization of Volatiles from Different Parts of A. altissima
#### 2.3.2 Collection of Plant Volatiles by Dynamic Headspace Method  
Volatiles emitted from (1) phloem from healthy and infected trees, (2) annual branches, and (3) foliage from seedlings were sampled from the field and collected by dynamic headspace method at the Forestry Bureau in Lingwu city. In the laboratory, each plant part was placed inside the collection bag (Oven Bags Turkey Size, Reynolds Consumer Products, Inc., Lake Forest, IL, USA) and tied tightly. After the air in the bag was drained by the atmospheric sampler (QC-1S, Beijing Municipal Institute of Labour Protection), the filtered air was pumped into the bag through the glass tube with activated charcoal using the atmospheric sampler. The other corner of the bag was attached to a glass tube with 70 mg of Porapak Q (80-100 mesh), the adsorbents were sandwiched between glass wool in the glass tube. The atmospheric sampler pumped clean air at a flow rate of 150 mL/min. Volatiles were collected by the adsorbents for 24 h and were then eluted with 800 \(\upmu\)L of n-hexane. The eluent was kept at \(-20\)\({}^{\circ}\)C and brought back to Beijing for analysis. Volatiles of empty collection bags were collected as a control.  
Volatiles collected by HS-SPME and dynamic headspace method were analyzed by GC-MS-QP2010 SE (Shimadzu, Kyoto, Japan) following the same procedure. The GC was equipped with a Restek Rtx-5 MS capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m). Helium was used as the carrier gas at a constant flow rate of 1 mL/min, the injection volume was 1.0 \(\upmu\)L in the split mode with a 40:1 split. The oven temperature started at 50 \({}^{\circ}\)C, and was increased at 6 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C and held for 6 min, then increased at 10 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C and held for 10 min. The mass spectra were recorded in the electron impact mode at 70 eV (source at 220 \({}^{\circ}\)C, scanned mass range: 29-500 m/z). Data analyses were performed using GCMS solution 4.1.1 (Shimadzu, Japan) with the National Institute of Standards and Technology (NIST) database. The relative content of each volatile compound was identified by peak area calculated by the normalized method [21].
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 7 Performance Analysis
### Computation Cost  
In this section, the computation cost in both the SN and UE side is conducted. _Enc_ and _Dec_ are shortened for symmetric encryption and decryption. Meanwhile, the exponential operation, and the pairing are solely defined as \(Ex\) and \(e\). Furthermore, \(H\), \(M\), and \(D\) denote the one-way hash function, multiplication operation, and division operation respectively. Finally, the point multiplication operation is denoted as \(p\). The comparison results on computation cost is presented in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scheme** & **SeDS [19]** & **LRSA [32]** & **GRAAD [12]** & **PPAKA [9]** & **Our Scheme** \\ \hline \hline Forgery Attack Resistance & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Replay Attack Resistance & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Provision to Identity Privacy Preserving & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Session Key Establishment & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Cerificateless Authentication & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) \\ Dynamic Key Updating & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) \\ Continuous Authentication & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison on Security Properties.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **SeDS [19]** & **LRSA [32]** & **GRAAD [12]** & **PPAKA [9]** & **Our Scheme** \\ \hline Storage (UE) & 2816 bits & 3040 bits & 3488 bits & 4704+192\(t\) bits & 2304+16\(t\) bits \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of Storage Overhead.  
Table 4, indicating that the relatively smaller computation cost is required for resource-limited UEs in our scheme.
## 4 Discussion  
### The Reliability of AIS Data  
It is crucial to consider the potential impact of AIS transmission rates on the overall data quality and accuracy in assessing fishing vessel activities. Mainland China's purse seine fishing vessels are active in the high seas of the North Pacific Ocean, the Western Pacific Ocean, the Southwest Atlantic Ocean, and the Indian Ocean. To better safeguard and optimize the utilization of fishery resources on the high seas, China's fisheries management authorities have implemented management measures for the areas where purse seine fishing vessels operate. Consequently, only authorized vessels are permitted to operate in the Arabian Sea of the Indian Ocean. The potential reasons for missing data from these vessels may include AIS equipment malfunctions or the captain's decision to deactivate the equipment, leading to a potential underestimation of fishing effort. It is recommended to enhance the security of the existing AIS using encryption technology to protect signals, or to introduce multi-factor authentication mechanisms to reduce the risk of the transmitters being turned off. Additionally, it is worth noting that an increase in vessel density can impact the reception quality of AIS signals. Signals are susceptible to interference and attenuation in high-density waters, resulting in a decrease in signal quality. Recognizing the importance of accurate data, China's fishery administration has recently mandated that fishing vessels operating on the high seas must ensure that their AIS equipment is activated. This proactive measure is expected to contribute to more precise and comprehensive data for fishery analysis and management in the future.  
Due to the limitations of the collected AIS data, we only analyzed the spatial distribution characteristics of fishing vessel operations for 2021-2022. However, this paper clearly outlines appropriate methods and spatial scales.
# Material Composition Testing Related to Measurement Instrument Enclosure Design and Safety
## 3 Results
### Classification of Test Samples by Material Mixture  
Table 5 and Figure 7 display the results grouped by base material and additives, demonstrating how mixtures within the same basic material group vary in their flammability categories. Material groups 3, 4, 7, 8, 9, 10, and 17 consistently fell into the same burning category across thicknesses and additive combinations. Conversely, groups 1, 2, 5, 6, 11, 12, 13, and 14 showed varied flammability results within their respective groupings. Some mixture combinations result in a different burning category. Material groups number 15 and 16 are consistent within one basic material but have different thicknesses. Figure 7 presents the vertical axis material burning categories and horizontal axis basic material group of samples (first and second column in Table 5). Each group of samples consists of different material composition samples. The results show which samples are in the same burning category and as such provide valuable information for manufacturers of insulation material regarding which material and additions combinations have the same burning category result. These findings are valuable for manufacturers in determining consistent material and additive combinations that reliably achieve the desired flammability performance, optimizing material selection and reducing the need for extensive testing. The sample size per material group was too small for robust statistical analyses, limiting the applicability of traditional significance tests. Nevertheless, several empirical observations are noteworthy. Certain inorganic pigments and fillers can improve flame resistance by promoting char formation during combustion, thus creating an insulating barrier on the polymer surface. This protective char layer reduces heat transfer, slows material degradation, and limits combustible gas release, improving the overall flammability performance even without specialized flame retardant additives. Although thicker samples generally exhibit better flame resistance due to their higher thermal mass, our results indicate that minor compositional variations, particularly the type of additives used, often overshadow thickness effects. Thus, additive selection appears more critical than thickness alone in determining the HB classification. Figure 6: Number of materials categorized by thickness and burning category.
## 4 Topology Optimization Design
### Variations in Effective Stress, Natural Frequency, and Rigidity
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Original Structure**} & \multicolumn{3}{c}{**After Design Improvement**} \\ \cline{2-5}  & \multicolumn{3}{c}{**Column + Base + Saddle**} \\ \hline \multirow{2}{*}{**Mode**} & **Natural Frequency (Hz)** & **Mode** & **Natural Frequency (Hz)** & **Variation (\%dd)** \\ \hline
1 & 70.75 & 3 & 137.76 & 94.72 \\ \hline
2 & 88.63 & 4 & 186.37 & 110.28 \\ \hline
3 & 131.16 & 5 & 212.87 & 62.30 \\ \hline
4 & 174.98 & 6 & 246.92 & 41.11 \\ \hline
5 & 204.74 & 7 & 320.52 & 56.55 \\ \hline
6 & 250.12 & 8 & 339.35 & 35.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of natural frequencies between the original structure and after design improvement for a machine tool system. Figure 19: Comparison of modal shapes between the original structure and after design improvement for a machine tool system. stiffness may be achieved up to 355.97% in the case of topology optimization of the column, base, and saddle subsystems when concurrently integrated together. This confirms that the topology optimization based on machining characteristics proposed in this study for structural design improvement and stiffness enhancement is feasible and effective. The variations, \(\varphi\), of the effective stress, natural frequencies, weight, and stiffnesses of a machine tool are defined as:  
\[\varphi=\frac{after\ design\ improvement-original\ structure}{original\ structure} \tag{3}\]  
The variations of the effective stress, natural frequencies, weight, and stiffnesses of a machine tool obtained from the finite element analyses are calculated by Equation (3) and shown in Tables 3-6, respectively. It is defined as the difference between the numerical simulation result after design improvement and that before design improvement, and then divided by the simulation result before design improvement (the original structure).
## 7 Discussion  
Results show that the developed algorithm achieved good results for both translations and rotations. In particular, the maximum difference in the rotation estimation was 3.2\({}^{\circ}\) for the nominal rotation of 45\({}^{\circ}\) (see Table 1). Figure 13 shows the optimization result for this case.  
The mean difference for the rotation is 1.5\({}^{\circ}\), while the standard deviation is 1.0\({}^{\circ}\).  
The maximum difference in the translation estimation was instead 4 cm for the nominal translation of 72 cm (see Table 2). As shown in Figure 11, it seems that, at the increase of the amount of the translation, the difference increases. Most likely, looking at Figure 14, this is due to how the vehicle appears in the equirectangular image. In this case, the vehicle appears quite far and small translations cannot be appreciated from the image point of view. Indeed, also in this case, at least visually, the difference between the real world and the synthetic image does not seem to be relevant (Figure 14).  
Figure 12: An example of the optimization result where the camera was rotated by 15\({}^{\circ}\). (**a**) Portion of the input equirectangular image taken by the 360\({}^{\circ}\) camera. (**b**) Result of the segmentation. (**c**) Optimization result in which the CAD is rendered in the final pose found by the optimization algorithm.  
Figure 13: Optimization result where the camera was rotated by 45\({}^{\circ}\). (**a**) Input equirectangular image taken by the 360\({}^{\circ}\) camera. (**b**) Result of the segmentation. (**c**) Optimization result in which the CAD is rendered in the final pose found by the optimization algorithm.  
The mean difference for the translation is 1.4 cm, while the standard deviation is 1.5 cm. The computational time of 20 min makes use of the proposed algorithm to be used offline. However, in the case of a video, once the pose is estimated in the first frame, the search field for the next frame is very limited because the vehicle will be in a pose very near to one of the previous frames. This will speed up the elaboration and the pose detection.
## Materials and Methods  
### Fungi and Cultivation Conditions  
_Schizophrenia commune_ IBPPM541, _Pleurotus ostreatus_ f. Florida IBPPM540, _Pleurotus ostreatus_ 336, _Pleurotus ostreatus_ D1 (Jacquin) [PERSON], _Lecanicillium aphanocladii_ [PERSON] IBPPM542, and _Fusarium oxysporum_ IBPPM543 were from the Collection of Rhizosphere Microorganisms (IBPPM RAS, Russia); _Trametes hirsuta_ LE-BIN072, _Trametes maxima_ LE-BIN0275, _Trametes ochracea_ LE-BIN093, _Trametes gibbosa_ LE-BIN1911, _Lenzites betulina_ LE-BIN2047, _Pleurotus ostreatus_ LE-BIN0432, and _Stecherinum murashkinskyi_ LE-BIN1963 were from the RAS Federal Research Centre \"Fundamentals of Biotechnology\" (Moscow, Russia); _Trametes versicolor_ MUT3403, _Pleurotus ostreatus_ MUT2977, _Bjerkandera adusta_ MUT3398, _Geotrichum candidium_ MUT4803, and _Cladosporium herbarium_ MUT3238 were from Mycotheca Universitatis Taurinensis (Turin, Italy); _Trametes versicolor_ DSM11269 and _Stropharia rugososannulata_ DSM11372 were from Helmholtz Centre for Environmental Research\(-\)UFZ (Leipzig, Germany) (Table S1).  
The fungi were grown in a basidomyces-rich medium with our modifications (g/L) [25]: NH\({}_{4}\)NO\({}_{3}\), 0.724; KH\({}_{2}\)PO\({}_{4}\), 1.0; MgSO\({}_{4}\)\(\times\) 7H\({}_{2}\)O, 1.0; KCl, 0.5; yeast extract, 0.5; FeSO\({}_{4}\)\(\times\) 7H\({}_{2}\)O, 0.001; ZnSO\({}_{4}\)\(\times\) 7H\({}_{2}\)O, 0.0028; CaCl\({}_{2}\)\(\times\) 2H\({}_{2}\)O, 0.033; \(D\)-glucose, 10.0; peptone, 10.0. Alternatively, the fungi were grown in [PERSON]'s medium with our modifications (g/L) [26]: KH\({}_{2}\)PO\({}_{4}\), 0.2; MgSO\({}_{4}\)\(\times\) 7H\({}_{2}\)O, 0.05; CaCl\({}_{2}\), 0.01; thiamine, 0.0025; NH\({}_{4}\)NO\({}_{3}\), 0.724. The pH of [PERSON]'s medium was maintained with 25 mM phosphate buffer (pH 6.0), and maltose (1%) served as the carbon and energy source. [PERSON]'s medium was supplemented with 0.1% Tween-80 to increase ligninolytic enzyme production. Cultivation was performed at 24-26 \({}^{\circ}\)C with shaking (130 rpm).  
Pollutants were added on day 2 of growth to final concentrations of 50 mg/L for PAHs (anthracene, phenanthrene, or fluorene) and anthraquinone dyes (Acid Blue 62 and Reactive Blue), 200 mg/L for a nonionic surfactant (neonol AF9-12) and isononylphenol (isomeric mixture), and 5 g/L for oil. PAHs and isononylphenol were added as a chloroform stock solution: 5 mg/100 \(\mu\)L and 20 mg/200 \(\mu\)L, respectively. Control treatments contained 100 \(\mu\)L of chloroform, which was added on day 2 of fungal growth. After 14 days, residual PAHs, neonol AF9-12, isononylphenol, and oil were extracted from the bulk of flasks (without separation of the cultivation medium and mycelia) with chloroform (5 mL, three times). The resulting extracts were evaporated and were analyzed as described below. The mycelium and the cultivation medium were separated by filtration. The mycelium increment (mg of dry biomass) was determined by weighing.