esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Research Prospects  
Optimizing cultivation conditions for _C. militaris_ is pivotal for maximizing the production of its bioactive compounds, thereby enhancing its therapeutic and commercial viability. Various environmental factors, including substrate type, temperature, humidity, light, and aeration, significantly influence the growth and metabolite output of this fungus. Here are specific strategies and research directions for improving cultivation conditions:  
* Substrate Optimization: Utilizing substrates enriched with complex carbohydrates or proteins can enhance the levels of specific metabolites such as polysaccharides and bioactive peptides, crucial for the medicinal properties of _C. militaris_. Investigating different substrate combinations and their effects on metabolite synthesis can lead to more effective and economical cultivation practices.
* Maintaining temperatures between 20 and 22 \({}^{\circ}\)C is essential for optimal fungal growth and metabolite production.
Controlling humidity and light exposure is critical to optimize fungal metabolism and secondary metabolite synthesis. Specific light regimes can be employed to stimulate the production of targeted bioactive compounds.
* Biotechnological Advances:
* Modifying genetic pathways in _C. militaris_ via genetic engineering can enhance the production of targeted metabolites like cordycepin. This approach can also improve the strain's resilience to environmental stressors, thereby increasing overall yield.
* Both solid-state and submerged fermentation technologies can be optimized to improve biomass production and consistency of bioactive compounds. Advances in these technologies can also reduce production costs and enhance scalability.
* Bioreactor Utilization: Employing bioreactors in submerged fermentation setups allows for automated and precise monitoring of growth conditions, significantly boosting production efficiency and consistency in bioactive compound profiles. bioreactors can provide controlled environments conducive to large-scale production, ensuring high-quality outputs.  
These strategies underscore the necessity of meticulous management of cultivation parameters to ensure robust fungal growth and significantly influence the quality and quantity of bioactive compounds produced. Future research should focus on further integrating these biotechnological approaches to refine cultivation techniques, thereby improving the efficiency and sustainability of _C. militaris_ production. This will ensure it remains a sustainable and economically viable natural health product to meet the increasing global demand.  
Moreover, exploring the synergistic effects of combining _C. militaris_ with other functional ingredients could open new avenues for product development. Research into consumer preferences and market trends can guide the creation of innovative products that deliver both health benefits and sensory appeal. By continuing to advance cultivation and processing techniques, the industry can harness the full potential of _C. militaris_, providing consumers with accessible and effective health-promoting products.
## Security Analysis
### Confidentiality  
It is assumed that the sender is \(UAV_{1}\) and the receiver is \(UAV_{2}\). Taking the \(UAV_{2}\) public key requested by \(UAV_{1}\) to the blockchain as an example, [PERSON]'s methods are used to prove the confidentiality of the proposed protocol. The protocol interaction process is as follows.  
(1) \(UAV_{1}\to B:\)  
\[C=\{E_{PK_{B}}(DID_{1},DID_{2},E_{SK_{1}}(Hash(DID_{1},DID_{2},Timestamp_{1}, PkRequest)),\] \[Timestamp_{1},PkRequest)\}\]  
(2) \(B\to UAV_{1}:\)  
\[C=\{E_{PK_{1}}(DID_{1},DID_{2},E_{SK_{B}}(Hash(DID_{1},DID_{2},Timestamp_{2}, PK_{2})),\] \[Timestamp_{2},PK_{2})\}\]  
The above protocol is formally described as follows.  
\[N_{1}(UAV_{1},B)P_{1} =\overline{\alpha_{1}(UAV_{1},B)}P_{1}\] \[=E_{PK_{B}}(E_{SK_{1}}(Hash(P_{1}))P_{1}).\]  
where \(\alpha_{1}(UAV_{1},B)=E_{PK_{B}}E_{SK_{1}}\), and \(P_{1}=(DID_{1},DID_{2},Timestamp_{1},PkRequest)\).  
\[N_{2}(UAV_{1},B)P_{2}=\overline{\beta_{1}(UAV_{1},B)N_{1}(UAV_{1},B)P_{2}}\] \[=\overline{\beta_{1}(UAV_{1},B)\alpha_{1}(UAV_{1},B)P_{2}}\] \[=\overline{E_{PK_{1}}E_{SK_{B}}D_{PK_{1}}D_{SK_{B}}E_{PK_{B}}E_{ SK_{1}}P_{2}}\] \[=E_{PK_{1}}(E_{SK_{B}}(Hash(P_{2}))P_{2}).\]  
where \(\beta_{1}(UAV_{1},B)=E_{PK_{1}}E_{SK_{B}}D_{PK_{1}}D_{SK_{B}}\), \(P_{2}=(DID_{1},DID_{2},Timestamp_{2},PK_{2})\), and \(E_{PK_{1}}D_{SK_{1}}=1\).  
In Dolev-Yao models, there is \(\gamma=D_{SK_{2}}\beta_{1}(UAV_{Z}\), \(B)\) that makes:  
\[\overline{\gamma N_{1}(UAV_{1},B)}=\overline{D_{SK_{2}}\beta_{1}( UAV_{Z},B)N_{1}(UAV_{1},B)}\] \[=\overline{D_{SK_{2}}E_{PK_{2}}E_{SK_{B}}E_{PK_{2}}D_{SK_{B}}E_{ PK_{B}}E_{SK_{1}}}\] \[=\overline{E_{SK_{B}}D_{PK_{2}}E_{SK_{1}}}\
eq\lambda.\]  
where \(SK_{Z}\) is the private key of any UAV in the system, and \(\lambda\) is an empty set.  
According to the method of [PERSON] and Yao, the proposed secure communication protocol satisfies the requirement of confidentiality.
## 3 A Proposed Robust Scheme Based on Hop-Count Analysis (MHA)  
### The Concept of MHA  
We illustrate the concept of MHA in Figure 2. The normal routes for a particular communication pair contain 5\(\sim\)6 hops; the route under a wormhole attack has a hop-count of 2. We see the route under the wormhole attack has a smaller hop-count than normal. As a result, users who avoid routes with relatively small hop-counts can avoid most wormhole attacks.  
In MHA, we first examine the hop-count values of all routes. Then we choose a safe set of routes for data transmission. Finally, we randomly transmit packets through safe routes. Even if the wormhole is not avoided in some severe cases, we can still minimize the rate of using the route path through the wormhole. The simulation results are shown in Section 4.  
In our work, we refer to RFC3561 [4], i.e., the AODV routing protocol, to set up MHA. We use the control packets as in RFC3561 and modify them to satisfy our requirements. Their modified functions are defined in the following section. For example, the RREQ packet is used for route discovery and the RREP packet is used for route reply, and so on. Figure 3 shows the RREQ packet format in MHA. While most fields stay as they were in RFC 3561, in addition, we define a field called _check flag_ (CF),which is used with RREQ ID to distinguish new RREQ packets from old ones in MHA. The CF is also included in the RREP packet. A RREQ packet with CF = 0 is served as a normal route discovery, while CF \(\
eq\) 0 represents the _graylist broadcast_, which is introduced in section 3.3. The source node triggers the gravlist broadcast based on the result of hop-count analysis.  
The MHA contains four parts. The route establishment is introduced in Section 3.2, and gravlist broadcast is illustrated in 3.3. The hop-count analysis scheme is proposed in 3.4. Finally, the route maintenance method is given in 3.5. The notations used are listed in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Represents** \\ \hline CF & The check flag value \\ \(\mathrm{IP_{S}}/\mathrm{IP_{D}}\) & The source/destination IP address \\ \(\mathrm{RREP_{lim}}\) & RREP number limit \\ \(\gamma\) & Number of received RREP packets \\ \(\alpha\), \(\beta\) & The lower, upper bound for route selection \\ m, n & The lowest, highest hop-count of legal routes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.  
Figure 3: RREQ packet format of MHA protocol.  
Figure 2: Distinguishing safe routes from hop-count values.
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 3 State of the Art
### Popularity and Priority  
There have been many different priority-based forwarding protocols, for both OppNets and other types of networks (e.g., [8; 12]). They usually assign some of the messages (e.g., disaster warnings, messages from police or fire brigade, etc.) a high priority level than for other messages and give preference to those messages when forwarding them. In addition, such messages are usually given higher priorities in the storage caches and forwarding buffers to avoid them being deleted. Some works do not pre-stamp the messages with priority levels, but compute the priority based on various parameters, e.g., the sender, the receiver, type or number of messages. For example, in [13], smaller messages are given higher priority.  
Popularity, on the other hand, reflects how people have perceived specific data items (e.g., videos)--i.e., liked or disliked them. It has already been applied for mobile content caching [14] to decide which content to pre-load on a base station for mobile users. To the best of our knowledge, popularity of data items has not been used before for data dissemination, mainly because it is not trivial to predict it without global historical data.
## 4 Experimental Results
### Experimental Results
A rigorous comparative evaluation was performed between the conventional binary cross-entropy (BCE) loss function and our novel optimization strategy under identical experimental configurations. Figure 4 systematically illustrates the loss trajectories throughout the training process, revealing a distinct convergence pattern: The proposed methodology achieves stable convergence at approximately 40,000 iterations, demonstrating a two-fold acceleration in convergence rate compared to the BCE baseline requiring 80,000 iterations. The generalizability of the proposed method is further validated by conducting experiments on WOS and AAPD, two widely used multi-label text classification datasets. Comparisons were made against Retrieval [3], TextRCNN [5], Hi-Match [6], Hi-AGM [5], HGCLR [34], MLCL-KNN [4], HiSR [8], HALB [7], DPT [35] and MLCL [26], with the results presented in Table 5.  
The experimental results show that the proposed method consistently outperforms state-of-the-art approaches across multiple datasets. The model achieved the highest micro-F1 score (88.24%) and a competitive macro-F1 score (80.24%) on the WOS dataset, as well as the leading micro-F1 score (81.21%) and a substantial macro-F1 score (57.65%) on the AAPD dataset. These results demonstrate its efficacy in general multi-label text classification tasks. The success of this method can be attributed primarily to its ability to effectively capture label semantics at multiple levels and address the challenge of imbalanced sample distribution. This further confirms that the semantic analysis of latent embedding space and the effective integration of cross-layer label semantic relationships represent a viable approach applicable to multi-label text classification tasks, including legal-specific domains. The proposed model exhibits superior domain-specific effectiveness and remarkable task generalization capability. Figure 4: Convergence Dynamics of Training Loss. Compared with the relatively lower accuracy in the legal text multi-label recognition task, the model achieved high accuracy on the WOS and AAPD datasets. Following the analysis, the differences in the accuracy of the model are mainly reflected in (1) the different languages of the datasets, (2) the differences in the number of labels, and (3) the differences in the sample size. The observed discrepancies in the experimental results further validate the viewpoint hypothesized in this study that \"legal multi-label classification represents a complex scenario characterized by abundant label types, demanding semantic correlations and strong hierarchical dependencies between labels\". Traditional multi-label recognition approaches often struggle to deliver satisfactory performance when handling such intricate tasks involving legal texts, particularly owing to their limitations in addressing the sophisticated hierarchical interdependencies and semantic associations inherent in legal label systems.
## 2 Materials and Methods
### ACC Deaminase Activity  
The evaluation of ACC deaminase (EC: 4.1.99.4) activity was performed following the protocol reported by [PERSON] et al. [32], utilizing fructose instead of glucose for _A. brasilense_ in DF salt minimal medium. A calibration curve was calculated with different concentration of the standard \(\alpha\)-ketobutyrate (Sigma) in 0.1 M TRIS-HCl (pH 8.0) (R\({}^{2}\) = 0.9945; y = 0.004x + 0.0063) and detecting the absorbance of the reaction mixture at 540 nm (Spectrophotometer VARIAN Cary 50 Bio). The protein amount was determined by Bradford method [33]. ACC deaminase activity of the strains was expressed as \(\upmu\)mol \(\alpha\)-ketobutyrate h \({}^{-1}\) mg protein \({}^{-1}\).
# Uncertainties in the Seismic Assessment of Historical Masonry Buildings
## 4 Results
### Holsteiner Hof
#### 4.1.2 Failure Analysis  
Figure 10 shows the results of 400 IDAs for the Holsteiner Hof model in terms of modal periods, fragility curve and the correlations between the input parameters and the predicted PGA at failure. At the start of each IDA, modal periods were determined to calculate the Rayleigh damping coefficients. The material parameters \(E_{m}\), \(G_{m}\) and \(f_{cm}\) correlate positively with the PGA at failure, with the strongest correlation detected for \(E_{m}\). The parameters \(E_{m}\), \(G_{m}\) and \(f_{cm}\) were set as correlated when the LHS sample was prepared; it was therefore expected that all correlate in a similar manner with the PGA at failure. A positive correlation between the PGA at failure and drift limit values was expected for models that developed mainly in-plane failures. The shear drift at collapse had a more significant correlation with the PGA at failure than the flexural drift at collapse. Rayleigh damping ratio was expected to be correlated with the PGA at failure for any failure mode of non-linear dynamic analyses. Floor stiffness and floor-to-wall connection parameters did not correlate with the PGA at failure, even though the simulations led to an almost equal number of out-of-plane and in-plane collapses. This was because the out-of-plane failures were located in the walls spanning parallel and failing out-of-plane perpendicular to the direction in which the beams spanned, and as such were not influenced by these parameters.  
Figure 9: Holsteiner Hof model Spearman correlation matrix for PGA 0.21 g.  
The failure PGA was not the only value impacted by the material and modelling parameters, as the portion of the building that fails was also influenced. The distribution of the types of failure can be seen in Figure 11. Interestingly, the number of the out-of-plane and in-plane failures was divided evenly. In-plane failures were again split between flexural and shear failures, with flexural failures accounting for a larger portion. A majority of the failures were localized in the second floor piers spanning in the y-direction for both out-of-plane and in-plane failures. Still, failure location was detected in all other parts as well.  
Figure 10: Holsteiner Hof model: (**a**) Distribution of modal periods. (**b**) Fragility curve. (**c**) Correlations between the PGA at failure and input parameters.
## 1 Introduction
Crystallization and DOC are not only influenced by solidification characteristics but also by packing parameters. In this study, we used the enthalpy transformation detection method to determine the crystallization time at various melt and mold temperatures under the appropriate packing parameters. First, a prediction model for the crystallization time was built using response surface methodology (RSM) [27; 28]. The applicability of the crystallization completion model was assessed by analyzing the warpage of the parts molded at various cooling times. When the post-filling time (i.e., the packing plus cooling time) was larger than the crystallization time, the part warpage remained relatively constant, indicating that incomplete crystallization did not significantly contribute to the warpage. Once crystallization completion is confirmed, molding conditions can be adjusted and optimized accordingly. The influence of packing-based parameters on part shrinkage, including packing pressure and time at various melt temperatures, was then analyzed using regression analysis. A prediction model for part shrinkage variation and time-averaged packing pressure was constructed. Both models were verified via experiments to test the accuracy of the predictions. The verified prediction models can serve as useful guidelines for improving and optimizing molded part shrinkage. For further understanding of processing conditions on part qualities based on the morphology view point, one can refer to the study by [PERSON] et al. [29]. Figure 2: Schematic of the infrared temperature sensor that measures the maximum temperature within the melt. In contrast, a thermocouple only measures the temperature at the contact point of the cavity surface.
# Development of a New Test-Rig for Wheel-Rail Contact Experiments under Large Slip Conditions
## 4 Experimental Research
### Experiments of Different Surface Contaminants  
Wheel-rail contact experiments under large slip conditions with different surface contaminants are carried out utilizing the test-rig. Oil contamination is created by smearing oil (SHELL, 54 VX32) on the surface of the rail. Then, a small amount of water or leaves are added to produce oil + water or oil + leaves contamination. Contamination with leaves is created by applying leaves along the rail with paper tapes. The applied axle load is 14 t (818.4 MPa) and the sprinkler system is suspended temporarily. At first, the test-rig runs at the speed of 40 km/h for a while without creepage, then sinusoidal creepage is generated between the wheel and rail, varying from 0 to 0.3 with a period of 12 s. Measured slip-adhesion curves are shown in Figure 15a and contaminant distributions on the rail surface of the test-rig are shown in Figure 15b.  
It is shown that with the above four contaminants, the second maximum of adhesion coefficient in the large slip stage does not exist. Unlike water, these contaminants seem to cause no adhesion recovery under large slip conditions. That is because, in these conditions, full hydrodynamic lubrication is formed between wheel and rail. The measured adhesion coefficient fits well with the theory. Particularly, with the application of oil, the slip-adhesion curves appear to coincide in the process of creepage rising and falling, and only in the leaf contaminant condition does the slip-adhesion curve show no coincidence.  
Figure 15: Wheel–rail contact experiments under large slip conditions with various surface contaminants: (**a**) measured slip–adhesion curves; (**b**) contaminants on rail surface of the test-rig.
## 3 Multipath Multimedia Dynamic Source Routing (MMDSR)  
In this section, we give a brief summary of the main features of the MMDSR routing protocol, whose detailed description was presented in [3]. In this present article, we further improve the routing scheme by designing a novel game-theoretical model to provide video-streaming in MANETs. The framework is able to provide video-streaming services over IEEE 802.11e [28] MANETs and to dynamically adapt to the changing network conditions inherent in MANETs.
# Low-Complexity Pupil Tracking for Sunglasses-Wearing Faces for Glasses-Free 3D HUDs
## 2 Methods
### Whole-Face Detection and Classification of Faces with Sunglasses  
We utilized the error-based learning (EBL) method published in our previous work [23], which utilizes the AdaBoost classifier [31; 32; 33] and local binary pattern (LBP) features [31; 32; 33] for facial detection tasks. The EBL framework trains only a small fraction (less than 5%) of the detection training image DBs in much shorter training times, while it improves the detection rate through three stages [23]. We used a cascaded classifier with N-boosting substages for each stage in EBL [23]. Multiscale block LBP feature space was adopted for the cascaded AdaBoost classifier [23]. Unlike our previous method, which used an eye-nose detector, for this study we constructed an entire face detector to address the issue of eye occlusion by sunglasses. The cascade-AdaBoost algorithm is used extensively for facial detection tasks. Although deep-learning-based facial detection approaches such as SqueezeDet [26], region-based convolutional neural networks (R-CNN) [34], and multitask CNNs [27] have been developed recently, such techniques require considerable GPU resources and, therefore, require network lightening for integration with automobiles, for example as part of AR 3D HUD systems, which adopt commercial vehicle-embedded computing boards with limited GPU resources. Therefore, fast processing must be married with low complexity to maximize the performance of the limited computational resources of AR 3D HUD systems.  
Our proposed detector is both practical and simple, requiring only a CPU. For the bare-face training dataset, we reutilized samples constructed for our previous study [23], which outlines the details of the EBL algorithm used for training involving the bare-face image datasets. For sunglasses-wearing faces, we constructed a new training image database containing 30,000 images of 37 people wearing sunglasses according to our own capturing and labeling annotations (Figure 3a). For the final face-detection training step, the 50,000 bare faces and 30,000 sunglasses-wearing faces from the training samples were used. To run two different eye-tracking modes according to the presence of sunglasses, we performed an image classification routine to separate the bare and sunglasses-wearing faces. The region surrounding the eyes was cropped using the statistical mean shape from our training dataset labeling (Figure 3b). In addition, the eye region classifications were assisted by the cascade-AdaBoost classifier and LBP features.
## 3 Datasets and Evaluation Metrics
### BRISQUE  
The Blind/Referenceless Image Spatial Quality Evaluator is a no-reference metric. Its calculation involves extracting features based on Natural Scene Statistics (NSSs) and using a pretrained regression model (Support Vector Regression--SVR) to predict a quality score from these features. Due to its reliance on a trained machine learning model, it does not have a closed-form mathematical formula that fully characterizes it [97]. Lower values indicate better perceptual quality.
## 2 Study Area and Experimental Data
### Data
#### 2.2.3 Land Cover  
The MCD12Q1 product provides global land cover type data at a spatial resolution of 500 m at an annual time step from 2001 to 2019. It is based on the supervised classification of MODIS reflectance data with six different classification schemes, including the IGBP (Annual International Geosphere-Biosphere Programme), which was widely utilized due to its high accuracy and widespread acceptance [33]. Thus, the IGBP classification method was utilized in this study. The land cover data from 2001 to 2019 were chosen to produce a spatially continuous dataset via mosaic. The filling data of MCD12Q1 was removed to reduce their effect on the results. Evergreen needleleaf forests, evergreen broadleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, and mixed forests were grouped into forests. Closed shrublands and open shrublands were grouped together as shrublands. Woody savannas and savannas were combined into savannas.