esa-sceva/satcom-chunk-collection huggingface dataset:
# Cell Response to Oxidative Stress in Antarctic Filamentous Fungi
## 8 Antioxidant Defence in Antarctic Fungi
### The Role of Cold Shock Proteins (CSPs)  
Upon exposure to cold temperatures, fungal cells quickly trigger the production of a substantial number of cold shock proteins (CSPs). These proteins play a crucial role in transcription and translation mechanisms [173]. In eukaryotes, CSPs are incorporated as cold shock domains in multidomain proteins, distinguished by their variable sequences at the N- and C-terminal regions. While the function of CSPs in the adaptation of bacteria to low-temperature environments is well understood, the function of cold shock domains in eukaryotes remains to be fully elucidated [234]. Furthermore, there is a paucity of information regarding the contributions of these proteins to fungal growth and survival in low-temperature environments. [PERSON] et al. [234] found that the up-regulation of CSP genes in the rust fungus _Melampora larici-populina_ significantly bolstered its cold stress tolerance. CSPs have been identified in _A. clavatus_ and _Metarhizium anisopliae_ growing under conditions of extremely low temperatures [235]. The study of [PERSON] et al. [236] provides a comprehensive overview of the functions of CSP-family proteins, emphasizing their significance in the stress response mechanisms of enteropathogenic _Yersinia_. In the context of budding yeast cultures, the identification of two distinct groups of genes encoding CSPs has been made. These groups are designated as the \"early response to low temperatures\" group and the \"late response\" group. The \"early response to low temperatures\" group is associated with transcription, transport, and the metabolism of lipids and fatty acids. The second group of genes encodes proteins that support carbohydrate metabolism and the metabolism of trehalose and glycogen. The results of this study demonstrate that yeast cells employ two distinct programs in their response to low temperatures, thereby enhancing their capacity to adapt [173].
## 2 Problem Assessment
### Requirements for Characterizing Android I/O  
Traditional I/O characterization study defines the I/O characteristics on four dimensions: I/O type (read _vs_. write), I/O size (KB), spatial locality (sequential _vs_. random), and temporal locality (hot _vs_. cold). To properly understand the I/O characteristics of Android platform, one needs to acquire the above mentioned four characteristics under various different contexts: subject to file types, filesystem block types, e.g., metadata, data, or journal, and Android apps, e.g., Facebook, Youtube, _etc_. For example, we need to understand what fraction of writes is synchronous for filesystem journal writes.  
However, as far as we are aware of, the existing tools that capture and analyze workload are not suitable to study the details of I/O behavior in the Android platform because the tools do not identify the file type nor the application that issued an access for a given block. To provide remedy for these issues, we implemented the following two features:  
* Identify the file type for a given block. We define six file types: SQLite DB, SQLite Journal, executable, multimedia, resources, and other files. From the logical block address captured by block access trace tool [16], we identify the type of a file to which the respective block belongs. This process consists of two steps. First, from a logical block address, the proposed tool identifies the inode number of the file to which the logical block address belongs. Then, from the inode number, it identifies the file type of the respective file;
* Identify the application for a given block. The proposed tool can identify the application that originally issued an I/O request. Different from legacy Linux kernel, Android I/O subsystem delegates the handling of the I/O requests to special daemon, mmcqd. From the block device layer, mmcqd is viewed as an application for a given I/O request. We need to find the original application for a given I/O, not mmcqd.
## 2 Related Work
### Maximum Power Point Tracking (MPPT) Approach  
The lookup table-based MPPT approach uses the tip speed ration (TSR) to control the DFIG-based wind power system [1; 16]. This technique is based on changing the range of rotor speeds of the DFIG system. In order to follow the reference rotor speed, this varies with wind speed. The reference speed ensures the optimal TSR that brings the power coefficient (\(C_{\text{P}}\)) of the wind turbine to its maximum value. The MPPT control-based TSR controller is presented.  
An extra anemometer is required to apply this scheme for the wind speed measurement and also has a pre-known value of the optimal TSR to convert the wind velocity measurement into its corresponding reference for optimal generator speed. Owing to the non-negligible inertia of WECS, the generator speed changes rather lethargically as compared with the change in the wind rate. On the other side, the TSR control technique provides the fastest control action as it directly measures the wind speed and provides the control reference instantaneously. Therefore, it gives more energy. Although, accurate wind measurement is not a small task, especially in the case of the large wind turbine. The anemometer provides limited measurements of wind speed only at the hub height and cannot cover for the whole span of large blades [36]. In addition, owing to the interaction between the rotor and the wind, this usual placement of anemometers on nacelles leads to inaccurate wind speed measurements in both upwind and downwind turbines. For TSR control, we keep the TSR constant at its optimal value by changing the rotor speed in the numerator. Meanwhile, the wind speed changed in the denominator of the above ratio. The generated power by the wind turbine is mathematically expressed as follows:  
\[\text{P}=0.5\rho\pi\;R^{2}V^{3}C_{\text{P}}\;(\lambda,\beta) \tag{5}\]where \(\rho\) is the air density (kg/m\({}^{3}\)), \(R\) is the radius of the turbine rotor, \(V\) denotes the speed of the wind, and \(C_{\rm P}\) represents the power coefficient that shows the power conversion efficiency of the wind turbine. The theoretical power available in the wind is the physical meaning of the \(C_{\rm P}\) curve; this is the ratio of the actual power delivered by the turbine. \(C_{\rm P}\) is a function of the TSR, \(\lambda\), in addition to the blade pitch angle (\(\beta\)) in a pitch-controlled wind turbine. The value of \(\lambda\) is given in Equation (6).  
\[\lambda=\text{R}\omega_{\rm r}/\text{V} \tag{6}\]  
where \(\omega_{\rm r}\) is the rotating speed of the wind turbine. Practically, the maximum \(C_{\rm P}\) can be 40%-45% [36]. The rotor proficiency curve \(C_{\rm P}\) (\(\lambda\)) is a nonlinear function of the TSR (\(\lambda\)), which is indomitable by the blade design and the pitch angle. The relationship between \(C_{\rm P}\) and TSR can also be summarised as follows: TSR = \(\omega_{\rm r}\)*R/V\({}_{\rm wind}\), where R is the radius of the blade.
# Oil Press-Cakes and Meals Valorization through Circular Economy Approaches: A Review
## 4 Anti-Nutritional Factors  
Anti-nutritional factors are substates of natural or synthetic origin, found in the human diet or animal feed, which can affect the health, and growth performance of livestock. These factors can have different effects on animals depending on their digestive process, for example, trypsin inhibitors have a negative effect on monogastric animals, but not in ruminants because it is degraded [109]. The direct utilization of oilseed cakes in human or animal nutrition is limited by the presence of antinutrients, which influences the organoleptic properties, protein digestibility, and macro-/micro-elements bioavailability [22]. The major antinutrient in oil cakes are presented in Table 2.  
Canola meal contains phenolics, phytic acid, and glucosinolates, that can cause problems in food. The main antinutritional factor (30 times higher compared to soybean) are the phenolics acid esters, especially sinapine. This binds to protein and creates a complex that confers dark color, bitter taste, and through oxidation, poor digestibility. Regarding glucosinolates, it was demonstrated to have a positive effect on health in low proportion [110]. When seeds are crushed, enzymes such as myrosinase are liberated, which are responsible for the hydrolyzation of glucosinolates in glucose and various toxic chemicals (isothiocyanate or thiocyanate ions) [10]. Cyanogenic glycosides are the major antinutrient in flaxseed cake. In the gastrointestinal tract, during digestion they form hydrogen cyanide, causing below 50 mg/Kg acute toxicity in adults. Linatine, another antinutritional factor can cause pyridoxine (B\({}_{6}\)) deficiency [111]. The majority of oilcakes (rapeseed/canola, sesame, sunflower, soybean, groundnut, hempseed) contain antinutrients such as phytic acid and trypsin inhibitors, which can diminish protein and vitamin utilization. More precisely the second one (trypsin inhibitors) connects to the digestive enzyme trypsin, thus reducing proteins adsorption and digestion. [112]. Saponins are a group of steroidal glycosides (natural foam producers) that cause hemolysis and interference in bile acids, lipid-soluble vitamins, cholesterol, and dietary lipids [111]. Phytic acid can bind with minerals, proteins and amino acids forming phytates and insoluble complexes, thus reducing essential nutrient bioavailability, and digestibility. They can also lower the minerals availability and amylase activity [67]. The elimination or inactivation of these toxic substances can be achieved by various methods: physical (dehulling, heat-cooking, autoclaving, toasting), chemical (ammoniation, the addition of choline, methionine, ferrous sulphate, sodium carbonate), enzymatic and fermentative.
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 4 Experiments
### Experiment Results
#### 4.2.1 Comparison with Recent DETR
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & **Backbone** & **Epoch** & **Params** & **GFLOPs** & \(\mathbf{\Lambda}\)\(\mathbf{P}_{\mathbf{50}}^{\mathrm{val}}\) & \(\mathbf{\Lambda}\)\(\mathbf{P}_{\mathbf{75}}^{\mathrm{val}}\) & \(\mathbf{AP}_{\mathbf{S}}^{\mathrm{val}}\) & \(\mathbf{AP}_{\mathbf{M}}^{\mathrm{val}}\) & \(\mathbf{AP}_{\mathbf{L}}^{\mathrm{val}}\) \\ \hline SMCA-DETR [29] & R101 & 108 & 58 & 218 & 46.3 & 66.6 & 50.2 & 27.2 & 50.5 & 63.2 \\ Deformable-DETR [8] & R50 & 50 & 40 & 173 & 46.2 & 65.2 & 50.0 & 28.8 & 49.2 & 61.7 \\ DAB-Deformable-DETR [9] & R50 & 50 & 48 & 195 & 46.9 & 66.0 & 50.8 & 30.1 & 50.4 & 62.5 \\ Dynamic-DETR [30] & R50 & 50 & - & - & 47.2 & 65.9 & 51.1 & 28.6 & 49.3 & 59.1 \\
**SQ-DETR (Ours)** & R18 & 72 & 20 & 60 & 47.3 & 64.6 & 51.0 & 29.7 & 51.1 & 62.8 \\ DAB-Deformable-DETR++ [9] & R50 & 50 & 47 & 194 & 48.7 & 67.2 & 53.0 & 31.4 & 51.6 & 63.9 \\ DN-Deformable-DETR [10] & R50 & 50 & 48 & 195 & 48.6 & 67.4 & 52.7 & 31.0 & 52.0 & 63.7 \\ Dynamic-DETR [30] & RX101 & 50 & - & - & 49.3 & 68.4 & 53.6 & 30.3 & 51.6 & 62.5 \\ DN-Deformable-DETR++ [10] & R50 & 50 & 47 & 279 & 49.5 & 67.6 & 53.8 & 31.3 & 52.6 & 65.4 \\ DINO-Deformable-DETR [2] & R50 & 36 & 47 & 240 & 50.9 & 69.0 & 55.3 & 34.6 & 54.1 & 64.6 \\
**SQ-DETR (Ours)** & R50 & 62 & 43 & 135 & 52.5 & 71.1 & 56.9 & 34.5 & 57.1 & 69.8 \\
**SQ-DETR (Ours)** & R50 & 72 & 43 & 136 & 52.6 & 71.3 & 56.7 & 34.6 & 57.1 & 70.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._As shown in Table 3, SQ-DETR achieved performance on par with the baseline RT-DETR models across all metrics, while reducing the decoder FLOPs by up to 7.8% (from 12.8G to 11.8G for ResNet-18 and by 6.6% from 21.1G to 19.7G for ResNet-50). Notably, SQ-DETR with ResNet-50 recorded an \(AP_{50}\) of **81.8%**, matching the performance of RT-DETR under the same training configuration, but with improved efficiency. The most significant reduction in computational cost was observed in the ResNet-18 configuration, where SQ-DETR lowered the decoder FLOPs by 7.8% while maintaining identical detection performance. These results confirm that SQ-DETR maintains strong detection performance even in densely crowded and highly occluded environments, while also reducing computational cost. This supports the method's robustness and practicality across diverse real-world scenarios.
## 4 Proposed Energy Neutral Operation
### Frame Structure  
Figure 2 shows the frame structure for the proposed ENO based on SWIPT. The frame is based on time division multiple access with time division duplexing (TDMA/TDD) and scheduling-based resource allocation is used for conflict-free transmission. Firstly, a beacon signal is broadcasted at the beginning of the frame for frame synchronization and to provide the frame configuration and scheduling information to all of the nodes in the cell. In compliance with the harvest-then-transmit protocol [21], the HAP transmits RF energy during the WET slot with length \(T_{e}\). During this WET period, all of the sensor nodes harvest energy to be used in the current frame.  
The remainder of the frame is divided and used for each cluster. The resources allocated to each cluster consist of multiple SWIPT slots for the member nodes in the cluster and one WIT slot for the CH. The total number of SWIPT slots for a certain cluster is the same as the number of member nodes in that cluster, and the length of a SWIPT slot is set to \(T_{s}\). Each SWIPT slot is reserved for each sensor node through a pre-scheduling mechanism so that access collision does not occur [41]. On the other hand,  
Figure 2: Frame structure for the proposed ENO based on SWIPT.  
the last WIT slot is dedicated to the CH only for WIT and has a length of \(T_{d}\). Hence, the total length of the frame depends on the number of clusters and the number of member nodes in each cluster.  
The sensor node transmits the sensing data to its CH in the SWIPT slot allocated to itself. Simultaneously, it transfers the remaining energy (if it exists) to the CH by using SWIPT based on either the PS or TS method. Each sensor node \(j\) uses a portion \(\alpha_{j}\) of power or time for WIT and the remaining portion \((1-\alpha_{j})\) for WET. During these SWIPT slots, the CH sequentially receives the sensing data while receiving additional energy from its member nodes. Then, the CH aggregates all of the sensing information and transmits the aggregated sensing data to the HAP at the last WIT slot. Here, the CH uses not only the energy initially provided by the HAP but also the energy additionally received from its member nodes. It is noted that we do not consider the ambient energy harvesting between any nodes [42] because the amount of energy harvested is negligible due to the long distance between nodes and the sensor nodes may inherently stay in sleep state except for its transmit and receive periods.
# Microwave Pyrolysis of Biomass: The Influence of Surface Area and Structure of a Layer
## 1 Introduction
### Microwave Pyrolysis of Biomass and Waste: A Review of Modern Researches
In comparison, the gas yield during catalytic microwave pyrolysis was about 40-60 wt% [35] without Al\({}_{2}\)O\({}_{3}\) and 70-72 wt% with added Al\({}_{2}\)O\({}_{3}\)[13]. The structure of the biomass and the dimensions of the feed sample are also important in the design and operation of microwave pyrolysis plants. In [47], microwave pyrolysis of pressed blocks of wheat and corn straw was studied (density of 80 kg/m\({}^{3}\), block size of \(1000\times 600\times 600\) mm). The large size of the straw block contributed to the uneven distribution of temperature inside it. [PERSON] et al. [47] found that the content of H\({}_{2}\) reached the highest value of 35 vol%, and the content of synthesis gas (H\({}_{2}\) and CO) exceeded 50 vol%. Research [48] focused on the microwave pyrolysis of oil palm biomass (fibers and shells) using char as a microwave absorber. Shells were used in the form of large particles (without grinding) with sizes ranging from 0.001 to 0.1 \(\upmu\)m, while the fibers were crushed to a finer size (300-600 \(\upmu\)m). For each experiment, the ratio of biomass and absorber was varied (1:0.25, 1:0.5, and 1:1). It was found that the addition of a microwave absorber to the biomass did not only increase the temperature, but also intensified the pyrolysis due to the formation of vapors. The maximum bed temperature at shell/absorber ratios of 1:0.25 and 1:0.5 was about 200 and 237 \({}^{\circ}\)C, respectively. The maximum gas yield was 30 wt% for shell/absorber ratios of 1:0.5 and 1:1. [PERSON] and [PERSON] [48] showed that microwave pyrolysis systems can use coarse particles, which reduces the cost of grinding and removing moisture. [PERSON] and [PERSON] [49] obtained the heating characteristics of a biomass layer (fruit tree branches in the form of a uniform layer and granules) using numerical simulation of the fuel sample heating mode in a multimode microwave system. It has been established that the height of the biomass layer and its shape (layer or granules) affect the temperature profile. This indicates that there is an optimal loading height at which the maximum material temperature can be reached for a particular furnace size. Studies [49; 50] confirm that the distribution of biomass has a direct effect on the yield of gases during microwave pyrolysis.
## 4 Hardware Security
### Physical Unclonable Functions (PUFs)
#### 4.1.4 PUFs as a Root of Trust
The software-based PUF (SW-PUF) combines physical chip variations with delays in software instructions to generate unique IDs within a secure Root of Trust (RoT). This approach supports secure boot and remote attestation, ensuring that only authenticated, tamper-free software is executed [267]. A secured IoT architecture proposed in [268] combines PUF with Trusted Platform Module (TPM), and Tangle Distributed Ledger Technology (DLT acts as a RoT, establishing a unique digital identity for each device. The proposed architecture implements a Security-by-Design  
Figure 19: Layered defense model. (SbD) approach at the hardware level, strengthens attack resistance, and defends device and data integrity. Quantum channels are vulnerable to diverse noise sources, which include environmental interactions and eavesdropping attempts. A key reconciliation protocol is proposed in [269], allowing transmission of a bit stream through insecure and noisy quantum channels. Also, the researchers claim that the proposed protocol can reconcile two PUF responses obtained from the same challenge but at a different time. Also, minor noise levels in the PUF responses are mitigated through the application of a fuzzy extractor, designed to produce stable cryptographic keys from marginally erratic PUF responses [270].
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 5 Conclusions  
With the further deepening of the demand for information and communication technology in the electric power industry, the emerging wireless optical technology will show its natural advantages in the traditional electric power industry in terms of large transmission capacity, strong confidentiality, and free from electromagnetic interference. Most of the existing researches focus on the theoretical level, so it is necessary to accelerate the practice and large-scale application of wireless optical technology in the power industry. Further, future work should focus more on the integration of power lines and indoor wireless optical transmission of high-speed, highly reliable two-way communication. With the key enabling technologies such as multi-input and multi-output, Non-Lambert beam characterization, beam forming, and wireless power transfer technologies, the transmission and coverage performance of power industry optical wireless technologies will be significantly improved and play a significant role in the construction of smart power and power industry.  
Conceptualization, J.D. and W.L.; methodology, J.D. and W.L.; validation, J.D. W.L., and C.-L.L; formal analysis, J.D. and W.L.; investigation, J.D., W.L. C.-L.I., H.Z., and H.M.; writing--original draft preparation, J.D. and W.L.; writing--review and editing, J.D., W.L., and H.M.; supervision, J.D., W.L., C.-L.I., and H.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (Grants No. 62061043), Natural Science Foundation of the Xinjiang Uygur Autonomous Region (Grants No. 2019D01C020), High-level Talents Introduction Project in Xinjiang Uygur Autonomous Region (Grants No. 042419004), Tianchi Doctor Program of the Xinjiang Uygur Autonomous Region (Grants No. 04231200728), Natural Science Foundation of Xinjiang University (Grants No. 62031224624), National Natural Science Foundation of China (Grants No. 61401420), and Postgraduate Research and Innovation Project of Xinjiang Uygur Autonomous Region (Grants No. X)[2020C063).  
The authors declare no conflict of interest.
# Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System
## 3 Proposed Method  
Given an input image, that is in the linear RGB space of a camera after removing black level and saturation level, the proposed method is to estimate a 3D vector as \(\hat{\ell}=[\hat{\ell}_{R},\hat{\ell}_{\zeta},\hat{\ell}_{B}]^{T}\) that represents the scene illuminant color for this image. An overview of the proposed method is illustrated in Figure 1.  
As shown in Figure 1, the training phase contains three major steps: feature extraction, image clustering, and ANFIS modeling. By a two-step clustering strategy, training images are classified into different clusters according to color distribution features (CDFs) and initial illumination features (IIFs). Then multiple ANFIS models are effectively trained to regress the underlying relationship between image features and illuminant. In the testing phase, CDFs and IIFs will be extracted and the corresponding combination weights will be calculated dynamically. The final illuminant estimation is obtained by fuzzy weighting all outputs of ANFIS models.