esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Security Proof for Twin-Field Quantum Key Distribution (QKD)
## 5 Evaluation
### A Discussion on the Asymmetric Nature of the Protocol  
It is worth taking a closer look as to why \(\Pi\)-Zero and \(\Pi\)-One perform differently from the standard version \(\Pi\)-Total. First, consider Equations (36) and (37). Note that, even under ideal conditions of \(p_{d}=0\), \(f=1\), and \(\alpha=\beta=1/\sqrt{2}\) (which is what would be expected if all devices were perfect and there were simply natural loss \(\eta\)), for any \(\eta>0\), it holds that \(Q_{Z,1}<Q_{Z,0}\). Similarly, \(Q_{X,1}<Q_{X,0}\). The same inequalities hold for imperfect devices (i.e., when \(p_{d}>0\) and \(f<1\)). This can be seen more clearly in Figure 3. Thus, anytime the server sends message \(0\), there is actually a greater chance of error than in the case of message \(1\). Therefore, under most conditions and under this channel scenario, discarding all messages of \(0\) actually improves the performance of the system. The users may decide, _after_ measuring the channel statistics, to determine which mode of operation to perform; thus, the users can always optimize their choice of protocol after the quantum data have been transmitted and can therefore always choose the mode that will return the higher number of key-bits. It would be interesting to analyze these three protocols under other channel scenarios, beyond depolarizing. Note that our security proof can handle _any_ channel scenario; however, we chose only depolarization channels for our evaluations in this section.  
Figure 1: (**left**) Asymptotic key-rate of the TF-QKD protocol version \(\Pi\)-Total. Here, \((x,y,z)\) implies \(p_{d}=10^{x}\), \(\alpha=\sqrt{1/2+y}\) and \(f=z\). We also compared it with the PLOB bound [25]. If \(d\) is the distance to Alice and Bob, we set \(\eta=10^{-(d/2)/10}\). (**right**) Asymptotic key-rate of TF-QKD protocol \(\Pi\)-Total, \(\Pi\)-Zero, and \(\Pi\)-One. Here we set \(p_{d}=10^{-5}\), \(\alpha=\sqrt{3/4}\), and \(f=0.8\). Note that our key-rate agrees asymptotically with that from [19] for \(\Pi\)-Total and so we do not plot a comparison; we are not aware of a key-rate result for \(\Pi\)-Zero or \(\Pi\)-One. We note that \(\Pi\)-One can yield strictly higher key-rates and support longer distances.  
Figure 2: (**left**) Key-rate as a function of the number of signals (note that \(x\)-axis is log-scale). Here, \(p_{d}=10^{-8}\), distance is 25 km; \(\alpha=\sqrt{3/4}\); and \(f=0.8\); (**right**) Finite key-rate as a function of distance; here, we use the same parameters as in the left figure, but with \(N=10^{15}\). Again, we note that \(\Pi\)-One can outperform the other two protocol modes of operation.
## 2 Literature Review
### Technology Acceptance Model (TAM)  
The Technology Acceptance Model (TAM) proposed by [PERSON] [17] is commonly used to analyze the factors that influence behavioral intention when people accept new technologies, which, in turn, provides an analytical framework for predicting the behavior of a target group in terms of using new technologies. [PERSON] [18] used TAM as the foundation theory to investigate college students' use of a learning management system (LMS) by constructing a structural equation model. The results showed that perceived usefulness was the most important predictor of students' use of the LMS. On the other hand, [PERSON] [19] used TAM as a theoretical framework to explore students' perceptions of using LMS in terms of perceived ease of use and usefulness. Two important dimensions of the model are \"perceived usefulness\" and \"perceived ease of use\", which directly influence people's attitudes toward new technologies and indirectly influence their willingness to use them. Based on different research areas, TAM is often supplemented with additional external variables, such as technology anxiety, self-efficacy, expectations, and trust, among others [20; 21; 22]. Although the underlying model of TAM is extremely reliable, researchers still need to consider broader environmental factors such as cultural differences and personality habits [23]. Online education is often considered to be a blend of education and technology, and numerous studies have used TAM as a basis for adding additional external variables to explore students' attitudes toward online learning to further predict students' behavioral intentions [10; 24].
###### Abstract  
This study explores existing research on neural networks, fuzzy logic-based models, and genetic algorithms applied to brain tumor classification. A systematic review of 53 studies was conducted following PRISMA guidelines, covering search strategy, selection criteria, quality assessment, and data extraction. Articles were collected from three scientific databases: Web of Science, Scopus, and IEEE. The review primarily focuses on practical contributions, with most studies emphasizing applications over conceptual insights. Key methods in the field demonstrate significant impact and innovation. Commonly used training and testing mechanisms include dataset splitting, augmentation, and validation techniques, highlighting their widespread adoption for performance evaluation. The analysis of evaluation metrics shows that accuracy and the DICE score are the most frequently used, alongside sensitivity, specificity, recall, and other domain-specific measures. The variety of metrics underscores the need for tailored approaches based on dataset characteristics and research objectives. By highlighting trends, challenges, and research gaps, this review provides actionable insights for advancing BTC research. It offers a comprehensive overview of techniques and evaluation methods to guide future developments in this critical domain.  
Keywords:brain tumor classification; magnetic resonance imaging (MRI); deep learning and machine learning models; segmentation and evaluation metrics +
Footnote †: journal: Applied sciences
# Distributed Inference Models and Algorithms for Heterogeneous Edge Systems Using Deep Learning
## 3 System Model and Problem Description
### Workload-Partitioning Method Based on Linear Programming Relaxation  
To solve the optimization problem described in Equation (18), the problem expressed in Equation (13) first undergoes relaxation to enable efficient computation. First, the computation latency is estimated for each individual DNN layer. Subsequently, the computation latency across multiple layers, including the fused block computation latency, is calculated. Next, the transmission latency is estimated based on the size of the fused blocks, and the energy consumption is computed accordingly. Finally, the total latency and energy consumption are used to determine the optimal workload-partitioning strategy (\(\pi\)). Based on this approach, a workload-partitioning algorithm using the linear programming relaxation is formulated as described in Algorithm 1.  
```
0: Edge Device: \(\mathcal{N}=\{U_{1},U_{2},...,U_{i},...,U_{N}\}\)
0: DNN Model: \(\mathbb{G}=\{\mathcal{L}_{1},\mathcal{L}_{2},...,\mathcal{L}_{j},..., \mathcal{L}_{L}\}\)
0: Configuration tuples: \(\mathbb{C}=(K,\mathcal{C}_{in},\mathcal{C}_{out},S,P)_{j},\forall j\in L\)
0: Resources tuples: \((P_{comp},P_{comm})_{i},\forall i\in N\)
0: Bandwidths:\(B\)
0: Dividing point:\(p,p\in L\)
0: Partitioning decision:\(\pi=\{\mu_{1},\mu_{2},...,\mu_{i},...,\mu_{N}\}\)
1: Procedure PARTITION(\(\mathcal{N}\))
2: Initializing the problem variables
3: Get the predict delay \(T^{pre}\) from Equation (10) //* Estimate the total inference latency
4: Get the predict energy consumption \(E^{pre}\) from Equation (11) //* Estimate the total inference energy consumption
5: Solve LP Problem Equation (18) to obtain \(\pi\)
6:if\(\pi\) satisfy Equation (19) then
7: return\(\pi\)
8:else
9: Find the minimum element \(\mu_{m}\) in \(\pi\)
10: \(\mathcal{N}\leftarrow\mathcal{N}-\{m\}\) //* Delete the edge device\(U_{m}\)
11: return PARTITION(\(\mathcal{N}\))
12:endif
```  
**Algorithm 1** Workload Partition Algorithm (WPA)  
The primary computational cost of Algorithm 1 arises from the recursive operation in line 11. However, the total number of recursive calls does not exceed N (i.e., the total number of edge devices). Therefore, the time complexity of Algorithm 1 is O(N).
## 3 Results and Discussion
### Statistical Modeling
#### 3.3.2 Performance Measures
Although the maximum strength is achieved when higher values of the input variables are used, Figure 17 highlights the fact that the fly ash content has a much higher influence on the obtained strength. As mentioned before, the behavior of the \(t_{L}\) and \(c_{SH}\) input variables is linear in \(\log q_{u}\), being almost flat.
## 3 System Model and Problem Formulation
### Physical Network  
In Figure 1, \(G=(\mathcal{V},\mathcal{L})\), an undirected graph is utilized to represent IoT-MEC networks, where \(\mathcal{V}\) represents the set of network nodes and comprises cloudlets (\(\mathcal{C}\)) and access point (AP) nodes, while \(\mathcal{L}\) denotes the set of edges. Two nodes, denoted as \(u\) and \(v\) (\(u,v\in\mathcal{V}\)), are connected by link \(uv\) (\(uv\in\mathcal{L}\)). Due to constrained computational resources in cloudlets, a predetermined quantity of VNFs may be instantiated exclusively within the VMs operating within the cloudlets. The set of all VNF instances is denoted by \(M\) in our study. Additionally, \(\mathcal{P}\) is utilized to indicate the set type of VNFs. Each VNF type is indicated by the variable \(p\) (\(p\in\mathcal{P}\)), including FW, DPI, load balance, HTTP, etc. The blue line is an example of SFC; when the IoT terminal makes a request, it requires traverse an ordered chain, i.e., SFC, which include a source node; VNFs; and destination node. In the network, APs are in charge of transforming the IoT-SRs to cloudlets, and an SDN controller is used to receive IoT-SRs from APs, i.e., the request from IoT terminals, and placing the SFC. Figure 1 is an example of the network.  
We use \(C_{c}^{cpu}\) and \(d_{c}\) to denote the CPU capability and process delay of cloudlet \(c\) (\(c\in\mathcal{C}\)). The bandwidth capacity, packet loss rate, and delay of link \(uv\) (\(uv\in\mathcal{L}\)) are represented by \(C_{uv}^{bw}\), \(C_{i}^{loss}\), \(C_{i}^{loss}\), and \(d_{uv}\), respectively. For each cloudlet (\(c\in\mathcal{C}\)), \(n_{c}^{p}\) is used to indicate the count of VNF type \(p\) instantiated in \(c\). Furthermore, the availability ratio of CPU for \(c\) and the availability ratio of bandwidth for \(uv\) are represented as \(\omega_{c}^{cpu}\) and \(\omega_{uv}^{bw}\), respectively.
## 4 Discussion
### Remote Sensing Carbon Flux  
Through a comparison and analysis of field measurement data from the Ulastai Station in the Middle Tien Shan grassland ecosystem, it was found that the GCFD data are applicable to the Ulastai region. The correlation coefficients between GCFD data and the measured values of NEE, GPP, and Reco are 0.59, 0.81, and 0.73, respectively. The accuracy of GPP and Reco in the GCFD dataset was higher than NEE, which is consistent with the conclusions reached by [PERSON] et al. [33]. The _RMSE_ values for the GCFD data compared to the measured values of NEE, GPP, and Reco are 5.21, 4.99, and 2.20, respectively, the _Bias_ values are 0.41, 0.19, and 0.94. The reason for this error may be due to the fact that the training samples were selected from 280 global sites, mainly distributed in Europe and North America. Additionally, there may be regions with a lack of observational data, resulting in inaccurate simulated accuracy and errors between the site measurements and the GCFD dataset. Furthermore, there is an imbalance in the temporal and spatial resolutions of the remote sensing data, meteorological data, and carbon flux data used in the GCFD dataset. To unify the spatiotemporal resolution, the time resolution for these three types of data is set to 10 days per step, and the spatial resolution is set to 1 km. This process may introduce deviations in the dataset results [63; 64].  
Figure 12 presents the remote sensing spatial distribution of carbon flux in the Tien Shan region of Xinjiang. The Ulastai area shows a significant carbon sink from April to September 2018, with the highest carbon sink value occurring in the IIi River Valley, which is consistent with previous studies [65]. Therefore, through the verification analysis of field measurement data from the eddy correlation system and GCFD data, it was shown that the errors in the GCFD dataset resulting from the uneven distribution of training sample sites and unified spatiotemporal resolution are not significant for evaluating global carbon cycling. The example of Ulastai Station provides a scientific basis for the application of GCFD data in other regions. It also verifies the feasibility of using a machine learning fusion algorithm to construct a carbon flux dataset and provide data support for areas with sparse measurements.
# Microalgal Proteins and Bioactives for Food, Feed, and Other Applications
## 1 Introduction
### Microalgae for Food and Functional Food Applications
#### 1.1.1 Proteins and Peptides
In terms of health, these proteins are also a source of bioactive peptides with a wide range of different health effects when consumed [42]. Many microalgae species also produce commercially attractive enzymes with a wide range of potential uses, for example, enzymes with antioxidant activity including superoxide dismutase, catalase and peroxidase activities [43]. Peptides are short sequences of amino acids between two and thirty in length, with mass values less than 10-kDa [44]. They provide a health benefit to the consumer that goes above and beyond basic, human nutrition. Microalgae peptides can be generated using enzymes or are native and encoded from the algae genome. Both peptide types are associated with a wide range of hormone-like, biological activities [45]. The use of bioactive peptides in pharmacology was first described in 1950, when peptides of dairy origin were shown to enhance bone calcification in racchit infants [46]. Table 3 lists the species of microalgae from which peptides with significant antimicrobial, antioxidant, anti-inflammatory, anti-hypertensive, and anti-atherosclerotic properties have been derived to date using enzymatic hydrolysis methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species** & **Protein** & **PDCAAS** & **Cell Wall Composition** \\ \hline _Tetraselmis succia_ & 31 [34] & n/d & Polysaccharides (high content of 3-deoxy-d-manno-oct-2-ulosonic acid, galacturonic acid, galactose) [41] \\ \hline n/d = not determined. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & \begin{tabular}{c} **Enzyme Used** \\ **for Hydrolysis** \\ \end{tabular} & **Peptide** & **Effect** & **References** \\ \hline _Arthospira_ & \begin{tabular}{c} Trypsin, \\ chymotrypsin, \\ and pepsin \\ \end{tabular} & \begin{tabular}{c} LDAVNR \\ MMLDF \\ \end{tabular} & \begin{tabular}{c} Anti- \\ inflammatory \\ \end{tabular} & [47] \\ \hline _Arthospira_ & \begin{tabular}{c} Thermolysin \\ _platensis_ \\ \end{tabular} & \begin{tabular}{c} FSESSAPEQHY \\ \end{tabular} & Antioxidant & [48] \\ \hline _Arthospira_ & \begin{tabular}{c} Trypsin \\ _platensis_ \\ \end{tabular} & \begin{tabular}{c} n/d \\ \end{tabular} & Antiumor & [49] \\ \hline _Arthospira_ & \begin{tabular}{c} Pepsin \\ \end{tabular} & \begin{tabular}{c} IAE \\ FAL \\ AEL \\ IAPG \\ VAF \\ \end{tabular} & ACE-1 inhibitory & [50] \\ \hline _Chlorella_ & \begin{tabular}{c} Pepsin \\ _ellipsoidea_ \\ \end{tabular} & \
\begin{tabular}{c} LNGDWW \\ \end{tabular} & Antioxidant & [51] \\ \hline _Chlorella_ &
\begin{tabular}{c} Papan \\ \end{tabular} & n/d & Antiumor & [52] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reported microalgae-derived peptides generated using enzymes found to have biological activity.
## 4 Conclusions  
CFD modeling was performed for the numerical simulation of the two-dimensional radial-flow PSA oxygen generation process. The gas distributions and pressure differences were investigated at different flow types, and the results indicated that the CP-\(\pi\)-typed adsorber renders better flow characteristics for PSA oxygen generation. A comparative study between centripetal and centrifugal \(\pi\)-flow types was carried out based on the first PSA cycle. The centripetal flow exhibited a much better performance on oxygen enrichment and adsorbent desorption. The steady state was reached after 8 cycles, and the product oxygen purity and recovery were 98.6% and 38.9%, respectively, at a product flow rate of 0.16 m\({}^{3}\) h\({}^{-1}\). By increasing the product flow rate from 0.16 m\({}^{3}\) h\({}^{-1}\) to 0.35 m\({}^{3}\) h\({}^{-1}\), the amount of generated oxygen increased by 2.19 times while the oxygen purity decreased from 98.6% to 80%. Lowering the oxygen purity can significantly save energy consumption if oxygen with a medium purity can meet practical requirements.  
Conceptualization, X.Y. and H.W.; methodology, H.W., J.C., C.Z. and Z.L.; writing, X.Y., and Z.L.; project administration, Y.L. and Y.X. This research was funded by the National Natural Science Foundation of China (51604016, 21808012, 21676025), National Key R&D Program of China (No. 2017 YFC0806304), Beijing Natural Science Foundation (No. 8182019), the National Postdoctoral Program for Innovative Talents (No. BX201700029), the China Postdoctoral Science Foundation (No. 2018M630075), the Fundamental Research Funds for the Central Universities (No. FRF-TP-17-063A1, FRF-BD-18-015A). The authors declare no conflict of interest.  
Figure 12: Effect of product flow rate on separation effect; (**a**) effect of product flow rate on oxygen product purity and recovery, (**b**) effect of product flow rate on non-recovered oxygen contributions on Cb and Pu step.
## 3 Materials and Methods
### Data Processing  
The magnetometry data were processed using OASIS Montaj 9.8 software, using conventional methods to calculate anomalies and produce residual anomaly maps through a variety of filters. In an initial phase, inaccurate values resulting from drone position, take-off and landing manoeuvres, orientation, excessive roll and temporal discrepancies between the sensor and the drone were filtered out. Points located at the most extreme extensions of the survey lines and other outliers were excluded using a one-dimensional median filter. Finally, a correction using the diurnal variation of the Earth's magnetic field values, caused by solar activity, was applied to adjust the data collected during the study period [24].  
Regarding the TDEM data, they were acquired using a \"coincident loop\" configuration and square loops (both emitter and receiver) of 75 m length per side. A minimum of 1000 measurement repetitions per channel were performed, and tests were carried out at different gain levels in order to minimise any influence of electromagnetic noise present in the environment. The results of the various decay curves obtained in the field were presented using the Monex GeoScope \"TEM Plotting Program v 2.0.0\". In addition, the same program was used to generate the corresponding geoelectric profile [25].
# Energy-Efficient Resource and Power Allocation for Underlay Multicast Device-to-Device Transmission
## 4 Proposed Heuristic Energy-Efficient Resource and Power Allocation Algorithm
This is because in the worst case, the maximization problem will be solved in \(M\) times. Here, \(f|\mathcal{K}_{m}|\) represents the size of each D2D multicast cluster. It can be seen that, compared with exhaustive searching based methods, (e.g., such as [16], where the complexity is \(\left(M^{3}\right)+(M\times N\times f|\mathcal{K}_{m}|)\)), the proposed heuristic solution can considerably reduce the computational complexity.
## 2 Materials and Methods
### Preliminary Concepts and Simulation Configuration  
One of the design guidelines of the mission has been the correct estimation of the satellite's lifespan and orbit lifetime. There are several internal and external factors that reduce the lifespan of the satellite (charged particles, solar radiation, extreme temperature variations, batteries' depth of discharge and number of charge/discharge cycles, aging of the electronic devices, etc.). On the other hand, there is the orbital decay, which is mostly due to atmospheric drag and is especially significant for low earth orbit (LEO) orbits. The reentry of the satellite is both inevitable and desirable, but only when the mission is finished. As with most CubeSats, the 3 Cat-3/MOTS will not have orbital maneuver capabilities; therefore, the satellite's lifespan and its orbit lifetime should be similar.
## 10 Results and Discussion
### Average Residual Energy  
Average residual energy is the average amount of energy remaining in a group of wireless nodes or devices after a certain time or a series of operations. The average residual energy with respect to time for the GBG-RPL and BCPS-RPL mechanisms is shown in Figure 9.  
For both the GBG-RPL and BCPS-RPL processes, Figure 9 shows the average residual energy's declining trend over time. It illustrates that as time goes on, both mechanisms'  
Figure 8: Energy consumption.  
Figure 9: Average residual energy.  
energy levels drop, showing that the CPS is using up its energy reserves. The GBG-RPL displays a high average residual energy throughout the simulation time, resulting in an enhanced network life. This means more availability of the CPS network. In both approaches, the nodes initially consume more energy during system initialization as trust in the system is established. However, in GBG-RPL, nodes solely observe and forward trust parameters to the root. In contrast, BCPS-RPL nodes not only observe neighbor behavior but also calculate trust scores, leading to increased energy consumption. This pattern of low average residual energy in BCPS-RPL and high average residual energy in GBG-RPL persists throughout the simulation. Additionally, when attacks are introduced into the CPS network, GBG-RPL detects the attacker faster than BCPS-RPL, contributing to energy savings for sensor nodes and improving network longevity.