esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Tbps and Kilometer-Range Transmission of Terahertz Signals
## 4 High-Sensitivity SIMO Space-Ground Communication Architecture
### Communication Models and System Designs for Terahertz Satellite-Ground Links  
For the ultra-long-distance communication scenario between a geostationary orbit satellite (Fengyun-4) and a ground station (Shanghai), we demonstrated 100 Gbps high-speed simulated transmission over 36,000 km in the 300 GHz frequency band with 16 QAM modulation through system-level simulation. System parameters are detailed in Table 1. Based on the Friis equation, the received signal power at the ground station is approximately \(-52.1\) dBm. The receiver employs a 25 dB gain THz LNA followed by a 30 dB gain PA. After down-conversion (with 10 dB insertion loss), the signal passes through an additional 30 dB gain LNA. Accounting for the receiver's 10 dB noise figure, the final signal power before demodulation reaches \(\sim\)13 dBm.  
To enable higher data rates, we implement the MIMO-MRC technique described in Section 3.2, replacing a single 4.5 m large-aperture antenna with four smaller-aperture antennas. With four receiver antennas, MRC combining theoretically quadruples the SNR (6 dB gain). Equivalent aperture calculations show each smaller antenna requires a diameter of 2.06 m. After diversity reception, advanced DSP techniques--including carrier demodulation, matched filtering, carrier recovery, and MRC--are applied. The system's workflow and schematic are illustrated in Figure 15.
## 3 Results: Network Performance
### Delay
\begin{array}{cccc}W_{2}^{ini}&W_{3}^{ini}&\ldots&W_{N}^{ini} \end{array} \right]\). The trivial case is to impose that \(W^{ini}=\left[ \begin{array}{cccc}0&..&0\end{array} \right]\) when the network operation starts. However, testing for different values for every node, it is possible to obtain a lower average delay in the FIFO case, as can be seen in Table 2. In this table, two cases have been shown for the \(W^{ini}\) vector for three network sizes and different packet rates: the cases shown in Table 2 are the best and the worst average delay of all possible cases (permutations). For example, when using an initial vector of \(W^{ini}=\left[ \begin{array}{cccc}2&4&1&0&1\end{array} \right]\) in the case of \(N=6\) and uniform packet rate of \(\lambda_{j}=\ \frac{1}{10}\), the maximum end-to-end delay is reduced to 13 slots, far from the case of 21 slots when using \(W^{ini}=\left[ \begin{array}{cccc}0&1&2&4&1\end{array} \right]\). In this particular case, this is a reduction of 38%, a very important benefit for delay in the continuous operation of the string network proposed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Round-Robin**} & \multicolumn{2}{c}{**FIFO**} \\ \cline{2-5}
**Size (N)** & **Max.** & **Average** & **Max.** & **Average** \\ \hline
4 & 7 & 4 & 7 & 4 \\
5 & 9 & 5.25 & 9 & 5.25 \\
6 & 14 & 7.796 & 14 & 7.7965 \\
7 & 17 & 8.996 & 17 & 8.996 \\
8 & 19 & 11.9869 & 23 & 11.993 \\
9 & 21 & 13.2329 & 25 & 13.2399 \\
10 & 30 & 13.8843 & 28 & 13.8833 \\
20 & 59 & 31.9368 & 63 & 31.9509 \\
50 \({}^{1}\) & 193 & 81.8638 & 161 & 81.8637 \\
100 \({}^{2}\) & 326 & 163.7764 & 327 & 163.7842 \\ \hline \multicolumn{5}{c}{\({}^{1}\) Simulation time: 5000 slots. \({}^{2}\) Simulation time: 10,000 slots.} \\ \end{tabular}
\end{table} 
Table 1: Delay of the optimal scheduling (slots). Simulation time: 2000 time slots. The number of packets delivered to the Gateway from every node is shown in the last column of Table 2. It can be seen that the two above mentioned \(\mathbf{W}^{ini}\) vectors significantly reduce the delay with minimal impact on that number of packets. Similar results for the case of Round-Robin service policy can be observed in Table 3.  
Finally, if the maximum delay is the main design objective, Table 4 shows again a similar comparison between both policies, suggesting that the service policy hardly affects the end-to-end delay. This effect can be easily observed in Figure 11, where it can be seen how the service policy has a minimal effect on the bounds (\(\min\)/\(\max\)) of average end-to-end delay. In order to represent the simulation results for all the possible \(\mathbf{W}^{ini}\) (permutations), an index vector has been used to name every different \(\mathbf{W}^{ini}\) vector. This index is equal to the position of the vector \(\mathbf{W}^{ini}\) in an ascending sorted list of all the permutations, i.e., index=1 for \(\mathbf{W}^{ini}=[0\;0\;0\;0\;0]\), index=2 for \(\mathbf{W}^{ini}=[0\;0\;0\;0\;1]\) and so on, up to index = 3125 (5\({}^{5}\) different vectors) for \(\mathbf{W}^{ini}=[4\;4\;4\;4\;4]\).
# Nanometer-Resolution Imaging of Living Cells Using Soft X-ray Contact Microscopy
## 2 Soft X-ray Contact Microscopy
### Radiation Sources  
Various types of X-ray sources in the \"water window\" were used in SXCM, including, X-ray tubes, synchrotrons, and plasma sources. X-ray tubes and synchrotrons were used in the early phase of SXCM research [27; 29]. The main disadvantage of using these sources in SXCM was the exposure of the sample for a relatively long time, leading to radiation damage and decreased resolution [30]. It was found that imaging hydrated, living biological specimens required exposure with a sufficient X-ray dose in a very short time to avoid blurring due to radiation-induced and natural movement, as well as the recording of an image of the sample before radiation damage occurred. Therefore, laser-produced plasma  
Figure 1: A flowchart of the contact X-ray microscopy imaging procedure: (**a**) exposure with radiation penetrating the biological sample and absorbed in the recording medium—photoresist, (**b**) chemical development of the photoresist converting spatial 2D modulation of the radiation density into the surface morphology, (**c**) digitization—conversion of the surface morphology into the digital image using high-resolution imaging methods, e.g., AFM or SEM.  
sources emitting soft X-ray radiation in the form of nanosecond pulses were proposed [31] and came to dominate the use of SXCM.  
A dense and high-temperature (10\({}^{6}\) K) plasma from the interaction of intense laser pulses with a target material (usually solid) is a bright source of soft X-rays. By choosing suitable target materials and laser parameters, the emitted radiation can be optimized for \"water window\" X-rays [32]. SXCM experiments were performed using various laser systems, including Nd:glass [33], Nd:YAG [31], iodine, excimer [34], and others, producing sub- and nanosecond pulses with energies varying from less than a Joule to several hundred Joules. The latter made it possible to obtain SXCM microscopy images due to a single-pulse exposure, which made it possible to prevent the radiation damage from limiting the resolution. However, for practical reasons, the most suitable compact laser systems, usually Nd:YAG, which are commercially available produce nanosecond laser pulses with energies up to a few Joules with high repetition.  
The use of solid targets is the most popular in SXCM systems. However, they are characterized by the problem of target debris, produced as a result of the laser ablation of the target material, which can cause sample damage when exposed to X-rays. To reduce the debris grazing incidences, X-ray optics in the form of a toroidal mirror was used to focus soft X-rays on a sample [35]. The debris problem was completely solved by applying laser-plasma soft X-ray sources based on a gas puff target in SXCM experiments [36].
## 3 Results
### Mixotrophic Cultivation Using the Synthetic Medium and STB at pH 3  
The biomass production after cultivation at an initial pH of 3 was 1.6-fold higher using the STB than with CM (Figure 3a). _E. gracilis_ cultures entered the exponential growth phase on day 2. The culture in the STB reactor entered the stationary phase on day 3, whereas the culture in the CM reactor entered the phase a day later. Paramylon production was 0.54 \(\pm\) 0.14 g L\({}^{-1}\) using STB on day 6. COD and TN consumptions were higher in STB cultures than in CM cultures regardless of the initial pH condition (Figure 3b,c). Approximately 59% of the nitrogen source was consumed in STB cultures. As shown in Figure S1, the green color in the STB reactor was darker than that in the CM reactor. A higher SGR (more than 2-fold) and CO\({}_{2}\) biofixation rate (more than 1.5-fold) were observed in STB cultures than in CM cultures (Table 3). Further, the CO\({}_{2}\) biofixation rate was more than 1.9-fold higher in the reactors providing CO\({}_{2}\) mixed gas (1.5 g L\({}^{-1}\) d\({}^{-1}\)) than in those without the gas flow (0.81 g L\({}^{-1}\) d\({}^{-1}\)).  
Figure 3: (**a**) Biomass production, (**b**) COD consumption, and (**c**) T-N consumption using STB and CM medium at an initial pH 3. Sample analyses were performed in duplicate, and the data were represented as average \(\pm\) standard error (\(n\) = 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Substrate**} & \multirow{2}{*}{**Initial pH**} & **Specific Growth Rate** & **Doubling Time** & **CO\({}_{2}\)** & **Biofixation Rate** \\  & & **(d\({}^{-1}\))** & **(d)** & **(g L\({}^{-1}\) d\({}^{-1}\))** \\ \hline STB & 3 & 1.51 & 0.46 & 2.85 \\ STB & 8 & 1.12 & 0.62 & 2.14 \\ CM & 3 & 0.79 & 0.88 & 1.50 \\ CM & 8 & 0.71 & 0.98 & 1.37 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specific growth rate, doubling time, and CO\({}_{2}\) biofixation rate using STB and CM using different initial pH conditions.  
Figure 2: Nutrient consumption throughout the cultivation period depending on glucose concentration: (**a**) COD and (**b**) T-N. The amount of consumed nutrients was calculated from the average values regarding the beginning and the end of the cultivation.
## 2 Features of Hydrogen Combustion Processes
### Formation of Pollutant Emissions: NOx  
NOx emissions consist mainly of nitric oxide (NO) and, to a lesser extent, of nitrogen dioxide (NO\({}_{2}\)) and nitrous oxide (N\({}_{2}\)O). Being a precursor to photochemical smog, NOx also contributes to acid rain and causes ozone depletion, which makes it a pollutant regulated by national and international organizations, specifically by ICAO.  
In a laminar flame and at the molecular level in the turbulent flame, the formation of NOx can be explained by four different chemical kinetic processes: thermal NOx, fast (instantaneous) NOx, fuel NOx, and N\({}_{2}\)O intermediate. Thermal nitrogen oxides are formed as a result of oxidation of the nitrogen present in the atmospheric air supplied for combustion; instantaneous nitrogen oxides are formed during high-speed reactions in the flame front, and fuel nitrogen oxides are formed in the process of oxidation of the nitrogen contained in the fuel. Under high pressure and oxygen-rich conditions, NOx can also originate from molecular nitrogen (N\({}_{2}\)) via N\({}_{2}\)O.  
[PERSON] [42] and [PERSON] [43] present the results of the studies into various mixtures of natural gas and hydrogen that show an exponential increase in NO emissions as the percentage of hydrogen increases (Figure 19). [PERSON] and [PERSON] [44,45] also report on the increased NOx emissions when adding hydrogen.  
Figure 19: The relationship between the NO emissions of natural gas and hydrogen and the volume fraction of hydrogen.  
Figure 18: CO and CO\({}_{2}\) emissions for various equivalence factors [41].  
Figure 19 shows that there is a significant increase in the rate of NO formation between 60% and 100% hydrogen, which in this study was most likely due to an increased rate of hydrogen formation by means of the thermal mechanism. However, in the studies by [PERSON] [4] and [PERSON] [46], the data were normalized to the same temperature in the flame front, which proves the fact that at the same combustion temperature, the concentration of nitrogen oxides in the combustion products of methane-hydrogen mixtures is higher than when burning pure methane (Figure 20). This may be due to both a decrease (compactification) of the flame front (which leads to an increased gas residence time in the high-temperature region) and to a change in the chemical mechanism of the nitrogen oxide formation. Thus, the problem of validating the kinetic mechanisms of the nitrogen oxide formation during hydrogen combustion becomes critical, as is described, for example, in the paper by [PERSON] [47].
## 3 Collaboration Performance Evaluation Based on Combination of AHP and EW
### Determining the Comprehensive Weight Based on Combination of AHP and EW
#### 3.2.3 Determining the Comprehensive Weight Based on Minimum Deviation  
After using the above two methods to calculate the subjective weight and the objective weight, respectively, the most common method is to perform linear weighting on them, and the obtained comprehensive index weight \(c_{i}\) is:  
\[c_{i}=\alpha_{i}z_{i}+(1-\alpha_{i})s_{i}\;,(i=1,2,\ldots n) \tag{10}\]  
where \(z_{i}\) represents the subjective weight of the \(i\)-th index obtained by AHP, and \(s_{i}\) represents the objective weight of the \(i\)-th index obtained by EW. \(\alpha_{i}\) is the preference coefficient of the subjective weight of the \(i\)-th index, and \(1-\alpha_{i}\) is the preference coefficient of the objective weight of the \(i\)-th index, but the value of \(\alpha_{i}\) is generally set based on experience, which is highly subjective.  
In order to reflect the subjectivity of decision makers in the decision-making process and the objectivity of data fluctuations, this paper adopts the combined weighting method based on the minimum deviation to determine the weight of comprehensive indicators.  
The reasonable comprehensive index weight is the weight that can minimize the total deviation of the decision result from the result under the subjective and objective weighting method. Therefore, we construct the following single-objective optimization model:  
\[min\sum_{i=1}^{n}\left[\alpha_{i}\frac{(c_{i}-z_{i})^{2}}{2}+(1-\alpha_{i}) \frac{(c_{i}-s_{i})^{2}}{2}\right] \tag{11}\]  
Since AHP is easily affected by subjective factors, the entropy weight method is introduced to determine an objective weight according to the fluctuation of the data, which reduces the influence of subjective factors and improves the dynamicity of the index weight. This provides the basic conditions for the accurate evaluation of enterprises' collaboration performance.
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 4 The Proposed Scheme
### Blockchain in the System  
In the proposed system, the blockchain is utilized for two purposes: to verify the integrity of the EHRs and for access control. In each block of the blockchain in our system, hash values of the records and access authority information are stored, and they are protected by the robust security of the blockchain system.  
Figure 5 shows the interactions between the data server and the blockchain for cases in which a user saves and requests the EHRs, separately. In this figure, we assume that the user is permitted to use the record. In terms of the EHR storage process, as shown in Figure 4(a), the hash value of the EHR is calculated and transmitted to the blockchain with other information, such as the access range, creation date, and saving date. When another user, such as a doctor, patient, or third party, enquires to use the EHR and receives the record as shown in Figure 4(b), the user calculates the hash value of the record and sends it to the blockchain to verify the integrity. Subsequently, the blockchain server verifies whether the hash value of the corresponding data is consistent with the hash value in the blockchain and returns the result to the user.  
In addition to integrity verification, the blockchain provides access control using a smart contract. As shown in Figure 4(a), the information revealing the user that can access the corresponding record is transmitted to the blockchain. As shown in Figure 4(b), when the server receives the data request from a user, it verifies the data accessibility from the  
Figure 4: Encryption and decryption processes for EHR.  
blockchain using the smart contract. Subsequently, when the blockchain returns a possible sign, the server delivers the EHR to the user.
## 2 Tumor-Tracking Oscillator and Sensor  
During laparoscopic surgery, tumors located inside the colon and stomach are difficult to locate in the cavity due to the peritoneum. This brings with it the risk of making incisions in the wrong locations during tumor extraction, which could lead to medical accidents. The X-band tissue search radar is also used to determine the location of the tumor, as shown in Figure 1[18; 19].  
Radars utilize oscillators to generate detection frequencies; such oscillators require high-\(\mathrm{Q_{L}}\) resonators and low phase noise. Existing resonator-based sensors used in tissue detection have \(\mathrm{Q_{L}}\) values of at least 50, as shown in Table 1. However, when these resonators are applied to oscillators, the phase noise of the signal generated during abnormality detection does not easily drop below even \(-100\) dBc/Hz at a 100 kHz offset [20; 21]. Therefore, resonators with higher \(\mathrm{Q_{L}}\) values are needed to further reduce the phase noise of oscillators; this need is amplified when considering the possibility of the generation of severe jitter noises on the radar, which would hinder the detection of the marker.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Resonant Frequency** & \multirow{2}{*}{\(\mathrm{Q_{L}}\)} & \multirow{2}{*}{**Detection Tissue**} \\  & **(GHz)** & & \\ \hline
[4] & 11.2 & 100 & cortisol \\
[5] & 12.3 & 50 & DNA \\
[6] & 7.54 & 130 & glucose \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resonator \(\mathrm{Q_{L}}\) for tissue marker applications.
# Improving Moving Insect Detection with Difference of Features Maps in YOLO Architecture
## 2 Materials and Methods
### Evaluation Metrics  
To evaluate the performance of the models, we use a set of standard object detection metrics: Precision, Recall, F1 Score, and mean Average Precision (mAP). These metrics provide a detailed assessment of the detection capabilities of the models across different  
Figure 8: RGB image of a honeybee at time \(k\) (**left**) and its corresponding motion likelihood map \(L_{k}^{3}\) (**right**). Left image reprinted with permission from Ref. [29]. Copyright ©2018 AU Signal Processing Group.  
Figure 9: Image at time \(k\) after motion enhancement (\(M\)).  
configurations. In addition, we report the number of GFLOPs (Giga Floating Point Operations) to quantify the computational cost associated with each model. FLOPs measure the total number of floating-point operations required to process a single input. One GFLOP corresponds to one billion (\(10^{9}\)) FLOPs. GFLOPs thus offer a convenient estimate of the inference complexity of the model.  
To better understand the evaluation process, we briefly describe the formulation of each metric used:  
* Precision: measures the proportion of correctly predicted positive instances among all predicted positive instances. It is defined by Equation (8). \[\text{Precision}=\frac{TP}{TP+FP}\] (8) where \(TP\) is the number of true positives and \(FP\) is the number of false positives.
* Recall: evaluates the proportion of correctly predicted positive instances among all actual positive instances. Equation (9) defines recall. \[\text{Recall}=\frac{TP}{TP+FN}\] (9) where \(FN\) is the number of false negatives.
* F1 Score: combines both Precision and Recall into a single measure. It is the harmonic mean of Precision and Recall, offering a balance between the two. F1 Score is given by Equation (10). \[\text{F1 Score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}}\] (10)
* Average Precision (\(AP\)): quantifies the area under the precision-recall curve for a single class. It is defined as the integral of the precision with respect to recall (\(p(r)\)), as shown in Equation (11). \[\text{AP}=\int_{0}^{1}p(r)\;dr\] (11) Although AP is not reported directly, it serves as the basis for computing the mAP, which averages the AP values across all classes.
* mean Average Precision at IoU = 0.50 (mAP50): summarizes the precision-recall curve across all object classes, using a fixed Intersection over Union (IoU) threshold of 0.5. A detection is considered correct only if its IoU with the ground truth is at least 0.5. The AP is calculated for each class under this criterion, and the results are the averaged across all classes, as shown in Equation (12). \[\text{mAP50}=\frac{1}{N}\sum_{i=1}^{N}AP_{i}^{IoU=0.5}\] (12)
# Study on the Detection Mechanism of Multi-Class Foreign Fiber under Semi-Supervised Learning
## 1 Introduction
Interestingly, unlabeled data are readily available and cost-effective compared to labeled data. Consequently, researchers are actively exploring ways to enhance recognition accuracy by harnessing unlabeled data, especially when dealing with limited labeled samples. Semi-supervised methods use unlabeled image information and a few labeled images to train models, reducing the performance degradation often seen in traditional unsupervised learning due to insufficient training samples. [PERSON] first introduced the concept of semi-supervised learning [10]. Existing semi-supervised learning algorithms primarily focus on image classification tasks and can be categorized into two main strategies: consistency regularization [11; 12; 13] and pseudo-labeling [14; 15; 16]. The consistency regularization strategy involves applying multiple random disturbances to unlabeled images and minimizing the differences between prediction results, effectively using unlabeled data for learning. The pseudo-labeling strategy, on the other hand, first trains with labeled data and subsequently predicts the unlabeled data to generate pseudo-labels, enabling self-training of the network. Semi-supervised learning combines aspects of both supervised and unsupervised learning. It utilizes a small amount of labeled data alongside unlabeled data for pattern recognition tasks and finds applications across various domains. Currently, semi-supervised methods based on deep learning have made initial progress in agricultural detection research. In this paper, we propose a semi-supervised foreign fiber detection algorithm, Efficient YOLOv5-cotton, based on the successful application of YOLO series algorithms in previous foreign fiber detection and the feasibility of semi-supervised algorithms. This approach is promising in achieving a high classification accuracy while reducing the dependence on labelled samples. It employs CBAM and SPPFCSPC to enhance the multiscale foreign fiber detection capability of the network, while C3 Ghost is used to reduce the model size for industrial applications. This study is based on the detection of cotton foreign fiber images collected under industrial reality, which provides a solid technical foundation for the automatic detection of foreign fiber in industrial production. At the same time, the semi-supervised algorithm proposed in this paper can reduce the time and workload needed among the textile industry practitioners in labelling foreign fiber images, which can be used to assist in the detection of foreign fiber in the textile industry.