esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Authentication Evolution: Novel Approaches for Securing Quantum Key Distribution
###### Abstract  
This study introduces a novel approach to bolstering quantum key distribution (QKD) security by implementing swift classical channel authentication within the SARG04 and BB84 protocols. We propose mono-authentication, a pioneering paradigm employing quantum-resistant signature algorithms--specifically, CRYSTALS-DILITHIUM and RAINBOW--to authenticate solely at the conclusion of communication. Our numerical analysis comprehensively examines the performance of these algorithms across various block sizes (128, 192, and 256 bits) in both block-based and continuous photon transmission scenarios. Through 100 iterations of simulations, we meticulously assess the impact of noise levels on authentication efficacy. Our results notably highlight CRYSTALS-DILITHIUM's consistent outperformance of RAINBOW, with signature overheads of approximately 0.5% for the QKD-BB84 protocol and 0.4% for the QKD-SARG04 one, when the quantum bit error rate (QBER) is augmented up to 8%. Moreover, our study unveils a correlation between higher security levels and increased authentication times, with CRYSTALS-DILITHIUM maintaining superior efficiency across all key rates up to 10,000 kb/s. These findings underscore the substantial cost and complexity reduction achieved by mono-authentication, particularly in noisy environments, paving the way for more resilient and efficient quantum communication systems.  
quantum key distribution; BB84; SARG04; quantum bit error rate; mono-authentication 2024 26 447 10.390/c26060447
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.1 The Results of the Five-Fold Crossover Experiments
The main idea of the integrity algorithm is to use the rule recognition results to correct the recognition boundary of the LSTM-CRF model and further improve the performance, which can be clearly observed in Figures 5 and 6. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Elements**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Accurate Evaluation**} & \multicolumn{3}{c}{**Coverage Evaluation**} \\ \cline{3-8}  & & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline \multirow{4}{*}{En} & LSTM-CRF & 56.38 & 38.53 & 45.75 & 70.28 & 60.43 & 64.96 \\  & Rules & - & - & - & - & - & - \\  & Our method & 64.39 & 45.08 & 52.98 & 77.33 & 65.82 & 71.09 \\ \hline \multirow{4}{*}{Attr} & LSTM-CRF & 71.35 & 58.67 & 64.19 & 81.79 & 70.57 & 75.69 \\  & Rules & 79.72 & 51.57 & 62.47 & 83.26 & 61.88 & 70.65 \\  & Our method & 81.86 & 63.59 & 71.47 & 86.46 & 74.17 & 79.74 \\ \hline \multirow{4}{*}{Val} & LSTM-CRF & 76.81 & 72.61 & 74.62 & 85.99 & 77.31 & 81.38 \\  & Rules & 81.55 & 57.01 & 66.86 & 82.83 & 67.87 & 74.52 \\ \cline{1-1}  & Our method & 86.09 & 73.62 & 79.34 & 90.17 & 81.84 & 85.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average the results of five-fold crossover experiments (%). (F = 78.77) was slightly higher than test set 1 (F = 77.67). Entities and attributes were domain-related. An entity is a unique name in a domain, and an attribute is the characteristics of the entity; therefore, the test set 1 entity and attribute recognition performances were slightly better. The attribute value was not relevant to the domain and represents the scope of an attribute, and its constituent elements often have similarities. Therefore, the recognition of results for the attribute values of test set 1 and test set 2 were close to each other, and test set 2 may even be slightly higher than test set 1. A comparison of the Avg (average value) of Table 8 with the Avg of each element in our method in Table 5 shows that the recognition results of our method were indeed higher than that shown in Table 5 because the LSTM-CRF model had field adaptability. Although the two sets of experiments prove that the cross-domain recognition performance was not as good as the training performance in the same field, the overall result shows that the recognition effect was not much different, which indicates that our method has a certain degree of versatility. Finally, a comparison of the results in Tables 6-8 shows that the F-scores of the entities, attributes, and attribute values in our model are much higher than that of the LSTM-CRF model and POS syntactic rules. This finding again shows that our method recognizes phrase elements and clause-level elements and thus can effectively improve the integrity of the elements. In addition, the experiment proves that our method also has advantages in other fields, which fully demonstrates that the model is domain independent.
# Effect of the Metro Train on the Smoke Back-Layering Length under Different Tunnel Cross-Sections
## 4 Numerical Simulation
### Validation  
In the case of a tunnel fire problem, validation of FDS capabilities should be carried out to evaluate the smoke back-layering length with a metro train blockage. In [PERSON]'s [25] study, the length of the smoke back-layering using the metro train blockage effect was verified. [PERSON] conducted a series of 1:4 small-scale experiments in a model tunnel with a dimension of 72 m \(\times\) 1.5 m \(\times\) 1.3 m. The tunnel ceiling and floor are made of fireproof boards, while the sidewalls are made of reinforced glass. Three different lengths (6 m, 12 m, and 18 m) of the metro train model are made of steel, with a cross-sectional geometry of 1 m \(\times\) 1 m. The square gas burner, which uses propane gas as fuel, with dimensions of 0.30 m \(\times\) 0.30 m \(\times\) 0.35 m (length \(\times\) width \(\times\) height), is located in the middle of the model tunnel, and the distance from the left end is 50 m. The gas temperature below the tunnel ceiling was measured by K-type thermocouples with a diameter of 0.5 mm, which are set at a distance of 3 cm beneath the ceiling at intervals of 0.5 m. Experiments with an HRR of 120 kW were selected for validation. We simulated the same experiments in FDS. Figure 7 shows the comparison of obtained smoke back-layering lengths from the FDS simulations with [PERSON]'s [25] experimental results. It can be seen that the FDS simulation results are in good harmony with the experimental results. Therefore, FDS is reliable for the prediction of smoke back-layering length with metro train blockage in tunnel fire simulations.
## 3 Test Results and Analysis
### Analysis of Influencing Factors and Failure Characteristics of Shear Strength
#### 3.2.4 Analysis of Shear Failure Characteristics  
Figure 9 shows the failure diagram of a shear test at 60\({}^{\circ}\) undulating angle and 8 mm undulating height of 3 MPa normal stress. As can be seen from Figure 9, when the saw-tooth of rock discontinuity is damaged, the through damage occurs from the root of the sawtooth. Among them, the rock discontinuity sawteeth at 60\({}^{\circ}\) undulation angle were all nibbled from the root of the footer sawteeth, and the fracture direction was almost parallel to the shear direction. This is because under the normal stress of 3 MPa, the rock discontinuities of the upper and lower plates mesh with each other, and the rock discontinuities of the lower plates move to the left under the action of the shear force. However, under the relatively high undulating angle of 60\({}^{\circ}\), due to the fixed height of the teeth, the larger the angle, the narrower the teeth, and the ability of the teeth to resist the shear action is weak, so the teeth are nipped off before climbing occurs. Except for the large sawtooth on the right side of the footer, all the other sawteeth were damaged at the height of 8 mm fluctuation. This is because when the angle of relief is fixed, the width of sawtooth decreases with the increase of the height of relief. Under the action of shear force, after the sawtooth with the smallest width on the left side was first nibbled, the shear force gradually transferred to the adjacent sawtooth, and with the increase of shear displacement, the small sawtooth that was damaged first turned over. Finally, except for the sawtooth with the largest width on the right side, all the other sawteeth were nibbled.
# The ConTAD Project: Detection of Multi-Container Loss for Safety of Navigation +
## 4 Closing the Technological Gap: The ConTAD Approach
### Ensuring Communication with a Floating Device  
When a container is lost at sea, there are several possible outcomes. It may end up floating on the surface and be destroyed on impact or sink to the sea floor. The container might float upside down or be partially submerged. This needs to be considered in the development process of a tracking unit that works on lost containers. Since radio communications will not work underwater, it is important to ensure that the antenna can float on the surface. Therefore, the tracking unit, or at least a communication part, needs to be able to disconnect from the container. The disadvantage is that the tracking unit's position is no longer the position of the container. Hence, a type of line connection or an underwater acoustic connection between the container and the tracking unit needs to be considered.
# Fault Diagnosis of HV Cable Metal Sheath Grounding System Based on LSTM
## 3 Analytical Calculations of Sheath Currents under Fault Conditions
### Cable Joint Breakdown Fault
\begin{array}{l}I_{\text{sa1}}=I_{\text{CL1}}-I_{\text{XL1}}-I_{\text {CR1}}\\ I_{\text{sa2}}=-I_{\text{i2}}-(I_{\text{CRB}_{1}}+I_{\text{CRC}_{2}}+I_{\text{ CRA}_{3}})+(I_{\text{CLB}_{1}}+I_{\text{CLC}_{2}}+I_{\text{CLA}_{3}})\\ I_{\text{sa3}}=I_{\text{CL3}}-I_{\text{XL3}}-I_{\text{CR3}}\\ I_{\text{sa4}}=I_{\text{i2}}+(I_{\text{CRB}_{1}}+I_{\text{CRC}_{2}}+I_{\text{ CRA}_{3}})-(I_{\text{CLB}_{1}}+I_{\text{CLC}_{2}}+I_{\text{CLA}_{3}})\\ I_{\text{sa5}}=I_{\text{XR3}}+I_{\text{CR3}}-I_{\text{CL3}}\\ I_{\text{sa6}}=I_{\text{XR1}}+I_{\text{CR1}}-I_{\text{CL1}}\end{array} \right. \tag{17}\]  
where \(I_{\text{CL},n}\) and \(I_{\text{CR},n}\) (\(n=1\), \(3\)) represent the leakage current components flowing into the left and right ends of the \(n\)th sheath loop.
## 2 Materials and Methods
### Individual Compounds (HPLC Analysis)  
The content of arbutin, corilagin, hydroquinone, hyperoside, methylarbutin, penta-O-galloyl-\(\beta\)-D-glucose, and picein was determined based on the previously described procedure [24] using a Varian ProStar HPLC separation system with UV detection (Varian Inc., Walnut Creek, CA, USA). In brief, the extracts were separated using a gradient mode, where eluent A was 0.1% HCOOH (formic acid) in H\({}_{2}\)O (\(\sigma/v\)) and eluent B was 0.1% HCOOH (formic acid) in ACN (acetonitrile) in the following mode: 0 min-4%B, 25 min-22%B, 40 min-25%B, 50 min-100%B, 55 min-100%B, 60 min-4%B, and 65 min-4%B. The temperature of separation was 25 \({}^{\circ}\)C, and the flow rate was set at 1 mL/min. The chromatograms for arbutin, corilagin, hydroquinone, methylarbutin, penta-O-galloyl-\(\beta\)-D-glucose, and picein were collected at 280 nm and at 350 nm for hyperoside.  
Oleanolic acid and unsolic acid contents were determined using the same equipment, temperature of separation, flow rate, andluents, but the following gradient mode was employed: 0 min-5%B, 20 min-80%B, 35 min-85%B, 40 min-100%B, 45 min-100%B, 50 min-5%B, and 65 min-5%B. The chromatograms for oleanolic acid and unsolic acid were collected at 210 nm.  
External calibration curves were applied for the quantification of the separated compounds, and the results were expressed as mg of individual compounds per g of plant material (dry weight).
# On a Framework for Federated Cluster Analysis
## 2 Materials and Methods
### Background: Related Work  
#### 2.1.1 Nonfederated \(k\)-Means Clustering  
Let \(X\) be a given dataset. The objective of the \(k\)-means clustering is to find cluster centers \(c=(c_{1},c_{2},\ldots,c_{K})\) (and corresponding assignments) such that the following expression is minimized (see, e.g., [23]):  
\[\hat{J}(c) =\sum_{k=1}^{K}\sum_{x\in X}I(x,k)||x-c_{k}||^{2} \tag{1}\] \[I(x,k) =\begin{cases}1,x\text{ is assigned to cluster }k,\\ 0,\text{ otherwise}.\end{cases}\]  
Each data point \(x\) is assigned to its closest cluster center, where closeness is defined by Euclidean distance. The assignment is given by function \(a:X\times\{c_{1},\ldots,c_{K}\}\to\{1,\ldots,K\}\):  
\[a(x,\{c_{k}\}_{k=1}^{K}):=\operatorname*{arg\,min}_{1\leq k\leq K}||c_{k}-x|| ^{2} \tag{2}\]  
A widely used iterative algorithm for finding such a clustering works is as follows [23]:  
1. Initialize cluster centers \(c_{1},\ldots,c_{K}\).
2. Assign clusters for all \(x\in X\) according to \(a(x,\{c_{k}\}_{k=1}^{K})\) (Equation (2)).
3. Recalculate cluster centers \(c_{k}\) by solving the following problem, i.e., calculating the mean of points assigned to the same cluster: \[c_{k}=\min_{m\in\mathbb{R}^{d}}\sum_{x\in X:a(x)=k}||x-m||^{2},k=1,\ldots,K.\]
4. Check if the convergence criterion is met, i.e., whether the assignment did not change (much) compared to the previous iteration. Let \(a_{i}^{(t-1)}\) be the assignment vector of the previous iteration for data point \(x_{i}\in X\), i.e., the \(k\)-th entry is \(1\) if \(a(x_{i},\{c_{k}\})=k\), and zero otherwise. Let \(a_{i}^{(t)}\) be the assignment of the current iteration. Further, let \(A^{(t-1)}\) and \(A^{(t)}\) be the assignment matrices, where the \(i\)-th row equals \(a_{i}^{(t-1)}\) and \(a_{i}^{(t)}\), respectively. Then, the algorithm converges if the difference between the two assignment matrices is smaller than some predefined \(\epsilon\): \[||A^{(t-1)}-A^{(t)}||^{2}<\epsilon.\] (3)  
If the algorithm did not converge yet, move back to step 2. If it did converge, terminate. \(\hat{J}(c)\) is monotonously decreasing with each iteration, but it is known that the algorithm might become stuck in a local minimum. In fact, it does not offer any performance guarantee, and [24] argues that it often fails due to its sensitivity to the initialization method. In [24], the still-popular initialization method \(k\)-means++ is introduced. It subsequently chooses random cluster centers that are likely to be far from already chosen centers. In our experiments, we use the scikit-learn implementation that applies the \(k\)-means++ initialization method, too [25].
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 3 Results and Discussion
### Ablation Resistance of Graphene-Enhanced CuW Electrical Contacts
Although the superiority of graphene addition has been demonstrated from the perspective of material properties and ablation resistance, successful preparation of graphene-CuW electrical contact products has not been achieved. Furthermore, no studies have been conducted on the performance of graphene-enhanced CuW electrical contacts under actual interruption conditions in circuit breakers, lacking the guiding significance in actual use [18; 28; 29; 30]. In this study, graphene-enhanced CuW80 composites and electrical contacts have been successfully prepared for the first time. Through circuit breaker type testing, the graphene-enhanced CuW80 electrical contacts were breaking 20 times under extreme short-circuit current conditions (peak voltage of 392 kV, current of 65 kA, and cumulative arc duration of 292.5 ms), demonstrating an improvement in their ablation resistance. This achievement holds significant importance in practical applications.
# A New, Fast Pseudo-Random Pattern Generator for Advanced Logic Built-In Self-Test Structures
## 7 Discussion and Conclusions
It is worth mentioning that the very short time necessary to find the separation coefficient between output channels of the DT-LFSR-TPG structure is also a distinctive property of that solution that overcomes the PRPGs designed on grounds of linear registers of the LHCA 90/150 type, two-dimensional and hierarchical CA, or GLFSR ones. Further investigations on pseudo-random pattern generators of the DT-LFSR-TPG type shall be dedicated to the reduction of hardware overhead introduced by these modules. The figures provided in Table 8 indicate that such an overhead is caused, for instance, by a high number of redundant D and T flip-flops that are not connected to any scan path. Therefore, the idea of reducing the overhead, in particular for TPGs with a large number of output channels, may consist in seeking DT-LFSR registers designed according to primitive polynomials but with the length of \(n\) much higher than the ones investigated in this paper. Hopefully, the suitable DT-LFSRs shall be found to enable the construction of DT-LFSR-TPG modules with only a slight number of redundant D and T flip-flops. Another opportunity to significantly reduce the hardware overhead introduced by a DT-LFSR Test Pattern Generator lies in the application of purposefully designed T-type flip-flops with low implementation cost (i.e., with a little area occupied on a silicon substrate), similarly to the solution disclosed in [65]. Then, the original seeding method can be applied to T-type flip-flops, as shown in [65; 66]. Another opportunity to seek for PRPGs offering some compromise between the maximum operating frequency and the implementation cost lies in further improvements of LFSR + PS and CA + PS test pattern generators. One has to be aware that the key factor that mitigates the maximum operating frequency is the time of signal propagation through a phase shifter wired to outputs of the LFSR or CA registers. Logic structures of that type can be described in a very instinctive manner by means of the Reed-Muller (RM) algebra [67] (pp. 44-46, 289-310), [68; 69; 70; 71], [67; 72]. The logic circuits based on the RM logic are pretty well investigated and numerous methods for optimization of propagation time have already been developed for them, where one of such methods is disclosed in [73]. The author expects that some of these methods can be used to draw up a new method for the engineering of phase shifters with propagation times much shorter than the ones reported in [34; 39]. This research was supported by the Polish Ministry of Science and Higher Education funding for statutory activities (BK-226/RAu12/2021). Not applicable. The author declares no conflict of interest.
# A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training
## 2 Sport Training  
Sports training is a continuous process between an athlete and their trainer. It is a pedagogically organized process where the role of the trainer is one of teacher and organizer, with respect to guiding the athlete's activities, and organizing their training sessions [8]. Training exercises are precisely defined tasks that demand physical effort, and should in some way improve the sports results of the trainee. Multiple training exercises are then organized into complete units called training sessions. The end goal of a training session is the _perfection_ of the athlete's abilities, in other words reaching their natural potential. The continuous process of training can be broken down briefly into the following four phases [6]:  
* **Planning** refers to the prescription of the proper exercise units. The cycle of sports training sessions are focused around the competition calendar. It is the phase in which the trainer prepares the exercise schedule for the athlete.
* **Realization** is the _execution_ phase of the prepared exercises. The roles of the trainer in this phase are: preparing (potential) equipment, conducting a psychophysical evaluation of the athlete before the session, monitoring the intensity of the session, and improving tactics in team based sports. The exercise data needed for further analysis are recorded in this step.
* **Control** is a comparison of the exercises actually performed by the athlete versus the planned exercises. This can be completed by the use of video analysis and contemporary computational technology. In individual sports, a bio-metric performance analysis can be performed, whereas notational analysis systems are used in team sports.
* **Evaluation** is the measurement of the athlete's performance. Two kinds of evaluations exist: (1) The evaluation of the single training load (short-term performance analysis) and the (2) evaluation of the total training cycle load (long-term performance analysis). The evaluation is the comparison between set goals versus achieved results, and the amount of planned versus actually performed exercises.  
The interconnection and continuous transition between the four mentioned phases is seen in Figure 1. Each cycle should provide the athlete with improved results. Since sports training is an activity of at least two parties, notably the trainer and the athlete, various computational-based approaches can be used to aid the decision-making of the trainer, or replace him altogether by introducing a virtual assistant. This allows the athlete to choose from a variety of possible training regimes without the need for employing an actual person to aid them in their training.
# Light-Controlled Microbots in Biomedical Application: A Review
## 2 Prospective Applications
### Targeted Drug Delivery  
The targeted drug delivery method is one effective strategy to deliver medicine in higher concentrations to some regions or portions of the body relative to others. This method of drug delivery to cancerous cells or tumors has been in use for a long period of time. An effective targeted drug delivery system must contain four fundamental needs, such as retain, evade, target, and release [50]. Moreover, drug targeting can be further classified into active and passive targeting. In passive targeting, also known as the enhanced permeation and retention (EPR) effect, drug accumulation in the vicinity of the tumor is established through a leaky vasculature. However, with passive targeting, only a tiny fraction (\(<\)5%) of the total administered formulation is actually delivered to the intended target site [51]. Whereas in active targeting, there is a specific interaction between the ligand and receptor [52; 53]. The drug reaches the targeted zone through the blood circulation system. This not only reduces the dosage of the drug, but also the adverse effects are decimated. This form of medicinal drug delivery is demonstrated, and numerous studies have been conducted to determine the prospective application of targeted drug delivery [54; 55]. The use of a targeted drug delivery system has already been proven to be effective [56; 57]. Researchers are now looking at using microbots to deliver drugs by functioning as active drug carriers. Many studies have already demonstrated the feasibility of using magnetic [58; 59; 60; 61] (which could be either gradient or rotating or oscillating magnetic field driven), motile microorganism-based [62; 63; 64], acoustic [65; 66; 67] or chemical [68; 69] actuated microbots. Whereas there are not many studies that claim the application of light-controlled microbots in vivo for targeted drug delivery. Light-driven drug carriers have the potential to be used as a targeted drug delivery vehicle, delivering drug payload to specific therapeutic zones. Figure 2 shows the viability of using microbots in targeted drug delivery. The main advantage of using light is that it is very accurate, and light is less invasive than any other control modalities. Nonetheless, they do pose some negative impacts on human health and the environment due to the biocompatibility problems [70].