esa-sceva/satcom-chunk-collection huggingface dataset:
# The Development and Evaluation of a Training Monitoring System for Amateur Rugby Union
## 4 Discussion
### Training Monitoring System Functionality  
The system interface was rated highly by the S&C coaches (\(4.2\pm 0.8\)) and the ease of use was rated highly by the players (\(3.8\pm 1.1\)). However, from a functionality perspective, an issue highlighted by half of the coaches and 24% of the players was the log-in process using a 4-digit team and player PIN. It would appear that this created an additional barrier in some players uploading their data, with several suggesting a smartphone application being a more appropriate forum for the system. Training load monitoring using a smartphone application has been concluded to be a feasible and practical tool to be used outside of a controlled laboratory setting [50]. Furthermore, five players suggested the addition of automated reminders before and after training would be beneficial, which would again lend support to using a smartphone application.
## 4 Numerical Results
Note that Algorithm 1, for the AP pre-coder optimization, becomes comparable to the SDR scheme as \(N\) becomes large, and thus the combination of Algorithms 1 and 2 is a reasonable solution when \(N\) is sufficiently large, which is also observable in Figure 2.  
Figure 3: SINR performance comparison over the number of reflecting elements \(N\) in the overloaded case. Here, \(P=10,M=4\) and \(K=8\). Figure 4: SINR performance comparison over the transmission power \(P\) with a small number of reflecting elements (\(N=32\)). Here, \(M=8\) and \(K=4\). Figure 5: SINR performance comparison over the transmission power \(P\) with a fair number of reflecting elements (\(N=128\)). Here, \(M=8\) and \(K=4\).
# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
## 3 Simultaneous Transmissions from Multiple Base Stations in the URN (Multi-BSs-URN)
\tag{10}\]The second probability \(prob\Big{(}R_{U_{o}}^{(2)}\geq\eta\Big{)}\) in (8c) can be expanded as  
\[prob\Big{(}R_{U_{o}}^{(2)}\geq\eta\Big{)} \tag{11a}\] \[=prob\Big{(}(1-\lambda)R_{UAV,U_{o}}^{2 nd}\geq\eta\Big{)}\] (11b) \[=prob(\frac{P_{UAV}S_{UAV,U_{o}}}{P_{mBS,U_{o}}^{2 nd}S_{mBS,U_{o}} +P_{sBS}S_{sBS,U_{o}}+\sigma_{U_{o}}^{2}}\geq 2^{\omega^{-}}-1)\] (11c) \[\stackrel{{(a)}}{{=}}E_{h_{UAV,U_{o}}}E_{h_{sBS,U_{o }}}[P(h_{mBS,U_{o}}\leq B_{1}h_{UAV,U_{o}}-B_{2}h_{sBS,U_{o}}-B_{3})]\] (11d) \[\stackrel{{(b)}}{{=}}1-E_{h_{UAV,U_{o}}}E_{h_{sBS,U_ {o}}}\Big{[}e^{-B_{1}h_{UAV,U_{o}}+B_{2}h_{sBS,U_{o}}+B_{3}}\Big{]}\] (11e) \[=1-E_{h_{UAV,U_{o}}}[e^{-B_{1}h_{UAV,U_{o}}}]E_{h_{sBS,U_{o}}} \Big{[}e^{B_{2}h_{sBS,U_{o}}}\Big{]}e^{B_{3}}\] (11f) \[\stackrel{{(c)}}{{=}}1-\bigg{(}\frac{k_{UAV,U_{o}} }{k_{UAV,U_{o}}+B_{1}}\bigg{)}^{k_{UAV,U_{o}}}\frac{1}{1-B_{2}}e^{B_{3}}, \tag{11g}\]  
where  
\[\omega^{-}=\frac{2\eta}{1-\lambda},\] \[B_{1}=\frac{l_{UAV,U_{o}}P_{UAV}}{(2^{\omega^{-}}-1)l_{mBS,U_{o} }(P_{mBS,U_{o}}^{1 st}+P_{mBS,UAV}^{1 st})},\] \[B_{2}=\frac{l_{sBS,U_{o}}P_{sBS}}{l_{mBS,U_{o}}(P_{mBS,U_{o}}^{1 st}+P_{mBS,UAV}^{1 st})},\] \[=prob\Big{(}(1-\lambda)R_{UAV,U_{o}}^{2 nd}\geq\eta\Big{)}\]0  
\((a)\) in (11d) takes the expectations with respect to \(h_{UAV,U_{o}}\), \(h_{sBS,U_{o}}\); \((b)\) in (11e) follows a cumulative distribution function (CDF) of exponential distribution because \(h_{mBS,U_{o}}\) has a exponential distribution of \(exp(1)\). \((c)\) in (11g) follows a moment-generating function (MGF) of a gamma distribution, \(h_{UAV,U_{o}}\)\(Gamma(k_{UAV,U_{o}},1/k_{UAV,U_{o}})\) and an exponential distribution \(h_{mBS,U_{o}}\)\(exp(1)\).
# Performance of Ship-Based QKD Under the Influence of Sea-Surface Atmospheric Turbulence
## 5 Conclusions  
Sea-surface atmospheric turbulence has a significant impact on the performances of a ship-based QKD, especially on the performances of ship-to-ship QKD systems, where strong turbulence can greatly reduce secure keys and horizontal transmission distances plummet from 32 km (weak turbulence) to 3 km (strong turbulence), or even become impossible to encode. Therefore, optical compensation, background light suppression, and coding protocol improvement should be used to obtain better performance in QKD experiments.  
Conceptualization, G.-Y.J.; methodology, G.-Y.J., X.-H.L., W.-D.Z. and Z.-A.W.; software, G.-Y.J.; validation, G.-Y.J.; formal analysis, G.-Y.J.; investigation, X.-H.L., W.-D.Z. and Z.-A.W.; resources, S.Z.; data curation, X.-H.L., Q.-M.Z. and P.-F.G.; writing--original draft preparation, G.-Y.J.; writing--review and editing, G.-Y.J.; supervision, Y.-P.L.; project administration, S.Z.; funding acquisition, Y.-P.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Wuhan grant number 2025060102030013.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Characterization of TPCB
#### 3.1.2 Particle Size Distribution of TPCB  
The particle size distribution of TPCB was investigated by laser size analysis. The curves corresponding to the Gaussian fit are shown in Figure 3. The size of TPCB nanoparticles is mainly  
Figure 2: FTIR spectrum of TPCB.  
distributed in the diameter range from 50 nm to 5000 nm. In detail, the D\({}_{10}\) (accumulation of 10%), D\({}_{50}\) (accumulation of 50%) and D\({}_{90}\) (accumulation of 90%) of TPCB are 200.35 nm, 481.65 nm, and 1159.35 nm, respectively, which indicates that the particle size distribution of TPCB is not uniform. The reason is that, on the one hand, in order to satisfy the requirements of characteristics at different positions of tires, different kinds of CB are added to the tires, so that TPCB is a compound of different kinds of CB from the raw material of tires; on the other hand, the coking substances generated in the pyrolysis process can affect the particle size distribution of TPCB.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 3 Related Work
### Endorsement Vulnerabilities  
[PERSON] et al. [43] identified that endorsers' identities are publicly visible within a channel, making them vulnerable to targeted DoS attacks. Their experimental results show that attacking two endorsers led to a 20% reduction in the throughput and a 75% increase in the latency. To mitigate the risk, they proposed randomized endorser selection using Verifiable Random Functions (VRFs) and pseudonymous endorsements. However, the proposed strategy sacrificed network efficiency for security, leaving room for further improvement. Building on this concern, [PERSON] et al. [44] identified that statically configured, publicly known endorsers are easy targets. It creates a vulnerability where attackers can target a small subset of these nodes to disrupt transaction processing. To mitigate this, they proposed a dynamic and randomized selection mechanism for endorsing peers using the SHA-256 hashing of public keys combined with time-based randomness. Their simulation results demonstrated a nearly 50% reduction in the success rate of such attacks. [PERSON] et al. [45] investigated Denial-of-Endorsement (DoE) attacks in HLF, where malicious nodes conspire to selectively refuse transaction endorsements. While mitigation strategies such as custom endorsement policies and increased trusted peer nodes have been proposed, there is no detailed design or empirical validation to test their effectiveness. [PERSON] et al. [46] developed a Petri Net-based modeling framework to simulate Sybil attacks on HLF's transaction flow. By forging multiple Sybil clients and malicious endorsers, these attacks significantly disrupt the transaction flow, increase the endorsement latency, and impact the multiversion concurrency control (MVCC) and ordering services. However, since their study focused specifically on Sybil attacks, it does not necessarily constitute a DoS attack in all cases.  
While several solutions like VRF-based selection and dynamic endorsement policies have been proposed, none have been validated collectively with other layers under a coordinated attack. This informed our attack scenario design targeting endorsement peers in combination with ordering services to explore cascading effects.
# Clone Node Detection Attacks and Mitigation Mechanisms in Static Wireless Sensor Networks
## 3 Cybersecurity Attacks in Wireless Sensor Networks
### Clone Node Attack (Replication Attack) in Static Wireless Sensor Networks
#### 3.3.1 Execution of the Clone Node Attack  
To effectively perform a replication attack, the attacker usually proceeds as follows:  
* The attacker physically captures the legitimate nodes in the network area.
* The attacker extracts all the sensitive information found in those legal nodes.
* The attacker uses all the obtained information to produce new nodes (malicious nodes) with the same ID of the legal nodes found in step 2.
* Finally, the attacker launches them back into a specific location of the network.  
The attacker, having the chance and possibility to allow himself to proceed with all of these four (4) steps, can drastically disturb the network performance at any time he wants by carrying out new attacks.
# Feature-Selection-Based DDoS Attack Detection Using AI Algorithms
## 4 DDoS Detection Model
### Decision Tree  
A decision tree is a graphical representation of a decision-making process that iteratively separates data into subgroups based on the values of input attributes. Each split produces a branching node that may lead to other splits or other outcomes. In classification and regression tasks, decision trees are used to generate predictions or choices based on patterns in the feature values of the data. Since DTs are simple to understand and analyze, figuring out the criteria that lead to DDoS attack detection is an easy task. Consistency in detection can be impacted by minor modifications in the data that lead to noticeably different tree architectures [10].
# A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather
## 3 Methodology
### Detection Network Module  
YOLOv3 utilizes a novel network architecture known as Darknet-53, which comprises 53 convolutional layers. Unlike previous models that use fully connected layers, YOLOv3 employs anchor boxes for predicting bounding boxes. It performs these predictions at three different scales, enabling more accurate detection of objects of varying sizes. Each prediction layer corresponds to a specific scale, enhancing the model's flexibility and precision. Darknet-53 leverages residual blocks, skip connections, and upsampling techniques to extract more meaningful features from images, thus improving learning and detection accuracy. Due to its outstanding performance in object detection, YOLOv3 is highly suitable for applications such as image editing, security surveillance, crowd detection, and autonomous driving [31; 32]. In the detection network presented in this paper, we employ an identical network architecture to detect obstacles in rail transit systems.
# Low-Complexity GSM Detection Based on Maximum Ratio Combining
## 1 Introduction
Finally, we conclude the paper in Section 6. Notation: Boldface uppercase letters denote matrices; boldface lowercase letters denote vectors. \((\cdot)^{T}\), \((\cdot)^{H}\), \((\cdot)^{-1}\) and \((\cdot)^{\dagger}\) represent the transpose, Hermitian transpose, inverse, pseudo-inverse operation of a vector or a matrix, respectively. \(\lfloor\cdot\rfloor\) denotes the floor operation. \(\|\cdot\|_{p}\) is the \(\ell_{p}\)-norm of a vector or a matrix. \(\left|\cdot\right|\) stands for the absolute value of a complex number or the cardinality of a given set. \(round(\cdot)\) indicates the operation of rounding a real number to the nearest integer. \(\text{mod}(\cdot,\cdot)\) is the modulo operation. \(\Re(\cdot)\) and \(\Im(\cdot)\) represent the real and imaginary parts of a complex-value variable, respectively. \(\mathbb{R}\) and \(\mathbb{C}\) represent the field of real and complex numbers, respectively. \(\mathbb{C}_{n}^{k}\) denotes the binomial coefficient, which is given by \(C_{n}^{k}=\frac{n!}{k!(n-k)!}\). If \(n<k\), the binomial coefficient \(C_{n}^{k}=0\). \(N-PAM=\{-N+1,-N+3,\cdots,-1,1,\cdots,N-3,N-1\}\), where \(N\) is a power of two.
# Solution Merging in Matheuristics for Resource Constrained Job Scheduling
## Appendix C Construct, Merge, Solve and Adapt Parameter Value Selection  
The construct, merge, solve and adapt (CMSA) parameters of interest are the mixed integer program (MIP) time limit (120 and 300 s), the number of iterations in ant colony optimisation (ACO) (500, 1000, 2000 and 5000 iterations) and the maximum age limit (3, 5 and 10). Ten runs per instance were conducted and all runs were conducted for 30 min with 10 cores. The results are presented in Table 1. For each parameter of interest, the results averaged across the results obtained by all possible settings of all the other parameters.  
Regarding the MIP time limit, we see that, overall, 120 s is preferable, particularly for eight machines or more. For four and six machines, 300 s is preferable; however, the results are close here.  
The results concerning the number of ACO iterations show that, generally, 5000 iterations provide the best average results across all machines. It is only for the instance with four machines that 2000 iterations works better. However, for these instances, the results are very close across all possible settings. As with MS, more ACO iterations could be considered, but this would lead to very few CMSA iterations being completed within the total time limit.  
The results concerning the maximum age parameter are not as clear. A low maximum age (3) seems the best option overall.
## 3 Results  
### Morphology  
Figure 1 shows some representative SEM micrographs of the produced samples. The SG method favors the formation of porous sponge-like structures (Figure 1a-d), mainly consisting of agglomerates of small particles. In the case of ES (Figure 1e-k), the use of PVP and PAN leads to different selectivity towards the formation of NFs. Fibrous structures are always observed in PAN-derived samples (Figure 1j-l), where beads rarely form. On the contrary, in some of the PVP-derived samples, fibers are hardly observed (Figure 1e,g), whereas, in the remaining ones, NFs and beaded structures often coexist (Figure 1f,i); in some samples, fibers are organized in a three-dimensional network (Figure 1h,i).  
Figure S2 shows the NF diameter distributions as automatically estimated by the analysis software of the SEM microscope. In PVP-derived samples, the distributions are centred on values increasing in the order 5 MK-a (580 nm) \(<\) 5 ME-a (630 nm) \(<\) 5 ML-a (770 nm). In PAN-derived samples, the distribution centre values increase in the order 5 MG-b (380 nm) \(<\) 5 MF-b (510 nm) \(<\) 5 MD-b (520 nm) \(<\) 5 ME-b (660 nm) \(<\) 5 ML-b (720 nm). The comparison of the results relative to samples 5 ME-a and 5 ML-a with those relative to samples 5 ME-b and 5 ML-b suggests a possible role of the metal combination in determining the variation of the NF diameter.  
Regardless of the sample morphology, preparation method and conditions, the dispersion of the composing elements is always spatially uniform within the samples, as revealed by SEM/EDX analysis (Figure S3).  
Figure 2 displays some representative HRTEM images of the investigated samples. Images with lower magnification are shown in Figure S4a-d. Regardless of their composition, samples produced by the SG route consist of aggregates of crystalline oxide particles (see the SAED pattern in the inset of Figure S4a), having polyhedral shape and size ranging between a few tens of nm to hundreds of nm (Figure 2a,e). The electrospun PAN-derived NFs are micrometers long and have diameters ranging between 150 and 450 nm. Independent of their composition, they are built-up by interconnected crystalline oxide grains (see the SAED patterns in the insets to Figure S4b-d) with similar shape and size to that of particles synthesized by the SG method (Figure 2b-d,f-h). For both synthesis routes, the STEM/EDX elemental maps (Figure S5) prove the homogeneity of the spatial distribution of metals and oxygen at the nanometer scale.  
Figure 2: HRTEM images of samples (**a**,**e**) 5 MF-sg, (**b**,**f**) 5 ME-b, (**c**,**g**) 5 MF-b and (**d**,**h**) 5 MG-b.
## 4 Experiments and Results
### CO\({}_{2}\) Concentration Outcomes
The integral path length is 580 m.  
Figure 18: The discrepancy between the measured CO\({}_{2}\) concentration by the IPDA system and in situ sensor on the 580 m horizontal path. Figure 20 shows the diurnal variation of the horizontal column-averaged CO\({}_{2}\) concentrations obtained by the improved system on 7 November 2019 over a 1.5 km path (red double arrow line in Figure 14). The diurnal variations measured by the IPDA lidar were consistent with the in situ sensor. The standard deviation of the comparison error between the IPDA lidar and the in situ sensor was about 5.987 ppm, as shown in Figure 21. Figure 22 shows the measurement results of the IPDA lidar on this path for five consecutive days (18-22 October 2019) in autumn. The weather was clear throughout the measurement time. The characteristics of the diurnal variations of CO\({}_{2}\) concentrations in five days are similar and obvious, showing a clear change rule of one peak and one valley. During the day, the CO\({}_{2}\) concentration reaches its lowest value around 14 o'clock, which is close to the atmospheric background concentration. This might be because the atmospheric convection and horizontal activities are more active during this period, which is conducive to the diffusion of pollutants. In addition, during this period, plants have strong photosynthetic activity and absorb a greater amount of CO\({}_{2}\). After 15 o'clock, the CO\({}_{2}\) generated by soil, biological respiration and industrial production accumulated in the near-surface atmosphere and the CO\({}_{2}\) concentration began to rise slowly, reaching its highest value before sunrise. After sunrise, the CO\({}_{2}\) concentration can be seen to drop rapidly.
# An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification
## 3 Materials and Methods
### Support Vector Machines (SVM)  
SVM is a widely and commonly used machine learning technique for classification. As shown in Algorithm 3, it works by determining the best margin for separating distinct types of data. SVM uses kernel functions to handle both linearly and non-linearly separable data. Its goal is to optimize the margin between classes while accurately categorizing data points. SVM is a strong supervised learning classifier that can be applied to both binary and multi-class classification applications.  
SVM have a hierarchical foundation made up of several layers. SVM is fundamentally made up of a training dataset, a training method, and a model structure. The training data consist of input characteristics and their related classifications, and the method uses support vectors to establish the ideal margin. The model structure includes information about the margin, support vectors, and feature weights. SVM sublayers contain the kernel function, which converts input to a higher-dimensional space for linear separation. Support vectors, which are critical for decision boundaries, are the data points that come closest to the margin. The decision margin defines the distance between the support vectors and the decision boundaries, with the goal of maximizing it. During classification, the trained SVM model is used to classify fresh data by translating it into a high-dimensional space and analyzing its location relative to the model's margins and bounds.  
In addition to its layered architecture, the SVM algorithm is enhanced with the inclusion of a pseudo code snippet, as detailed in Algorithm 3. This pseudo code provides a structured representation of the algorithmic steps involved in SVM classification. Additional layers of SVM, such as the direct inference extension and the multi-class SVM layer, further contribute to its classification efficiency and handling of multi-class classification issues.  
```
\(X\): Training matrix (each row represents a training sample) \(y\): Corresponding classifications (values can be +1 or \(-1\)) \(C\): Penalty parameter (measures the allowance for errors) epsilon: A small value for stopping criterion (convergence threshold) max_iterations: The maximum number of iterations Steps: 1. Initialize parameters:  Set weights w to zeros  Set bias b to zero  Set iteration counter t to 0 2. Repeat until convergence or reaching the maximum number of iterations: Increment t by 1 For each training sample \((x\_i,y\_i)\) in \(X\), \(y\):  Compute the margin : margin \(=y\_i\ *\ (dot\_product(w,x\_i)+b)\) If margin \(<1\):  Compute the gradient of the loss function with respect to  w : gradient \(=w-C\ *\ (y\_i\ *\ x\_i)\)  Compute the gradient of the loss function with respect to  b: gradient_b \(=-C*y\_i-\ Update\) weights: \(\text{w}=w-(1\ /\ t)\ *\ gradient\_w-Update\text{ bias : b}=b-(1\ /\ t)\ *\ gradient\_b\) Return w, b
```  
**Algorithm 3** The Pseudo Code of SVM Algorithm
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms
### Fuzzy Logic Based Methods  
Fuzzy Logic is a multivalued logic that allows intermediate values to be defined between conventional evaluations, such as true/false, yes/no, high/low. Fuzzy systems refer to those that are directly related to fuzzy logic. These systems are mainly composed of fuzzification, knowledge bases, fuzzy inference engines and defuzzification. Fuzzification is the conversion of the input of a system to fuzzy sets with some degree of membership anywhere within the interval using a membership function. A membership function is a curve that defines how each point in the input space is mapped to a membership value. The knowledge base stores all information on the fuzzy controller, including knowledge and required control objectives in the specific application field. These core factors determine the performance of the fuzzy controller. The function of the fuzzy inference engine is to convert the fuzzy \"if-then\" rule into a type of mapping according to the fuzzy logic rule. Defuzzification is the conversion of the fuzzy output quantity into clear output.  
Fuzzy logic is also applicable to image fusion. In this process, local features of the image are extracted and combined with fuzzy logic to compute weights for each pixel [115; 116]. The fuzzy logic-based fusion rule is often used to cope with blurry image fusion. As a fusion rule, it could be further combined with DWT [117], NSCT [118; 35; 119] and NSST [120] for medical imaging. In this application, DWT/NSCT/NSST was performed on source images to obtain high- and low-frequency sub-bands. Next, a logic-based fusion rule was applied for the fusion of high [35; 117; 118] or low [120] sub-bands, and for the enhancing of the global contrast of the image [119]. The Fuzzy logic algorithm could overcome defects of losing edge information and color distortion in DWT/NSCT/NSST to improve image contrast. Neuro-fuzzy combines artificial neural networks with fuzzy logic to generate a resulting hybrid intelligent system. The humanlike reasoning style fuzzy system is combined with the learning procedure of artificial neural networks. This approach utilizes an artificial neural network to train the parameters of the membership function, and has been used in the fusion of MRI/CT images [121; 122]. Similar to fuzzy logic fusion, neuro-fuzzy logic can be combined with WT, Contourlet transform [123], NSST [124] and NSCT [125] to optimize fusion performance.