esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient High-Dimensional Kernel k-Means++ with Random Projection
## 4 Experiments
### Findings
#### 4.6.2 Clustering Performance of Our Method  
We evaluated the clustering performance on six different real-world datasets. Figure 4 indicates that the overall execution time improvement in the projected space can be seen in all experiments. With enough dimensionality such that no performance degradation is observed, it is shown that our method generally reduces the execution time by over 50%. With the bioinformatics dataset, kernel k-means++ in the projected space runs 10-20 times faster than the ordinary kernel k-means in the original input data space. Table 3 summarizes the time reduced in our experiments. With the execution time of the baseline, it is not always the case that k-means++ can shorten the clustering process. The execution time on the bioinformatics dataset GLI-85 and SMK-187 indicate that, on a dataset with high dimensionality and limited data points (e.g., bioinformatics data), the k-means++ takes much longer time to find the initial centers than the computation of kernel k-means clustering.  
Figure 4: Reduced training time.  
Figure 5 illustrates the clustering performance of the proposed method against the increasing dimensionality. There are two horizontal lines in green and blue, corresponding to kernel k-means and kernel k-means++, respectively, in the input feature space (the baseline) with the clustering performance of our methods in black and red against an increasing number of dimensions.  
The results with the six experiments generally indicate that the clustering performance of the kernel k-means++ is either better than or close to that of kernel k-means in the original input space or the projected feature space. In the projected data space, WCSS is much higher than that of the baseline only when the dimensionality is very low. In other words, the performance deteriorates a lot when the data have been projected into an extremely low dimensional space. The clustering performance converges to the performance of the baseline when the number of dimensions increases. In general, the performance with projection converges to that of the baseline when the dimensionality is between 100 and 200.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **D** & **d** & _Proposed_1  & _Baseline_2  & _Speed-up_ \\ \hline MNist & 784 & 100 & 0.42 & 2.22 & 5.4\(\times\) \\ ImageNet-Cat & 1000 & 200 & 19.80 & 81.82 & 4.13\(\times\) \\ SMK-187 & 19,993 & 100 & 0.53 & 13.80 & 26.1\(\times\) \\ GLI-85 & 22,283 & 100 & 0.36 & 3.24 & 9.10\(\times\) \\ Reuters & 929 & 100 & 110.2 & 603.3 & 5.47\(\times\) \\
20 Newsgroups & 947 & 200 & 27.8 & 65.9 & 2.37\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training time reduced with our method compared to that of vanilla kernel k-means. In general, the higher the original dimensionality (D), the more the speed-up.  
Figure 5: WCSS of approximation close to original kernel k-means.
# Detecting and Mitigating Attacks on GPS Devices
## 5 Detection Techniques and Their Comparison
### GPS jamming Detection  
Most commercial GPS receivers do not have anti-jam or jamming detection features; however, there are some examples of commercial products that have jamming detection features. The authors of [55] presented a study evaluating the performance of various GPS receivers in the presence of jamming. One of the devices, the u-blox NEO-6 GPS receiver, was equipped with jamming detection capability. The NEO-6 provides a jamming level estimator (the jamIND field in the MON-HW message) that is intended to assess the likelihood of an ongoing jamming attack. A higher value indicates that a jamming signal is likely present. A narrowband jammer signal near the L1 center frequency interfered with the receiver and disrupted navigation; however, the NEO-6 reported an extremely low probability of a jamming attack. This result is unsurprising given the low-cost nature of most commercial GPS receivers. Existing commercial jamming detection capabilities are unlikely to perform well.  
There is significant research on GPS jamming attack mitigation; however, there are far fewer papers on GPS jamming detection. Historically, simple energy detection and thresholding have been used for jammer detection. This approach works well for high-power jammer systems; however, energy-based approaches do not perform as well when the incident jammer energy is comparable to the target signal energy. Some of the papers that proposed GPS jamming mitigation approaches also embedded a form of jamming detection as part of their overall approach, but generally, those approaches are not stand-alone in nature. Overall, existing GPS jamming detection methods appear to fall into one of three primary categories: (1) based on the statistical properties of the received signal, a derivative of energy-detection approaches; (2) based on antenna array hardware approaches; and (3) based on ML approaches.  
Research has historically focused on the first two categories: statistical signal properties-based and antenna-based approaches. Newer proposed approaches have attempted to leverage ML-based approaches that can consider the GPS signal properties, the jammer signal, and the GPS receiver to support detection decisions. Open questions for these approaches are (1) how they perform in complex propagation environments, such as urban canyons, and (2) how they perform with GPS receiver equipment, given the variation in GPS receiver performance that has been demonstrated previously. Another consideration is the complexity of the proposed solutions and whether they are practical for small UAS platforms with limited computational capability. This question is particularly relevant for ML-based approaches and whether they can be computationally optimized to execute on unmanned platforms with low processing capabilities, small amounts of volatile memory, and limited persistent storage space. These approaches are summarized in Table 9.
# Utilizing Graphite Waste from the Acheson Furnace as Anode Material in Lithium-Ion Batteries
## 2 Materials and Methods
### X-Ray Diffraction (XRD)  
The crystallinity of JMGW and its spheronized derivatives was assessed using XRD analysis. The samples were analyzed on a D-max 2500 VPC (Rigaku, Tokyo, Japan) diffractometer with Cu K\(\alpha\) radiation (\(\lambda\) = 1.5406 A). The scans were performed over a 20 range of 10\({}^{\circ}\) to 90\({}^{\circ}\) at a scanning rate of 0.5\({}^{\circ}\)/min. For normalization purposes, 20 wt.% of pure silicon (Kojundo Chemical, Sakado, Japan) relative to the target material was introduced as a standard reference. The interlayer spacing of (002) plane (d\({}_{002}\)) and graphitization degree (GD) were calculated using Bragg's law and Franklin Equation (Equations (1)-(3)) [31; 32; 33]. The value of d\({}_{002}\) is a fundamental parameter that describes the distance between adjacent graphene layers in graphite materials. A smaller d\({}_{002}\) value signifies higher crystallinity, which is crucial for enhancing the electrical conductivity and lithium-ion intercalation properties of anode materials in LIBs. The graphitization degree (GD) is another important parameter that quantifies the structural order of graphite. It is calculated by comparing the measured d\({}_{002}\) value with theoretical benchmarks for ideal graphite (0.3354 nm) and disordered carbon (0.3440 nm). A higher GD reflects superior crystallinity, which directly contributes to improved electrochemical performance by reducing internal resistance and increasing electrical conductivity.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Challenges
#### 8.2.8 Implementation and Maintenance Cost  
KDN requires additional software, hardware, and human resources to collect, store, and analyze data using a machine learning model or another heuristic model-based method compared to SDN. The hardware and software costs are expensive to purchase and maintain, making it challenging for organizations to justify the investment. Hardware such as processing units, memory resources, additional sensors for data capture, data integration middleware, etc., and additional software such as database engines, machine learning models, knowledge-representing data structures, rule engines, rule generators, ontology editors, etc. are required. Particularly, machine learning models require high computational and memory resources to implement, which can make it challenging to purchase hardware satisfying such requirements. In order to reduce the cost of software, organizations may consider open-source options. Furthermore, KDN requires special personnel with experience in networking, data analysis, machine learning, knowledge representation models, communication protocols, languages, etc. who may provide their services for a high cost. If trained personnel are not available, they need to be educated and trained to implement and maintain KDN, which will also cost organizations.
# Therapeutic Potential of Polymer-Coated Mesoporous Silica Nanoparticles
## 5 Polymer Coated MSNs for Infectious Diseases  
Growing antibiotic resistance and persistent biofilms are the major reasons for the failure of potent antibiotics to combat infections. Nanomedicine, particularly MSNs, have recently demonstrated their capability in the treatment of bacterial infections owing to enhanced penetration through biological barriers [69].  
SBA-15-type MSNs coated with PDA containing silver as an antibacterial agent were prepared by [PERSON] et al. to prolong the inhibition of bacterial growth via in situ formed silver nanoparticles. PDA coated MSNs control the release of silver particles and a prolonged inhibitory effect was observed with _E. coli_ (60 h) and _S. aureus_ (36 h) bacterial strains [57]. Similarly, whisker-like SBA-15 particles coated with 10% tannic acid were utilized as a carrier system for metronidazole for the treatment of trichomoniasis. The coating of tannic acid over the surface of nanoparticles was assisted by glutaraldehyde. The presence of tannic acid allowed pH sensitive drug release and improved the drug loading capacity of metronidazoleable by 7%. The coated MSNs demonstrate 100% protozoan growth inhibition for 180 min in vitro, which was superior to metronidazoleable solution [58].  
In one of our own studies, the antibacterial properties of the naturally derived polymer chitosan were used as a coating on silver-doped MSNs to introduce multimodal antibacterial properties [70]. Also, in this case, a clear shape-dependent effect was observed especially on _E. coli_. when administered concomitantly with the antibiotic drug kanamycin, an enhanced antibacterial effect against _Vibrio cholerae_ (_V. cholerae_) was achieved. Since MSNs are especially suitable for modular design, great promise can be envisaged in producing multifunctional MSNs including several antibacterial constructs in one system, prospectively making them more efficient in combating antibacterial resistance than drugs alone.  
[PERSON] et al. reported the preparation of gentamicin loaded MSNs, coated with Nafion polymer as a thin film on a slide (Figure 12). Nafion polymer provides stability to the film along with pH responsive release behaviour. Prepared films of MSNs were found stable for up to 90 days at pH 7.4 and release the loaded drug sustainably over up to 38 h in a mildly acidic solution, whereas up to 56 days at physiological pH. The utilization of this novel thin film system to protect against bacterial infections was proposed for implantable medical devices [59].  
Figure 12: Pictorial presentation of thin film preparation of MSNs coated with Nafion. Reproduced with permission from reference [59].
# Seamless Indoor-Outdoor Localization Through Transition Detection
## 3 Handover Strategy Based on Three State Density Functions
### GNSS-Based Model  
The GNSS-based distribution model \(P^{gmss}(s)\) considers the carrier-to-noise density in decibels (\(C/N_{0}\)) and the number of satellites used in fix (\(n^{sat}\)) to determine the probabilities of each state. The cases are broken into two by checking if \(n^{sat}\) is below 4 or not, taking into account that at least 4 satellites are required for an accurate GNSS fix. After considering the number of satellites, it takes the mean of the carrier-to-noise density (\(C/N_{0}\)) for four satellites with the highest value as the input and returns the probabilities of states at the \(k\)th location \(P_{k}^{gmss}(s)\), which is also an arbitrarily designed nonlinear function, as shown below.  
\[P_{k}^{GNSS}(s=in|n^{sat}<4)=\begin{cases}1&,\text{ if }C/N_{0,k}<30\\ \frac{40-C/N_{0,k}}{10}&,\text{ if }30\leq C/N_{0,k}\leq 40\\ 0&,\text{ if }C/N_{0,k}>40\end{cases} \tag{10}\]  
\[P_{k}^{GNSS}(s=transition|n^{sat}<4)=\begin{cases}0&,\text{ if }C/N_{0,k}<30\\ \frac{C/N_{0,k}-40}{10}&,\text{ if }30\leq C/N_{0,k}\leq 40\\ 1&,\text{ if }C/N_{0,k}>40\end{cases} \tag{11}\]  
\[P_{k}^{GNSS}(s=out|n^{sat}<4)=0 \tag{12}\]  
\[P_{k}^{GNSS}(s=in|n^{sat}\geq 4)=\begin{cases}1&,\text{ if }C/N_{0,k}<30\\ max(\frac{35-C/N_{0,k}}{5},0)&,\text{ if }30\leq C/N_{0,k}\leq 40\\ 0&,\text{ if }C/N_{0,k}>40\end{cases} \tag{13}\]  
\[P_{k}^{GNSS}(s=transition|n^{sat}\geq 4)=\begin{cases}0&,\text{ if }C/N_{0,k}<30\\ 1-\lfloor\frac{35-C/N_{0,k}}{5}\rfloor&,\text{ if }30\leq C/N_{0,k}\leq 40\\ 0&,\text{ if }C/N_{0,k}>40\end{cases} \tag{14}\]  
\[P_{k}^{GNSS}(s=out|n^{sat}\geq 4)=\begin{cases}0&,\text{ if }C/N_{0,k}<30\\ max(\frac{35-C/N_{0,k}}{5},0)&,\text{ if }30\leq C/N_{0,k}\leq 40\\ 1&,\text{ if }C/N_{0,k}>40\end{cases} \tag{15}\]  
For the case where \(n^{sat}\geq 4\), \(P^{gns}(s=transition)\) rises from 0 at \(C/N_{0}=30\) and reaches the highest value of 1 at \(C/N_{0}=35\), decreasing back to 1 at \(C/N_{0}=40\). \(P^{gns}(s=indoor)\) starts to decrease from 1 at \(C/N_{0}=30\) and reaches a minimum value of 0 at \(C/N_{0}=35\), where \(P^{gns}(s=outdoor)\) starts to increase from 0 until it reaches a value of 1 at \(C/N_{0}=40\).
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 3 Proposed Scheme  
### Overall Framework of the Proposed Scheme  
The overall framework of RDH-HEI based on EC-EG is shown in Figure 3. Firstly, the image is divided into non-overlapping \(A,B,C\), and then the space is reserved by the PEE algorithm. The image owner encodes the pixel values of image onto the points of the elliptic curve. Finally, the MCC is constructed after encrypting image with the public key \(pk\). The data hider divides the encrypted image into non-overlapping \(A_{E},B_{E},C_{E}\) in the same way as the image owner, scrambles the additional data with the hidden key \(k_{2}\) and then encrypts it with \(pk\). Finally, the data hider embeds the encrypted additional data into the \(n\) LSBs of the target pixels by homomorphic addition in ciphertexts. The addition operation embeds the encrypted additional data into the lower n bits of the target pixel. When the receiver owns the private key \(sk\) and the hidden key \(k_{2}\) of the data hider, the homomorphic subtraction in ciphertexts is used to extract the encrypted information, which is then decrypted by \(sk\) and the additional data are restored by \(k_{2}\). When the receiver only has the private key \(sk\) of the image owner, a directly decrypted image containing additional data can be obtained. When has the private key \(sk\), the self-embedded key \(k_{1}\), and the hidden key \(k_{2}\), the receiver can extract additional data after decryption and restore the cover image losslessly.
# Underwater Communications for Video Surveillance Systems at 2.4 GHz
## 3 Scenario and Test Bench  
The measurements have been performed in fresh water under specific conditions in a controlled space. This section present the scenario where tests have been performed and the equipment and the software used in our tests. Finally, we will explain the different tests, the parameters changed to perform our measurement and the statistical tools used to define the relationships between parameters.
# Multi-Criteria Optimization in Operations Scheduling Applying Selected Priority Rules
## 4 Experimental Study
### Optimization Model
#### 4.3.2 Single-Objective Optimizations  
The presented approach demanded to find the minimum and maximum value for each of five selected production goals with respect to constraints presented in Table 4. We used a set of single-objective optimizations for this purpose instead of an estimation [24; 38]. The objective functions for obtaining ideal and worst points were optimized according to the type of production objective. The objective functions and target of optimization for each of them are shown in Table 5.  
Simulation environment Witness Horizon includes the optimization module Experimenter with its Advanced Mode for the performance of simulation-based optimization. Implemented optimization algorithms comprise Random solutions, Hill Climb, Min/Mid/Max, and All Combinations algorithms, and metaheuristic, such as Adaptive Thermostatistical Simulated Annealing (SA). The optimum of the individual objective functions representing production goals was obtained via simulation-based optimization in two steps. Firstly, employing a metaheuristic algorithm briefly explored the design space with respect to the local optima at the beginning of optimization process. None of the values reached by SA represented global optimum, only local ones, therefore we did not present them. Secondly, we used the Brute Force algorithm All Combinations, which runs all constrained combinations of design parameters. The optimization process via the All Combinations algorithm was time consuming, but with the guarantee that the optimal solution will be found. For all experiments, the length of the one simulation run was set up to 1440 min and the warm-up period was 90 min. We used one replication for one simulation run due to the deterministic nature of the model. The resulted values of the ideal and worst points are listed in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Objective Function** & \
\begin{tabular}{c} **Optimization Target When** \\ **Finding Ideal Point** \\ \end{tabular} &
\begin{tabular}{c} **Optimization Target When** \\ **Finding Worst Point** \\ \end{tabular} \\ \hline Flow time & minimize & maximize \\ Costs per part unit & minimize & maximize \\ Work in progress & minimize & maximize \\ Machine utilization & maximize & minimize \\ Number of products & maximize & minimize \\ \hline \hline \end{tabular}
\end{table}
Table 5: The target of the single-objective optimization.
# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 3 System and Channel Modeling
### Channel Modeling  
Channel modeling is different in aerial channels, unlike the terrestrial counterpart. The percentage of LoS components is high in urban areas and less in rural areas in an A-to-G channel. That is, in urban scenarios, the A-to-G channel shows Rician fading as the LoS path is predominant [1]. However, in suburban scenarios, Rayleigh fading is observed due to stronger Non-LoS signals. As the Rayleigh channel can be considered as a particular case of Rician fading, we use Rician fading in this work [39; 40]. Therefore, the channel model can be represented as  
\[\mathbf{H}=\sqrt{\frac{\kappa}{1+\kappa}}\mathbf{H_{LoS}}+\sqrt{\frac{1}{1+ \kappa}}\mathbf{H_{NLoS}} \tag{2}\]  
where \(\mathbf{H_{LoS}}\) and \(\mathbf{H_{NLoS}}\) represent the channel matrix corresponding to LoS and NLoS paths. Hence, \(\mathbf{H}\) represents complex fading coefficients and the dimensions of the matrix are  
Figure 1: Drone–user connectivity under atmospheric disturbances.  
\(N_{R}\times N_{T}\), where \(N_{R}\) and \(N_{T}\) represent the number of receiving and transmitting antennas. The Rician factor \(\kappa\) is [41]  
\[\kappa=\frac{\sigma_{LoS}^{2}}{\sigma_{NLoS}^{2}}, \tag{3}\]  
where \(\sigma_{LoS}^{2}\) and \(\sigma_{NLoS}^{2}\) are the LoS and NLoS power, respectively. We consider the transmitter and receiver having multiple antennas, and the MIMO channel is assumed to be static. The MIMO channel's LoS part is given by [41]  
\[\mathbf{H_{LoS}}=\begin{bmatrix}1\\ e^{2\pi\frac{d_{R}}{\lambda}Sin(AoA_{R})}\\ \vdots\\ e^{2\pi\frac{d_{T}}{\lambda}(M-1)sin(AoA_{R})}\end{bmatrix}\begin{bmatrix}1\\ e^{2\pi\frac{d_{T}}{\lambda}Sin(AoD_{T})}\\ \vdots\\ e^{2\pi\frac{d_{T}}{\lambda}(N-1)sin(AoD_{T})}\end{bmatrix}^{T} \tag{4}\]  
where \(d_{T}\) and \(d_{R}\) represent the separation of antennas in the transmitter and receiver, respectively. \(AoA_{T}\) and \(AoD_{R}\) represent Angle-of-Arrival and Angle-of-Departure concerning the transmitter and receiver, respectively. The \(\mathbf{H_{NLoS}}\) follows a Rayleigh distribution and has the same dimension as that of \(\mathbf{H_{LoS}}\).
## 2 Methods and Data
### Model
#### 2.3.2 Logistic Regression  
In this study, sets 0 and 1 denote the off and on states of the mechanical ventilation system's status, respectively. Thus, the prediction model can be a binary classification problem. Logistic regression algorithm is a classic classification algorithm [29] and hasbeen widely used in modeling occupant behavior [22; 23; 25; 30]. It can be represented by the probability function, as described by Equation (3):  
\[logit\ p=log\bigg{(}\frac{p}{1-p}\bigg{)}=b+c_{1}x_{1}+c_{2}x_{2}+\ldots+c_{n}x_ {n} \tag{3}\]  
where \(p\) is the probability of the mechanical ventilation system turning on/off event, b is the intercept, \(c_{1}\), \(c_{2}\),..., \(c_{n}\) are the coefficients of variables and \(x_{1}\), \(x_{2}\),..., \(x_{n}\) are the explanatory variables (i.e., indoor and outdoor environmental parameters and time parameters).