esa-sceva/satcom-chunk-collection huggingface dataset:
# Loop-Back Quantum Key Distribution (QKD) for Secure and Scalable Multi-Node Quantum Networks
## 4 Discussion
### Security Considerations  
A key feature of the Loop-Back QKD protocol is its inherent robustness against depolarization in the quantum channel. Unlike conventional QKD protocols, where quantum bit errors directly impact the key rate, Loop-Back QKD relies solely on the basis used by [PERSON] rather than the exact transmitted state. Since [PERSON] does not measure the incoming quantum state but instead applies a unitary polarization transformation in a randomly chosen basis, the final measurement performed by [PERSON] is only sensitive to the basis alignment, not to the specific state received.  
For example, if [PERSON] transmits \(0_{Z}\) and [PERSON] chooses the basis \(X\), he applies a polarization operation resulting in \(0_{X}\). The quantum channel may induce a depolarization, flipping the state to \(1_{X}\). However, when [PERSON] measures in the original \(Z\) basis, she obtains \(1_{Z}\), which still provides unambiguous information about [PERSON]'s basis choice. This property ensures that the protocol remains functional even in the presence of channel-induced depolarization, significantly improving its robustness against noise.  
Then, what is the origin of the transmission errors in this protocol? As shown in Table 6, errors arise from false positives in cases where [PERSON] polarizes the state in the same basis in which it was prepared by [PERSON], but the channel depolarizes it during its return. This causes [PERSON] to detect it as an orthogonal state, thereby generating an error (assuming ideal detectors).  
Depolarization is influenced by the length of the path, which in this protocol is twice the distance of a unidirectional link. However, according to [26], the QBER increases linearly with distance, while at long distances, fiber losses become the dominant factor contributing to the total QBER.  
Since the error-free scenario (when [PERSON] measures in a basis opposite to [PERSON]'s) and the error-prone scenario (when [PERSON] measures in the same basis as [PERSON]) are equally probable, the average channel error rate is expected to be comparable to that of a unidirectional link, as in the BB84 protocol.  
However, as depolarization increases, the protocol's error rate remains below 25%. This behavior arises from the distribution of outcomes, wherein half of the cases remain unaffected by errors, as demonstrated in Table 6. In other words, even if complete depolarization occurs in all cases where [PERSON] polarizes in the same basis as the state prepared by [PERSON], the channel error rate would still be bounded at 25%, assuming ideal detector efficiency. An experimental verification of this model will be essential to confirm these limits.  
On the other hand, we expect that the absence of measurement basis disclosure in this protocol provides a significant advantage in mitigating more advanced attacks, such as collective and coherent attacks. These attacks typically optimize the measurement of intercepted states by exploiting the publicly disclosed information from [PERSON] [27].
## 2 Related Work  
In recent years, several system architectures and optimization models based on UAV-MEC have been developed to address the mobile users' main challenges where the computation offloading concept is applied. Most of these approaches have only considered MEC networks with single [20, 21] or multiple edge servers [22, 23], although a few approaches have considered MEC networks with multi-edge servers and a centralized cloud [16, 19, 24]. This section provided a brief overview of popular approaches based on MEC networks with single and multi-edge servers with and without a centralized cloud.
# Effects of Shading on the Growth and Carbon Storage of _Enhalus acoroides_
## 4 Discussion  
### Response of Seagrass Growth to Shading  
Changes in the leaf morphology of seagrass can be used to visualize its response to changes in the light environment [4]. Studies have shown that light reduction significantly reduces the leaf length and width of _Zostera marina_[22]. At 64% and 75% shading intensities, the number of leaves of _T. hemprichii_ was significantly lower than that in the full-light treatment [23]. In the present study, the leaf length and width of _E. acoroides_ decreased with increasing shading intensity, and the number of leaves also tended to decrease. When light is insufficient, seagrasses are able to persist in this stressful environment by altering their leaf morphology or maintaining a small number of leaves to effectively reduce their respiratory demand in the low-light environment. In contrast, an increase in the leaf length can enhance the capture of light by seagrasses in low-light environments [24]. The reason for this difference could be due to the higher shading intensity and longer shading period in this study. In addition, the shoot density of _E. acoroides_ did not change significantly at 3 months of shading treatment, which may be related to the time of shading initiation. We started the shading treatment of _E. acoroides_ in winter, when the growth rate of _E. acoroides_ was low. Indeed, carbohydrates were accumulated by seagrasses in the spring and summer when the sunlight was sufficient to meet their carbon demand in the low-light environment in the early period. The shoot density of _E. acoroides_ decreased significantly with the increasing duration of the shading treatment, which was in summer. Seagrasses exposed to high temperatures may require more light to maintain a carbon balance and are thus more susceptible to light reduction [25].
# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
###### Abstract  
Atrial fibrillation (AF) is still a major cause of disease morbidity and mortality, making its early diagnosis desirable and urging researchers to develop efficient methods devoted to automatic AF detection. Till now, the analysis of Holter-ECG recordings remains the gold-standard technique to screen AF. This is usually achieved by studying either RR interval time series analysis, P-wave detection or combinations of both morphological characteristics. After extraction and selection of meaningful features, each of the AF detection methods might be conducted through univariate and multivariate data analysis. Many of these automatic techniques have been proposed over the last years. This work presents an overview of research studies of AF detection based on RR interval time series. The aim of this paper is to provide the scientific community and newcomers to the field of AF screening with a resource that presents introductory concepts, clinical features, and a literature review that describes the techniques that are mostly followed when RR interval time series are used for accurate detection of AF.  
Keywords:atrial fibrillation; arrhythmia; RR time series; diagnosis-based-data; univariate analysis +
Footnote †: journal: _Algorithms_
## 4 Numerical Experiments  
This section explores the effectiveness and performance of the POGA-DP method in UCSP-BJFU instances. We compare the proposed approach with various traditional algorithms, including a genetic algorithm (GA), the Producer-Scrounger Method (PSM), Ant Colony Optimization (ACO), and particle swarm optimization (PSO). Additionally, we analyze and validate the impact of the scheduling similarity between adjacent weeks on the final scheduling results.  
GAs have been successfully applied in various fields [41]. To facilitate their implementation in the UCSP, this study employs a single-point crossover operation with a crossover probability of 0.3. The mutation operation is performed by randomly selecting a teacher and modifying their course time slot with a mutation probability of 0.01.  
The PSM is a population-based optimization algorithm proposed by [PERSON] [32]. It enhances the problem-solving capability by simulating different roles and behavioral patterns within a group. In each iteration, the producers attempt to explore better solutions, the scroungers move toward the producers, and dispersed members perform random movements to increase the solution diversity and prevent the algorithm from becoming trapped in local optima. To optimize the university course scheduling problem, this study employs a swapping selection probability of 0.3 in the PSM, enhancing the algorithm's search capability and solution efficiency.  
PSO is a swarm-intelligence-based optimization method [42; 43] that enhances the problem-solving capabilities by simulating the collaborative search behavior of particle populations. In each iteration, a particle's velocity and position are updated based on its personal best position and the global best position. To optimize the university course  
Figure 4: Classroom scheduling implemented using dynamic programming.  
scheduling problem, this study adopts an inertia weight of 0.8 to balance the global and local search capabilities. Additionally, both the individual learning factor (c1) and the social learning factor (c2) are set to 1.5, ensuring that the particles can explore new solution spaces while efficiently converging to high-quality solutions.  
ACO is a bio-inspired optimization method that simulates the pheromone deposition and sensing mechanisms of ants during foraging to identify the optimal paths. In this study, the pheromone evaporation factor is set to 0.5 to prevent premature convergence to local optima while enhancing the global search capability. Additionally, a global pheromone update strategy is adopted, where only the best solution found in each iteration reinforces the pheromone trail, thereby improving the solution quality. To optimize the university course scheduling problem, the pheromone importance parameter \(\alpha\) is set to 1, and the heuristic information importance parameter \(\beta\) is set to 2. This configuration ensures a balance between exploring new solutions and leveraging the historical best solutions, thereby enhancing the stability of the scheduling results and the overall optimization performance.
# Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
## 2 The Considered Class of Flexible Job Shops and Their Representation
### Basic Assumptions
#### 2.1.4 The Objectives  
The main objective of this study is to propose a method to minimize the total energy needed to perform a given production plan while satisfying due time constraints for each job. The energy of interest is composed of the processing energy of the different operations on their assigned machines and of the internal logistics energy spent to transport materials from one machine to the next. Here, it is assumed implicitly that there is a unique source of energy, electricity, but this hypothesis could easily be revised.
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 4 Proposed Strategy
### Slice Isolation by Resource Allocation (SIRA) Scheme
#### 4.2.2 LC-SIRA-Based Decision Making  
NSR nodes in the physical infrastructure must be routed in the most efficient manner possible. The efficiency of the PI resources can be improved by providing the shortest route for NSR nodes. There are some situations in which the shortest route can be linked to existing NSRs. To do this, we need to first find all of the connections between NSR nodes and then organise them in a static link array with the lengths of the paths between them taken into account. This array can be used to connect NSRs in PI to each other. Connection building is expected to correspond to each of the most direct routes, one after the other. When it comes to link provisioning, the link array always uses the least time-consuming option among the available methods.
## 3 Hardware Development
### PCB Design  
Table 6 summarizes the list of main electronic components used to manufacture the IoT-based sensor PCB and their commercial cost per unit. To calculate the approximate unit cost, considering the manufacture of 5000 pieces, manufacturing costs, testing costs, payment of certifications, housing cost, and development management, among others, had to be taken into account. This gave a rounded cost of 40.00 USD per unit.  
Once all the components were selected and the circuit diagrams designed, the PCB concept was created with the help of a CAD tool [15]. It is worth mentioning that a design methodology must be followed to reduce the size and decrease losses and power consumption [40]. Likewise, one must avoid designing a PCB that is difficult to assemble and expensive to produce.  
The footprint of the Hall-effect sensors PCB is shown in Figure 8a and its 3D model is shown in Figure 8b. When designing the host card, it was considered to avoid placing devices or traces in the vicinity of the wireless communication antenna to avoid noise. In addition, it was also considered that the MCU would be placed in a socket for quick replacement. The footprint of the host card PCB is displayed in Figure 8c, and its 3D model in Figure 8d.  
Once the correct operation of the electronic designs was verified by prototyping, the PCBs were manufactured.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device** & **PCB Designation** & **Price (USD)** & **Quantity** \\ \hline \multicolumn{4}{c}{**Sensors 1**} \\ \hline DRV5055 & Sen\_A, Sen\_Z & 0.418 & 2 \\ GRM152R61A104 KE19D & C100-C103 & 0.029 & 4 \\ RC0402 FR-0710 KL & R100, R101 & 0.012 & 2 \\ \hline \multicolumn{4}{c}{**Host 2**} \\ \hline PIC12F683-I/P & U100 & 1.62 & 1 \\ ESP8266-12E/ESP-12E & U101 & 0.80 & 1 \\ TLV755P & U10R & 0.123 & 1 \\ MIC5205 & U11R & 0.410 & 2 \\ RES-402 & R000 & 0.044 & 1 \\ RES-402 & R100P - R104E & 0.0680 & 6 \\ SF-0603F063-2 & F100R & 0.55200 & 1 \\ GRM31 MR61A106M & FC103E, C106R & 0.2439 & 2 \\ D4V5H1U2 LP161 & D100 & 0.102 & 1 \\ DMN2020 LSN-7 & D101 & 0.230 & 1 \\ D4V5H1U2 LP1610-7 & D103 & 0.102 & 1 \\
640456-2 & CON101,CON102 & 0.0973 & 2 \\ GRM152R61A104 KE19D & C100P - C101E & 0.029 & 4 \\ GRM219R61A105 MA19D & C101P - C105R & 0.1196 & 1 \\ C1206C106M8 PACTU & C102P & 0.170 & 1 \\ UKL1A221 MPDAANA & C200E & 0.3560 & 1 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 6: Main electronic components used for the IoT-GL-Sensor.
## 3 Results
### Akt1/Src/Cav1/Catalase Signaling Promotes Radioresistance by Buffering Oxidative Stress in RD Cells
Because RMS therapy is based on the use of a multiagent cocktail including vincristine, actinomycin D, and cyclophosphamide (VAC), we tested the combination of statins and actinomycin D. Although actinomycin D is an inhibitor of RNA synthesis, it has been reported to act as an oxidative stress inducer as well [29]. Interestingly, actinomycin D treatment reduced cell survival and increased apoptosis in the RD and RH30 lines but not in the radioresistant lines, as evaluated by neutral red assay (Figure 6A,B) and FACS analysis (Figure 6C,D). The combination of statins and actinomycin D led to chemosensitization, impairing cell viability (Figure 6A,B) and increasing apoptosis (Figure 6C,D) in all cell lines. Overall, these data suggest that statin treatment may be critical for promoting radio- and chemosensitization in RMS cells. Figure 4: Statins downregulate Akt1 and Cav1 protein levels in myrAkt1 cells and increase ROS production in all RMS lines. (**A**) Cells were treated for 4 h with statins or vehicle 24 h after plating control and myrAkt1 cells (1.5 \(\times\) 10\({}^{5}\)) in 60 mm dishes, then processed by IB analysis (\(n\) = 2). Densitometric quantifications are reported in the graphs after tubulin normalization. Data are mean \(\pm\) SEM, **\(p\)**-value \(<\) 0.001; ***\(p\)-value \(<\) 0.0001; one-way ANOVA test vs. vehicle-treated cells. (**B**) Cells (3 \(\times\) 10\({}^{3}\)) were plated in black 96-well plates in triplicates. ROS production was evaluated by administering the CM-H2 DCFDA probe to all the indicated RMS lines after receiving statins or vehicle at the indicated doses for 24 h (\(n\) = 2). Data are mean \(\pm\) SEM, *\(p\)-value \(<\) 0.05; **\(p\)-value \(<\) 0.001; one-way ANOVA test vs. vehicle-treated cells. Figure 5: The combination of statins and IR promotes radiosensitization in RMS lines. (**A**) Subconfluent cells from RD, RH30, myrAkt1, and RDCav1 lines were treated for 2 h with statins or vehicle before IR treatment. Cell survival was evaluated by clonogenic assay (\(n=2\)). Data are mean \(\pm\) SEM, ***\(p\)-value \(<\) 0.0001; one-way ANOVA test vs. vehicle-treated irradiated cells. Representative images were taken after crystal violet incorporation. (**B**) IF analysis of nuclear \(\gamma\)H2 AX staining in myrAkt1 and RDCav1 cells pretreated for 2 h with statins or vehicle prior to IR and evaluated at the indicated time points using a fluorescent microscope at 63 X magnification. The scale bar corresponds to 50 \(\upmu\)m. The quantification is relative to the average number of \(\gamma\)H2 AX-positive cells counted in ten different fields (\(n=2\)). Data are mean \(\pm\) SEM, ***\(p\)-value \(<\) 0.001; ***\(p\)-value \(<\) 0.0001; one-way ANOVA test vs. vehicle-treated irradiated cells for each time-point. (**C**) Cells (4 \(\times\) 10\({}^{4}\) for myrAkt1 and RDCav1, or 6 \(\times\) 10\({}^{4}\) for RD and RH30) were plated in duplicates into 6-well plates. FACS analysis was performed on all RMS lines after treatment with lovastatin or simvastatin and IR for 72 h (\(n=2\)).
# Real-Time Vision through Haze Based on Polarization Imaging
## 4 Target Enhancement Algorithms Based on Polarization Information
### CLAHE Image Enhancement Algorithm Based on Bilinear Interpolation  
While the overall quality and contrast were significantly improved in the polarization reconstructed scenes, local details were not adequately enhanced, and overexposure was noted in the sky portion of the image [19]. To resolve these issues, we used a bilinear interpolation CLAHE algorithm to further enhance the polarization reconstructed images via the following steps:  
Step 1: Divide the image into several area blocks to perform individual contrast enhancement based on the block's histogram. The local contrast enhancement is represented by Equation (10):  
\[x_{i,j}^{\prime}=m_{i,j}+k\big{(}x_{i,j}-m_{i,j}\big{)} \tag{10}\]  
where \(x_{i,j}\) and \(x_{i,j}^{\prime}\) denote the grayscale values before and after enhancement, respectively, and \(m_{i,j}=\frac{1}{m\times n}\sum\limits_{(i,j)\in W}x_{i,j}\) denotes the average grayscale value of the pixels in block \(W\). Parameter \(k\) can be represented as in Equation (11):  
\[k=k^{\prime}\Bigg{[}\Bigg{(}\frac{\sigma_{i,j}^{2}}{\sigma_{n}^{2}}\Bigg{)}-1 \Bigg{]} \tag{11}\]  
where \(k^{\prime}\) denotes the scale factor, \(\sigma_{n}^{2}\) the noise variance of the whole image, and \(\sigma_{i,j}^{2}\) the grayscale variance of block \(W\).  
Step 2: Stitch neighboring regions by means of bilinear interpolation to effectively eliminate the artifacts between neighboring regions introduced post local contrast enhancement. Assume that the  
Figure 4: Flowchart of image registration algorithm.  
values of four points \(K_{11}=(x_{1},y_{1})\), \(K_{12}=(x_{1},y_{2})\), \(K_{21}=(x_{2},y_{1})\), and \(K_{22}=(x_{2},y_{2})\) of a function \(f(x)\) are known; then, the value of a point \(H=(x,y)\) of the \(f(x)\) function can be derived by linear interpolation.  
First, linear interpolation is performed along the x-direction to obtain the value as represented by Equations (12) and (13):  
\[f(H_{1})\approx\frac{x_{2}-x}{x_{2}-x_{1}}f(K_{11})+\frac{x-x_{1}}{x_{2}-x_{1 }}f(K_{21}) \tag{12}\]  
\[H_{1}=(x,y_{1}) \tag{13}\]  
\[f(H_{2})\approx\frac{x_{2}-x}{x_{2}-x_{1}}f(K_{12})+\frac{x-x_{1}}{x_{2}-x_{1 }}f(K_{22}) \tag{14}\]  
\[H_{2}=(x,y_{2}) \tag{15}\]  
Subsequently, the same operation is performed along the y-direction as represented in Equation (14):  
\[f(H)\approx\frac{y_{2}-x}{y_{2}-y_{1}}f(H_{1})+\frac{y-y_{1}}{y_{2}-y_{1}}f(H_ {2}) \tag{16}\]
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Mobile Networks
#### 4.5.4 5G  
5G promises very high throughput, low latency, High Definition (HD) video, and unbounded connectivity. This is expected to provide smart healthcare with the use of connected smart wireless wearable sensor devices. 5G uses the mm-Wave. The mm-wave spectrum spans 3 GHz to 300 GHz in the electromagnetic spectrum. Within the mm-wave spectrum, only 57-64 GHz and 164-200 GHz are unsuitable for communication purposes, as shown in Figure 12[117].  
Media access techniques similar to those of 4G technology, such as Orthogonal Frequency Division Multiple Access (OFDMA), can be used with 5G. Advanced antenna systems such as massive Multiple Input Multiple Output (MIMO) in the physical layer improve capacity and bandwidth, which is highly required for 5G wireless communication. 5G is envisaged as possessing the following features: [20; 40; 114; 117; 118]:  
1. A high multi-peak data rate of 10 Gbps;
2. Ultra-low latency;
3. Massive network capacity with high bandwidth;  
Figure 12: Available mm-wave Communication Spectrum (3 GHz–300 GHz).  
A very large number of connected devices; this is crucial for the vision of a breakthrough in IoT applications, which 5G can achieve using small cell sizes such as femtocell, picocell, and microcell;
* Availability of six \"nines\" (99.9999%), that is, 5G networks should always be available to deliver as much capacity as possible;
* Maximum coverage;
* Low energy consumption;
* High user experience, with 5G expected to be user-centric rather than operator-centric.
The proposed secondary control strategy is designed to be fully distributed and can handle constant power and resistive loads. Furthermore, in designing the control parameters, the proposed controller does not necessitate knowledge of power line resistances, while still achieving rigorous stability analysis. To assess the effectiveness and resilience of the proposed distributed secondary control technique in achieving balanced current distribution and voltage restoration within DC microgrids, real-time experiments were carried out under varying operating conditions using a real-time digital simulator Speed goatTM facility. The following sections of the article are organised as follows: Section 2 covers the fundamentals of DC microgrid. Section 3 presents the test model of the DC microgrid with the novel fuzzy-based secondary control system. The analysis of the simulation results with a detailed discussion is presented in Section 4. Section 5 offers the real-time experimental implementation of the proposed secondary control to validate the simulation results. The concluding section of this article summarizes and concludes the complete corpus of the study.