esa-sceva/satcom-chunk-collection huggingface dataset:
# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
###### Abstract  
With the rapid advancement of the Internet of Things and the emergence of 6G networks in smart city environments, a growth in the generation of data, commonly known as big data, is expected to consequently lead to higher latency. To mitigate this latency, mobile edge computing has been proposed to alleviate a portion of the workload from mobile devices by offloading it to nearby edge servers equipped with appropriate computational resources. However, existing solutions often exhibit poor performance when confronted with complex network topologies. Thus, this paper introduces a decentralized mechanism aimed at determining the locations of network edge nodes in such complex network topologies, characterized by lengthy execution times. Our proposal provides performance improvements and offers scalability and flexibility as networks become more complex. Experimental evaluations are conducted using the Shanghai Telecom dataset to validate our proposed approach.  
next-generation networks; mobile edge computing; decentralized mechanism Article
# The Potential of Visible and Far-Red to Near-Infrared Light in Glaucoma Neuroprotection
## 8 The Future of Clinical Trials for the Treatment of Glaucoma  
Generally, glaucoma is a disease that progresses slowly; hence, there have been concerns regarding the feasibility of conducting clinical trials to evaluate the efficacy of new treatments for slowing or preventing vision loss [65]. It is generally believed that such trials would require very large sample sizes or long follow-ups. Nevertheless, clinical trials for glaucoma neuroprotection are not impossible. When treatment efficacy is evaluated by (1) difference in incidence of pointwise event-based progression, and (2) difference in rate of visual field mean deviation (MD) change between groups using linear mixed models (LMMs) versus conventional event-based survival analysis, the feasibility of future glaucoma clinical trials can be improved substantially [66].  
The studies described in this review highlight the relevance of some intracellular pathways which could be novel therapeutic targets, with the intent of lowering IOP on one side and stimulating tissue regeneration on the other side. The potentiation of mitochondrial activity associated with ion flux control could be proposed as a metabolic approach that is worthy of being pursued, and the therapy with red light could be used with this aim. In addition, a thermophysical approach to glaucoma has recently been developed by improving the comprehension of the biophysical bases of the light-receptor mechanism in order to design new possible devices for therapies [67].  
If PBM usefulness could be established through large clinical trials, it would offer a non-invasive and inexpensive approach that could easily be delivered by medical providers, or even by patients themselves, for the prevention or slowing down of the progress of retinal pathologies [10], including glaucoma.
## 3 Historical Review to State-of-the-Art
### Ceres  
The Clouds and the Earth's Radiant Energy System [CERES; 5] launched its first instrument in 1997. Via 7 different instruments on 5 different low-Earth orbiting satellites, CERES continues to provide ERB satellite observations at the time of writing this paper. The extended CERES data record has permitted multiple generations of shortwave ADMs that are summarized in this section.  
Figure 5: ERBE maximum likelihood estimate schematic for identifying scene type over ocean. Adapted from [PERSON] et al. [37].
# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 2 Background
### Architecture Selection and Implementation
The convergence behavior shows that training stabilizes around 400 epochs, with both losses decreasing steadily and minimal gap between training and validation curves, indicating good generalization without overfitting. The additional 100 epochs ensure stable optimization and model robustness. With this understanding of GNSS multipath effects and our chosen deep learning approach, we now present our core methodological contribution: the adaptation and implementation of LRP for interpreting LSTM-based GNSS multipath detection.
## 3 Channel Model
### Attenuation  
The attenuation also affects the desired signal. The attenuation depends on distance \(d\) from the source to the destination and on selected frequency \(f\). The attenuation \(A(d,f)\) is given as [43]:  
\[A(d,f)=A_{o}d^{k}\alpha(f) \tag{3}\]  
The \(k\) represents the spreading factor, and \(\alpha(f)\) represents the absorption coefficient, respectively. Following is the equation to express attenuation in dB.  
\[10 logA(d,f)/A_{o}=k.10 log(d)+d.10 log\alpha(f) \tag{4}\]  
The spreading loss is represented by the first term of the above equation, and the absorption loss is represented by the second term, respectively. The \(k\) is taken as 1.5 for practical spreading. Using [PERSON]'s formula, the absorption coefficient \(\alpha(f)\) in dB/Km for frequency \(f\) less than 0.4 kHz and above this is given by the following equations, respectively.  
\[10 log\alpha(f)=\frac{0.11\times f^{2}}{1+f^{2}}+\frac{44\times f^{2}}{4100+f^ {2}}+\frac{2.75\times f^{2}}{10^{4}}+0.003 \tag{5}\]  
\[10 log\alpha(f)=0.002+\frac{0.11\times f^{2}}{1+f^{2}}+0.11f^{2} \tag{6}\]
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 3 Congestion Detection and Identification
### Congestion Determination
#### 3.2.2 Congested Flow Identification  
Switch buffers handle traffic from different senders. When congestion occurs, pausing or slowing down all flows affects the transmission of non-congested flows. Therefore, it is necessary to identify the real-congested flow and adjust its transmission rate after dividing the degree of congestion.  
The ideal solution identifies a congested flow based on a hash of the flow identifier (FID) for the flow five-tuple information, and the upstream switch reduces the delivery of that flow. This option is theoretically feasible, but it is difficult to implement in practice. The switch would need to record information about all passing flows and look up information about congested flows, which would take up a lot of resources.  
From another perspective, once the degree of congestion in the switch buffer is detected, the percentage of packets per flow in the current queue is calculated to determine the congested flow. To monitor queue occupancy at each point in time, we establish a register on the switch to keep track of packets. This register records the number of packets coming from different flows. Upon detecting congestion, the register is read to calculate buffer occupancy in real time. The greater the percentage of packets from the same flow in the buffer, the greater the contribution of that flow to the current congestion of the switch. The real-congested flow can be identified by analyzing the percentage of packets in the queue.  
Programmable switches provide different types of state objects to maintain the state between packets, such as tables, counters, meters, and registers. Among these, registers serve as one of the defining features of next-generation programmable switches, providing a register memory accessible in the data plane for packets to read and write various states at line rate. In the mechanism of identifying real-congested flows, a register serves as a counting function with no more complex operations. Therefore, it does not affect the read and write operations of the registers regardless of the state of the network.
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 4 DVB-T2/C2 Demodulator Architecture
### Channel Equalizer Unit  
There are various scattered pilot patterns in DVB-T2 and DVB-C2 uses a part of the scattered pilot patterns in DVB-T2. Figure 12 shows the block diagram of the channel equalizer based on various scattered pilot patterns. The channel estimator performs pilot symbol-assisted channel estimation using pilots and their respective positions in order to support various pilot patterns. It is implemented by the least squares (LS) estimator. The interpolator performs frequency interpolation by using a linear interpolator.  
Figure 11: Block diagram of variable FFT.
## 2 Mechanism Description
### MPLS Label Distribution Procedure  
We want to clarify how our mechanism differs from MPLS multipath routing. MPLS makes a selection from predefined (candidate) paths and depending on the constraints selected paths are used. In contrast, our approach dynamically creates new paths between PE nodes on demand, driven by network-wide link utilization monitored by an SDN controller. Our mechanism does not switch between predefined MPLS paths, but always adds new paths that are less utilized in a given moment in time. What is important, the old paths are still used by existing flows, but new flows are transferred via new paths. Old paths remain active until all flows traversing these paths are completed.  
Our mechanism does not implement RSVP or LDP label distribution protocols. In our mechanism, only MPLS tagging is important. It does not matter how the MPLS labels are distributed amongst the PE and P nodes. The important assumption for our mechanism is that we use MPLS labels that are global for a particular MPLS domain. Each used label represents a particular PE node. A few paths can connect two PE nodes. In this case, a few MPLS labels represent the same destination node.  
The controller assigns constant labels while maintaining a consistent mapping of labels to specific PE nodes. In the case of RSVP or LDP, labels have a local meaning. Due to our mechanism requiring only static label distribution representing PE nodes, segment routing can be applied to distribute these labels in the network. Moreover, each mechanism which can install labels in MPLS routers can be applied.  
One of our main objectives is to implement our mechanism with minimal modifications to existing solutions using available devices off-the-shelf. Specifically, the PE nodes need to be modified. P nodes can remain as standard MPLS routers, OpenFlow switches, or switches that only support P node functionality (e.g., Arista devices). When using existing equipment, only static label distribution is required, which can be achieved using protocols such as NetConf or SNMP.
## 4 Throughput Optimization Strategy of Hybrid Service
### Multi-Agent Modeling in Hybrid Service
To access as many mMTC services as possible, we set the reward function for the mMTC communication link agent as follows:  
\[M_{j}=\left\{ \begin{array}{l}C_{\max}-\mu_{1}C_{URLLC}-\mu_{2}C_{mMTC},whenC _{re-mMTC}\geq 0\\ \lambda,otherwise\end{array} \right. \tag{8}\]  
where \(C_{\max}\) is the total capacity of Shannon in the current bandwidth. \(\mu_{1}\in\{0,1\}\) is the agent factor of the URLLC link, indicating whether there is any URLLC service that needs to be transmitted in the current sub-bandwidth. \(\mu_{2}\in\{0,1\}\)is the agent factor of the mMTC link: when \(\mu_{1}=1\), it indicates that there is URLLC service transmission within the  
Figure 2: The Architecture of Multi-agent Resource Scheduling. bandwidth. Similarly, \(\mu_{2}\) represents the transmission status of mMTC services. \(C_{re-mMTC}\) is the remaining mMTC service volume. \(\lambda\) denotes a hyperparameter used to adjust the willingness of the network to access mMTC services. Therefore, the reward function in each step \(t\) is calculated as follows:  
\[r_{t+1}=\omega_{URLLC}\sum_{h\in H}C_{URLLC,t}+\omega_{ mMTC}\sum_{j}^{J}M_{j,t} \tag{9}\]  
where \(\omega_{URLLC}\) and \(\omega_{ mMTC}\) are weight coefficients used to adjust the transmission traffic volume of these two services.
# Emerging Applications of Machine Learning in 3D Printing
## 3 Results
### ML in Patient Data Analysis for the Selection of 3D-Printed Medical Devices  
We live in an information society, a knowledge-based society--the entire world is based on information, and information determines the value of a company or a person's social status. It is, therefore, worth paying great attention to the following:  
* Collecting data in repositories, such as the Bydgoszcz repository of medical images;
* Data audit (including for the purposes of their further use by ML);
* Data protection--the effects of neglecting security can be catastrophic for individuals or the operations of companies;
* Analysis of patient data, including within the paradigms of Industry 4.0 or e-health [2; 3; 4].  
In the context of selecting personalized, 3D-printed medical devices, patient data analysis plays a key role. Thanks to machine learning algorithms, it is possible to quickly process large data sets, such as computed tomography images or genetic test results, which significantly speeds up the device design process. Machine Learning (ML) enables the identification of individual patient needs, which allows for precise matching of the device design to the user's anatomy [2]. These systems can also predict potential problemsrelated to the functioning of the device, which increases the safety and effectiveness of the therapy. Ensuring compliance with data protection regulations is also an important aspect, as ML algorithms require access to sensitive information. Implementing such technologies requires high-quality input data, as well as mechanisms for their verification, which increases the importance of data auditing. An integrated approach to data analysis and device design also allows for the creation of predictive models that support decision-making by doctors [3]. As part of Industry 4.0, combining data from different sources--such as hospitals, laboratories, or IoT devices--allows for a more comprehensive approach to diagnostics and treatment [4]. Examples of such applications can be found in systems supporting the printing of implants adapted to specific diseases or bone defects with high predictive accuracy scores of 0.9173 and 0.8772 [19; 20]. ML-based analysis also contributes to the optimization of the production process, which allows for saving raw materials and costs [21; 22]. In the future, these technologies may play a key role in the development of biocompatible materials dedicated to 3D printing [23; 24; 25]. Effective analysis of patient data and the use of ML is not only a step toward better medicine but also sustainable development of technology.
## 3 Results and Discussion
### Physicochemical Parameters  
Analysis of the results showed that there was no statistically significant difference (\(p<0.05\)) in the values of the pH, TA and TSS parameters between the two methods of configure production. Regardless of the method of confiture production (thermomix or evaporation under reduced pressure), the pH value of the product was 3.0 \(\pm\) 0.2, the acidity was 1.0 \(\pm\) 0.1 g of citric acid/100 mL and the TSS was 40 \(\pm\) 1\({}^{\circ}\) Brix. Moreover, during the entire storage period, these parameters remained consistent. The lack of changes in physicochemical parameters was mainly due to similar production times using two different methods. In both cases, there was no hydrolysis or degradation of sugars, which are mostly responsible for the formation of TSS. Furthermore, the low temperature at which the confitures were stored in airtight vessels allowed the content of organic acids and sugars to remain at similar levels.  
It is recommended that the pH of fruit preserves should be in the range of 2.8 to 3.2 to ensure effective gelation of pectin [14]. [PERSON] et al. [22] observed lower pH in honeycomb per pure in the range of 2.6-2.9, depending on the variety and heat treatment time. Studies on strawberry jams obtained at atmospheric pressure and reduced pressure suggested no significant effect of pressure on pH and TA [13]. Other researchers obtained significantly higher TSS and pH values in apple pekmez using open-pan evaporation method, while no change was observed in TA [23]. Depending on the cooking time used, there may be significant differences between jams concentrated by the two methods. The extract of jujube jam that was vacuum-heated for 20 min differed significantly from jam that was subjected to 15 min of cooking at atmospheric pressure [14]. Similar observations to those of our work were indicated by [PERSON] et al. [24]--the production of tomato paste by vacuum and with atmospheric evaporation did not result in differences in TSS of the final product.
# Polymeric Nanoparticles--Tools in a Drug Delivery System in Selected Cancer Therapies
## 3 Nanoparticles in Cancer Therapy--Passive and Active Mechanisms
### Active Mechanism  
In cancer progression, an increase in the expression of certain receptors on the surface of the cancer cell is also observed [6]. The active mechanism, as compared to the passive one, is based on exploiting this phenomenon and applying a selected polymeric nanoparticle in combination with a cell-specific ligand [35; 36]. Due to this effect, the polymeric nanoparticle with the incorporated drug can better reach the tumor site and also what is highly desirable, limiting the interaction only to tumor cells and thus causing a reduction in systemic toxicity [36]. The most commonly used ligands attached to PNPs are biotin, folic acid and antibodies directed against specific cellular antigens or proteins [26]. As an example, in 2015, a study by the team of [PERSON] et al. [37] showed that the use of d,l-PLGA with paclitaxel (20% \(w/w\) dry weight of PLGA) nanoparticles conjugated with 50 \(\upmu\)g of vascular endothelial growth factor (VEGF) formulated by single emulsion solvent evaporation better increases the affinity for human umbilical vein endothelial cells (HUVECs) than PLGA NPs without conjugation to VEGF. Additionally, they showed greater antiproliferative effects, which  
Figure 3: Mechanisms of action of polymeric nanoparticles: (**a**) passive mechanism; (**b**) active mechanism.  
is crucial for anticancer therapy [26; 37]. However, the use of a combination of polymeric nanoparticles drug-loaded with a surface ligand has some limitations. The main one is the reduced circulation time in the bloodstream due to faster uptake by the phagocytic system, as well as the variability in the expression of selected receptors depending on the stage of the cancer [26; 36]. Despite these disadvantages, this system is more commonly used than one based on a passive mechanism.
## 3 Results and Discussion  
### First-Order Statistics  
The local energy dissipation of the flow is part of the total energy consumption of the energy dissipater. Figure 9 shows the contour map of the average vertical velocity of the jet when m = 0.15. The dashed lines in Figure 9a,b are isolines with average velocity \((W/W_{j})\) = 0.95 and 0.1, respectively. The isolines \((W/W_{j})\) = 0.95 and 0.1 represent the outer boundary of the potential core and the shear layer, respectively. As shown in Figure 9a, when the crushing needle is not added, the vertical average velocity \((W/W_{j})\) takes the maximum value in the center of the nozzle at the bottom of the section (X/D = 0, Z/D = 0), which is \(W_{max}=W_{j}\), that is, the average velocity of the vertical jet pipeline nozzle section. Based on this, the Reynolds number based on the maximum flow rate can be calculated as \(Re_{max}=W_{max}H/\
u\) = 32,639, which is a fully developed turbulence [14]. The fluid flows from the nozzle to the outer boundary of the shear layer at the jet nozzle. With the vertical development of the flow, the vertical average velocity is getting smaller and smaller. In contrast, near the free surface near the outer boundary of the shear layer (X/D = \(-\)0.32, Z/D = 6.26) and (X/D = 0.32, Z/D = 6.26), the fluid flows from the upper part of the jet to the bottom of the jet and forms a semi-closed loop with reverse flow in the nozzle jet. With the vertical development of the flow, the vertical average velocity increases. As shown in Figure 9b, after adding the crushing needles, the vertical average velocity \((W/W_{j})\) reaches the maximum value at (X/D = 0, Z/D = 0), which is \(W_{max}=W_{j}\). Adding crushing needles will hinder upward diffusion of the jet, reduce the height of the jet, and accelerate the attenuation of the jet velocity. As shown in Figure 9b, when the jet diffuses upward, there is no obvious separation trend in Z/D < 2.12. As the jet continues to develop along the vertical direction, the vertical velocity loss gradually increases and the lateral movement dominates. The two jets begin to separate. Subsequently, the fluid at the bottom of the jet is brought to the upper part of the jet. On the left and right sides of the upper part of the jet, the vertical average velocity direction is downward, and the upper fluid is brought to the bottom of the jet. The two processes form a semi-closed loop, which forms a channel for fluid exchange between upward jetting and downward free-falling at the cross section  
Figure 8: Jet height verification: (**a**) m = 0.1; (**b**) m = 0.15; (**c**) m = 0.2. Z is the vertical coordinate.  
of the jet nozzle. It is concluded that when there are no crushing needles, the rain curtain falling into the water cushion pool is concentrated, and the impact force on the water cushion pool is large; when there are a large number of crushing needles, the rain curtain falling into the water cushion pool is scattered and the impact on the water cushion pool is small, which is conducive to the safety of the project.
## 1 Introduction
### Paper Outline  
The paper is organized as follows. In Section 2, the technical background of system model for D&F relay node co-operative is introduced. Section 3 introduces the proposed D&F radio access hardware architecture and the completely functional testbench framework based on SDR and MatlabTM software. Section 4 describes the evaluated outdoor-to-indoor scenario. The obtained results and their explanation are carried out in Section 5, where the key performance indicators EVM (error vector magnitude), BER (bit error rate), and throughput are considered. Section 6 presents a summary of the obtained results and future works. Finally, Section 7 states the conclusions of this work.