esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Within this study, diffusion coefficients and activation energies of diffusion were determined experimentally from desorption kinetics into the gas phase. Activation energies of diffusion for a broad range of molecules below and above the glass transition temperature are not available in the scientific literature, which is the novelty of this study. From experimentally determined activation energies of diffusion, parameters for the prediction of the diffusion coefficients in GPPS and HIPS were established below and above the glass transition temperature of both polymers. These parameters \(a\) and \(b\) in Equation (3) were derived from the correlation between the pre-exponential factors D\({}_{0}\) and the activation energies of diffusion E\({}_{\text{A}}\). The parameters \(c\) and \(d\) were derived from the correlation between the activation energies of diffusion E\({}_{\text{A}}\) and the molecular volume V of the investigated substances. From Equation (3) and the parameters \(a\) to \(d\) the diffusion coefficients D\({}_{\text{P}}\) of other, non-tested organic substances can be predicted. In addition to the desorption kinetics, diffusion coefficients of the homologous row of 1-alcohols were determined by permeation tests through a thin GPPS film. The results of both methods, desorption and permeation kinetics, are in good agreement. Due to the fact that all diffusion coefficients were determined in gas phase kinetics, swelling effects and interactions between solvents and PS can be excluded. Therefore, the prediction parameters established within this study can be considered a pure diffusion coefficients. Most of the diffusion coefficients found in the scientific literature might include also swelling effects of the polymers with simulant media. Pure diffusion coefficients in polystyrene polymers might be useful for the understanding of the diffusion and migration processes. Migration modelling based on experimentally determined activation energies of diffusion, as established within this study, might therefore offer a basis for a more realistic estimation of the migration of polymer constituents into food.  
Funding: The study on GPPS was financed by Fraunhofer IVV. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author.  
Thanks are due to [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] (all Fraunhofer IVV) for experimental contributions to this work. Further thanks are due to [PERSON] (Ineos), [PERSON] (Ineos) and [PERSON] (Systox) for proofreading. Conflicts of Interest: The author declares no conflict of interest.
# Advancing the State of the Fog Computing to Enable 5G Network Technologies
## 5 Overview of Fog Computing
### Fog Physical Network Architecture
#### 5.2.1 Application Architecture  
* **Device Layer:** The description of how end-user devices are connected to the fog network in this layer. It includes IoT devices such as mobile devices (eMBB), sensors, gateways (mMTC), and actuators. Data transferred may perform data transfer with the network or directly with devices themselves (peer-to-peer). Moreover, this layer hosts computational tasks either by software running on the operating system or by embedded coding.
* **Fog Layer:** This layer consists of intermediate network devices located between the cloud layer and the end-devices (device layer). The Remote Radio Head is the initial point of offload in this layer and the small cell connected by fiber front haul to the core of networks, where incoming processing data reduces the burden on front haul. The macrocells from the point of offloading transmit processed data to the core networks via backhaul links. Intermediate devices and ethernet links are used for the realization of both the backhaul and front haul. It forms a potential area for computational processing and storage of data that can be offloaded.
* **Cloud Layer:** It is the apex in the architectural hierarchy, where the offload points are the cloud virtual machines. This layer can handle intensive computational processes with large storage that cannot be performed on edge computing due to the cloud layer's high-end infrastructure and scalability. It contains a Based Band Unit (BBU) at the application layer processing data coming from the RRHs and small cells. The data passes through the first haul and route processed data to application servers [15; 39]. FC architecture is known as decentralized, dynamic multi-layered, and resourceful, unlike another computing mode. Fog nodes act as a common network constituent linking the cloud, smart devices, and users with other Fog nodes.
# LaRaWAN Metering Infrastructure Planning in Smart Cities
## 5 Conclusions  
Algorithms to support gateway placement combined with radio coverage modeling in urban environments are challenges facing computer simulation environments. This paper presents a new algorithm to locate gateways collecting data from wireless IoT sensor networks. The research was conducted on the example of a 330,000-strong city in Poland. The proposed algorithm, according to the authors, enables efficient deployment of data collection gateways. The authors' involvement in a research agenda focused on data transmission from electricity meters enables a comparison between the results of simulation tests and real-world metering networks employing LoRaWAN technology. As far as the authors are aware, this is the first study that employs machine-learning (ML) techniques to determine the location of data-collecting nodes in LoRaWAN networks.  
The proposed solution is the first stage of building a simulation environment, which does not yet include traffic models that allow accurate simulation of uplink and downlink traffic. Assumptions have been made to avoid collisions and message overlap, but an accurate model will take these aspects into account. No less, the application created at this stage allows planning the deployment of network nodes for measurement. The implemented mechanisms for communication with geospatial databases will in the future be able to be applied to areas with varying degrees of urbanization with the possibility of using different path loss radio models.  
It is noteworthy that the algorithm presented in the paper can have an impact on the careful design and implementation of networks to reduce energy consumption and radio bandwidth usage. Moreover, when the required coverage and continuous monitoring are provided, it will be possible to draw realistic conclusions and take action based on the data collected. Thus, this could become the next step toward the implementation of a smart city concept, whose functionality and capabilities will be optimized for the convenience and safety of residents.  
M.P., P.Z. and B.M.: conceptualization, validation, and writing--review and editing; M.P., B.M. and P.Z.: methodology; M.P.: simulations, data analysis, investigation, resources, software, visualization, and writing--original draft; P.Z.: project administration and supervision. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the grant to maintain research potential of Kazimierz Wielki University (Ministry of Education and Science, grant 2023).  
**Institutional Review Board Statement:** No applicable.  
Figure 7: Locations of shops and retail outlets as metered electricity consumption sites, together with the location of LoRaWAN gateways determined using the K-means algorithm (Bydgoszcz, Poland).
## 4 Experiments and Discussion
### Ablation Studies
#### 4.3.4 Two-Step Inference  
Since we have tried to construct a two-step training network, it is also interesting to devise a two-step inference method. Moreover, as can be seen in Figure 7, P2P-Worms are frequently classified as Trojan-Downloader. However, they are in totally different parent categories. We believe this is caused by its small amount of data, resulting in the instability of its Normal Vector. But we find that although the data for P2P-Worm are rare, its parent category, i.e., Worm, has sufficient samples. Consequently, we train the model in training step #2 again and obtain the Normal Vectors for all the parent classes, i.e., Backdoor, Worm, Trojan, and misc., to make full use of these samples. Hence, we have the Normal Vectors for categories and their sub-categories.  
Figure 6: The confusion matrix (with data on accuracy) of the results of training step #2. From top to bottom, from left to right, there are Worm, P2P-Worm, Trojan-Spy, Net-Worm, Packed, Trojan-Downloader, Trojan-PSW, Trojan-Ransom, Misc, and Trojan-FakeAV.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Category** & **F1-Score** & **Overall Accuracy** \\ \hline Worm & 0.89 & \\ P2P-Worm & 0.29 & \\ Trojan-Spy & 0.88 & \\ Net-Worm & 0.78 & \\ Packed & 0.93 & \\ Trojan-Downloader & 0.71 & 0.92 \\ Trojan-PSW & 0.96 & \\ Trojan-Ransom & 0.85 & \\ Misc & 0.55 & \\ Trojan-FakeAV & 0.99 & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Quantitative results of training step #2.  
At reference time, given a new sample \(x\), we first compare \(x\) with the Normal Vectors of the parent categories and find the optimal parent category \(i_{p}\) by choosing the maximum similarity. Then, we compare \(x\) with the Normal Vectors of subcategories in \(i_{p}\). Finally, the subcategories \(i_{s}\) with maximum similarity is determined as the label of \(x\).  
Results of two-step inference are shown in Table 10 and Figure 7, and the F1-scores of these categories have improved a lot, especially P2P-Worm, and the grid of P2P-Worm in the confusion matrix is much darker than in Figure 6, demonstrating the effectiveness of the two-step inference.
## 3 Problem Formulation and Modeling  
### Problem Description  
In scenarios where public transport and private vehicles coexist on the same road sections, the control strategy must account for the efficiency of public transport vehicles and their impact on private vehicles, with the goal of improving overall traffic efficiency. The control strategy will generate a sequence of control actions within the action space, using information from the current-state space. Additionally, it will evaluate whether the current decisions can optimize overall traffic efficiency by employing reward and punishment functions, allowing for adjustments in subsequent decisions.  
Studies have adopted the compulsory TSP strategy. In the control strategy for private vehicles, additional rewards and punishments will be introduced, including measures to avoid disruptions to private vehicles and reduce road congestion. This ensures that the priority given to buses does not compromise the overall road network's performance. Level 4 self-driving has been achieved in laboratory settings [29]. Buses and private vehicles on the road are equipped with CACC technology. Considering the above factors, the following assumptions were formulated:  
1. It is assumed that buses and private vehicles on the road are equipped with CACC.
2. The PATH laboratory proposed the following CACC model [30] after verifying it through small-scale platooning experiments. Consequently, real-time information, such as vehicle speed, vehicle position, vehicle class, vehicle arrival time, and the length and signal time of the private vehicle, can be obtained.
3. The buses and the traffic lights are equipped with communication capabilities, enabling them to exchange information with each other.
4. The primary control objective of the traffic lights is to minimize the waiting time for private vehicles while ensuring traffic safety and flow.  
Based on these fundamental assumptions, the goal of real-time signal-priority control for self-driving-bus arterial lines is to minimize the waiting time for private vehicles by effectively controlling the traffic lights when the bus approaches intersections. This, in turn, enhances the overall efficiency of private vehicles. Specifically, by continuously monitoring and analyzing the state of the bus, the system determines the optimal signal light and green-light durations, allowing the bus to pass through the intersection promptly. This optimization improves the bus's operating efficiency and reduces the waiting time for private vehicles. Importantly, this approach prioritizes traffic safety and considers the interests of all participants, ensuring a smooth and safe flow of traffic.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### Laboratory-Scale Implementation Considerations  
The experimental parameters (12 kHz PPM carrier, 180 Hz information signals) reflect laboratory-scale validation using available instrumentation and components. This approach follows established research methodology where proof-of-concept validation precedes system scaling. The frequency selection ensures adequate separation between carrier and information signal frequencies while remaining within the operational capabilities of the test equipment.  
The principle demonstrated at these frequencies remains proportional when extended to higher frequencies suitable for optical wireless communication (OWC) applications. The fundamental mathematical relationships governing derivative-based PPM generation remain valid regardless of operating frequency, with implementation constraints determined by component specifications rather than theoretical limitations.
## 2 Materials and Methods  
A thorough search of the literature was performed on Medline via PubMed, Cochrane Database of Systematic Reviews (CDSR) of the Cochrane Library and Physiotherapy Evidence Databases (PEDro) up to November 2021. In the study were included only systematic reviews and meta-analysis in the English language, published from 2010 to date. A specific research protocol was not registered before the study's start. To perform this search, we used an association of the Mesh terms \"plantar fascitiis\" and \"shock wave\", \"radial extracorporeal shockwave therapy\" (\"RSWT\"), \"focal shockwave therapy\" (\"FSWT\") or \"extracorporeal shock wave therapy\" (ESWT) connected with different Boolean operators (research terms are reported in Supplementary Paper S1). We then screened our results searching for systematic reviews and meta-analyses. Two operators (F.A. and A.B.) independently screened and reviewed all the studies found by the search. Full text articles were obtained when the article was considered important, and duplicated articles were excluded. Disagreement and inconsistencies were overcome by discussion between reviewers and consulting a third reviewer (M.P.). We included systematic reviews and meta-analysis about the use of extracorporeal shock wave therapy (ESWT) in patients diagnosed for plantar fascitiis, so defined by the trial author, regardless of the timing of the symptom's arousal. We included only studies focusing on humans, which enrolled adult people (\(\geq\)18 years old). We considered as interventions ESWT treatment (focused and radial extracorporeal shockwave therapy), compared with other physical/pharmacological therapies or with placebo. We considered as outcomes of interest the following: treatment success and pain relief, analyzed with numerical or other specific rating scores. We also screened the bibliography of the studies included, searching for more systematic reviews or meta-analyses following the inclusion criteria. We excluded articles that were not possible to obtain, articles without an abstract, and articles that considered PF together with other painful foot pathologies. We also excluded articles when ESWT was associated with other physical or pharmacological therapies. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement guidelines were followed to write this umbrella review [25]. Articles eligible for inclusion were assessed for methodological quality using the AMSTAR checklist [26; 27].
# Engineering Design of the European DEMO HCPB Breeding Blanket Breader Zone Mockup
## 2 Design of the Mockup
### Basic Considerations to Obtain Average Nusselt Number  
For obtaining the average Nusselt number through the thermal hydraulics experiment, the Equation (1) could be used. Here, \(Nu\) is the Nusselt number, \(D_{h}\) is the hydraulic diameter; \(\lambda_{f}\) is the thermal conductivity of coolant at bulk temperature; \(h\) is the convective heat transfer coefficient. \(q_{w}^{\prime\prime}\) is the wall-to-fluid heat flux; \(T_{w}\) is the temperature at the coolant-contacting wall; \(T_{f}\) is the bulk temperature of coolant. Once the geometry is given, the \(D_{h}\) is known. The unknowns that have to be derived from measured data are \(\lambda_{f}\), \(q_{w}^{\prime\prime}\), \(T_{w}\), and \(T_{f}\). \(q_{w}^{\prime\prime}\) can be calculated approximately by Equation (2), where \(\lambda_{s}\) is thermal conductivity of steel. \(T_{f}\) can be obtained approximately by Equation (3). Where \(T_{inlet}\) is coolant inlet temperature, \(Q(x)\) is the integrated power up to location x, \(c_{p}\) is the specific heat of the coolant, and \(\dot{m}\) is the mass flow rate. The \(\lambda_{f}\) could be obtained from literature data, once the bulk temperature is known. \(T_{w}\) can be obtained by Equation (4). \(T_{I}\) is a measured thermocouple temperature, \(r_{1}\) is the radius, at which place the thermocouple is installed, \(r_{w}\) is the radius of inner surface of the annular channel, see Figure 2.  
\[Nu=\frac{D_{h}}{\lambda_{f}}*h=\frac{D_{h}}{\lambda_{f}}*\frac{q_{w}^{\prime \prime}}{T_{w}-T_{f}} \tag{1}\]  
\[q_{w}^{\prime\prime}=-\lambda_{s}*\frac{dT}{dx} \tag{2}\]  
\[T_{f}(x)=T_{inlet}+\frac{Q(x)}{c_{p}*\dot{m}} \tag{3}\]  
\[T_{w}=T_{1}-\left|\frac{q_{w}^{\prime\prime}}{\lambda_{s}}*r_{w}*\ln\left( \frac{r_{w}}{r_{1}}\right)\right| \tag{4}\]
## 3 Results
### Antibioticfilm Activity of S. pubescens  
_S. mutans_ attachment onto the surface of the tooth has been implicated as a primary causative agent of dental caries in humans. The results of the examination of whether SPY, SPC, and SPS have inhibitory effects on biofilm formation are shown in Figure 4: the result of SPY showed inhibitory effects of approximately 84.61%, 84.51%, and 86.08% at the concentrations of 0.31, 0.63, and 1.25 mg/m; SPC showed 83.48% and 88.40% at 6.25 and 12.50 mg/mL; and SPS showed about 82.98%, 87.06%, 88.26%, 90.11%, and 92.27% inhibitory effects from 9.38 to 150.00 mg/mL.  
Figure 4: \(Cont.\)  
Figure 3: Measurement of pH and cell growth of _S. mutans_ treated with _S. pubescens_ extracts: SPY **(a)**, SPC **(b)**, and SPS **(c)**. After treating _S. mutans_ with the MIC value of SPY, SPC, and SPS for 24 h, pH and cell growth were measured. The values are mean \(\pm\) standard deviations (SDs); ### \(p\) \(<\) 0.001 versus the non-treated group; \({}^{*}p<\) 0.05, \({}^{**}p<\) 0.01, and \({}^{***}p<\) 0.001 versus the 1% sucrose-treated group.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rate of Change** & **SPY** & **SPC** & **SPS** \\ \hline \multirow{6}{*}{\(\mathrm{pH}\) increase (\%)} & 14.91 & 3.10 & 6.03 \\  & 15.42 & 21.44 & 20.35 \\  & 15.56 & 54.26 & 30.83 \\  & 66.68 & 62.52 & 35.13 \\  & 66.94 & 63.72 & 51.76 \\  & 67.73 & 64.98 & 54.20 \\ \hline \multirow{6}{*}{\(\mathrm{Cell}\) decrease (\%)} & 20.60 & 12.14 & 20.92 \\  & 25.00 & 62.86 & 30.39 \\ \cline{1-1}  & 25.60 & 91.54 & 58.89 \\ \cline{1-1}  & 93.16 & 92.67 & 75.73 \\ \cline{1-1}  & 93.53 & 92.93 & 85.13 \\ \cline{1-1}  & 93.80 & 93.35 & 86.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rate of change regarding pH and growth.
## 2 Deep Echo State Network  
### Leaky Integrator Echo State Network  
A leaky integrator echo state network [18], (LI-ESN), as shown in Figure 1, is a recurrent neural network with three layers: input layer \(\mathbf{u}(t)\in\mathbb{R}^{N_{x}\times 1}\), hidden layer \(\mathbf{x}(t)\in\mathbb{R}^{N_{x}\times 1}\), output layer \(\mathbf{y}(t)\in\mathbb{R}^{N_{y}\times 1}\). \(t\) denotes time sequence order. The hidden layer is regarded as a reservoir, which holds the memory of foregone information, and \(\mathbf{x}(t)\) is refreshed by the state transition function:  
\[\mathbf{x}(t)=\alpha\mathbf{x}(t-1)+(1-\alpha)\tanh(\mathbf{W}_{i}\mathbf{u}( t-1)+\mathbf{W}_{r}\mathbf{x}(t-1)), \tag{1}\]  
where \(\mathbf{W}_{i}\in\mathbb{R}^{N_{x}\times N_{t}}\)is the input weight matrix randomly generated before training, and \(\mathbf{W}_{r}\in\mathbb{R}^{N_{x}\times N_{x}}\) is the reservoir weight matrix previously given before training. \(\alpha\in[0,1]\) is the leaky parameter. \(\tanh(\bullet)\) is the activation function of the hidden layer. The reservoir weights in \(\mathbf{W}_{r}\) must be initialized to satisfy the echo state property (ESP) [19; 20], denoting by \(\rho(\bullet)\) the spectral radius operator (i.e., the largest absolute eigenvalue of its matrix argument), and the necessary condition for the ESP is expressed as follows:  
\[\rho((1-\alpha)\mathbf{I}+\alpha\mathbf{W}_{r})<1. \tag{2}\]  
Accordingly, the values in matrix \(\mathbf{W}_{r}\) are randomly selected from a uniform distribution (e.g., \(U[-1,1]\)), and then rescaled to satisfy the above condition in Equation (2).  
The output \(\mathbf{y}(t)\) can be calculated through a linear combination of reservoir states as follows:  
\[\mathbf{y}(t)=\mathbf{W}_{o}\mathbf{x}(t), \tag{3}\]  
where \(\mathbf{W}_{o}\in\mathbb{R}^{N_{y}\times N_{t}}\)is the output weight matrix.  
During training, the states of reservoir neurons are collected in a training state matrix \(\mathbf{X}_{train}=[\mathbf{x}(1),\mathbf{x}(2)\ldots\mathbf{x}(L_{train})]\), and an output target matrix \(\mathbf{Y}_{train}=[\hat{\mathbf{y}}(1),\hat{\mathbf{y}}(2)\ldots\hat{ \mathbf{y}}(L_{train})]\) is collected correspondingly, where \(L_{train}\) is the number of training samples.  
Figure 1: Structure of LI-ESN.  
The output weights in \(\mathbf{W}_{o}\) can be calculated by ridge regression as follows:  
\[\mathbf{W}_{o}=\Big{(}(\mathbf{X}_{train}^{T}\mathbf{X}_{train}+\lambda\mathbf{ I})^{-1}\mathbf{X}_{train}^{T}\mathbf{Y}_{train}\Big{)}^{T}, \tag{4}\]  
where \((\bullet)^{T}\) represents matrix transpose, \((\bullet)^{-1}\) represents matrix inversion, \(\lambda\mathbf{I}\) is a regularization term ensuring \(\mathbf{X}_{train}^{T}\mathbf{X}_{train}\) is invertible.
# An Efficient Routing Protocol for Quantum Key Distribution Networks
## 2 The Proposed QOLSR Protocol
### The Proposed QOLSR Protocol  
According to the previous analysis, OLSR is a better fit for QKD networks than AODV or DSDV. Therefore, an optimized routing protocol based on OLSR that is more efficient and more suitable for QKD networks is proposed in this study. The main drawbacks of  
Figure 2: Schematic diagram of OLSR routing protocol.  
QKD networks are the frequent changes in link-state, which cause low quantum secure key utilization. The proposed routing protocol, QOLSR, can reduce the frequency of changes in link-state and enhance quantum secure key utilization of QKD networks through link-state awareness and a path optimization algorithm.