esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### The Influence of Number of CPU Cores  
Figure 7a,b shows \(P_{b}\) and \(P_{r}\) as a function of \(B_{2}\) for different number of CPU cores of a first-tier MEC server \(c_{1}\), respectively. We can find that \(P_{r}\) for M/M/3 (\(c_{1}\) = 3) is higher than \(P_{r}\) for M/M/2 (\(c_{1}\) = 3); \(P_{r}\) for M/M/2 (\(c_{1}\) = 2) is higher than \(P_{r}\) for M/M/1 (\(c_{1}\) = 1). \(P_{b}\) for M/M/3 (\(c_{1}\) = 3) is lower than \(P_{b}\) for M/M/2 (\(c_{1}\) = 2); \(P_{b}\) for M/M/2 (\(c_{1}\) = 2) is lower than \(P_{b}\) for M/M/1 (\(c_{1}\) = 1). More CPU cores in the MEC server can reduce the waiting time, so it can relieve blocking. From the results, we can see that a single-core edge server can be replaced by a dual-core edge server with a low service rate, which can achieve better performance. In Figure 7a,b, the optimal bias factor \(B_{2}\) for \(P_{b}\) and \(P_{r}\) (M/M/3) is smallest, and optimal \(B_{2}\) for \(P_{b}\) and \(P_{r}\) (M/M/1) is largest. This is because an MEC server with more CPU cores can achieve better performance. Equations (6) and (7) show that multi-core servers increase \(\pi_{k,0}\) (idle probability), aligning with \(P_{r}\) and \(P_{b}\) improvements. Multi-core servers mitigate blocking by increasing parallel processing capacity, even at lower service rates.
###### Abstract  
This paper deals with a Power Electronic Transformer (PET) topology for a 3-phase AC distribution grid. In the discussed topology, a Modular Multilevel Converter (MMC) and a Full-Bridge converter are employed for the medium voltage (MV) and the low voltage (LV) side, respectively. By using the space vector, approach a mathematical model for the MMC is presented and a grid-synchronous algorithm is implemented to easily control the power flow through the structure. The MV and LV side converters are linked through a High Frequency (HF) transformer, whose control strategy is a Dual-Active Phase-Shift Control (PSC) with Square Wave Modulation (SQM). This technique is combined with a predictive algorithm, which is able to keep each leg's capacitors' voltages balanced both in stationary and in transient conditions. The proposed algorithm is numerically validated in the Matlab/Simulink(r) environment.  
Keywords:power electronic transformer; modular multilevel converter; square wave modulation; voltage balancing; dual active phase shift control; predictive control algorithm; modulation technique +
Footnote †: journal: Applied
## 5 Convolutional Neural Network for Indoor Positioning
### Residual Network  
ResNet features two main types of residual blocks: the basic block and the bottleneck block. The basic block consists of two \(3\times 3\) convolutional layers and a residual connection with a stride of 1, while the bottleneck block includes a \(1\times 1\) convolutional layer, a \(3\times 3\) convolutional layer, another \(1\times 1\) convolutional layer, and a residual connection. The \(1\times 1\) convolutional layer in the bottleneck block is primarily used to decrease the dimension of the feature map, thereby reducing the computation and parameter count.  
In this paper, we consider a basic block of residual blocks. The structure of residual blocks is shown in Figure 3. The core of ResNet's residual blocks lies in its residual connection, which adds the output of the previous layer to the current layer's output, enabling the current layer to incorporate information from the previous layer. This design effectively alleviates the problem of gradients vanishing, making the model easier to train and optimize.  
The ResNet consists of residual blocks that are based on the convolutional layer. For the purpose of accelerating the model convergence speed and improving model performance, we added BN and activation function layers between convolutional layers in the residual structure of ResNet. The Batch Normalization (BN) layer normalizes the input of each layer in a deep neural network for each batch, which stabilizes the input distribution of each layer and accelerates model convergence speed. Assuming the input after augmentation is \(x\), for a layer with multiple input dimensions, the BN operation [56] for each dimension can be expressed as:  
\[\hat{x}=\frac{x-E(x)}{\sqrt{Var(x)+\epsilon}} \tag{13}\]  
where \(E(x)\) and \(\sqrt{Var(x)}\) present the expectation and variance of the input mini-batch respectively, and \(\epsilon\) is a small coefficient to prevent the denominator from being zero, which approximates to 0.  
Alternatively, the activation function layer, ReLU function, is used in the structure due to its simplicity and non-linearity. This function is added after the BN operation to  
Figure 3: Structure of a residual block.  
effectively mitigate the gradient vanishing problem and enhances the model's performance. The activation function can be mathematically presented as  
\[\sigma(\cdot)=ReLU(\hat{x})=max(\hat{x},0), \tag{14}\]
# Track Modulus Assessment of Engineered Interspersed Concrete Sleepers in Ballasted Track
## 3 Field Experimentation Methods
### Single-Point Load Method for Track Modulus Calculation  
The track modulus values were calculated using the single-point load method, which was developed and documented by [PERSON] and [PERSON] [34]. This method requires knowledge of the rail's sectional properties, the wheel load, and the track deflection. Using these inputs, and based on the BOEF fundamentals, the track modulus was computed. The equation used to calculate track modulus is shown below:  
\[k=\frac{\left(\frac{p}{w_{m}}\right)^{4/3}}{\left(64 EI\right)^{1/3}} \tag{5}\]  
Figure 6: The test zone, with the timber and EIC sleepers shown with the instrumentation layout.  
Figure 7: The linear potentiometer and rod system used to measure the vertical track displacement.  
where \(k\) is the track modulus (kN/mm, lb./in./in.); \(w_{m}\) is the maximum track deflection (mm, in); \(P\) is the wheel load (kN, lb.); \(E\) is the modulus of elasticity of steel (MPa, psi) = 210, 000 MPa, 30 \(\times\) 106 psi; \(I\) is the moment of inertia of rail (mm\({}^{4}\), in\({}^{4}\)) = 36,700,000 mm\({}^{4}\), 88.2 in\({}^{4}\) (132 RE Rail); \(EI\) is the flexural rigidity of the rail.  
Other methods to calculate the track modulus require additional knowledge of the entire deflection basin and its area, which is more complicated to quantify analytically. For this study, the single-point load method was deemed appropriate. This study required a deviation from the traditional single-point load method for determining the track modulus because of the interaction between the wheels applying loads adjacent to the point in which the peaks were extracted, so there was no true single point that applied the load. For the rolling stock used, this interaction effect of the adjacent wheels was negligible (approximately 0.8% based on the BOEF analysis). Additionally, the interaction influenced both the EIC sleepers and timber sleepers in the same manner, which mitigated its potential negative impact on the results, which were largely comparative in nature.  
For the single-point load method, there is typically no adjustment for potential slack in the system due to gaps beneath the sleepers. While this could lead to errors based on visual deflection observations that appeared to indicate gaps were present, the large number of sleepers for which data were collected (23 total) increased our confidence in the data, and quantified the representative behavior of each sleeper type while mitigating the presence of gaps below any given crosstie.
## 4 System Model
### Single-Level MPC Method
For example, in Figure 5, Case 3 shows that the pumped hydro storage cannot change its operating state for the first 15 min, but it can change its operating state once when the new half-hour period begins. We can also explain the above process in a different way: when \(t_{s}=0\) (Case 0), the MPC controller, fly wheel, and pumped hydro storage start working, the MPC controller provides the decision, and then the fly wheel and pumped hydro storage systems perform the actions based on the decision. In Case 0, the decision variable of pumped storage belongs to the control variable. Once the pumped storage changes its operating state, it must remain in this state for half an hour. In the following Cases 1-5, the decision variable of the pumped storage is a constant at the initial time, not a variable. In other words, in Cases 1-5, the variable of pumped storage does not take part in the on-line optimization, and it is assigned according to the value of Case 0. When \(t_{s}=1\), \(2\ldots 5\), the corresponding conditions are Case 1\(\ldots\)Case 5, and when \(t_{s}=6\), the corresponding condition is Case 0. The relationship between \(t_{s}\) and the case can be described by Equation (1):  
\[\text{Case}\,n=\text{mod}(t_{s},6) \tag{1}\]  
where mod is the remainder operator, and the relationship is shown in Figure 6.  
Figure 5: Single-level MPC performing rolling optimization (based on pumped storage and fly wheel). In Figure 6, the Ci represent Case i, the roll optimization based pumped storage and fly wheel is repeated after six scheduling intervals.
# Low-Cost _Ka_-Band Satellite Receiver Data Preprocessing for Tropospheric Propagation Studies
## 4 Conclusions  
In this paper, we illustrated, with the aid of received signal and concurrent meteorological recordings, some of the difficulties one faces when trying to perform a millimeter wave satellite beacon propagation experiment lasting over periods of years when affordable measurement equipment is utilized.  
The main challenge was determining a sequence of reference levels representing the 0 dB excess attenuation level (template). There is an underlying daily cycle in the received satellite signal, caused mainly by thermal effects on the outdoor unit electronics. Antenna mispointing can also play a significant role, but not in our case, since we used a large beamwidth antenna. The observed cycles are more remarkable when the temperature dynamic range is larger. It was shown that during cloudy days, the signal excursion is smaller and the daily average levels may be lower due to the presence of water vapor and cloud attenuation. We also observed how it is possible to detect water condensation on the radome, giving rise to deformations of the expected template.  
Conceptualization, V.P.-S. and F.P.-F.; methodology, V.P.-S., D.N., and F.P.-F.; software, V.P.-S., F.M., and D.N.; validation, V.P.-S., F.M., and D.N.; formal analysis, V.P.-S., F.M. and D.N.; investigation, V.P.-S., F.M., D.N., and F.P.-F.; resources, F.M. and F.P.-F.; data curation, V.P.-S. and F.M.; writing--original draft preparation, V.P.-S., F.M., D.N., and F.P.-F.; writing--review and editing, V.P.-S., F.M., D.N., and F.P.-F.; visualization, V.P.-S., F.M., and D.N.; supervision, F.P.-F.; project administration, F.P.-F.; funding acquisition, F.P.-F. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Grant PID2020-113240 RB-I00 funded by MCIN/AEI/10.13039/501100011033.  
Not applicable.  
The authors would like to thank [PERSON] from the Universidade de Vigo (Spain) for providing the disdrometer data. They would also like to thank [PERSON] and [PERSON], students from Ecole Nationale de l'Aviation Civile (ENAC), Toulouse, France, for performing the most of the data compilation and signal preprocessing during their fourth-year stage at an institution abroad.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; nor in the decision to publish the results.
# Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition
## 4 Discussion
Dependence on Data Quality: The model's performance and generalizability depend heavily on the quality and granularity of the input data, which can vary significantly across different healthcare settings. To address this, the further validation of the model on diverse datasets from varied healthcare environments is essential. 3. Ethical Consideration: As machine-learning systems become more prevalent in healthcare, ensuring adherence to the highest standards of data protection and ethical use is crucial. Its integration into clinical practice must consider privacy and data security implications carefully. 4. Feature Selection: To address the limitations in initial feature selection, future work will incorporate a broader and more complex array of variables. This includes integrating clinical notes via natural language processing (NLP) techniques to extract predictors such as patient symptoms, previous medical interventions, and social determinants of health. 5. External Validation: The absence of external validation is a significant limitation. Our future research agenda includes conducting external validations and cross-institutional studies to enhance the model's credibility and applicability across diverse healthcare systems. Collaborations with multiple institutions to access varied datasets will allow the evaluation of the model across different populations and clinical conditions. 6. Our model is designed as a decision-support tool to assist healthcare professionals, not to replace them. The final decision regarding patient disposition should always be made by a qualified medical practitioner who can take into account the complete clinical picture. Before full-scale deployment, our model will undergo extensive pilot testing in a controlled clinical environment. This will help identify any potential issues with false negatives and allow for adjustments based on real-world feedback. Besides these general problems, the most challenging and concrete step is to engage healthcare professionals by presenting those (or similar) results to them and eliciting a positive comment or counterargument; this could be the basis of a long and fruitful applied research agenda, as well as a means to train medical experts in how to deal with the application of ML techniques in several aspects of their work--after all, reviewing one's own work vis a vis the recommendations from an ML-based system could be fruitful even on personal and professional basis. Despite these challenges, the potential of AutoML in medicine is immense and researchers are actively working on these issues.
## 2 System Design
### Processing Unit and Transmission  
Individual sensor nodes commonly have four main parts: a data gathering and processing unit, transmission unit, power management and sensors. Performance of each of these sections in terms of power consumption and reliability greatly affects the overall performance of the sensor nodes and network.  
Figure 2 illustrates a general schematic of different sections of the proposed sensor node for pipeline monitoring.  
Figure 1: General schematic of the proposed Underground Wireless Sensor Network (UWSN) for a pipeline monitoring system.  
Figure 2: Schematic of the sensor node and its components.  
The Micro Controller Unit (MCU) is responsible for gathering measurements from the sensors, processing data, managing the power regime of the nodes and sending data to the transmitter. The performance of the MCU highly affects the overall performance of the node. A careful balance between the processing capability and power consumption is required to achieve optimum overall performance.  
The power management unit is responsible for converting the input voltage from the battery to a usable voltage for the MCU and other components.  
The Transmitter is one of the main parts of any sensor node. This unit is responsible for transmission of the data which is collected by MCU to other nodes via RF signals.  
Signal conditioning is composed of a voltage divider circuitry and passive filters to regulate and condition the signals from the sensors before they are transferred to the analogue to digital converter in the MCU. Another aspect which affects the performance of the sensor node is its firmware. This can greatly affect both the power consumption and reliability. Figure 3 illustrates the latest version of the sensor node without packaging.  
The sensor node illustrated in Figure 3 was designed and developed based on commercially available components in order to keep the overall costs down. In choosing the components special attention was given to power consumption. The MCU of the sensor node plays a major role in its power consumption; a PIC16 LF1827 microcontroller was selected for its low power consumption characteristics (nano Watt XLP Technology). The main feature of this microcontroller is its low power consumption during sleep mode. This is especially important as the node will spend most of its time in sleep mode. The other main component of the sensor node is its RF transceiver; an eRA400 TRS 433 MHz transceiver was selected for its low power requirements and flexible firmware.
## 6 The Description of Contextual Data for E-Service Provision of Multimodal Freight Transportation
Such data are used by the dispatch  
Figure 5: A process diagram of the recommended data transfer and transportation process in multimodal transport (source: modified according to [40]). center of the marine transport terminals to supervise cargo processing and ship dispatch effectively. Integrating the IT system with marine terminals guarantees the synchronization of port operations with shipping schedules and cargo needs. Freight transport is transferred from road to rail and water transportation management. Each transportation management system is responsible for managing certain parts of the route, so ensuring a smooth transition between these systems is essential to protect cargo integrity and stay on schedule. Figure 6: Stages of data transfer in multimodal transportation. The Customs Information System and the Agency of Statistics cooperate with the IS to monitor compliance with the legislation and ensure accurate data submission. The interrelationship between these agencies is critical as they ensure compliance and provide essential statistics for legal and operational transparency. Custom's brokers handle customs formalities at road, rail, and sea transport terminals. Brokers assist in the legal movement of goods across international borders by coordinating with various modes of transportation to ensure regulatory compliance and smooth transitions. The consignee takes over the final shipment, which ends the entire transportation and data transfer procedure. The recipient's preferences and input can influence future transport organization and logistics management strategies, thereby completing the supply chain cycle. The entire process is determined by data from the shipper, which flow through the information system to the transport management systems. This ensures that every step is well informed and coordinated. Each step, such as road, rail, and sea, depends on successfully completing and accurately transferring data from previous steps to function effectively. Cooperation with customs and statistical authorities ensures that all regulatory obligations are met and data are accurately documented for processing, storage, and analysis. In summary, it can be said that the interconnections between different modes of transport guarantee that the system is efficient, well coordinated, and can adapt to different operational and regulatory needs. Therefore, it is important to ensure the integrity of the mutual systems of all participants in the process and the continuous dissemination and management of information and processes. The efficient movement of goods depends on the smooth transfer between different vehicles, made possible by accurate data and well-coordinated activities.
## 4 Extension to Constrained Designs  
### Design of Frequency Modulation Rate \(k_{r}\)  
According to (6), the FM slope trimmed signal can be expressed as follows:  
\[\mathbf{\tilde{J}}(t)=\exp\left[j\pi(k_{r}+\Delta k_{r})t^{2}\right] \tag{15}\]  
where \(k_{r}\) is the FM slope and \(\Delta k_{r}\) is the FM slope mismatch value.  
Two FM slope signals are selected, corresponding to \(k_{r1}=0.9733k_{r}\) and \(k_{r2}=1.0274k_{r}\). Using the matched slope signals as reference signals, pulse compression is performed on these two signals, and the results are shown in Figure 6a. Zooming in on Figure 6a (see Figure 6b), the time width spreads widely, and the pulse compression output amplitude drops rapidly.  
Figure 5: Joint communication and jamming system flow based on FBMC chirp signal.  
In Figure 7, the black dotted line shows the normalized peak and effective time width of the pulse compression output of the FM slope mismatch signal when the bandwidth is varied and the time width is 50 \(\upmu\)s. When the bandwidth is 15 MHz and the time width is 50 \(\upmu\)s, the signal is in the matched state. The peak of pulse compression is the highest, while the effective time width of the output is the smallest, forming an effective main lobe spike.  
The signal uses a multiple false target jamming style, which requires a chirp jamming signal, while the radar signal for the phase reference relationship requires that the tuning frequency does not mismatch to retain the pulse compression gain, i.e.,  
\[\sqrt{|k_{r}|}\cdot T_{r}\geq\sqrt{BT_{r}} \tag{16}\]
## 3 How to Treat and Protect Cultural Heritage Assets against Biodeteriogens?  
According to [PERSON] [1] and [PERSON] [5], to control biological colonization, indirect and direct methods may be applied. The choice is dependent on various factors, which include the biodeteriogen types and their degree of extension, and the nature and status of conservation of the artefact, as first suggested by the Italian Committee [6].  
Indirect methods are intended to modify or limit the effects of some harmful environmental factors (pH, RH, T, light, etc.), which directly affect the manufact, contributing to the microorganisms growth (Figure 1).  
As these do not act directly on the object, they may be considered fully green in terms of conservation. They are suitable for indoor interventions, while they are difficult to adopt in outdoor (Figure 2). It is pleonastic to add that the control of environmental factors is almost unachievable for outdoor cultural heritage assets, since these may cover a large area and, hence, be subjected to climate changes and pollution.  
Figure 1: Example of physicochemical parameters affecting an indoor environment. The presence of visitors requires lights and produces an increase of temperature, relative humidity, and release of CO2, dramatically changing the delicate equilibrium of these environments. The image shows the Crypt of Popes in St. Callistus Catacombs, Rome, Italy, during the CATS project (photo, [PERSON]).  
Figure 2: Example of outdoor archeological area. Remains of the antique Roman town of Ercolano (Naples, Italy). In general, outdoor environmental parameters such as sun irradiation, rain, raising humidity, wind, etc. can only be partially controlled due to the different exposition and the extension of the area. Indirect methods aim to keep environmental parameters in a range of acceptability. Image taken during the project POR Calabria FESR, project “NANOPROTECH” (NANO PROtection Technology for Cultural Heritage, J24E07000380005). (photo, [PERSON]).  
In order to control the growth of organisms in most conservation procedures, the use of direct methods (mechanical, physical, chemical, and biological) is the only solution. In this scenario, whatever method is chosen, any intervention has to be carefully planned in order to reduce the risks of unwanted recolonization. To prevent this drawback, a multistep approach, which takes into account the whole of the factors influencing medium and/or long-term conservation, has to be considered.  
Direct methods, acting on the material's surface, are potentially harmful to the artifact. For this, it is important to be aware of the advantages/disadvantages that the different methods entail.