esa-sceva/satcom-chunk-collection huggingface dataset:
# Stochastic Model Predictive Control for Urban Traffic Networks
## 4 Optimization Method
### Uncertain Function Approximation  
Neural networks (NNs) (also referred to as connectionist systems) are a computational approach, which is inspired by the current understanding of using biological brain to solve problems. It is a class of adaptive systems consisting of a number of simple processing elements, called artificial neurons. Neural networks (NNs) typically consist of multiple layers or a cube design, and the signal path traverses from front to back. An important advantage of NNs is the ability to learn to perform operations, not only for inputs exactly like the training data, but also for new data that may be incomplete or noisy. It has been widely recognized from literature [28; 29; 30; 38] that neural networks (NNs) have the ability to approximate continuous or discontinuous functions and achieve a high speed of operation. Therefore, we decided to train a feedforward NN to approximate the uncertain function (23), aiming at speeding up the solution process of the optimization problem (17).  
As pointed out in the user's guide of the neural network toolbox, a fairly simple neural network can fit any practical function. In this study, we create a single-hidden layer feedforward neural network (SFNN) by using the neural network toolbox of MATLAB (R2016a, MathWorks, Beijing, China). The number of input neurons and that of output neurons are defined as \(N_{in}\) and \(N_{out}\), which are both decided by the function needed to be approximated. Since the number of input neurons is bigger than that of the output neurons, we set \(N_{hide}\) the number of neurons in the hidden layer is proportional to that of the input neurons. That is, \(N_{hide}=\gamma N_{in}\) (\(\gamma>1\) is a constant). In order to use the input and output data to train the SFNN, we need to preprocess the data first. In practical application, both input data and output data are usually normalized in the interval \([0,1]\).
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 4 Discussion
### Impact of Increasing Satellite Number  
In order to validate the efficacy of the algorithm proposed in this study further under conditions of large-scale satellite deployment, we conduct additional comparisons on energy consumption and algorithm running time across various quantities of LEO satellites. In these experiments, each LEO satellite is assumed to contain an average of three missions.  
As depicted in Figure 11, the total energy consumption under each strategy increases correspondingly with the rising number of satellites. This is to be expected as more satellites invariably lead to more missions. Notably, the energy consumption under the SSPCO strategy consistently remains lower than that under other strategies, affirming the superior energy-saving capabilities of the SSPCO method.  
Moreover, Figure 11 demonstrates the inevitable increase in the algorithm's running time resulting from the growth in satellite quantity, as a larger number of satellites engenders an expanded solution space. Remarkably, the running time of SSPCO is significantly shorter than that of PSOCO and GACO. This is due to the design of SSPCO, which constructs an improved flow network, thereby avoiding extensive ineffective searching like other algorithms and effectively reducing the runtime.  
The combined results of Figure 11 clearly exhibit the effectiveness and superiority of the proposed SSPCO when applied to a large-scale satellite deployment.  
Figure 11: Impact of the different number of LEO satellites on the total energy consumption and the running time. (**a**) Total energy consumption. (**b**) Running time of 20 experiments.
###### Abstract  
The effects of climate change and urbanisation, such as more intense rainfall and changing land use patterns, are putting increasing pressure on urban drainage systems. This study proposes a comprehensive methodology for evaluating the effectiveness of sustainable urban drainage systems (SUDSs) in mitigating flooding and managing stormwater in both current and future scenarios. The approach integrates geospatial data, including digital elevation models (DEMs) and land use information, to delineate catchments and characterise hydrological parameters. Historical rainfall records and hydrological modelling were employed to define two baseline storm events: an extreme storm involving 422 mm of rainfall over 2 h, and an average storm involving 2.84 mm of rainfall over 1 h and 18 min. Future scenarios were developed by updating these baseline events using annual rates of change in maximum and average precipitation derived from climate projections between 2025 and 2100. The analysis incorporates seven CMIP6 climate scenarios: SSP1-1.9, SSP1-2.6, SSP4-3.4, SSP4-2.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5. A stochastic simulation of 1000 storms per year was carried out using a custom-built conceptual hydrological model based on CN and developed in Python, which reflects interannual variability. The results show that extreme storm volumes could increase by up to seven times and average storm volumes by up to two and a half times. Additionally, discharge peaks could exceed baseline values by up to 20% in some years, suggesting an increased occurrence of extreme runoff events. The methodology assesses SUDS performance by comparing runoff and hydrological responses between baseline and future estimates. This framework enables vulnerabilities and adaptation needs to be identified, ensuring the long-term effectiveness of SUDSs in managing urban flood risk. Addressing uncertainties in climate and land use projections emphasises the importance of integrating SUDS assessments into wider urban resilience strategies.  
sustainable urban drainage systems (SUDSs); climate change adaptation; urban hydrology; flood risk management; hydrological modelling
# A Software-Defined Radio for Future Wireless Communication Systems at 60 GHz
## 5 Conclusions  
In this article, a novel software-defined radio solution for future wireless communication systems is presented and validated as a complete multi-Gigabit/s radio in-the-loop OFDM transmission scheme with mmWave 60 GHz RF front-end to cope with UHD multi-streams.  
The SDR hardware choice was based on the overall criteria of low time-to-market and reduced development costs. On the one hand, VC707 FPGA was the choice for the embedded DSP engine, since at that time it was the best compromise among available logic resources, highly-flexible, high-speed serial bus I/O interface, and cost. In addition, both FMC230/FMC126 were selected based on their inherent high sampling rates, bit resolution, and their compatibility with the VC707 FMC I/O interface connectivity.  
Figure 22: Photograph of the proposed testbed: (**a**) Techdays 2017, Aveiro; and (**b**) Ciência 2018, Lisbon.  
Moreover, firmware connectivity issues between the embedded system and both FMC230/FMC126 have been outlined in this work, as well as the considered workarounds to enable a GSPS real-time communication system. Currently, the SDR is configured to 1.25 GSPS, which, as discussed, is 5G future-proof. In addition, both ADC/DAC converters were deeply characterized in terms of SNR, ENOB, SINAD, and SFDR, and their advantages of a digital IF RF architecture has been extensively discussed.  
Additionally, a complete multi-Gigabit/s radio in-the-loop OFDM communication system was implemented for high data rate applications considering 4-, 16-, 64-, and 256-QAM, which was validated. EVM results in both scenarios were below 2%, which is quite remarkable considering the current norm for employed 256-QAM OFDM transmissions is 2.5% [31].  
A complete overview on mmWave front-end was done, which together with the baseband unit form a robust communication system with rates up to 1 Gbps. The chosen use case for this work was UHD video stream that was achieved with a 180\({}^{\circ}\) field of view setup available at IT.  
In summary, this work details the required steps to achieve a real-time testbed that meets 5G requirements on the mmWave frequency range with 1 Gbps and low latency main characteristics.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing and visualization--L.D., R.G., C.R. and R.F.S.C.; Supervision, project administration and funding acquisition--C.R. and R.F.S.C.  
This research was partially supported by the Portuguese Government under the project RETOT--Reflectometry Technologies to Enhance the Future Internet of Things and Cyber-Physical Systems, reference POCI-01-0145-FEDER-016432 (SAICT-45- 2015-03/16432), funded by FEDER within COMPETE 2020: Instituto de Telecomunicacoes under the projects 5G TESTBED and 5G MIMO TESTBED (UID/EEA/50008/2019).  
The authors declare no conflict of interest.
## 6 Challenges and Future Directions
### AI in Industrial Wireless Communications  
We can design and implement practical communication technologies for various wireless channels in actual application environments when we have channel models that accurately depict the physical effects of these channels on transmitted radio signals. Traditional channel modeling techniques, such as stored channel impulse responses, deterministic channel models, and stochastic channel models, require an in-depth understanding of the subject matter and technical proficiency in radio signal propagation modeling through electromagnetic fields. They are extremely complex, involve several parameters, are unsuitable for predicting the statistical characteristics of wireless channels, and are not transferable to other communication settings. To avoid these difficulties and complexities, machine learning and artificial intelligence approaches can help. For example, a generative adversarial network (GAN) framework was proposed in [72] to address the autonomous wireless channel modeling issue without requiring extensive theoretical investigation or data processing. Another example is the interplay between RIS and AI in wireless communications, which was explored in [73].  
Modern manufacturing industries have progressed from electronic automation systems to industrial digital machine communication. In order to gather and analyze data for guiding dynamic, intelligent systems in manufacturing, these systems need a high-speed connection. When 5G communications are combined with AI and big data, it becomes possible to analyze industrial data more quickly and make precise decisions based on the data. Smart factories provide better-connected machine ecosystems by using big data from interconnected devices powered by AI and 5G connectivity [74].
## 3 Channel Estimation in Different Frequency Bands
### Channel Estimation for RIS Systems in Low Frequency Bands  
In this section, the focus is on the channel estimation method for RIS systems in low frequency bands. At low frequency bands, due to its rich multipath scattering, an unstructured channel model is used, but it is difficult to describe the overall characteristics of the propagation environment with the method. Unstructured CSI requires a large training overhead; therefore, many channel estimation methods have been proposed to reduce the training overhead.  
The channel estimation methods in low frequency bands are described in Figure 4. Methods are based on fully-passive RIS and semi-passive RIS in different scenarios, which are divided into four main aspects: MISO, MIMO, massive MIMO, and wideband system.  
The channel estimation methods of different RIS systems are presented by different RIS architectures. Similar to the high band, the fully-passive RIS mostly uses the estimation method of cascaded channels, while the semi-passive can use the method of separate channel estimation.
# A Design of a Parcel Delivery System for Point to Point Delivery with IoT Technology
## 3 Improved Parcel Delivery System Design
### Shortest Path Check Stage  
At this stage, we should know how to identify the shortest route to add a new destination. At the destination, other parcels can simply stop and move to the next logistics hub. The process is as follows. First, get GPS data from stage Section 3.2. It then calculates the distance between the current GPS data and the destination. In case the distance is shorter than the threshold value, then send the parcel data to the ThingPlug server via LoRa and send ID info of an IoT device to a transmitter's smartphone via Bluetooth. Then, add new destination information on the IoT device and update the logistics route. Finally, complete the moving step and move onto stage Section 3.4. If the distance is longer than the threshold value, wait for a certain time delay before the loop restarts.  
Figure 4: Sequence diagram of Improved Parcel Delivery System: IoT Devices Oriented.
## 6 Federated Learning for Early Health Prediction
### Federated Learning for Disease and Mortality Prediction
#### 6.2.2 Heart Diseases  
[PERSON] et al. in [87] presented a novel application of federated learning in the context of heart disease prediction. Their research introduced a federated learning framework that allows multiple healthcare institutions to collaboratively train a predictive model while keeping patient data decentralized and secure. By aggregating local model updates from different data sources, this approach achieves improved accuracy in heart disease prediction, facilitating early detection and intervention. The study highlights the potential of federated learning to harness the collective knowledge of distributed healthcare providers, enhancing the quality of predictive models without compromising patient data privacy, which is crucial for advancing cardiovascular disease management in a privacy-aware healthcare landscape.
## 2 Minimally Invasive Therapies for HCC and Their Tissular Effects
### Thermal Ablative Therapies
#### 2.1.4 High-Intensity Focused Ultrasound  
High-intensity focused ultrasound (HIFU) is based on the transmission of mechanical waves emitted by piezoelectric transducers and focused through acoustic lenses; the ultrasounds are emitted at a low frequency (0.8-1.6 MHz) on a specific target [56]. Single or multi-focal HCC disease may be addressed through this method, though success rates with complete ablation are reported in 50 to 100% of cases [57; 58]. Data on the improvement in  
Figure 4: MRI 5 weeks after laser ablation of an HCC nodule in the right liver lobe. The treated nodule (arrow) is isointense on T1 fat sat (**a**), hyperintense on T2 fat sat (**d**), has no arterial enhancement on the subtraction images (**b**), no restricted diffusion (high signal intensity on both DWI (**e**) and ADC (**f**)) and low contrast uptake on the hepatobilary phase (**c**).  
survival are scarce, and studies on the application of HIFU in HCC generally yield small populations. One of the major advantages of HIFU is that it does not require puncturing the tumor [59] and the heat is quickly generated so dissipation through blood vessels is negligible [23]; however, in order to optimize the ultrasound transmission window, several invasive gestures may be applied, such as rib removal, instillation of saline solution in the pleura or peritoneal cavity, or filling the lumina of the stomach or colon [56]. Conversely, the method might prove useful in patients with advanced liver disease and ascites where other minimally invasive procedures are not indicated.  
The effects of HIFU are mixed, and include both thermal and mechanical effects. Thermal effects are caused by the ultrasound energy that raises the temperature and induces coagulative necrosis. Excessive temperatures may be obtained and vaporization may also be achieved through this method [60]. The mechanical effects are represented by acoustic cavitation, a process that implies the creation of microbubbles that subsequently expand and implode, inducing extreme pressure variations that injure the adjacent cells, a process called the \"popcorn effect\" [60].  
HIFU has been deemed safe and effective in ablating small HCC by various studies; despite this, it may face difficulties when attempting the treatment of nodules in areas of the liver that require interventions to optimize acoustic transmission [57; 58; 61].
## 7 Conclusions  
In this paper, we studied the joint VNF placement, user association, and traffic routing in B5G networks targeting energy efficiency maximization, while ensuring a high UE acceptance ratio. We modeled the aforementioned problem as a MILP with minimal assumptions, which captured all characteristics of the employed technologies, resource, and service types as well as their constraints and power consumption. To tackle the prohibitive complexity of the studied problem, we proposed HERO, an energy-efficient resource planning heuristic, which was shown to significantly outperform the SoA, while achieving up to 78% of the optimal value, with up to 742 times lower complexity. Finally, we described the Orchestration Framework that is responsible for the dynamic and automated orchestration of Vertical-driven services in tailored E2E Network Slices, and explained how such a framework is assisted by the proposed algorithms, which jointly compute the optimal allocation of both network and compute resources across the radio, core, and transport network segments. Although the proposed formulation and heuristic algorithm can be extended to also handle service requests dynamically arriving in time (i.e., they can be used to implement an **online** joint computational and communications resource allocation policy), the computational complexity of the heuristic may still be too high to properly adapt to the small time scale of request arrivals. To this end, in the future, we plan to explore learning-based techniques (inspired from approximate reinforcement learning) to construct low-complexity online energy-efficient resource allocation algorithms. Conceptualization, M.G. and A.M.; architecture design, F.M. and G.L. ; methodology, A.M. and M.G. ; prototype implementation, L.L. ; validation, A.M. and M.G. ; formal analysis, M.G. ; resources, G.K.; writing--original draft preparation, M.G. and A.M.; writing--review and editing, G.K., N.P. and F.M. ; visualization, A.M.; supervision, N.P. and N.C.; project administration, G.K. and F.M. ; funding acquisition, G.K. and N.C. All authors have read and agreed to the published version of the manuscript. Figure 9: Percentage of active nodes and links of all algorithms for different numbers of UEs per gNB area. **Funding:** This work was funded by H2020 5G-COMPLETE (GA 871900). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** No new data were created or analyzed in this study. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Lithium-Ion Battery Modeling and Experimental Setup
### Experimental Setup  
The AW-IMR14500 lithium-ion batteries are used in this paper. The cathode material is LiCoO2, and the anode material is graphite. The basic specifications of the cells are given in Table 1. The TIBQ27 GDK000 EVM system is used to test the cells, and ESPEC is used to maintain a constant ambient temperature. The tests were carried out in three cells to ensure reproducibility. The test cells all reached the standard discharge cutoff current/voltage and were fully relaxed to ensure the same initial discharge conditions before each test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specification** \\ \hline Nominal Capacity & 700 mAh \\ Charge Mode & CC 1/CV 2 \\ Charge Voltage & 4.2 V \\ Standard Charge Current & 140 mA \\ Fast Charge Current & 1400 mA \\ Charge Cut-off Current & 14 mA \\ Discharge End Voltage & 3.0 V \\ Standard Discharge Current & 140 mA \\ \hline \hline \end{tabular}  
* Constant Current, 2 Constant Voltage.  
\end{table}
Table 1: Battery details.  
Figure 2: The Thevenin model.  
The tests consist of the incremental OCV test, the low-current OCV test, and the dynamic test. Constant current discharge and relaxation operations are alternated in the incremental OCV test. Furthermore, the discharge current set to 1C (700 mA), and the relaxation time is set 2 h for each release of 35 mAh (5% SOC). The test stops when the cell reaches the discharge cutoff voltage in the last discharge process and then relax for 2 h. The low current OCV test uses a constant current discharge mode setting the discharge current to C/50 (35 mA). The test stops when the cell reaches the discharge cutoff voltage and then relaxes for 2 h. Dynamic discharge current and relaxation operations are alternated in the dynamic discharge test. The dynamic discharge process is set to continuously discharge at 700 mA for 200 s, 500 mA for 100 s, 100 mA for 100 s, 400 mA for 100 s, 50 mA for 100 s and 200 mA for 100 s. The relaxation time is set at 2 h. The dynamic test stops when the cell reaches the discharge cutoff voltage in the last constant discharge process and then relaxes for 2 h. The discharge current settings for the above incremental OCV test and the dynamic test are shown in Figure 3.  
State of Charge (SOC) Estimation with Fused Open Circuit Voltage (OCV) Curve Based on Unscented Kalman Filter (UKF)
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
## 4 Results  
The 3-, 5-, 7-, and 9-day arc solutions are displayed from different aspects to fully evaluate the accuracy and stability of SLR-only orbit determination for BDS satellites. A total of 173 RMS groups of SLR multi-day arc solutions are analysed from the half-year time series in 2019. To avoid the contamination of bad multi-day arc solutions, these bad RMS of multi-day arc solutions are regarded as failure solutions, based on the strategy described in Section 2.2. The success rate of multi-day solutions for all nine BDS satellites is presented in Section 4.1. Then Section 4.2 shows the \(D\) -value, the pattern of each average RMS value, and the Sun elevation angle of \(\left|\beta\right|\), as well as the overall average RMS values of the orbit overlaps and comparison with WUM orbits in RTN directions. The overall average 3D-RMS is presented in Section 4.3. Section 4.4 analyses the median RMS of 9-day solutions.
###### Abstract  
Carrier aggregation (CA) was introduced in mobile communication systems in response to the demand for higher network capacity. CA was conceived as a technique to achieve higher data rates by aggregating multiple blocks of spectrum from the same or different frequency bands. This work explores a different point of view, where CA is employed not as a way to increase capacity through using more bandwidth, but as a diversity technique in order to increase the spectral efficiency of the existing spectrum, and therefore, achieve higher capacity without needing additional spectrum. A mathematical model and set of closed-form expressions are provided, which can be used to characterise the performance of CA as a diversity technique (in terms of both ergodic capacity and secrecy capacity) and determine the impact of various relevant configuration parameters. The numerical results obtained by evaluating the mathematical expressions derived in this work are in line with our previous simulation studies and demonstrate that CA can be effectively exploited as a diversity technique to improve the capacity and performance of mobile communication systems compared to the case of single-carrier transmission over the same amount of bandwidth.  
mobile communications; carrier aggregation; diversity techniques; performance analysis Article