esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low Energy IoT Application Using Beacon for Indoor Localization
## 2 Related Works
When the reader generates an electromagnetic field, the tag, passing through the field itself, transmits its information to the reader. Once the tag has decoded the reader signal as correct, it \"responds\" to it by reflecting, through its antenna, and modulating the field emitted by the reader [20; 21]. The communication between reader and tag are described in specific standards, while the information that the tag transmits to the reader is contained ina certain amount of memory that each tag contains within it. Currently, the standardization process sees organizations such as ISO, ETSI and EPC-global as protagonists. Among the various standards adopted for this RFID technology we find the following [22]:  
* ISO/IEC 15961: \"RFID for Item Management: Host Interrogator; Tag functional commands and other syntax features\";
* ISO/IEC 15962: \"RFID for Item Management: Data Syntax\";
* ISO/IEC 15963: \"Unique Identification of RF tag and Registration Authority to manage the uniqueness\". In the application field, the use of an RFID system is very broad. For example, it is possible to exploit this technology in storage sites for the tracking of goods in transit, for access control through the use of tickets, for identification by season tickets or cards, and even for the location of livestock in the pasture or wild animals. In short, it is a technology that has many applications but which also faces some problems, such as privacy and security, high cost, and possibility of uncertain reading in the case of a large number of articles. The uncertainty in the reading may depend on strong interference due to the presence of both liquid masses, which absorb the signals, and to metal surfaces, which reflect them. Table 1 summarizes the main technologies used for an indoor localization system in relation to accuracy, power consumption and cost. Some recent works have shown how it is possible to use passive Wi-Fi tag and Wi-Fi fingerprint techniques to obtain an indoor location and other possible ones on UWB [23; 24; 25]. One of the main problems highlighted in the literature are interference problems. In fact, in order to have a good response, there is a need to have systems that are sufficiently performant or to be in charge of environmental disturbances, such as electromagnetic sources, shielded materials or even cases of low consumption techniques that the same people constitute as problems.
## 1 Introduction
In previous research, the processes of crystal growth or aggregation of amino acids and proteins by light-induced force have been observed primarily via fluorescence imaging, light-scattering imaging, or fluorescence spectrum measurement [26; 27]. However, fluorescence signals generally reflect only the lowest excited electronic states, and light-scattering signal intensity is too weak to provide the properties of micro-aggregates. Thus, it is difficult to obtain detailed information about aggregation states such as aggregation numbers from the spectroscopic methods listed above. We have successfully identified in the previous report [48] that the aggregation states of porphyrin molecules, 4-[10,15,20-tris(4-sulfophenyl)-21,24-dihydroporphyrin-5-yl]benzenesulfonic acid (TPPS), are formed in an unsaturated solution with continuous-wave laser irradiation, off-resonant from main molecular absorption peaks. We have observed absorbance change, \(\Delta\)A \(\sim\) 10\({}^{-3}\), in the resonance region of the optical transition to the aggregation state. Surprisingly, this absorbance change is approximately 10\({}^{4}\)-10\({}^{5}\) times larger than the ratio of the optical trapping potential energy to the thermal energy (10\({}^{-7}\)-10\({}^{-8}\)), and its physical mechanism is unknown. Differing from previous studies [10; 26; 27; 28; 29; 30], our study employs neither a metal nanostructure to enhance the optical force via a plasmonic effect [15; 16; 17; 18] nor other surface effects [30; 31; 32]. In this paper, to determine how to elucidate the mechanism of the phenomenon, we investigate the solvent dependence of the light-induced absorbance change.
# Research on Joint Resource Allocation for Multibeam Satellite Based on Metaheuristic Algorithms
## 3 Constraint Handling Method
### Non-Dominated Beam Coding  
We assume that beams with different polarizations are independent; therefore, we focus on the bandwidth overlap between adjacent beams with the same polarization. We assume that beam B dominates beam A when beam A is adjacent to beam B and that the bandwidth of beam A is influenced by beam B. If we treat the bandwidths of the beams sequentially, the dominance phenomenon is almost ubiquitous; however, if we assign values to the bandwidth beams at intervals, there is no influence between them.  
As depicted in Figure 4, the adjacent non-dominant beams dominate the bandwidth of the dominant beam, and there is no bandwidth overlap constraint between non-dominant beams. The bandwidth vector (\(XB_{ND}\)) can be encoded as follows:  
\[xb_{j}=r,r\sim\mathrm{U}(0,1),j\in BS_{ND}, \tag{17}\]  
\[XB_{ND}=[xb_{1},xb_{2},\cdots,xb_{N^{nd}}]. \tag{18}\]  
As demonstrated in Figure 5, the NDBC method encodes only the non-dominated beams and generates directly corresponding bandwidth values, whereas the bandwidth of the dominated beams is determined by their respective adjacent dominated beams.  
Once the population has evolved during the population evolution session, it must be converted to a bandwidth allocation vector in order to ensure that the bandwidth constraint is met. The conversion method is as follows:  
\[bw_{j}=xb_{i}\times B_{tot},\forall j=nd_{i}\in BS_{ND},i\in\left\{1,2,\cdots, N^{nd}\right\}, \tag{19}\]  
\[bw_{j}=\left\{\begin{array}{c}\left[1-\max(xb_{a},xb_{b})\right]\times B_{ tot},\exists(a,j,b)_{adj,p}\\ \left(1-xb_{a}\right)\times B_{tot},only\exists(a,j)_{adj,p}\end{array}\right.,for\left\{\begin{array}{c}\forall j\
otin BS_{ND}\\ \forall a,b\in BS_{ND}\&a\
eq b\end{array}\right.. \tag{20}\]
# Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding
## 2 The HES Architecture and Modeling in Stateflow
### Implementing EMS Using STATEFLOW  
The EMS controls the energy flow among different sources, i.e., PV, ESU, and Gen, with the load. Its objective is to maintain the power balance while satisfying load demand under the operational constraints of the system. In this particular study, the EMS is designed to accomplish two main tasks: (1) to ensure uninterrupted supply of electricity in the presence of load shedding, and (2) to optimize the utilization of available energy generated from both renewable and conventional sources. The HES can be modeled in SF where the states represent different modes of operation for HES. The HES switches between these states according to the predefined operating conditions of the EMS. Figure 4 shows the SF chart for the HES operation.  
Figure 4: The HES operation in SF.  
The variables P_Grid, P_PV, and P_Load are inputs to the chart, while the ESU power (P_ESU) is defined as local data in the super-state HES_Operation. It is based on SOC and defined as the maximum power that ESU can constantly deliver for 1 h. Similarly, P_Gen is defined in the Gen_Mode state as constant data. The term \"h\" represents the time-step and is expressed in hours. At the output, Grid_Mode, PV_Mode, ESU_Mode, and Gen_Mode track the energy transactions (for each time-step) among various parts of the system. The variable SOC\({}_{\text{C}}\) represents the current SOC; it maintains the record of SOC for each hour.  
The state diagram is hierarchical with both AND and OR compositions. The names of the states suggest the operation of different units. The HES_Operation, which represents the overall operation of HES, has OR composition with two child states: Grid_Connected_Mode and Islanded_Mode. The former is the normal (default) operation, where the electrical grid is expected to fulfill the entire load demand. On the other hand, the Islanded_Mode specifies the operation using the alternative sources (PV, ESU, and Gen). This state is executed when load shedding occurs. Further, Grid_Connected_Mode has AND composition of two child-states, i.e., PV_Mode and the Grid_Mode, whereas the Islanded_Mode contains two child states, RES_Mode and Gen_Mode, with OR composition.  
The chart utilizes two types of transitions: inter-state and the intra-state. The inter-state transition is prioritized; it changes the operation from Grid_Connected_Mode to Islanded_Mode and vice versa (according to CFT). On the other hand, the intra-state transition is evaluated if the inter-state transition is not executed. It has a temporal logic operator (timer) to represent the _for-loop_ construct. Table 1 describes the events and the CFT for the HES_Operation state. Based on the hourly values of the available data, the operating scenario is created for each time-step (hour) of the chart execution. Under different operating scenarios, different operational modes of HES are activated. The operating modes implemented using states can be seen in Figure 4.
## 2 Materials and Methods
### Terra Climate Datasets  
TerraClimate is a gridded dataset of monthly climate and climate water balance over the global land surface. These data provide important inputs for global-scale ecological and hydrological studies that require high-spatial-resolution and time-varying data. All the data have a monthly temporal resolution and a spatial resolution of 4 km (1/24 th of a degree). The data cover the period 1958-2022. In addition to maximum and minimum temperatures and precipitation (Pre), TerraClimate provides derived variables (evapotranspiration (ET), vapor pressure deficit (VPD), and palmer drought severity index (PDSI)) and water balance indicators (runoff, snow water equivalent, soil moisture, and climate moisture deficit). Its strength lies in combining fine spatial resolution climatology with temporal information from 1958 to the present; in addition to the standard monthly climate summaries, TerraClimate provides more directly ecologically and hydrologically relevant surface hydrological climate variables that are readily available for download ([[https://www.ecmwf.int](https://www.ecmwf.int)]([https://www.ecmwf.int](https://www.ecmwf.int)), accessed on 8 April 2023) [39].  
We selected the monthly data for max air temperature (Tmax), min air temperature (Tmin), short-wave incoming radiation (SWdown), PDSI, Pre, ET, and runoff from the TerraClimate dataset. To facilitate annual analysis, the monthly Tmax, Tmin, SWdown, and PDSI data were aggregated into annual means by averaging across the months within each year for the period 1986-2016 [40]. Correspondingly, the monthly Pre, ET, and runoff data were aggregated into annual totals by summation over the same period.
## 4 Comparison of Different Techniques and Discussion
The AUC values for each feature are given in Figure 14, and the AUC values presented for each feature and fusion combination are presented in Figure 15. The combination of features refers to the values given in Figure 9. It could be observed that fusion with averaging and saliency performs best in almost all fusion combinations. Additionally, the defect detection performance of radiographic features (except for the radiography amplitude feature) can be improved by the fusion application of the best performing ultrasonic signals (U2, U4, U6). In addition, the visual examination of the fusion results presented in Figure 13 reveals that fused results give additional information about the structure. It is obvious that radiography reveals the inclusion type defects very well; however, ultrasonic results provide additional information about the structure--additional disbond regions around the edges of the inclusions are visible.
# Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review
## 3 Applications of Smart Drug Delivery Devices  
IoT-based drug delivery systems are becoming increasingly popular in the healthcare sector due to their potential to enhance the accuracy, efficiency, and effectiveness of drug delivery. In smart drug delivery systems, various algorithms are involved to analyse data, make intelligent decisions, and optimise treatment. Closed-loop control algorithms are used in systems such as closed-loop insulin delivery for diabetes management, where real-time sensor data are continuously monitored and analysed to determine the appropriate drug dosage or delivery rate. Pharmacokine and pharmacodynamic algorithms utilise mathematical models to optimise dosing regimens and predict drug responses based on factors such as drug absorption, distribution, metabolism, and excretion. Machine learning algorithms can analyse large datasets to identify patterns or correlations that inform drug dosing decisions and optimise treatment outcomes. These algorithms enable personalised dosing recommendations, adaptive dosing adjustments, and real-time alerts, enhancing the effectiveness and safety of drug delivery systems [14]. Smart drug delivery systems hold significant potential in improving medication compliance and reducing the burden of complex regimens. IoT-based systems provide unique advantages for smart drug delivery, leveraging their connectivity and seamless data exchange capabilities. With real-time monitoring and data analytics, IoT sensors continuously track drug delivery parameters, patient responses, and environmental factors, enabling prompt intervention and optimised treatment protocols [3]. Remote access and connectivity allow healthcare providers to remotely monitor patient progress, making them particularly valuable for chronic disease management and telemedicine services. Enhanced patient engagement through mobile apps, wearables, and interactive interfaces empowers patients to actively participate in their treatment, leading to improved self-management and treatment outcomes [13]. The scalability and interoperability of IoT infrastructure enable seamless integration with existing healthcare systems, allowing smart drug delivery systems to adapt to evolving needs. Predictive maintenance and data-driven insights optimise system reliability, resource allocation, and workflow efficiency. The classification of IoT-based drug delivery systems is shown in Figure 5.  
Figure 4: Body sensors commonly used in healthcare applications.
# Meta-Extreme Learning Machine for Short-Term Traffic Flow Forecasting
## 3 Experiments
### Discussion  
The purpose of this paper is to propose a more accurate and efficient forecasting model to address the problems of existing short-term traffic flow forecasting methods. From the results of the experiments, our proposed hybrid model achieved the expected results. In the results of comparing each model using experimental evaluation criteria, our proposed ABC-ELM model achieved more accurate predictions. First, we attribute all this mainly to the fact that the hybrid model unlocks the potential of the forecasting model using the features of the optimization algorithm. Since the parameters of ELM are determined randomly, this indicates a huge optimization space for the learning process of ELM. We unlocked the potential of ELM using the ABC optimization algorithm. Second, the experiment shows that GA-ELM, GSA-ELM, and ABC-ELM all use optimization algorithms to optimize ELM, but the ABC-ELM achieved higher prediction accuracy. This is mainly due to the fact that the ABC algorithm uses fewer control parameters but maintains a certain level of robustness. It also finds the current local solution and the global optimal solution just by comparing the advantages and disadvantages of the problem in each round  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Aggregation Intervals** & **Data A1** & **Data A2** & **Data A4** & **Data A8** \\ \hline
5 min & 12.68 & 11.50 & 11.68 & 13.03 \\
10 min & **11.40** & **9.95** & **11.26** & **11.90** \\
20 min & 13.44 & 12.98 & 15.68 & 15.79 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The MAPE(%) of ABC-ELM at different aggregation intervals.  
Figure 8: Comparison of MAPE (**a,b**) values of three learning models ELM, GSA-ELM, and ABC-ELM at 5 min and 20 min aggregation interval. ABC-ELM has the lowest MAPE values in all four datasets A1, A2, A4, A8.  
of iterations. So, the probability of finding the optimal solution increases significantly. However, there are still shortcomings in this paper. First, the experiments designed in this paper are more applicable to ABC-ELM models with a priori defined variables and implemented data aggregation. Second, our proposed ABC-ELM model and the experimental design in the paper do not use preprocessing of irregular time series. This leads to traditional methods used to process time series data introducing bias and failing to model the temporal irregularities of incomplete time series. We consider the two irregular time series preprocessing methods proposed in [54] to be constructive. In the future, we will introduce the irregular time series preprocessing methods, and the idea of hybrid models can be applied to many regression problems, such as predicting spatial CO\({}_{2}\) concentration, forecasting airport passenger traffic, etc.
## 1 Introduction
### Related Work  
State-of-the-art LPFs usually target specific wireless communication systems with a limited configurable filter parameter set, mostly focused on bandwidth tuning. In most reported cases, bandwidth selection is limited to several pre-defined values and a tuning ratio not exceeding 10 [19; 20]. Small signal linearity performance of wideband filters, expressed as in-band input-referred third-order intercept point (IIP3), is typically around 20 dBm. Reported linearity-related improvements, like feedforward compensation techniques in operational amplifiers, or use of additional active stages, can enhance linearity performance at the expense of power consumption and design complexity [20; 21]. Furthermore, LPF parameters such as noise figure (NF), linearity performance, for both small and large signal conditions, are typically dependent on the settings of filter bandwidth, with no means of compensation [19; 20; 22].  
Most of the reported flexible channel selection filters are based on active-RC or gm-C designs. While wideband designs can use gm-C filters, they are more suited for low power solutions, have poor linearity and dynamic range performance compared to active-RC designs [23]. Channel selection filter implementation typically does not exceed the 8 th order, with most common values being 2 nd, 4 th, or 6 th [20; 24]. None of the reported filters have functionality to reconfigure order of the filter.  
Although most of the reviewed filters use either a Butterworth or a Chebyshev approximation, there are reports of elliptical filter implementations for wideband low-pass filters [25]. While elliptical filters with transmission zero in stopband in theory could offer sharper transition from passband to stopband while maintaining a flat passband response, they require extreme gain-bandwidth product (GBWP) for large bandwidths to meet the specifications of passband linearity and have an accurate realization of the active-RC filter [22]. Implementations of wideband filters can suffer from PVT and hence complex tuning schemes are usually needed. Without precise calibration of quality factor (Q), elliptical filters can produce a response that deviates from the initial design, while still requiring additional elements to implement them [25].  
In this work, a wideband reconfigurable integrated LPF for 5G NR compatible SDR solutions is designed and validated. The proposed LPF has programmable bandwidth and filter order, as well as process, voltage, temperature (PVT) compensation, and power management features. This article consists of four chapters. In the second chapter, LPF and operation amplifier design is presented. In the third chapter, post layout simulation results of the LPF are reported. The final chapter concludes the presentation.
## 3 Design of Improved Multi-Structural Morphology Filtering Approach
### Connected Domain Calculation Based on Moment Features  
The morphological features of a vessel can be obtained by using the contour-based connectivity domain marking method [54]. The moment characteristics of the connected region are expressed as follows:\[m_{pq}=\sum_{i=-\infty}^{\infty}\sum_{j=-\infty}^{\infty}i^{p}j^{q}f(i,j) \tag{25}\] \[u_{pq}=\sum_{i=-\infty}^{\infty}\sum_{j=-\infty}^{\infty}(i-i_{c}) ^{p}(j-j_{c})^{q}f(i,j) \tag{26}\]  
where \(m_{pq}\) represents the region of the \((p+q)\)-order moment, \(u_{pq}\) denotes the central moment, \(i\) and \(j\) are the region point coordinates, and \(i_{c}=\frac{m_{10}}{m_{00}}\) and \(j_{c}=\frac{m_{01}}{m_{00}}\) represent the region centroid coordinates. In this study, the length of the region \(l=\max(j-j_{c})\), the height of the region \(h=\max(i-i_{c})\), the aspect ratio \(r=\frac{l}{h}\), and the area of the region \(c=m_{00}\) are used to describe the target vessel. These four quantities form a four-dimensional target feature vector \(V(l,h,r,c)\) which is directly deleted when it does not meet the set threshold in the binary image [55]. The feature vector \(V(l,h,r,c)\) must execute the vessel's contour moment function.
## 4 Results and Discussion
### Parametric Effect Analysis of Temperature  
Figure 6a shows the polarization curves at different temperatures obtained from the dynamic mechanistic model in the previous section. At an operating current density of 0.2 A/cm\({}^{2}\), activation loss is the major part of the voltage loss, and the output voltage increases with temperature. This is because the increase in catalyst activity and the acceleration of the electrochemical reaction rate lead to a decrease in the activation loss. When the stack is operated at a current density of 0.6 A/cm\({}^{2}\), the activation loss ceases to increase, and the linear increase in ohmic loss with current density makes it a major part of the voltage loss. Excessive temperatures (90 \({}^{\circ}\)C) cause the output voltage to drop. At an operating current density of 1.2 A/cm\({}^{2}\), the output voltage at 90 \({}^{\circ}\)C is significantly lower than at other temperatures due to a significant increase in the ohmic loss and concentration loss. The reason is that the excessively high operating temperature vaporizes the water in the MEA, the water content of the membrane decreases, and then the proton transport capacity decreases.  
The variation in voltage losses at different temperatures is shown in Figure 6b. The activation loss decreases with increasing temperature, and it is more evident at high current density than at low current density because a suitable temperature promotes the electrochemical reaction in the stack. The temperature increase accelerates the reaction rate, leading to a decrease in the activation loss. The ohmic loss increases dramatically at high temperatures due to the decrease in the water content of the proton exchange membrane caused by high temperatures. Temperature increase leads to a slight reduction in the concentration loss at high current densities.