esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Process and Equipment  
The research team contacted the Portuguese Basketball Federation to organize the data collection process. After the project was approved, the regional selections were contacted and provided with informed consent forms to be signed by the players' legal guardians. Once all players' participation was authorized, the equipment to be used during data collection was prepared.  
Each player was equipped with a WIMUProTM inertial device that was attached and placed in a specific custom vest fitted tightly to the back of the upper torso, as is typically used in games. The SVIVOTM software (v. 923.4) automatically analyzed all the data collected by the inertial device and sent it to the computer screen in real time. This UWB system solves the problem of satellite referencing, using time-based positioning techniques in which the signal propagates from the transmitter to the receiver [44]. The UWB system was adjusted to the reference field before the start of the investigation, covering the perimeter of the field so that it would be recognized as a reference system [25]. This system consisted of eight antennas placed in a rectangle outside the playing field (Figure 1) [45]. The ANT+ transmitter emits a wireless signal for several seconds, and inertial devices include ANT+ receivers that register a mark in the software when they receive a signal. This solution enables automatic synchronization of time and positioning data in the software (SPROTM) [46]. The WIMU ProTM inertial device, the UWB system, and the software come from the same organization [47].  
For the analysis of the competition, the UWB system was calibrated 1 h before the start of the first games of the day, and the WIMUProTM inertial devices were synchronized with the UWB system through ANT+ technology. Following the protocol suggested by the device manufacturers, the court perimeter was traversed with two devices placed in close proximity, and the constancy of the distance between them was assessed using the UWB monitoring system at a sampling frequency of 100 Hz. The total measurement error was 0.08 \(\pm\) 0.05 m. This margin of error is optimal for basketball monitoring, and the calibration of both the UWB system and the inertial devices was therefore deemed acceptable for this  
Figure 1: UWB system on basketball court [6].  
study. Each player was equipped with the inertial device 20 min before the start of the match. In this way, there was a familiarization period during the warm-up. Once the match started, total and live times were calculated using the SVIVO\({}^{\text{TM}}\) software, with total time referring to the entire time a player was on the field, including all stoppages of play, but excluding intervals between quarters and free time. Live time corresponds to the time when the game clock was running and the player was on the field. The SPRO\({}^{\text{TM}}\) software was used to automatically analyze all the information collected by the inertial device [48].
# Multi-User Activity Recognition Using Plot Images Based on Ambiental Sensors
###### Abstract  
Artificial intelligence has increasingly taken over various aspects of daily life, resulting in the proliferation of smart devices and the development of smart living and working environments. One significant domain within this technological advancement is human activity recognition, which includes a broad spectrum of applications such as patient monitoring and supervision of children's activities. In this research, we endeavor to design a human activity recognition system that effectively analyzes multi-user data through a machine learning framework centered on graphical plot images. The proposed methodology uses a PIR sensor-based system. This system uses a two-stage process; the first one involves generating new image datasets as density map images and graphical representations based on the Kyoto CASAS multi-user dataset. In the second stage, the generated data are provided to a sequential convolutional neural network, which predicts the 16 activities developed by two users. To generate the new datasets, we only used data from ambient sensors, which were organized in windows. We tested many types of window dimensions and extra features such as temporal aspect and the limitation of two activities in one window. The neural network was optimized by increasing the deconvolutional layers and adding the AdamW optimizer. The results demonstrate the viability of this method, evidencing an accuracy rate of 83% for multi-user activity and an accuracy rate of 99% for single-user activity. This study successfully achieved its objective of identifying an efficient activity recognition methodology and a data image representation. Furthermore, future enhancements are anticipated by integrating data sourced from PIR sensors, with information gathered from user-personal devices such as smartphones. This approach is also applicable to real-time recognition systems.  
multi-user activity recognition; CASAS; plot image generation; plot image generation analysis; Kyoto multiresident ADL activities set; Keras +
Footnote †: journal: Applied sciences
# On Optimal Cell Flashing for Reducing Delay and Saving Energy in Wireless Networks
## 2 System Model
The system can be modeled by an \(M/GI/1\) multi-class processor sharing system, which reflects the fact that users see different service rates and file sizes based on their locations [24]. We also consider infinitely many classes because we address this problem in a continuous space \(\mathcal{L}\). The average number of flows at BS \(i\) is then simply given by \(\frac{\rho_{i}(\beta)}{1-\rho_{i}(\beta)}\) and total number of flows in all base stations is \(\sum_{i\in\mathcal{B}}\frac{\rho_{i}(\beta)}{1-\rho_{i}(\beta)}\)[28]. From [PERSON]'s formula, minimizing the average number of flows is equivalent to minimizing the average delay experienced by a typical flow. Minimizing \(\sum_{i}\frac{\rho_{i}(\beta)}{1-\rho_{i}(\beta)}\) is equivalent to minimizing \(\sum_{i}\frac{1}{1-\rho_{i}(\beta)}\) because \(\sum_{i}\left(\frac{\rho_{i}(\beta)}{1-\rho_{i}(\beta)}+1\right)=\sum_{i}\frac {1}{1-\rho_{i}(\beta)}\), which serves as one part of our objective functions in Equations (6) and (8). Our notations are summarized in Table 1. \begin{table}
\begin{tabular}{l l} \hline \hline \(x\) & location in continuous space \(\mathcal{L}\) \\ \(i\in\mathcal{B}\) & BS index \\ \(\beta\) & cell flashing ratio \\ \(\lambda(x)\) & flow arrival rate per unit area \\ \(1/\mu(x)\) & average file size at \(x\) \\ \(\gamma(x)\) & \(:=\frac{\lambda(x)}{\mu(x)}\), inhomogeneous traffic load density \\ \(c_{i}^{0}(x)\) & the physical capacity at \(x\) from BS \(i\) during Partition 0 \\ \(c_{i}^{1}(x)\) & the physical capacity at \(x\) from BS \(i\) during Partition 1, 2 or 3 \\ \(c_{i}(x,\beta)\) & \(:=(1-\beta)c_{i}^{0}(x)+\frac{\beta}{3}\ c_{i}^{1}(x)\), reference capacity \\ \(\rho_{i}(x,\beta)\) & \(:=\frac{\gamma(x)}{c_{i}(x,\beta)}\) system-load density (fractional time) \\ \(\mathcal{L}_{i}\) & the coverage of BS \(i\) \\ \(\rho_{i}(\beta)\) & \(:=\)\(\int_{\mathcal{L}_{i}}\rho_{i}(x,\beta)dx\), load of BS \(i\) having \(\beta\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation summary (BS: base station).
# Design and Simulation of Low-Orbit Satellite Broadcast Signal Receiving and Processing Terminal
## 3 Key Technologies
### Overall Machine Design
The beam steering FPGA uses Fudan Microelectronics' JFM7K325T (K7 for short) chip which contains a large number of DSP units inside the chip. The DSP unit is mainly used for digital down-conversion and phase-shifting processes of multi-beams, narrow-band anti-interference, etc. [44]. The synthesized beams complete narrow-band anti-interference inside the FPGA. After processing, it is sent to the baseband processing FPGA. The chip contains a large number of logic resources, which is sufficient for multi-channel capture, tracking, and decoding processes. The schematic block diagram of the baseband processing board is shown in Figure 15.
# A Mathematical Model for Scheduling and Assignment of Customers in Hospital Waste Collection Routes
## 4 Results
### Computational Experiments and Findings  
The proposed model was programmed using AMPL and solved with GUROBI 9.0 and CPLEX 12.10 solvers. The computer has an Intel Core i3-3217U processor with 4 GB RAM Windows 10 64-bit operating system. In the tables, CPU time is the time the solver requires to obtain the solution. The gap is the percentage between the best bound obtained and the model's best integer solution. In addition, all instances were executed with a time limit of 35,000 s (9.7 h). The proposed approach was applied twice, with GUROBI and CPLEX, to each region independently to compare both solvers' performance.  
The algorithm for solving the proposed mixed-integer linear programming model is called branch-and-bound. Initially, this procedure considers the original model by removing all integrality constraints (13). The resulting linear programming model (LP) is called the relaxation of the original model. The LP model is solved, and if the obtained result satisfies all of the integrality constraints, the approach has obtained the optimal solution for the original problem. If not, as is usually the case, then the standard procedure is to pick some variable that must be constrained to be an integer whose value in the LP relaxation is fractional. This variable is then called a branching variable, and the algorithm must branch on it, producing the two new sub-MIP problems P1 and P2. If we can compute optimal solutions for each of P1 and P2, the algorithm takes the better of these two solutions and will be optimal to the original model. The algorithm now applies the same idea to these two MIPs, solving the corresponding LP relaxations and, if necessary, selecting branching variables. Therefore, a search tree is generated. The MIPs generated by the search procedure is called the tree's nodes, with the original model designated as the root  
\begin{table}
\begin{tabular}{l c c c} \hline
**Frequency** & **Special Waste Customers** & **Percentage** & **Cumulative Percentage** \\ \hline Monthly & 444 & 54.41\% & 54.41\% \\ Weekly & 214 & 26.23\% & 80.64\% \\ Biweekly & 99 & 12.13\% & 92.77\% \\ Semi-weekly & 30 & 3.68\% & 96.45\% \\ Thrice-weekly & 23 & 2.82\% & 99.27\% \\ Daily & 6 & 0.73\% & 100.00\% \\ \hline Total & 816 & 100.00\% & \\ \hline \end{tabular} Source: By the authors based on data obtained from company.  
\end{table}
Table 1: Customer visit frequency.  
\begin{table}
\begin{tabular}{c c c} \hline
**Range in Kilograms (X)** & **No. Customers** & **\% Customers** \\ \hline ine X \(\leq\) 10 & 517 & 63.36\% \\ 10 \(\leq\) X \(\leq\) 100 & 215 & 26.35\% \\ 100 \(\leq\) X \(\leq\) 1000 & 76 & 9.31\% \\ 1000 \(\leq\) X & 8 & 0.98\% \\ \hline Total & 816 & 100.00\% \\ \hline \end{tabular} Source: By the authors based on data obtained from company.  
\end{table}
Table 2: Customer classification.  
node. In general, the algorithm has solved the original MIP if it reaches a point at which it can solve or otherwise dispose of all leaf nodes.
## 2 Materials and Methods
### Stage 4: Statistical Sampling and Subset Selection  
Since our goal is to find the best routing policy and technology combinations, we need to make sure that the selected systems really are the best. To do so, we used ranking and selection approaches. Usually, they determine the amount of data to sample or the number of replications from each population, or allocate the number of replications to each alternative from a fixed total computing budget. There are several approaches developed by researchers over the years, including the indifference-zone approach, the optimal computing budget allocation sequential approach (OCBA), and the enhanced two-stage selection (ETSS) [31; 32; 33]. However, they all present different drawbacks. Due to the least configuration assumption (LFC), the indifference-zone approach is known to be conservative as LFC is rarely encountered in reality. The OCBA, on the other hand, assumes that the true standard deviation of each alternative is known. Lastly, for the ETSS, there is no guarantee that the probability of correct selection is minimal [31]. Although the indifference-zone approach is conservative, it presents the advantage of being able to pre-specify a probability of correct selection, and set up a mean difference threshold based on the data at hand. Our goal is to rank the different policy combinations based on specific differences in the efficiency values. Therefore, we adopt the indifference-zone approach, more specifically the method developed by [34].  
The Koenig and Law method is a two-stage approach of selecting a subset of systems containing the best performing ones. The first stage consists of implementing a number of initial replications and finding the first-stage sample means. Based on the specified indifference amount and the desired probability of correct selection, additional random observations and the second-stage sample means are determined. Finally, the weights are defined to obtain the weighted sample means. Two systems are considered equal in performance if the difference between their mean efficiencies is less than the indifference amount. Figure 1 summarizes the process followed to estimate outputs and cost components to determine the cost efficiency and select the efficient policies' combinations.
## 3 Challenges and Research Motivations
Furthermore, if the load balance is not considered, the nodes located in the center of the body and closer to the sink will face congestion issues. Besides, these schemes, because of a lack of multiobjective features, can only achieve one objective at the expense of others in the given time. Thus, these schemes cannot maximize the performance and solve the challenges associated with monitoring the human body; thus, they are quite vulnerable to body channel fluctuations in BASNs. Hence, a novel routing protocol is greatly needed for BASN-based health monitoring applications. All aforesaid facts motivated our research to develop a novel, multiobjective, lion mating optimization inspired, clustering-based routing protocol that simultaneously supports various Quality of Service (QoS) requirements for BASN-based IoT health monitoring applications. Inthe proposed scheme, the entire routing problem has been modeled using mixed-integer linear programming (MILP). This research has the following main contributions:  
* A novel, multiobjective lion mating optimization algorithm is proposed to avoid local search problems during tackling the various objectives in the given problem search space. * A multiobjective, lion mating optimization based routing mechanism is proposed to provide robust, reliable, and energy-efficient delivery of patient data to the medical data center in dynamic situations, where doctors or autodiagnostic systems can react to abnormal situations. * Extensive simulation studies are performed using MATLAB 9.5 (R2018b) to validate the performance of the proposed scheme against the existing routing protocols designed for BASN-based health monitoring applications.
## 2 Materials and Methods
### DLS Measurements  
Dynamic light scattering (DLS) and \(\zeta\)-potential measurements were performed by means of a Zetasizer Nano ZS90 instrument (Malvern, UK). The instrument uses a He/Ne laser (wavelength 633 nm) and the scattering angle is 173\({}^{\circ}\). Hydrodynamic diameters are expressed as Z-average values. PSD curves were obtained by the instrument software using the non-negative least square fitting method. Number distribution curves were calculated on the basis of the Mie theory using the optical properties of bulk magnetite (refractive index, n: 2.3602; absorption coefficient, k: 0.14716). \(\zeta\)-potential values were determined from the electrophoretic mobility measurements applying the Smoluchowski approximation. NPs were dispersed in DDW. The pH of the aqueous dispersions was ca. 6.5. The concentration of all samples was of the order of 0.05 mg/mL and the measurements were performed at 25 \({}^{\circ}\)C. The reported values of the Z-average diameter and of the polydispersity index (PDI) are the average of three sets of acquisitions. The dispersions were sonicated (35 kHz, ultrasonic nominal power 80 W) for 30 min before DLS measurements.
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 3 Design and Implementation
### Code Structure  
The system code is composed of four executable Python files and three text files, which are used to save the received packets.  
1. The modified GNU Radio code file: This Python file contains the main functions that execute the GMSK transmitter and receiver codes. It initiates the ports and makes the connections. The modifications allow it to be run and terminated without opening the GNU Radio program. Its parameters, such as the port numbers, data rate, running duration and operating frequency, is changed by introducing a \"parameter set\" function.
2. Data management functions file: This creates the functions to deal with the transmitted and received data. It differentiates between the received packet types and save them into the correct files. It generates the signaling packets and the feedback replies.
3. Subsystem control file: This is the file that is run when the satellite starts sending. It creates the threads and arranges them. It specifies the durations and delays. It controls the overall operation, as well as the subsystem and its parameters.
4. Setting File: The code of this file prepares the communication parameters based on the feedback.
## 1 Introduction
### Case Study  
In our study, we focused on the case of milk fermentation. Milk is a liquid foodstuff that is consumed in large quantities. It contains many nutrients, such as proteins, lactose, minerals, and vitamins. The composition of milk varies according to its origin and type (e.g., cow, buffalo, sheep, and goat). Its nutritional value can be increased further by the fermentation process. The final products can improve digestibility and have health benefits and organoleptic properties ([PERSON], 1958; [PERSON], 1958). Depending on the microorganisms used in the fermentation process, products consist of yogurt, kefir, sour cream, kumis, cheese, and many others. During the fermentation, carbon dioxide, acetic acid, diacetyl, acetaldehyde, ethyl alcohol, and several other substances are formed, and these give the products their characteristic fresh taste and aroma. There is a great interest in the optimisation of these bioprocesses, especially because of the unique health effects of fermentation products. For the presentation of the applicability of adaptive control techniques to control a specific bioprocess, milk fermentation with kefir grains was considered as a case study.  
Traditionally, kefir is produced by inoculating kefir grains, which are a mixture of proteins, polysaccharides, mesophilic, homofermentative, and heteroformentative lactic acid streptococci, thermophilic and mesophilic lactobacilli, acetic acid bacteria, and yeast. Fermentation of the milk by the inoculum proceeds for 24 h, during which time homofermentative lactic acid streptococci grow rapidly, initially causing a drop in pH. This low pH favours the growth of lactobacilli, but causes the streptococci number to decline. The presence of yeasts in the mixture, together with the fermentation temperature (21-23 \({}^{\circ}\)C), encourages the growth of aroma-producing heterofermatterative streptococci. As fermentation proceeds, the growth of lactic acid bacteria is favoured over the growth of yeasts and acetic acid bacteria ([PERSON], 1958).  
Milk fermentation with kefir grains' propagation is an inherently very complex process because of the specific nature of the microbial metabolism, as well as the non-linearity of its kinetics. Therefore, monitoring and control are extremely important to develop models that would be able to provide an accurate description of the progress of the process.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 4 Integration of Quantum Money with Quantum Networks
### Infrastructure for Quantum Money Transfers
#### 4.3.1 Satellite-Based Entanglement Distribution  
For global and long-distance transfers, satellite-based systems could be used to distribute entangled particles over vast distances. This approach is already being explored in quantum key distribution (QKD) and would be suitable for quantum money transfers, connecting users in remote or international locations.  
In satellite-based entanglement distribution, pairs of entangled photons are generated and transmitted directly from a satellite--typically orbiting in low-Earth orbit (LEO)--to two optical ground stations. Figure 2 illustrates this process. Once ground stations A and B share an entangled state, they can perform quantum teleportation of an arbitrary qubit, following the procedure described in Section 4.1. Recent experimental advances have demonstrated the feasibility of satellite-based entanglement distribution between two locations separated by 1203 km on Earth [34].
## 5 Case Study: NCS Performance over WirelessHART
### Study of Alternative Physical Layer Technologies  
In this part, we demonstrate the ability of the simulation framework to compare different physical layer options. In particular, we run the batch reactor benchmark over both legacy 802.15.4 PHY and over two different 802.15.4 g-based PHYs (both the 200-kbit/s and 400-kbit/s modes) and compare the ratio of stable outcomes between the two simulations. All of the simulation parameters are identical to those in Table 1; only the K-factor is increased to 10 dB, making the channel fading less pronounced. The quadratic control error (with respect to the impulse response of the delay-free feedback loop with a 10-ms sample interval) is shown in Figure 13 for the high-correlation flat fading channel, for sample intervals between 20 ms and 50 ms. Unstable outcomes are excluded from the mean error calculation, but we provide the 95 th percentile error over all 100 independent drops. The 802.15.4 g 200 kbit/s PHY outperforms the other two PHYs; the feedback system remained always stable, and there is very little variation in the control error over the runs.
# Developmental Screening System for Patient Vibration Signals with Knee Disorder
## 3 Results and Discussion
### Swing Angles in Analyses of Sound Changes  
To observe the swing angle and the variations in sound signals, we used a six-axis inertial measurement unit to capture angle signals. In both the experimental and control groups, the knee joints were assessed at the ACL sensing point. The swing cycle was 4 s. Patients with left-knee ACL rupture exhibited the maximum amplitude in the sound signal change generated when the knee joint movement was changed from flexion to extension (0-90\({}^{\circ}\)); the actual changes in angle were 30-90\({}^{\circ}\). Bending from extension back to flexion (90-0\({}^{\circ}\)) also affected the amplitude. For the experimental group, the patients' right knees were not diagnosed as ACL rupture. In accordance with the signal waveform, the signal amplitude was most obvious during the knee movement from flexion to extension. The amplitude variations from extension to flexion were smaller.  
When the sound signals are being recorded, the maximum angle of action may be limited, e.g., when the patient cannot fully extend the knee and has a maximum angle of 60\({}^{\circ}\) as opposed to 90\({}^{\circ}\). Thus, the maximum bending angle of the joint can also be captured using the swing-angle measurement described. As such, the complete bending angle variation of the injured knee joint can be determined before and after treatment.