esa-sceva/satcom-chunk-collection huggingface dataset:
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 2 Jumping Animals and Biomimetic Miniature Jumping Robots
### Invertebrates
#### 2.1.6 Froghopper Inspired Jumping Robots  
Froghoppers have a ventral protrusion from a coxa, which engages with a protrusion from the dorsal surface of the femur. This means that both hind legs are held in fully cocked position without moving for a few seconds. At some point, hind legs depress and extend completely in less than 1 ms and a click sound occurs at the start of a jump, which possibly originates from the initial movements of the coxo-trochanteral joint or from the disengagement of the coxa and femur [61; 62].  
[PERSON] et al. [134] found that, during jump, froghoppers can control the take-off angle by adjusting the relative positions of hind legs' tibae, realizing synchronous jumps. Based on this, [PERSON] et al. proposed a froghopper-inspired robot with adjustable take-off direction [135] by means of a gear mechanism placed at the hip joints of hind legs. In particular, by manually acting on these gears, it is possible to preadjust the knee joint angles to achieve the controllability of take-off direction. Although these knee joints cannot be adjusted actively, this solution provides a new bioinspired idea to solve the control of the jumping direction of miniature jumping robots.
###### Abstract  
Quickly designing digital circuits that are both correct and efficient poses significant challenges. Electronics, especially those incorporating sequential logic circuits, are complex to design and test. While Electronic Design Automation (EDA) tools aid designers, they do not fully automate the creation of synthesisable circuits that can be directly translated into hardware. This paper introduces a system that employs Grammatical Evolution (GE) to automatically generate synthesisable Hardware Description Language (HDL) code for the Finite State Machine (FSM) of a Multi-Sequence Detector (MSD). This MSD differs significantly from prior work as it can detect multiple sequences in contrast to the single-sequence detectors discussed in existing literature. Sequence Detectors (SDs) are essential in circuits that detect sequences of specific events to produce timely alerts. The proposed MSD applies to a real-time vending machine scenario, enabling customer selections upon successful payment. However, this technique can evolve any MSD, such as a traffic light control system or a robot navigation system. We examine two parent selection techniques, Tournament Selection (TS) and Lexicase Selection (LS), demonstrating that LS performs better than TS, although both techniques successfully produce synthesisable hardware solutions. Both hand-crafted \"Gold\" and evolved circuits are synthesised using Generic Process Design Kit (GPDK) technologies at 45 nm, 90 nm, and 180 nm scales, demonstrating their efficacy.  
electronic design automation; evolvable hardware; sequence detectors; grammatical evolution; synthesisable sequential logic circuits; hardware description language design
## 1 Introduction
Different technologies have different characteristics, such as power and energy density, efficiency, response time, lifetime, investment and operation costs, and technical maturity. Therefore, application specific requirements must be well understood [9]. It is expected that in the future solar PV power producers will be required to implement production profile manipulation via external energy storages. Basic energy and power capacity requirements for the PV integrated ESSs are already known. For example, they have been derived from the so-called worst fluctuation model verified both with real measurement data from PV power plants and simulations [10]. The next step is to find out the requirements for the ESS when more sophisticated PV power production profile shaping is needed. One interesting topic is the operation of large grid connected utility scale PV power plants feeding the generated power to the grid on the electricity market. When feed-in tariffs are not available in the future, PV power production must meet the ISP requirements on the electricity market. This paper examines the energy and power capacity requirements for an ESS in a grid connected PV power plant meeting the electricity market requirements. The analysis is done for three different PV system sizes and two different ISP lengths. Similar production profile shaping studies have been done before [11]. The novelty of this paper lies in its deeper analysis of ESS requirements. Particularly, energy capacity requirements were analyzed thoroughly. For example, by altering the observation period length it was found that highest energy capacity requirements take place very seldom and quite randomly around the year. Therefore, there is potential for savings when sizing the energy storage by recognizing the instances that cause the highest energy capacity requirements beforehand.
# Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE
## 6 Conclusions  
In this paper, a novel automatic method for estimating QoS thresholds to be integrated in user utility functions on a per-service basis in an LTE system is proposed. The method relies on the collection of radio connection traces. In the first stage, connection traces are classified into application groups based on QCI and traffic descriptors registered per connection. Then, a minimum QoS threshold is inferred on a per-service basis by analyzing the QoS indicator with the largest impact on user experience and the traffic indicator that best reflects user behavior. The method has been tested with traces taken from live LTE networks, resulting in a minimum DL user throughput of 5 Mbps for full-buffer data services, 30 Mbps for streaming services and a maximum DL packet delay of 20 ms for VoIP services. The proposed data-driven method can be fully automated, eliminating the need for time-consuming subjective tests. Likewise, it can deal with the large diversity of system and human factors, which cannot be taken into account in lab environments. Due to its low computational load, it can be executed periodically to track changes in user trends. Additional analysis can be extended to 5G and broadband Internet satellite systems to check the impact of network capabilities on general user behavior.  
The contributions of authors are as follows: Conceptualization, A.J.G., M.T. and S.L.-R.; methodology, A.J.G. and M.T.; software, A.J.G. and M.T.; validation, A.J.G. and M.T.; formal analysis, A.J.G.; investigation, A.J.G. and M.T.; resources, M.T. and S.L.-R.; data curation, A.J.G. and C.G.; writing--original draft preparation, A.J.G.; writing--review and editing, M.T. and S.L.-R.; visualization, A.J.G.; supervision, M.T. and S.L.-R.; project administration, M.T. and S.L.-R.; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the Spanish Ministry of Science, Innovation and Universities (RTI2018-099148-BI00), the Junta de Andalucia (UMA18-FEDERJA256) and Ericsson Spain.  
Restrictions apply to the availability of these data. Data were obtained from Ericsson Spain and are available from A.J.G. with the permission of Ericsson Spain.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BDA & Big Data Analytics \\ CDR & Charge Data Record \\ CEM & Customer Experience Management \\ CM & Configuration Management \\ CRM & Customer Relationship Management \\ CTF & Configuration Trace File \\ CTR & Cell Traffic Recording \\ DL & Downlink \\ \end{tabular}
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 2 LoRaWAN Outline
### Architecture  
The network architecture of LoRaWAN [9] is depicted in Figure 1. It includes four entities: (_i_) the ENs, which are in charge of sensing variables, and transmit them via LoRaWAN radio frequency (RF) protocol, (_ii_) the GWs, which receives the information from the ENs and retransmit it to a server via LoRaWAN TCP/IP, (_iii_) the network server (NS) that receives the information from the GWs, deletes duplicates, and exhibits some services to send information to the applications by using different protocols like message queue telemetry transport (MQTT) or reverse hypertext transport protocol (Reverse HTTP), and (_iv_) application servers that receive information from the NS and store it, analyze it, or visualize it. Mainly, if an EN needs to transmit a frame, it sends a broadcast message that various GWs can receive. In this case, the same frame can be retransmitted by more than one GW, so the NS filters the information and puts the information into an MQTT broker or a cloud-based server.  
Figure 1: LoRaWAN architecture. A typical LoRaWAN deployment includes end nodes, gateways, a network server, and application servers.
## 4 Computational Experiments
### Input Data and Parameters  
Before optimization, some basic data need to be input, including train operation data, passenger OD demand and other parameters involved in the model.  
In this experiment, we designed a 4-h train timetable from 7:00-11:00. Passenger OD demand during this time horizon on a weekday in 2018 is shown in Table 8. It can be found that the largest passenger demand is 1735 between station \(A\) and station \(J\). Except for the terminal stations, Stations \(D\), \(E\) and \(G\) have a relatively large passenger demand.  
Considering that the passenger demand in Table 8 is not correlated with the expected departure time, we assume that the expected departure times during the whole study period obey the normal distribution [31] in order to obtain the passenger expected departure times. Then, the passenger demand with expected departure time is simulated by Equations (17) and (18).  
\[p(t)=\frac{1}{\sqrt{2\pi\sigma}}*\exp\left(-\frac{(t-\mu)^{2}}{2\sigma^{2}}\right) \tag{17}\]  
\[\varphi(t)=q*p(t) \tag{18}\]  
At each station, \(p(t)\) indicates the proportion of passengers expecting to depart at time \(t\); \(\varphi(t)\) presents the number of passengers expecting to depart at time \(t\); and \(q\) indicates the total passenger demand for an OD pair.  
In this way, the number of passengers with the expected departure times from one station to another are obtained. Distances and pure train running times of all sections are shown in Table 9, and other parameters involved in the model are listed in Table 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Station** & \(B\) & \(C\) & \(D\) & \(E\) & \(F\) & \(G\) & \(H\) & \(I\) & \(J\) \\ \hline \(A\) & 373 & 151 & 786 & 801 & 28 & 944 & 18 & 118 & 1735 \\ \(B\) & 0 & 131 & 125 & 83 & 7 & 127 & 6 & 36 & 479 \\ \(C\) & 0 & 0 &
## 3 Experimental Work
### Comparison of Sample-Discharging Rates  
Figure 19 shows the energy transfer rate waveforms of each sample. The reference measurement of the pure PCM sample shows that the phase change time (solidification process) that takes place from 53.5 \({}^{\circ}\)C to 51 \({}^{\circ}\)C has a sample time of 3740 s.  
The PCM with the copper spiral achieved a phase change time of 2295 s. The sample achieved an increase in phase change acceleration of 38.6% compared to the reference measurement.  
The PCM with the steel twisted matrix had a time of 1890 s. This shows that the phase change was faster compared to the reference sample by 49.46% and faster by 17.65% compared to the copper spiral.  
The PCM with the copper matrix showed promising results during the solidification process. Its time was 930 s, which means 75.13% acceleration compared to the reference sample. Compared to the steel twisted matrix, this difference is 50.79%, indicating that this method is relatively efficient and applicable for practical use.  
A summary of the results in terms of increasing the transfer of latent heat energy from the PCM to the heat transfer fluid is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment Description** & **Latent Heat (J)** & **\% Difference Compared to Pure PCM** \\ \hline Pure PCM sample & 3678.4 & - \\ PCM with copper matrix & 5183.2 & 41.0\% increase \\ PCM with steel twisted matrix & 4681.6 & 27.3\% increase \\ PCM with copper spiral & 6186.4 & 68.3\% increase \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of latent heat transfer rates in experiments.  
Figure 19: Comparison of sample-discharging rates.
## 4 Simulation Analysis
### Resource Utilization  
To manage large-scale data communication under the constraints of limited satellite resources, the time complexity, space complexity, and network overhead of the algorithm are critical factors. Regarding time complexity, the proposed algorithm employs a Q-table to determine optimal forwarding strategies. The Q-table is updated based on the node's local link information and the link states of neighboring nodes, which are used to compute rewards and refine the Q-table. In contrast, centralized routing algorithms necessitate global link-state information and employ double-loop traversal to identify the shortest path, leading to a time complexity of \(O\big{(}N^{2}\big{)}\). This highlights the efficiency of the proposed approach in reducing computational overhead while maintaining robust routing performance.  
In terms of space complexity, the centralized routing algorithm needs to store node and edge information in the network graph, with \(O(N_{E}+N)\) space complexity. \(N_{E}\) is the number of edges and \(N\) is the number of nodes. In the proposed algorithm, each satellite needs to store its local link information. Consequently, the proposed algorithm exhibits  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Total satellites & 49 \\ Number of orbits & 7 \\ Number of satellites per orbital plane & 7 \\ Orbital altitude & 780 km \\ Orbital inclination & 86.4\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 4: Constellation scene parameter setting.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Maximum queue length \(q_{max}\) & 200 \\ Load weight \(\omega_{1}\) & 5 \\ Delay weight \(\omega_{2}\) & 1 \\ Discount factor \(\gamma\) & 0.9 \\ Greedy factor \(\varepsilon\) & 0.8 \\ \(\varepsilon\) decay factor \(\mu\) & 0.998 \\ Number of episodes & 40 \\ Number of steps peer episode & 300 \\ Learning rate for Q-table Initialization & 0.7 \\ Learning rate for operational phase & 0.3 \\ \hline \end{tabular}
\end{table}
Table 5: Simulation parameter setting.  
superior performance in terms of both time and space complexity when compared to centralized routing algorithms. MARL-JR significantly reduces the computational and storage overhead, making it more suitable for large-scale satellite networks with limited resources.  
In centralized routing approaches, each satellite node is required to acquire global network state information, which is propagated to all nodes concurrently with data packet transmission. This process results in significant network overhead and increased computational load on satellites. In contrast, this study employs the periodic broadcasting of link-state information, where each satellite disseminates only its local state information to neighboring nodes and updates its Q-table utilizing the state information received from neighbors. This decentralized strategy significantly reduces the computational load on satellites and enhances the scalability and efficiency of the satellite network.
## 3 Feature-Selection Method and Application
### Fast Correlation-Based Filter (FCBF)  
This research uses the FCBF feature-selection method, which was proposed in 2004. It uses the SU value to replace the information gain and performs the calculation of selection [28]. This method can be divided into two parts. First use the features to sort the SU value of the fault type and use the threshold setting to delete the less influential features. Then compare the features, as shown in Figure 4. Because the correlation between features \(T_{1}\) and \(T_{2}\), \(T_{4}\) is higher than the relationship between \(T_{2}\), \(T_{4}\) and category, consider \(T_{2}\), \(T_{4}\) as redundant features with less correlation and delete them. The advantage of this method is that you can compare the correlation of features and perform feature selection at the same time and use features with higher correlation to filter other features that have not been deleted. In this way, the efficiency of calculation while filtering is achieved and the calculation is accelerated, and the recognition rate is improved.  
The steps of the process of FCBF feature selection are as follows:  
* Set the data set \((x_{i},t_{i}),i=1,\ldots,N\) and \(x_{i}{=[x_{i1},x_{i2},\ldots\ldots,x_{id}]^{T}\in R^{n},t_{i}{=[t_{i1},t_{i2 },\ldots,t_{im}]^{T}\in R^{m}\). Sample category is \(Y=(y_{1},y_{2},\ldots y_{N})\);
* Calculate the SU value between each feature \(t_{i}\) and category \(Y\);
* Store the \(SU(t_{i},Y)\) values of each feature and category in descending order into the \(S\) set;
* Calculate the sum of SU in the first \(n\) items in the \(S\) set (expressed as \(SU\_all\));
* If \(SU\_all<r\), then \(n{+}1\) repeat to _Step 4_ until \(S\_all>r\) stops, where \(r\) is 90% the sum of the \(S\) set;
* Remove features after nth from S (Remove features with less influence);
* Select the feature \(T_{1}\) with the largest \(SU(t_{i},Y)\) value from \(S\) as the main feature for selecting;
* Calculate the \(SU(t_{i},T_{1})\) of other features and main features in order and the \(SU(t_{i},Y)\) values between this feature and category \(Y\);
* If \(SU(t_{i},T_{1})\geq SU(t_{i},Y)\), it is regarded as a redundant feature and deleted from \(S\);
* The main feature is stored in \(S^{{}^{\prime}}\) and deleted from \(S\);
* Repeat _Step 7_ to _Step 10_ until \(S\) is the empty set and stop;
* The output S' is expressed as an important feature.  
Among them, the process of FCBF feature screening is divided into two stages. First, the SU value is used to distinguish the features, and second, by comparing the correlation between the features and the features, to distinguish whether the features are redundant. The flowchart of this method is shown in Figure 5.  
Figure 4: Method of fast correlation-based filter (FCBF) delete redundant feature.
# Effect of Organic and Conventional Production on the Quality of Lemon \"Fino 49\"
## 3 Results and Discussion  
### Physical and Chemical Analysis  
Growing lemon in a conventional way has been shown to obtain fruits with greater weight (121.75 g), greater fruit equatorial diameter (58.35 mm), and fruit polar diameter (79.66 mm) than fruits grown organically (Table 2). On the other hand, it was organic lemons that showed a better relationship between equatorial and polar diameter (0.78) and a lower skin thickness (5.14 mm). These values were slightly lower than those reported by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [17].  
Overall, color is considered a determining factor by the consumer for their purchase. This color change occurs due to the degradation of chlorophylls present in the skin and the synthesis of carotenoids, which are the ones that provide the yellow color to citrus fruits [40]. The color obtained for the evaluated lemon skin samples was like that obtained by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [13] in Italian cultivars (Table 3). In general, there were no significant differences between the samples (skin and juice), except for the lightness parameter (\(L^{*}\)), in which conventional lemons presented the highest values (73.38). However, it should be noted that differences of less than three units are imperceptible to the human eye [41], and therefore, it is not a notable difference. The high values in the parameter \(b^{*}\) indicate a yellowish color in the peel of the evaluated lemons [42], both conventional and organic. Likewise, the negative values in the \(a^{*}\) parameter in the juice samples are representative of pale juices with slight greenish tones [17].  
Some authors have compared the color of conventional and organic fruits. It has been found that the use of fertilization (nitrogen) could affect the color of these fruits, due to the increase of nitrogenous substances and the lower content of carbohydrates in fruits (apples and grapes), or due to decreased activity of enzymes that modulate pigment synthesis (grapevines and mangoes) [43].
## 2 Materials and Methods
### Device and Software for Data Collection  
We used a low-cost handheld NIR device, which is the DLP(r) NIRscan(tm) Nano Evaluation Module (Texas Instruments, Dallas, TX, USA) [31], to measure the NIR spectra of fish samples (Figure 2). This spectrometer leverages digital light processing (DLP) technology, which replaces the traditional linear array detector with a digital micromirror device (DMD)  
Figure 1: The complete workflow of our study.  
for wavelength selection and a single-point detector. By sequentially scanning through the columns of the DMD, a particular wavelength of light is directed to the detector and captured. This NIR scanner supports both Bluetooth low energy and USB communications, which enable mobile and computer lab measurements. For NIR data collection, we employed the DLP NIRscan Nano GUI v2.1.0, a software that came with the device, to start the scanning process and transfer recorded spectra from the spectrometer to our Windows computer through a USB cable. We did not apply any calibration method. Regarding scanning methods, the Column method selects one wavelength at a time, while the Hadamard method creates a set with several wavelengths multiplexed at a time and then decodes the individual wavelengths. We have chosen the Hadamard method since it collects much more light and offers a greater signal-to-noise ratio than the Column one.  
In the scanning process, part of the radiation in the NIR range emitted from the device was absorbed by the dissected samples. The remainder that is not absorbed is reflected back to the device sensor or transmitted through those substances. According to this, we could achieve absorbance, reflectance, and transmittance spectra simultaneously. Each spectrum consists of 228 wavelengths in the range of 900-1700 nm, i.e., a resolution of 3.5 nm per wavelength point. Among the three types of spectra, we decided to use the absorption spectrum to conduct experiments for the classification of urea content in fish.