esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Technology Communication Platform for Urban Mobile Sensing
## 5 Results
### Network Analysis  
The following subsections aim to characterize and portray the performance of our multi-technology opportunistic platform through: (1) the performance analysis of the implemented multi-gateway LoRa MAC protocol; and (2) the behavioral examination of the opportunistic and delay-tolerant network (mOVE) considering the previously proposed forwarding strategies.  
Keeping in mind the dissimilarities of the network nodes' mobility patterns between the evaluations and to better understand the network behavior, namely its multi-technology capabilities, a multi-technology map from one of the evaluation days was arbitrarily chosen. Figure 13 presents the multi-technology map from the second day of experiments. This map represents:  
* The temporal instant in which each node achieved a successful transmission of a data packet through LoRa technology; and
* The temporal instant in which each node entered in the WiFi neighborhood of other network nodes (moliceiros or RSU).  
This multi-technology evaluation allows us to observe the overall performance of each node's connectivity. For the LoRa technology, the noticeable lack of periodicity in sending the node's geographical information to the gateway is due to the lack of connectivity between the network nodes and the LoRa gateways. An improvement in order to handle the connectivity issues would be the change of the LoRa communication mode employed in this evaluation. This mode can be changed to a different one with a lower transmission rate in favor of higher achievable ranges. Also, it is important to notice that these evaluations were performed over a dense urban center, which is more susceptible to the loss of a communication link when compared to rural fields. On the other hand, regarding the WiFi communications, the interactions between the several network nodes are noticeable. These interactions can be shorter, in the case of two moleciros crossing paths, or longer, if both nodes are stopped within the range of each other.  
The co-existence of simultaneous LoRa and WiFi communications results from the fact that our multi-technology platform allows for the sending of differentiated data from each technology interface, increasing the platform's connectivity. Since we are taking advantage of two communication technologies with different radio frequency physical layers (different operating frequencies), it is ensured that there is no radio interference between them.
## 3 Computational Principle and Numerical Model
### Computational Principle
#### 3.1.2 Finite Difference Forms of Differential Equations  
We discretize the bridge piles from top to bottom [29; 30], as shown in Figure 5. In the figure, the discretization points 0 and N are the top and bottom of the pile, respectively, and the discretization points K and K + 5 are the junction points of the free section and the loaded section.  
Equations (5)-(9) are finite difference equations.  
\[z=ih \tag{5}\]  
\[\frac{dx}{dz}=\frac{x_{i+1}-x_{i}}{h} \tag{6}\]  
\[\frac{d^{2}x}{d^{2}z}=\frac{x_{i+1}-2x_{i}+x_{i-1}}{h^{2}} \tag{7}\]  
\[\frac{d^{3}x}{d^{3}z}=\frac{x_{i+2}-2x_{i+1}+2x_{i-1}-x_{i-2}}{2h^{3}} \tag{8}\]  
\[\frac{d^{4}x}{d^{4}z}=\frac{x_{i+2}-4x_{i+1}+6x_{i}-4x_{i-1}+x_{i-2}}{h^{4}} \tag{9}\]  
Figure 4: Microelement force analysis diagram. (**a**) Free section; (**b**) Loaded section.  
The finite difference equations for the free and loaded segments are expressed as Equations (10) and (11) by inserting Equations (5)-(9) into Equations (1) and (2).  
\[EIx_{i+2}+\left(-2 EI+P_{0}l^{2}+f_{i}\dot{h}^{4}\right)x_{i+1}-\left(-2 EI+P_{0}h^ {2}+f_{1}\dot{h}^{3}\right)x_{i-1}-EIx_{i-2}=2h^{3}Q \tag{10}\]  
\[\begin{array}{c}EIx_{i+2}+\left(-4 EI+P_{0}h^{2}+f_{2}\dot{h}^{3}+f_{2}h^{3} \right)x_{i+1}\\ +(6 EI-2P_{0}h^{2}-2f_{2}\dot{h}^{3}-f_{2}h^{3}+k^{2}\dot{n}^{4}h^{4+4})x_{i}\\ +\left(-4 EI+P_{0}h^{2}+f_{2}\dot{h}^{3}\right)x_{i-1}+EIx_{i-2}=a^{2}h^{6}+b \dot{h}^{5}+ch^{4}\end{array} \tag{11}\]  
Similarly, the boundary conditions can be simplified as follows:  
\[\left\{\begin{array}{c}EI\frac{x_{i+1}-2x_{i+1}-x_{i-1}}{h^{2}}|_{i=0}=M_{0 }\\ \left[EI\frac{x_{i+2}-2x_{i+1}+2x_{i-1}-x_{i-2}}{2h^{3}}+(P_{0}+f_{1}\dot{h}) \frac{x_{i+1}-x_{i-1}}{2h}\right]|_{i=0}=Q_{0}\end{array}\right. \tag{12}\]  
\[\left\{\begin{array}{c}EI\frac{x_{i+1}-2x_{i+1}+2x_{i-1}}{h^{2}}|_{i=bottom} =0\\ \left[EI\frac{x_{i+2}-2x_{i+1}+2x_{i-1}-x_{i-2}}{2h^{3}}+(P_{0}+f_{2}\dot{h}) \frac{x_{i+1}-x_{i-1}}{2h}\right]|_{i=bottom}=0\end{array}\right. \tag{13}\]  
The displacement, corner, shear force, and bending moment continuity conditions at the junction of the free and loaded sections of the pile are as follows:  
\[\left\{\begin{array}{c}x_{K}=x_{K+5}\\ x_{K+1}-x_{K-1}=x_{K+4}-x_{K+6}\\ 2x_{K+2}-x_{K+1}+2x_{K-1}-x_{K-2}=-2x_{K+7}+x_{K+6}-2x_{K+4}+x_{K+3}\\ x_{K+1}-2x_{K}+x_{K-1}=x_{K+6}-2x_{K+5}+x_{K+4}\end{array}\right. \tag{14}\]  
According to the above, the finite difference equations can be solved jointly for each node displacement. Then, the peripile soil resistance, pile bending moment, and pile shear force can be solved according to the following equations:  
\[\left\{\begin{array}{c}\sigma_{i}=-k(ih)^{n}x_{i}\\ M_{i}=\frac{EI}{2h}(x_{i+1}+x_{i-1}-2x_{i})\\ Q_{i}=\frac{EI}{2h^{3}}(-x_{i-2}+2x_{i-1}-2x_{i+1}+x_{i+2})\end{array}\right. \tag{15}\]
## 2 Structure of Hybrid AC/DC Microgrid
### AC-DC Coupled Hybrid Microgrid  
In this topology, as shown in Figure 1, both AC and DC buses are connected with Distributed generators, energy storage systems, and load. The interlinking converters have been employed as an interface between AC and DC subgrids. This configuration limits higher interfacing, which in turn reduces the cost and enhances the overall efficiency. This topology is the most attractive solution for a paradigm of future smart grids.  
Figure 1: (**a**) AC coupled, (**b**) DC coupled, and (**c**) AC-DC coupled hybrid microgrid [9].
## 1 Introduction  
Football, also known as soccer, is the world's most popular sport, but most high-profile games are played in Europe [1]. Professional football clubs' rewards associated with successful investment in youth academies have helped focus attention on talent identification and development models [2, 3]. Research and analysis of talent detection and development have grown considerably over the last 20 years [4]. These are the primary goals of football academies, which follow the athletes during their growth and guide them in the transition to the professional level [5, 6, 7].  
The relationship between anthropometrical features and physical performance found its origins in Greek populations, and it is now recognized as Kinanthropometry [8]. To date, many studies have enhanced the relevant contribution of body shapes and proportions in specific performance, leading to the selection of professional players in several sports [9, 10]. However, most of the studies related to this topic are based on cross-sectional data, which do not allow for evaluation of the growth trend of a single boy, and nothing is known about everything that happened before. During growth, the relationships between anthropometric and performance characteristics show a higher level of complexity, and evident differences can be found due to the players' morphological growth and physiological development [11; 12]. Thus, cross-sectional studies cannot provide definitive information about cause-and-effect relationships, and longitudinal studies are required.  
Few longitudinal reports or well-controlled experimental studies are available on the anthropometric and performance characteristics of football players in the literature, and this limits current knowledge concerning youth football players' development [6; 12; 13; 14].  
For this purpose, a recent systematic review of young football players dealt with synthesizing the available information regarding longitudinal data addressing young football players' motor performance changes [2]. The review found a series of inconsistencies and gaps in the literature, as the studies tended to use too many different tests to assess physical performance in football, and it highlights the need for coaches and stakeholders to consider players' physical performance over time whilst considering biological maturation, biological characteristics, and training stimuli.  
Despite the large body of scientific evidence on anthropometric and sport physiology characteristics in young football players, to the best of our knowledge, no longitudinal studies regarding young Italian players have been carried out.  
Therefore, considering that most professional academies seek to optimize the early detection and physical development of their young players, this study aimed to (1) investigate how anthropometric characteristics and physical performance changed during growth in adolescent Italian football players and (2) investigate how age-related anthropometric and body composition changes affected variations in players' physical performance.
# Inverse Kinematics of a Serial Manipulator with a Free Joint for Aerial Manipulation
## 5 Simulation Results  
### Manipulator with Fixed Base  
First, the algorithm was tested considering the manipulator attached to a fixed base through the undamped revolute joint. This case corresponds to a WidowX 250s robotic arm modified substituting the actuated shoulder joint with a free revolute joint.  
The joint angles calculated through our approach for the trajectory tracking task are reported in Figure 6 together with the torque applied on the non-actuated joint. Resulting joint angles respected the joint limits reported in Table A3. The calculated torque on the non-actuated joint was negligible (maximum value below \(7\cdot 10^{-6}\) Nm) and was due to discretization error; a reduction in the timestep used for the solution of the inverse kinematics would result in a reduction in this torque. Stroboscopic views of the manipulator following the circular trajectory are reported in Figure 7. The position error due to the time discretization was below \(2\cdot 10^{-4}\) m at all times during the simulation.  
After the first case, the manipulator attached to a fixed base through the damped revolute joint performing the load picking task was solved. The stroboscopic views of the manipulator performing the task are shown in Figure 8, while joint angles and torque on the first joint are reported in Figure 9. As expected, the torque on the first joint is not negligible because of the presence of the damper. When the load is grasped, there is a sharp increase in the torque applied to the base (up to \(9\cdot 10^{-3}\) Nm) and some vibrations are triggered; however, these vibrations quickly disappear because of the presence of the damper.  
Figure 6: Trajectory tracking for the 3-DOF manipulator with fixed base. (**left**) Joint angles and (**right**) torque applied to the base during the task.  
Figure 7: Stroboscopic view of the 3-DOF manipulator with fixed base executing the trajectory tracking task. (**left**) First stage (time \(<\) 5 s). (**right**) Second stage (time \(>\) 5 s). The green marker represents the UAV’CoG, the red marker represents the end effector, and the black circles represent the manipulator’s revolute joints.  
It is worth noticing that, when the manipulator is not moving, the largest contribution to the torque on the first joint is due to gravity. Therefore, at the end of the simulation, the combined CoG of the manipulator and the payload must be vertically aligned to the first joint. For this reason, final joint angles are different from initial joint angles. Also in this case, joint limits are satisfied and the position error was below \(5\cdot 10^{-5}\) m.
# Pixel Circuit Designs for Active Matrix Displays
## 3 OLED Pixel Circuits
### OLED Novel Pixel Circuits
#### 3.2.2 Mo Tft OLED Pixel Circuits  
The \(V_{\text{TH}}\) instability of MO TFTs increases the OLED current error in a pixel circuit. [PERSON] proposed an IGZO TFT 5T2C pixel circuit (Figure 7b) that utilises the common diode-connected transistor method to compensate for spatial and temporal \(V_{\text{TH}}\) variations in the driving TFT [79]. During the \(V_{\text{TH}}\) detection phase, capacitor C1 is charged to \(V_{\text{ref}}\)-\(V_{\text{TH}}\), ensuring that the OLED current is unaffected by changes in the driver transistor threshold voltage during the emission phase. The circuit provides a constant current to the OLED, with an error is 1.05% (\(I_{\text{OLED}}\) = 2.5 \(\mu\)A). Compared to other pixel circuits [80, 81], this circuit displays the smallest \(I_{\text{OLED}}\) error.  
To improve the AM-OLED panel's frame rate, [PERSON], [PERSON] et al. from Shanghai Jiaotong University proposed 6T2C voltage-programmed pixel circuits with a simplified working cycle [82]. Its working cycle includes only the initial phase, the data input phase, and the emission phase. Its working cycle is a shorter \(V_{\text{TH}}\) generation cycle compared with the classical voltage-programmed pixel circuit. This design may achieve a high frame rate.
# Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature
###### Abstract  
Many works have been conducted relevant to rainfall measurements, while the first relevant ones were based on the power loss estimation function from wireless links located back to the early 1940s. It is notable, though, that this innovative idea conduced to many theoretical models correlating the signal attenuation to the rainfall intensity. This type of parameter strongly contributes to the mechanism of frequency attenuation above 10 GHz. Consequently, in the last twenty years, there has been a significant boost to this research topic. Researchers all around the world have worked thoroughly on the issue of estimating rain with the use of earth-to-earth microwave signal attenuation. Nevertheless, the issue remains intriguing and challenging. This paper presents a literature survey, of the last decade, on this challenging issue focusing on measurements from backhaul cellular microwave links and experimental setups. Research challenges and future trends are also presented.  
microwave links; rain attenuation; measurements; signal propagation; experimental setups; backhaul cellular networks +
Footnote †: journal: 25 August 2020  
_Review_  
Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature
## 6 Simulation Results
### Transmission Using a SWIPT-Enabled Relay  
We now consider the operation of \(R\) if it were to operate solely through SWIPT. With SWIPT, the system is capable of harvesting energy while transmitting data to \(D\) and addresses one of the main issues that was identified in the AF-based relay system discussed earlier.  
The SNR achieved at \(D\) when using SWIPT is presented in Figure 7, and the energy harvesting capability when using SWIPT exclusively at the relay node for different source positions in the warehouse plane is shown in Figure 8. The results reveal that energy harvesting performance varies significantly with source location, achieving maximum efficiency when the source node is positioned closer to the relay. This spatial dependency of energy harvesting is particularly evident in the central regions of the warehouse (around the relay position at (3.5, 3.5)), where harvested energy levels reach their peak values. However, as the source moves towards the warehouse boundaries, especially at higher y  
Figure 6: Battery discharge performance during a single cycle of operation with AF relay-based transmission.  
coordinates, the harvesting efficiency decreases substantially due to increased path loss and potential signal degradation. This pattern highlights a key limitation of relying solely on SWIPT--while it provides excellent energy harvesting capabilities under favorable conditions, its performance becomes suboptimal when the source-relay distance increases or when channel conditions deteriorate.  
The energy harvested at \(R\) with the SWIPT system allows for a considerable increment in the system's energy gain. With the movements of \(S\), the amount of energy harvested varies, but is still capable of maintaining appreciable performance especially when in locations where the \(S\) node is closer to the \(R\) node.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 2 Related Work
### The ETSI MEC Framework and Its Reference Architecture
#### 2.1.3 Other MEC-Related Work  
Besides architectural aspects, MEC also defines the Application Programming Interfaces (APIs) that should be used at its reference points: to this end, Reference [30] provides a general overview of the RESTful ME service APIs, while specific examples APIs are given, for instance, in References [31; 32].  
Additionally, ETSI MEC publishes informative white papers. In particular, Reference [27] describes several different approaches to deploying an ME system in a 4G mobile network, to offload \"over the top\" ME apps from the cumbersome task of dealing with the tunneling protocols used in mobile networks.  
The ME system proposed in this paper adopts the insertion approach usually referred to as \"Distributed SGW with Local Breakout\" [27], which will be described in detail in the next sections.  
The integration of the MEC and NFV framework is specified in Reference [10]. We will briefly summarize the state of the art on this topic in Section 2.3, after introducing some relevant NFV concepts.
## 2 Measurements Campaign  
The applications that make up the IoT ecosystem generate a large demand for traffic. Therefore, broadband spectrum is required for the 5G cellular networks to provide access to the use cases defined in the ITU-M2150-1 recommendation. To achieve this, the authors in [2] propose combining the spectrum between the low, mid, and high bands to successfully enable 5G-IoT interaction. Similarly, the potential of the new mid-band and extended mid-band spectrum for 5G cellular networks is shown in [14]. Table 1 shows some services related to the 5G use cases and their corresponding spectrum and coverage.  
Figure 2: Smart university campus scenario based on IoT and 5G.  
The 5G use cases offer the possibility to access different services through the IMT-2020 specifications. These specifications are a set of Radio Interface Technologies (RITs). For example, Long-term Evolution (LTE) and New Radio (NR), both components of RIT, have been developed by the 3 rd Generation Partnership Project (3 GPP) and use frequency bands below 6 GHz. The NR can also use the frequency bands above 6 GHz. LTE RIT uses Frequency Division Duplex (FDD) and Time Division Duplex (TDD), supporting transmission bandwidths from 1.4 MHz to 640 MHz and peak data rates of up to 32 Gbps in the downlink (DL) and 13.6 Gbps in the uplink (UL). The DL transmission scheme is also based on Orthogonal Frequency Division Multiplexing (OFDM) and the UL transmission scheme is based on Discrete Fourier Transform Spread OFDM (DFTS-OFDM). The NR RIT uses FDD and TDD and supports channel bandwidths up to 400 MHz and peak data rates up to 140 Gbps in DL and 65 Gbps in UL. LTE RIT also includes multi-antenna transmission schemes. This enables support for enhanced MTC (eMTC), Narrow-band Internet of Things (NB-IoT), and Multicast Broadcast Multimedia Service (MBMS) with modulation schemes in DL up to 1024 QAM in the case of LTE RIT and QPSK for NB-IoT, with up to 200 kHz bandwidth. In addition, eMTC and NB-IoT have extended the original LTE coverage area by \(\sim\)15 dB and \(\sim\)20 dB, respectively. The NR RIT supports eMMB, URLLC, Industrial IoT (IIoT), V2X, private networks, and others. NR RIT supports in-band coexistence with NB-IoT and eMTC [3].
## 2 Results  
### Impact of Harvesting Time on Fruit Drop, Mold Incidence, and Quality of Golden Mulberry Fruit  
To investigate the effects of different harvesting time on mulberry fruit quality, samples were classified into four developmental stages based on the number of days after flowering--T1 (18 days), T2 (21 days), T3 (24 days), and T4 (27 days)--representing progressively ripening stages used for subsequent analyses. As shown in Table 1, with delays in harvesting, the fruit drop percentage and mold percentage of mulberry fruits showed an overall increasing trend and the marketable fruit percentage showed a decreasing trend, with the latter declining the most seriously at the T4 yellow-white period (27 days after flowering), which was only 65.7% \(\pm\) 3.21%, in which the percentage of fruit drop was the main reason affecting the quality of mulberry fruits in the T4 period.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Fruit Drop (\%)** & **Mold (\%)** & **Marketable Fruit (\%)** \\ \hline T1 & 1.30 \(\pm\) 0.60 \({}^{\text{d}}\) & 0.00 \(\pm\) 0.00 \({}^{\text{c}}\) & 98.73 \(\pm\) 0.64 \({}^{\text{a}}\) \\ T2 & 3.30 \(\pm\) 0.60 \({}^{\text{c}}\) & 0.00 \(\pm\) 0.00 \({}^{\text{c}}\) & 96.75 \(\pm\) 0.61 \({}^{\text{b}}\) \\ T3 & 9.30 \(\pm\) 1.50 \({}^{\text{b}}\) & 1.72 \(\pm\) 0.16 \({}^{\text{b}}\) & 89.01 \(\pm\) 1.72 \({}^{\text{c}}\) \\ T4 & 28.71 \(\pm\) 2.11 \({}^{\text{a}}\) & 5.72 \(\pm\) 1.20 \({}^{\text{a}}\) & 65.73 \(\pm\) 3.21 \({}^{\text{d}}\) \\ \hline \hline \end{tabular} Note: Data in the table are expressed as means \(\pm\) standard deviation. For each line, different lowercase letters correspond to values significantly different at \(p<\) 0.05.  
\end{table}
Table 1: The percentage of fruit drop, mold and marketable fruit of golden mulberry at different harvesting times.
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 3 Methodology
### Classification of Unseen Data  
Partitioning of the data is useful for the training and evaluation of machine learning models. The dataset is usually divided into two non-overlapping groups: training data and testing data. The training data are used for the modeling and feature set development. The test data are used to measure the model's performance. We have divided our dataset into 80% training data and 20% testing data.  
Figure 8: Bidirectional long short-term memory (BLSTM) architecture.  
```
Input ads = Audio dataset \(\mathrm{Labels}\) = Number of classes \(\mathrm{Labels}\) = Define class labels Output YPredicted = Predicted labels \(\mathrm{AlexNet\_Accuracy}\) = Model accuracy Algorithm Begin \(\mathrm{FeatureTrain}\) \(\leftarrow\) ExtractFeature(\(\mathrm{adsTrain}\)), \(\mathrm{FeatureTest}\)[ ] \(\leftarrow\) ExtractFeature(\(\mathrm{adsTest}\)) \(\mathrm{FeatureYTrain}\)[ ] \(\leftarrow\) FeatureTrain.labels, \(\mathrm{FeatureYTest}\)[ ] \(\leftarrow\) FeatureTest.Labels  Define Image Size \(\rightarrow\) inputSize[ ] fori \(\gets\) 1:numObservationsTrain \(\mathrm{sequence}\)\(\leftarrow\) FeaturesTrain[i] \(\leftarrow\) sequenceLengthsTrain(i) \(\leftarrow\) size(sequence, 2) endfor
[sequenceLengthsTrain, idx] \(\leftarrow\) sort(sequenceLengthsTrain) fori \(\leftarrow\) 1:numObservationsTest \(\mathrm{sequence}\)\(\leftarrow\) FeaturesTest[i] \(\leftarrow\) size(sequence, 2) endfor
[sequenceLengthsTest, idx] \(\leftarrow\) sort(sequenceLengthsTest) \(\mathrm{XTrain}\)\(\leftarrow\) FeaturesTrain(idx), \(\mathrm{YTrain}\)\(\leftarrow\) FeatureYTrain(idx) \(\mathrm{XTest}\)\(\leftarrow\) FeaturesTest(idx), \(\mathrm{YTest}\)\(\leftarrow\) FeatureYTest(idx) network \(\rightarrow\) BLSTM net \(\leftarrow\) trainNetwork(\(\mathrm{XTrain}\), \(\mathrm{YTrain}\), layers, options) [YPredicted, Probability] \(\leftarrow\) classify(net, \(\mathrm{YTest}\)) BLSTM_Accuracy \(\leftarrow\) mean(\(\mathrm{YPredicted}\)[ ] == \(\mathrm{YTest}\)[ ]) Confusion_Matrix \(\leftarrow\) confusionchart(\(\mathrm{YTest}\), \(\mathrm{YPred}\)) End
```  
**Algorithm 3**Classification task using a recurrent neural network (BLSTM).