esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Future Research Works  
In Section 5.2, we mentioned a procedure for the network startup through which nodes get to know their IP addresses, device level, and service IDs. The procedure we have in mind is an extension of the _neighbor discovery_ mechanism foreseen for the 6 LoWPAN nodes bootstrap. Indeed, the identification of the service type with a code (Service ID) make it possible for 6 LoWPAN nodes to advertise their services along with their IP addresses as soon as they get to know their default router. This would allow more performing nodes to create a distributed _network service registry_ that can prevent most constrained nodes from being massively queried for service discovery procedures. Basically, the most constrained nodes would be queried only if they do offer the sought service. Future research works will be focused on evaluating drawbacks and benefits of the envisioned _service advertising_ and _discovery_ mechanisms.  
As announced in the _research problem statement_ in Section 2, the proposed solution tackles large transmission problems caused by _large resource representation_. This is in line with the focus of the paper, which focuses on the access to constrained nodes' services. However, future research works may investigate the extension of the use of the SM packet for other _large transmission_ cases (_i.e._, network management operations like _nodes' firmware update_). The logic of the link-layer burst suggested in [22] may be exploited. As already mentioned, the solution is very interesting, but it is valid just for IEEE 802.15.4 nodes. The exploitation of the SM packet would make it independent from the _low layer technologies_ since it works at the Network Level.  
Finally, further future research works will be finalized to enriching the envisioned _Service ID_ encoding in the light of the semantic solutions for constrained nodes highlighted in Section 6. This would let the proposed mechanism evolving towards the perspective depicted in [34], where middleware solutions enhanced by semantic technologies are pointed as the most promising technologies to tackle interoperability, self-management, and autonomous issues.
## 4 Azimuth-Variant Doppler Characteristics Analysis  
As azimuth-variant geometry brings a challenge for developing high-precision data processing algorithm, an analysis of the azimuth-variant characteristics, such as Doppler and two-dimensional spectrum, is necessary for developing an efficient image formation algorithm. Note that parallel flying tracks and flat Earth model are assumed in this section.
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 7 Experimental Results
### GPS Accuracy Measurement  
Similar to compass, GPS accuracy depends on the calibration before the USV moves. If the calibration procedure is performed correctly, a disorientation position could not occur. Besides, GPS accuracy is also determined by the number of satellites that GPS reads. The minimum number of satellites is 12 satellites from 32 satellites [29]. The more satellites are read, the more accurate the position is read by GPS. The number of satellites when sailing with the Autonomous mode in this work can be seen in Figure 14. The conclusion drawn from the graph in Figure 14 is the minimum number of satellites that GPS reads as many as 17 Satellites. Meanwhile, the GPS data rate acquired is around 1.52 bytes/second.  
Figure 13: Magnetic field versus time.  
Another technique for analyzing GPS accuracy is to analyze the Horizontal Dilution of Precision (HDOP) value of GPS, a number used to express the horizontal geometric strength of the satellite constellation. GPS accuracy is significant if the HDOP value is below 1.5 and is very poor if above 2 [29]. Graph HDOP versus time when USV sailing is plotted in Figure 15. The conclusion drawn from the graph is the maximum HDOP value is 0.8, and the HDOP value vacillates between 0.6 and 0.8, which according to other studies, indicates high signal quality [17]. The final conclusion is that the accuracy value of a GPS is large because the HDOP value is below 1.5 and the number of satellites read by GPS is above 12 satellites.  
Figure 14: The number of satellites read by GPS on the USV.  
Figure 15: HDOP values versus time variables.
## 3 Results
### Temporal Changes in ESV
#### 3.1.2 Changes in Individual ESVs  
The EVs can be divided into 11 types: food production, raw material production, water resource supply, gas regulation, climate regulation, environmental purification, hydrological regulation, soil conservation, nutrient cycling maintenance, biodiversity maintenance, and aesthetic landscape provision. Detailed calculations have been made for the ESV of each service and its changes (Table 5). From 1991 to 2021, the region along the Beijing-Hangzhou Grand Canal exhibited different trends in individual ecosystem services. Among them, the values of hydrological regulation, water resource supply, and aesthetic landscape experienced fluctuations of first increasing and then decreasing, but showed an overall increasing trend, indicating that these ecological services possess considerable resilience and recovery capabilities in the region. However, the two ecosystem services of environmental purification and biodiversity showed a trend of \"first increasing and then decreasing\", but overall, they exhibited a decreasing trend. Food production, raw material production, gas regulation, climate regulation, soil conservation, and nutrient cycling maintenance all continued to decrease in ESV due to the expansion of construction land into woodland and grassland areas, which damaged the ecological environment. The decrease in the ESVs of food production and raw material production was mainly attributed to the high volume of cultivated land transfer. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1991–2006**} & \multicolumn{2}{c}{**2006–2021**} & \multicolumn{2}{c}{**1991–2021**} \\ \cline{2-7}
**Land Use Type** & **Value Change** & **Value Change Rate** & **(CNY 100)** & **Value Change Rate** & **Value Change Rate** & **Value Change Rate** \\  & **(CNY 100 Million)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Cultivated land & \(-212.59\) & \(-8.66\) & \(-146.25\) & \(-6.52\) & \(-358.85\) & \(-14.62\) \\ Woodland & \(2.35\) & \(0.10\) & \(-14.89\) & \(-0.63\) & \(-12.54\) & \(-0.53\) \\ Grassland & \(-46.98\) & \(-27.15\) & \(-35.43\) & \(-28.11\) & \(-82.41\) & \(-47.63\) \\ Water body & \(726.42\) & \(15.36\) & \(-493.33\) & \(-8.05\) & \(287.09\) & \(6.07\) \\ Wetland & \(-0.01\) & \(-10.86\) & \(0.00\) & \(-2.79\) & \(-0.01\) & \(-13.35\) \\ Construction land & \(0.00\) & \(0.00\) & \(0.00\) & \(0.00\) & \(0.00\) & \(0.00\) \\ Unused land & \(-0.04\) & \(-24.81\) & \(-0.09\) & \(-81.35\) & \(-0.13\) & \(-85.98\) \\ Total value & \(469.16\) & \(4.83\) & \(-636.00\) & \(-6.25\) & \(-166.84\) & \(-1.72\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Changes in the EVs of individual land use types along the Grand Canal from 1991 to 2021.
## 3 Model Validation and Numerical Examples  
In this section, it will be revealed that the modified K&C model has advantages over the modified HJC model for the description of low-porosity RPC material properties in RPC-FST under explosion load. Modified parameters of the K&C model, damage function \(\eta(\lambda)\), and equation of state are shown in Tables 1-3[18]. The modified HJC model is shown in Appendix A. Air equation of state parameters are shown in Table 4. The parameters of TNT are shown in Table 5. The material properties of the steel tube are shown in Table 6.  
Figure 2: Modified K&C model process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Mass density \(R_{0}\) (g/mm\({}^{3}\)) & 0.002 & Poisson’s ratio \(PR\) & 0.2 \\ Uniaxial tensile strength \(T\) (MPa) & 9 & Failure surface parameter \(a_{0}\) & \(3.3\times 10^{7}\) \\ Failure surface parameter \(a_{1}\) & 0.45 & Failure surface parameter \(a_{2}\) & \(7.4\times 10^{-10}\) \\ Compressive damage parameter \(B_{1}\) & 1.6 & Tensile damage scaling & 1.4 \\ Damage scaling coefficient \(B_{3}\) & 1.15 & Initial yield surface coefficient \(a_{0y}\) & \(2.5\times 10^{7}\) \\ Initial yield surface coefficient \(a_{1y}\) & 0.63 & Initial yield surface coefficient \(a_{2y}\) & \(2.3\times 10^{-9}\) \\ Residual failure surface & & Residual failure surface & \\ coefficient \(a_{1f}\) & 0.44 & coefficient \(a_{2f}\) & \(1.1\times 10^{-9}\) \\ Aggregate diameter \(LOCV\) (mm) & 0.03 & Post peak dilatancy decay \(EDROP\) & 1 \\ Stretch factor \(S_{\lambda}\) & 100 & Fractional dilatancy \(OMEGA\) & 0.5 \\ Unit conversion for length \(RSIZE\) & 0.04 & Unit conversion for stress \(UCF\) & 145 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Modified K&C model parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{3}{c}{**Scale Factor \(\lambda\)**} & \multicolumn{5}{c}{**Damage Function \(\eta\)**} \\ \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\lambda
## 2 Methods and System Setup
### System Introduction  
The MCLLRTR activity recognition process is shown in Figure 1. First, the height and weight of the patient are measured, and the therapist makes a functional assessment of the patient. Then, the essential data are input into MATLAB and compared with the standard model, and a detection threshold is specified for each patient off-chip. In step two, zero-point correction of the tension sensor is performed, the abnormal gait trigger position settings for the photoelectric sensors are set, and preparations are made to begin training. In the third step, the training data are transmitted to the STM32 main control board after Kalman filtering and analog/ digital (AD) conversion. Based on the sample features and offline thresholds from the first step, the proposed first-stage classifier (PNN) determines whether the data indicate a 'fall' or a 'normal gait'. If a fall has occurred, protection measures are launched, and the robot initiates safety protection measures; otherwise, rehabilitation training continues. The rehabilitation control strategies adaptively adjust based on the patient's performance, and all data are transmitted to the host computer through the wireless serial port in real time. As the final step, the data are uploaded for detailed analysis and classification during training, training reports are generated, new patient data are sent to the database, the neural network training parameters are updated, and the fall determination thresholds are updated.  
Figure 1: Mobile chaperone lower limb rehabilitation-training robot (MCLLRTR) prototype.
## 4 Results and Discussion
### Hyperparameter Experiments  
The main hyperparameters that affect the predictive performance of the model include historical length, embedding size, and node embedding size. In this paper, the hyperparameters are adjusted mainly by the raster search method [67]. The setting range of hyperparameters mainly refers to the existing literature [68]. In addition, to fully evaluate the influence of hyperparameters on experimental results, this paper carried out hyperparameter analysis experiments. Table 5 shows the values of the model hyperparameters. Figures 8-10 shows the MAE values of hyperparameter experiments. Based on the experimental results, the following conclusions can be drawn:  
1. The node embedding size has the least influence on the prediction results of DHGAR. The main reason for this is that the dynamic graph learning component does not require a particularly large number of parameters to obtain a graph structure that accurately models variable correlations.
2. The historical length has the greatest influence on the prediction accuracy of DHGAR. On one hand, a too-large historical length can lead to model overfitting. On the other hand, a too-small historical length may not provide enough information. Therefore, finding a proper balance in historical length is crucial.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Models** & **MAE (KW)** & **RMSE (KW)** & **P\({}_{\text{MAE}}\)** & **P\({}_{\text{RMSE}}\)** \\ \hline \multirow{4}{*}{\#1} & **DHGAR** & **76.135** & **114.295** & **-** & **-** \\  & W/O DG & 78.143 & 119.257 & 2.570 & 4.161 \\  & W/O HA & 78.225 & 118.933 & 2.672 & 3.900 \\  & W/O GA & 92.154 & 140.243 & 17.383 & 18.502 \\ \hline \multirow{4}{*}{\#2} & **DHGAR** & **67.424** & **93.418** & **-** & **-** \\  & W/O DG & 69.314 & 98.145 & 2.727 & 4.816 \\  & W/O HA & 69.759 & 99.023 & 3.347 & 5.660 \\  & W/O GA & 79.433 & 115.423 & 15.118 & 19.065 \\ \hline \multirow{4}{*}{\#3} & **DHGAR** & **84.216** & **121.547** & **-** & **-** \\  & W/O DG & 86.158 & 125.741 & 2.254 & 3.335 \\ \cline{1-1}  & W/O HA & 86.345 & 125.644 & 2.466 & 3.261 \\ \cline{1-1}  & W/O GA & 98.324 & 150.29 & 14.348 & 19.125 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The time series statistical analysis indexes of the ablation experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Config** & **Values** \\ \hline optimizer & Adam \\ learning rate & 0.002 \\ dropout & 0.3 \\ historical length & 5 \\ embedding size & 128 \\ node embedding size & 64 \\ learning rate schedule & MultiStepLR \\ milestone & 1, 25, 50, 75 \\ gamme & 0.5 \\ epoch & 100 \\ batch size & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The main hyperparameters of DHGAR.  
Figure 8: MAE values of different embedding sizes.  
Figure 10: MAE values of different historical lengths.  
Figure 9: MAE values of different node embedding sizes.
## 2 Multi-Agent Hour-Ahead EM Modeling
In order to generate the LMPs of all nodes as well as the corresponding supply and demand power schedules for the next hour \(t\), ISO must solve the congestion management model as follows [41]:  
\[\textbf{Max}_{p_{g,t},\forall i,P_{g,t},\forall i,P_{g,t},\forall j} \sum_{j=1}^{N_{d}}\left[k_{dj,t}(-\frac{1}{2}c_{j}P_{dj,t}{}^{2}+d_ {i}P_{dj,t})\right]-\sum_{i=1}^{N_{g1}}\left[k_{g,i,t}(\frac{1}{2}a_{i}P_{g,i,t }{}^{2}+b_{i}P_{g,i,t})\right] \tag{5}\] \[\text{s.t.}\sum_{i=1}^{N_{g1}}P_{g,t}+\sum_{v=1}^{N_{g2}}P_{rv,t}- \sum_{j=1}^{N_{d}}P_{dj,t}=0\] (6) \[P_{l}^{\min}\leq\sum_{z=1}^{Z}P_{Gz,t}\times sf_{l,Gz}-\sum_{z=1}^{Z}P _{Dz,t}\times sf_{l,Dz}\leq P_{l}^{\max},\forall l\] (7) \[P_{Gz,t}=\sum_{i\in Gz}P_{g,i,t}+\sum_{v\in Gz}P_{rv,t}\] (8) \[SF_{i,t}(P_{g;t,t},k_{g;t,t})=k_{g;t,t}(a_{i}P_{g;t,t}+b_{i}),\quad P_{g;t}\in \left[P_{g;\min},P_{g;\max}\right]\, \tag{1}\]0 (9) \[SF_{i,t}(P_{g;t,t},k_{g;t,t})=k_{g;t,t}(a_{i}P_{g;t,t}+b_{i}),\quad P_{g;t}\in \left[P_{g;\min},P_{g;\max}\right]\, \tag{1}\]1 (10) \[SF_{i,t}(P_{g;t,t},k_{g;t,t})=k_{g;t,t}(a_{i}P_{g;t,t}+b_{i}),\quad P_{g;t}\in \left[P_{g;\min},P_{g;\max}\right]\, \tag{1}\]2  
where \(N_{g1}\) is the number of NRGenCOs, \(N_{g2}\) is the number of RPGenCOs, and \(N_{d}\) is the number of DisCOs. Equation (5) shows that the objective of ISO is to pursue the maximization of social welfare. Equation (6) represents the power balance constraint of the whole system; Equations (7)-(9) represent the power flow constraints in each transmission line \(l\)[41]. In this paper, it is assumed that the power production of RPGenCO \(v\) (\(v=1,2,\ldots,N_{g2}\)) for hour \(t\), which is represented as \(P_{rv,t}\), is an exogenous stochastic parameter in our proposed congestion management model.
# Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
## 4 Channel Access
### Importance of Adaptive Guard Interval  
We can define the percentage of the network usage as the time amount that a user is transmitting information over the total time slot that is allocated for that user:  
\[N_{\rm u}=\frac{T_{\rm frame}}{T_{\rm frame}+T_{\rm guard}}. \tag{3}\]  
Ideally, it would be desirable that when an user stops transmitting, the next one could start immediately. As the acoustic wave has a low underwater propagation speed, the next node should wait that the most distant AUV receives all the signal.  
For a better illustration, we consider that the guard interval is dominated by the term \(d_{\rm max}/c\). We computed the effective network usage for distinct AUVs distance values. This result is depicted in Figure 6. In this figure, the x-axis is the real distance between the two most far away vehicles. The blue curve corresponds to the case that this information is perfectly known, leading to the usage of a \(T_{\rm guard}\) value which provides the maximum possible network usage rate. The left side of this curve, called the boundary, corresponds to the cases where the choice of \(T_{\rm guard}\) value results in no packet collision on the network. On the other hand, the usage of a \(T_{\rm guard}\) value which corresponds to an equivalent distance between transmitter and receiver lower than the real one, might result in packet collision.  
In the same figure, we also plotted the network usage rate for the cases where the guard interval is set as a fixed parameter, independent of the network topology. As observed, this approach does not explore all the time resources (left side) or it prompts transmission collisions (right side). This leads to the necessity of a strategy such that the system can operate always under the blue curve, but the closest as possible to it. A possibility is to adapt this parameter according to the network topology. Knowing that each AUV periodically broadcasts its location and speed, we can take advantage of this data for adapting the guard interval.  
Figure 5: TDMA scheme.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 5 Measurement and Analysis
### Function and Precision Test  
According to the real-time kinematic (RTK) technical specifications of the global navigation satellite system, the test environment is divided into two categories according to signal strength and terrain complexity: Class A (open flat area, satellite visibility \(>80\%\)) and Class B (urban canyon/dense forest area, satellite visibility \(<50\%\)). In order to comprehensively evaluate the environmental adaptability of the measurement system, three typical test scenarios were selected in this study: a standard football field (fully open environment), a campus dormitory (semi-closed environment), and a high-rise building intensive area with a weak signal. The test period covers different meteorological conditions, such as sunny, cloudy, and rainy, and repeated measurements are carried out at a fixed time of day to eliminate the daily periodic effect of ionospheric delay.
# Multi-Beam Circular Polarized Reflectarray on Parabolic Reflector by Variable Rotation Technique
###### Abstract  
Multi-beams antennas are currently being used for direct broadcast satellite, personal communication satellite, military communication satellite, and high-speed internet applications. In this work, a circularly polarized (CP) multi-spot beam satellite parabolic reflectarray antenna is designed to provide six spot beams at 19.7 GHz. For this purpose, an easy technique to compute the required phase shifts to produce two focused beams in specular directions for a CP parabolic reflectarray is proposed. These required phase shifts are added to the reflected fields by the variable rotation of the reflectarray elements printed on the surface of a parabolic antenna which are fed by a dual-CP feed-horn. For this purpose, a reflectarray cell made of a conductive cross embedded in a grounded multilayered substrate is optimized to produce very linear phase-shift and low cross-polarization level. To demonstrate the multibeam capacity, a 1.8-meter offset parabolic reflectarray made of the optimized reflectarray element was designed to generate six focused beams in dual-CP with three dual-CP feed-horns. The six main spots fulfill the typical multi spot satellite requirement with angular separation less than 0.56\({}^{\circ}\), 0.4 dB loss in the gain, and cross-polarization level below 35 dB with respect to the maximum of radiation.  
reflectarray; satellite antennas; multibeam antennas; moment method +
Footnote †: journal: Applied Physics Letters
# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 3 Methodology
### Dataset Cleaning, Pre-Processing and Annotation  
To avoid the noise in the corpus, we performed cleaning of the datasets and pre-processing via a Python script. To reduce spam, retweets were excluded. In addition, non-Arabic tweets were removed, by filtering by language (lang: ar), as translation damages the classifier efficiency.  
Additionally, unnecessary features in the tweets that might lower accuracy were removed from the tweet corpus before applying classifiers, such as user mentions (@user), emoticon, numbers, operators (+ = -$) and stop words (\",\",\",\",\",\"). The emoticon is deleted because we noticed that the classifier misunderstanding between the parentheses in the quote and in the emoticon as found by ([PERSON] 2016). In addition, reference [85] proved that the classification with keeping the emoticon decreased the performance of the classifier and they stated this due to the way of writing the Arabic sentience from the right-to-left and what causes from interchanging in the emoticons.  
Moreover, tweets with a uniform resource locator (URL) were excluded, as most of them were news or spam. Then, the tweet corpus was processed using the natural language toolkit (NLTK) library in Python for normalization and tokenization. The words in the tweets were tokenized, which means that the sentences were segmented into words for easier analysis. Then, the tweets were normalized. Normalization is the unification technique for the types of certain Arabic letters of different shapes. As stemming algorithms do not perform well with dialectical Arabic words [86] they were not applied. Examples before and after pre-processing (AraCust) [84] are shown in Tables 5 and 6.  
To annotate AraCust corpus, three annotators were hired in this work following [16]. Our annotators, A1, A2, and A3, were all computer science graduates, native speakers of the Saudi dialect, and had prior annotation experience. In this research, we classified the corpora using binary classification (negative vs. positive) to predict customer satisfaction toward the telecom company, following many studies that used binary sentiment classification with Arabic text [16; 87; 88; 89].
## 6 Conclusions  
This paper proposes an _EBDC_ mechanism for mobile sinks to collect data generated by all sensors. Initially, the circular monitoring region is geographically partitioned into a number of circular tracks. Then, the mobile sink moves along the scheduled trajectory \(J_{\rightleftharpoons}^{{}^{\mathit{EB},\mathit{T}}}\) for data collection such that the energy consumptions of all sensors can totally be balanced. The proposed _EBDC_ mechanism mainly consists of three phases: _Initialization_, _Energy Estimation_, and _Trajectory Construction Phases_. The _Initialization Phase_ evaluates the number of sensors \(|S_{l}|\) in each track \(k_{i}\) while the _Energy Estimation Phase_ derives the energy consumption required for each sensor when mobile sink traverses any track for one sweep repetition. Eventually, the _Trajectory Construction Phase_ schedules the movement trajectory \(J_{\rightleftharpoons}^{{}^{\mathit{EB},\mathit{T}}}\) for mobile sink. When mobile sink completes the movement of trajectory \(J_{\rightleftharpoons}^{{}^{\mathit{EB},\mathit{T}}}\) in each round, the data-relaying workloads can be totally shared by all sensors. That is to say, the energy consumptions of all sensors can be balanced. Theoretical analysis and performance evaluation reveal that the proposed _EBDC_ mechanism outperforms existing approaches in terms of network lifetime and the degree of energy balancing achieved.