esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This paper proposes the integration of ZigBee communication, Wi-Fi communication, and Internet connection capabilities onto a traditional Wi-Fi AP by combining the AP with ZigBee standard. With the proposed approach, the original Wi-Fi AP can be upgraded to an IoT AP. The novelty and contribution of the proposed IoT AP is summarized below. (1) IoT AP adaptively provides negotiation of channel usages between Wi-Fi network and ZigBee networks and help ZigBee network to allocate channel in a shorter response time; (2) Wi-Fi interference can compete with other Wi-Fi devices and reserve the allocated bandwidth resource for ZigBee network. This function fully resolves the existing problem of unbalanced resource competition between Wi-Fi and ZigBee interferences; (3) IoT AP supports dual modes of ZigBee operations, including power saving mode and emergency mode. By reducing the frame size, the IoT AP provides ZigBee devices with more opportunities to transmit data in a short cycle; and (4) the data collected from IoT devices can be forwarded directly to the data server  
Figure 16: Comparison of energy consumption.  
or data center via the Internet in a more efficient way, as compared with the existing solutions [12,24,25]. This helps the IoT AP can save more energy consumptions and reduce the response time. Performance results show that the proposed IoT AP not only prevents the ZigBee network contention and collision, but also saves energy consumption, as compared with the existing solutions [12,24].  
The authors would like to thank the Ministry of Science and Technology of the Republic of China for financially supporting this research under Contract No. MOST 104-3115-E-007-001-, MOST 103-2221-E-032-020-MY3 and MOST 104-2627-E-119-001-.  
[PERSON] and [PERSON] frequently discussed the design of IoT applications, system architecture, technique depth and challenges with [PERSON] and examined the checkpoints of IoT implementation. [PERSON] mainly implemented the designed IoT gateway to support the IoT functionalities, such as the data exchange and packet format transforming among heterogeneous devices with Wi-Fi, ZigBee or Ethernet interface, the ISM bandwidth coordination between ZigBee and Wi-Fi communications, and so forth. [PERSON] provided technical guiding to support the IoT implementation for Smart Home. [PERSON] also did the paper writing work for presenting the contributions of IoT implementation in the smart home environment.  
The authors declare no conflict of interest.
## 3 Design and Fabrication of Hydrocooler
### Structure of Spray Hydrocooling System  
The precooling device was designed with a precooling capacity of 1000 kg lithchi per day and a precooling time of 8 h. If six batches were precooled per hour, the precooling capacity of each batch was 21 kg. The size of packing basket selected was 0.35 \(\times\) 0.48 \(\times\) 0.16 m\({}^{3}\), and each basket could hold 11.5 kg of lithchi. Figure 6 shows the schematic diagram and photograph  
\begin{table}
\begin{tabular}{c c c c} \hline
**Component** & **Size or Model** & **Material or Company** & **Values** \\ \hline Compressor & QXL-30E & Zhejiang Boyang, China & 3180 W (\(-\)10 \({}^{\circ}\)C) \\ Condenser & Small 3-HP & SIMCO & 18 m\({}^{2}\)\(\times\) 2 \\ Evaporation coil & 0.016 m diameter & Copper & 43.4 m \\ Circulating water pipe & 0.032 m diameter & PVC & 5 m \\ Thermal energy storage tank & 1.7 \(\times\) 0.85 \(\times\) 1 m\({}^{3}\) & Stainless steel & 1.4 m\({}^{3}\) \\ Spray precooling box & 0.75 \(\times\) 0.52 \(\times\) 0.45 m\({}^{3}\) & Acrylic & 0.18 m\({}^{3}\) \\ Nozzle & 0.0015 m diameter & Silica gel & Variable size \\ Sprinkle & 0.25 \(\times\) 0.25 \(\times\) 0.006 m\({}^{3}\) & Stainless steel & 3–10 L min\({}^{-1}\)\(\times\) 6 \\ Precooling basket & 0.35 \(\times\) 0.48 \(\times\) 0.16 m\({}^{3}\) & Plastic & 200 g \\ Water pump & SD-750 & Weller & Frequency conversion \\ \hline \end{tabular}
\end{table}
Table 3: Selection of spray hydrocooler components.  
Figure 5: Structure diagram of TES tank and refrigeration system. (**a**) The main view and. (**b**) The left view.  
of the spray precooling box. Sprinklers were mounted at the top and a perforated plate was used at the bottom of the spray hydrocooling box. The outside of the box was covered with 1 cm thick insulation cotton. Six square stainless-steel sprinklers had nozzles made of silica gel. The nozzles were 1.5 mm in diameter under zero-pressure conditions and could enlarge with water pressure. The spray flow rate was controlled by the variable frequency water pump. Litchi packed in perforated baskets was kept in the spray precooling box for precooling. The cold water sprayed over the litchi initiates rapid heat exchange. The water then flows through the perforated bottom plate and enters the TES tank.
## 8 Performance Analysis
### Energy Dissipation and Node Lifetime Analysis  
We use the radio energy model, as that in [47]. The energy dissipation of transmitting (resp., receiving) one bit of information is given by: \(E_{Tx}=E_{elec}+E_{amp}\times Dis^{a}\) (resp., \(E_{Rx}=E_{elec}\)), where: \(Dis\) is the distance separating two nodes, and \(a\) is the attenuation factor of the environment and can be between 2 for free space and 4 for urban environment. The parameters that are used in [47] are as follows: \(E_{elec}=50 nI/bit\), \(E_{amp}=100 pI/bit/m^{2}\). Let \(D\_{Pkt}\) and \(B\_{Pkt}\) be the size in bit of data packet and beacon packet sent at each time period \(TB\), respectively. The tree-based routing protocol with and without the proposed IDS is symmetric in the sense that every node executes the same code, i.e., every time that a node sends a message, it has to receive the same type of message from each neighbor node. Subsequently, the energy dissipated by a node under a tree-based routing protocol is:  
\[E_{W}=(D\_Pkt+B\_Pkt)(E_{Tx}+d_{max}E_{Rx})\]  
In the case of the same routing protocol with the proposed IDS, the energy dissipated by a node is composed of two terms: The first term is the energy dissipated during the initialization phase, and it is computed as:  
\[E_{IDS}^{a}=((d_{max}^{2}+2d_{max})\log_{2}N+(d_{max}+1)RN)(E_{Tx}+d_{max}E_{Rx})\]  
The second term, which is consumed periodically at each \(TB\), is computed as:  
\[E_{IDS}^{b}=(D\_Pkt+B\_Pkt+d_{max}+\log_{2}N+TC)(E_{Tx}+d_{max}E_{Rx})\]  
Let the energy initial of each node be \(E_{init}\). The node lifetime under the tree-based protocol is  
\[T_{W}=(\frac{E_{init}}{E_{w}})\times TB\]  
The node lifetime under the IDS is:  
\[T_{IDS}=(\frac{E_{init}-E_{IDS}^{a}}{E_{IDS}^{b}})\times TB\]  
\begin{table}
\begin{tabular}{|l|l|} \hline CC (Initial) & \(O(2d_{max}+3)\) \\ \hline CC (Periodic) & 0 \\ \hline MC (Initial) & \(O((d_{max}^{2}+2d_{max})\log_{2}N+(d_{max}+1)RN)\) \\ \hline MC (Periodic) & \(O(d_{max}+\log_{2}N+TC)\) \\ \hline \end{tabular}
\end{table}
Table 1: Complexity analysis of the proposed IDS.  
Let \(N=1024\), \(d_{max}=10\), \(RN=32\), \(TC=32\), and \(\alpha=2\). For the rest of parameters, we use the following, as in [48]: \(D\_Pkt=4000\) bytes, \(B\_Pkt=20\) bytes, \(Dis=50\) m, and \(E_{init}=3\)J. Therefore, we obtain: \(E_{W}=25.728 mJ\), \(E_{IDS}^{a}=1.241 mJ\), \(E_{IDS}^{b}=25.769 mJ\), \(T_{W}=116.60\times TB\), and \(T_{IDS}=116.37\times TB\).
## 3 Methods
### Resampling Module  
The original imbalanced dataset is made more balanced by the resampling module. The ESA-GCN model combines undersampling and oversampling methods to address the imbalanced dataset problem. The Edited Nearest Neighbor (ENN) undersampling method, used by the ESA-GCN, reduces the majority class nodes based on their density in the feature space. It removes low-density majority class nodes, reducing noise and duplicate samples in the majority class samples of the imbalanced dataset and improving the accuracy of the classifier. Then, the ESA-GCN uses the Synthetic Minority Oversampling Technique (SMOTE) to generate minority class nodes. SMOTE synthesizes new minority class nodes to increase their sample count and interpolates them in the feature space with neighboring minority class nodes, thereby enhancing the classifier's ability to learn from the minority class. The resampling module includes both the ENN undersampling process and the SMOTE oversampling process.
# Domain Adaptation for Arabic Machine Translation: Financial Texts as a Case Study
## 4 Results and Discussion
### Human Evaluation  
In addition to the automatic evaluations reported above, we decided to assess the quality of our models' translations using human evaluation. Machine translation metrics such as BLEU only measure the linguistic proximity of outputs to the gold standard of reference. On the other hand, COMET and BERTScore which overcome this aforementioned issue, still exhibit discrepancy issues. To gain further insights into these results, we conducted a human evaluation. To this end, we recruited three native speakers and domain experts (post-graduate students in finance) to rate the acceptability of 50 randomly selected sentences from the test set. Similar to [7], we conducted a bilingual evaluation, whereby the evaluators rated both the original source sentences and translations generated by the MT models. The human evaluators were asked to rate each of the sentences based on the scale proposed by [67], ranging from 1 to 4, and outlined as follows:  
* 4 = Ideal: Not necessarily a perfect translation, but grammatically correct, with all information accurately transferred.  
* 3 = Acceptable: Not perfect (stylistically or grammatically odd), but definitely comprehensible, and with the accurate transfer of all important information.
* 2 = Possibly Acceptable: Possibly comprehensible (given enough context and/or time to work it out); some information transferred accurately.
* 1 = Unacceptable: Absolutely not comprehensible and/or little or no information is accurately transferred.  
We first asked the three human evaluators to rate one model's output and then we conducted an inter-rater reliability analysis on their ratings. The result of weighted Cohen's Kappa is 0.87. Then, we asked each evaluator to rate the outputs of the remaining models and provide justification for their responses were \"Ideal\" or \"Unacceptable\". The mean value of the raters' scores was averaged for each system, as shown in Table 3.  
Overall, the outcome of human evaluation corroborates the automatic evaluation results. In Setups 1 and 2, where ChatGPT-3.5 achieved the best performance in lexical and semantic metrics, human evaluation confirmed this result with a top score of 3.1.  
However, in Setup 3, even though the automatic metrics are degraded for all models, except BERTScore, the human evaluation shows that the translation quality of all models is comparable.  
Thus, we find that BERTScore correlates with human judgment more than COMET which has been recently reported to correlate highly with with human judgment [68]. This finding opens a great investigation for the future into whether semantic metrics correlate with human judgment and to what extent, in particular, when ChatGPT-3.5 is applied.
# The Design and Investigation of Hybrid a Microfluidic Micromizer
## 2 Materials and Methods
### Mathematical Modelling
#### 2.3.1 Mass and Momentum Conservation  
The governing equations related to mass and momentum conservation, such as the equation of continuity and the Navier-Stokes equations for compressible Newtonian fluids, are given below [45].  
\[\frac{\partial\rho}{\partial t}+\
abla\times(\rho v)=0 \tag{4}\]  
\[\rho\frac{\partial v}{\partial t}+\rho(v\times\
abla)v=-\
abla p+\mu\
abla^{2} v+\beta\mu\
abla(\
abla\times v) \tag{5}\]  
where \(\rho\) is the fluid density, \(\mu\) is the dynamic viscosity of the fluid, \(\beta\) represents the viscosity ratio, \(p\) and \(v\) describes the pressure and velocity of the flowing fluid, respectively.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Deployment Related Problems in WSNs
#### 3.3.3 Network Lifetime  
There are several research work relating to the deployment of sensor network while considering network lifetime along with other conflicting or non conflicting objectives. For example, optimal node deployment has been investigated in [111] to maximize optimal sensing coverage and network lifetime of wireless sensor networks. In [112], an optimal deployment of sensor network has been considered to simultaneously maximize network lifetime and coverage.
# Railway Overhead Wiring Structures in Australia: Review and Structural Assessment
## 3 OWS in Australia
### Common Structural Forms of OWS in Australia
#### 3.1.1 Single Mast OWS  
Single mast OWS are usually constructed from steel H-sections (called universal columns in Australia) with a fixed base. An example is shown in Figure 3. The base fixity is provided either by a base plate and holding down bolts embedded in a reinforced concrete footing, or a long length of steel section potted in an augured hole with reinforced concrete surround. The catenary wire is supported by a cantilever jib via a suspension insulator. The jib is commonly clamped onto the H-section instead of permanent welding or bolting to facilitate adjustment of its elevation. Single mast OWS are typically used for a single-track arrangement. Common durability issues of single masts include corrosion, loss of grout/crack at base plate, and deviation from plumb.
## 4 Evaluation of the CAN-Ethernet Network Time-Synchronization Performance  
Figure 6 shows the experimental setup for the performance evaluation of the CAN-Ethernet network synchronization. Five NXP MPC5748-LCEVB modules were used for the nodes and gateway. MPC5748 is a chipset for vehicle use and supports CAN (CAN FD) and 100-Mbps Ethernet networks. The network topology for the performance evaluation comprised a master node (Ethernet), gateway, and three slave nodes (CAN), and an oscilloscope was used to measure the synchronization error. To emulate a CAN network with many nodes, CAN slave 3 was connected with a 15 m cable. To generate timestamps, each node used a software-based timestamp.  
Figure 5: Synchronizing process for sharing the delay.  
Figure 6: Experimental environment for the evaluation of synchronization in the CAN–Ethernet network.  
The synchronization error is defined as the difference in the perceived global times. To measure the error, slave nodes were programmed to output trigger signals to a certain pin so that the oscilloscope captured the time instants. In addition, the synchronization procedure was periodically repeated every 1 s to limit the effect of difference in clock rates. The trigger signal for measuring synchronization performance was set to output approximately 800 ms after the time synchronization ended.  
Figure 7 demonstrates the time difference of the trigger signals from the nodes before and after time synchronization was performed. The rising edges of the output signals from the nodes were not aligned before synchronization while the edges were rising almost simultaneously after synchronization. These results confirmed that the proposed algorithm enables time synchronization in the CAN-Ethernet network.  
Figure 8 shows the oscilloscope measurement results of the synchronization error occurring in the four nodes after performing time synchronization in the CAN-Ethernet network. According to the measurements, when the proposed synchronization algorithm was applied, the maximum synchronization error between the master and slave was approximately 9.68 \(\upmu\)s. Table 1 presents the synchronization error when the above experiment was repeated ten times. The average of the synchronization errors was approximately 8.18 \(\upmu\)s, with a worst-case error of 9.68 \(\upmu\)s. Although various factors can cause this error, the timestamp function, which measures and records time, is most likely to significantly influence this error, because it is operated by software.  
Figure 8: Evaluation results of the synchronization in the CAN-Ethernet network.  
Figure 7: Comparison of the trigger signals before and after synchronization.
## 3 Materials and Methods
### Data Transmission
#### 3.5.1 Network Management User Interface  
Graphical Network Simulator-3 (GNS3) is an open-source network software written in Python that utilizes to present the network topology. GNS3 can host Docker containers, network switch, firewalls, or Ubuntu servers. A two-way data connection is possible via telnet. This combination of components allows the simulation of complex network environments [30].
# Women's Singles Tennis Match Analysis and Probability of Winning a Point
## 3 Results  
### Analysis of Factors Affecting Performance in Women's Singles Tennis in Grand Slam Tournaments  
Table 2 provides a descriptive analysis of the research and presents a comparison of the study variables according to the type of court (inter-criteria \(\chi\)2 test). \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Variables** & & **Clay** & & **Grass** & & **Hard** & \(\chi\)**2 Inter-Criteria** \\ \hline \multirow{4}{*}{Service} & DF & 59 & 4.6 & 50 & 5.2 & 56 & 5.6 & \(\chi\)2 = 2039 \\  & FS & 804 & 62.5 & 589 & 62.1 & 607 & 60.3 & \(p\) = 0.729 \\  & SS & 423 & 32.9 & 308 & 32.5 & 343 & 34.0 & \\ \hline \multirow{4}{*}{Rally length} & LN & 120 & 9.3 & 90 & 9.5 & 88 & 8.7 & \(\chi\)2 = 0.718 \\  & MD & 263 & 20.4 & 184 & 19.4 & 202 & 20.0 & \(p\) = 0.949 \\  & SH & 903 & 70.2 & 673 & 71.0 & 716 & 71.1 & \\ \hline \multirow{4}{*}{Bounce zone} & SZ & 105 & 8.2 & 130 & 13.7 & 122 & 12.1 & \(\chi\)2 = 23,445 \\  & ZB1 & 607 & 47.2 & 413 & 43.6 & 463 & 46.0 & \(p\) = 0.009 \\ \cline{1-1}  & ZB2 & 419 & 32.5 & 282 & 29.8 & 298 & 29.6 & \\ \cline{1-1}  & ZB3 & 95 & 7.4 & 85 & 9.0 & 78 & 7.7 & \\ \cline{1-1}  & ZB4 & 36 & 2.8 & 20 & 2.1 & 26 & 2.6 & \\ \cline{1-1}  & ZB5 & 24 & 1.9 & 17 & 1.8 & 19 & 1.9 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of performance variables for matches played on different court surfaces during the 2021 season, including a comparative assessment of these surfaces. The vast majority of points, regardless of the type of court surface on which they were played, started with a first serve (range: 60.3-62.5%) and ended with a short rally (range: 70.2-71.1%), with no differences depending on the type of court (\(p>0.005\)). In terms of aces, grass was the surface for which the highest number of aces was recorded (13.7%), with similar values for hard courts (12.1%) and lower values on clay (8.2%). Most of the observed points ended after the player hit the ball after a bounce in zone 1 (43.6-47.2%) or zone 2 (29.6-32.5%). The most common finish zone on any type of court was zone 1, although this was more pronounced on grass (20.0%) compared to clay (17.8%) and hard courts (17.4%). In terms of the effectiveness of winning points with a serve, grass led with 62.2%, followed by hard court (60.7%) and clay (56.1%), with statistically significant differences (\(p=0.002\)). Regarding serving, the highest number of winners was recorded for grass (23.2%), with similar figures for hard and clay courts (22% and 20%, respectively). Returning, it was observed that the surface with the most winners was clay (12.3%), with a higher figure than grass and hard court (9.3 and 9%, respectively). The values of unforced errors in both serving and returning were similar on all three surfaces (ranges: 29.4-32.6% and 23-26.3%,
## 3 Data Denoising  
### Data Decomposition  
One drawback of the empirical mode decomposition (EMD) method is the presence of mode mixing, which means that multiple frequency components can exist within a single intrinsic mode function (IMF). Ensemble empirical mode decomposition (EEMD) is a novel decomposition algorithm that improves upon EMD based on empirical validation. EEMD involves adding Gaussian white noise to the original signal sequence and then averaging the intrinsic mode function components. The intermittent phenomena of the original signal can be eliminated by the statistical characteristics of the white noise's uniform frequency distribution, thereby suppressing the mode mixing effect of EMD and improving the decomposition efficiency and accuracy. The principle is as follows:  
Step 1: Add a random Gaussian white noise sequence to the original signal, i.e.,  
\[x_{n}(t)=x(t)+km_{n}(t) \tag{4}\]  
In the equation, \(k\) is the amplitude coefficient of the added Gaussian white noise sequence.  
Step 2: Decompose the signal with the added Gaussian white noise using EMD to obtain a set of the IMF.  
Step 3: Repeat the above two steps \(n\) times and compute the average of the IMF components obtained from each decomposition to eliminate the influence of Gaussian white noise on the true IMF components. The final obtained IMF components and the residual \(r(t)\) are, respectively, as follows:  
\[c_{j}(t)=\frac{1}{n}\sum_{i=1}^{n}c_{ij}(t) \tag{5}\]  
\[r(t)=\frac{1}{n}\sum_{i=1}^{n}r_{i}(t) \tag{6}\]  
In the equation, \(c_{j}(t)\) is the \(j\)-th IMF component obtained from the ensemble empirical mode decomposition of the original signal.  
Step 4: Finally, the original signal is represented as follows:  
\[x(t)=\sum_{j=1}^{k}c_{j}(t)+r(t) \tag{7}\]
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Prediction Model  
Partial least square regression (PLSR), a popular multivariate data analysis method is applicable to spectral analysis [19]. In this study, PLSR models were individually established to represent quantitative relationships between information data (full spectra, key wavelengths, texture data, and fusion data) and three reference WHC traits of the samples. Full cross validation was applied to validate the prediction and avoid over-fitting of the PLSR models [11]. Finally, the samples from an independent prediction set were used to verify the predictive ability of the established PLSR model. The model performance was evaluated by the coefficient of calibration (Rc), the root-mean-square error estimated by calibration (RMSEc), the coefficient of cross-validation (Rcv), the root-mean-square error estimated by cross-validation (RMSEcv), the coefficient of independent prediction (Rp), and the root-mean-square error estimated by independent prediction (RMSEp). Commonly, the model, which has high coefficients of determination (Rc, Rcv, and Rp) and low root-mean-square error (RMSEc, RMSEcv, and RMSEp) as well as a small difference between RMSEc, RMSEcv, and RMSEp, is considered to be the desired model [27]. All predictive model development procedures were carried out by Unscrambler X 10.1 software (CAMO, Trondheim, Norway).