esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Hardware Section  
The hardware component of our prototype, as shown in Figure 1, comprises a load cell, an HX711 amplifier, a laptop, and an Arduino board. The primary function of this section is to weigh various fruits and vegetables and transmit their corresponding weight values to the graphical user interface through serial communication between the laptop and Arduino. The load cell we have used is a type of transducer that converts mechanical deformation into electrical signals, such as voltages. Specifically, we have used strain gauges as our load cell type. Strain gauges are made up of a metal bar with attached strain gauges, and they work based on the Wheatstone bridge principle. When an external force is applied to the load cell, such as by placing fruits or vegetables on it, the resistance of the strain gauges varies. This variation in resistance is directly proportional to the applied force, and is reflected in the form of voltages. The voltages generated are typically in the millivolt range and require amplification for further processing. To amplify the analog voltage readings of the load cell, we have integrated an HX711 amplifier. The amplifier comprises a microcontroller with an analog-to-digital converter that transforms the analog readings into digital values. We have employed the \"HX711.h\" library to facilitate this conversion process and make the necessary calibrations for accurate results. The laptop is used to program the Arduino board and interface it with the load cell through the HX711 amplifier. It is also responsible for receiving the weight values transmitted by the Arduino through serial communication. The Arduino board obtains the digital weight values from the HX711 amplifier and communicates them to the laptop via serial communication.  
Overall, our prototype provides precise measurement of the weight of individual fruits and vegetables, enabling accurate prediction of their market prices. Figure 2 provides a comprehensive depiction of the complete weighing process.
# Adaptive Abnormal Oil Temperature Diagnosis Method of Transformer Based on Concept Drift
## 2 Materials and Methods
### Abnormal Detection of Oil Temperature Based on Bagging Ensemble Learning
Output: k base classifiers C and their power weights \(\mathrm{w}\), working state of transformer S. The Steps of Algorithm 2: Step 1. Input the present transformer's working condition data TS into k trained base classifiers C, respectively, and obtain k values of prediction \(\{\mathrm{v}_{1}^{\prime},\mathrm{v}_{2}^{\prime},\ldots,\mathrm{v}_{k}^{ \prime}\}\). Step 2. Calculate the expected value of k prediction results \(\mathrm{Y}\mathrm{v}^{\prime}=\sum_{k=1}^{k}\mathrm{w}_{1}\mathrm{Y}_{1}^{ \prime}\) as the present predicted oil temperature. The steps of Algorithm 2 are described as follows: Step 2.1. If \(|\mathrm{Y}\mathrm{v}^{\prime}-\mathrm{Y}|/\mathrm{Y}\leq\alpha\), then the working state of transformer S = 'FINE', and the algorithm ends. Otherwise, it goes to the next step. Step 2.2. If \(\alpha<|\mathrm{Y}\mathrm{v}^{\prime}-\mathrm{Y}|/\mathrm{Y}<\beta\), then the working state of transformer S = 'WARNING', which means the transformer has potential problems but can still work, and the algorithm ends. Otherwise, go to the next step. Step 2.3. If \(|\mathrm{Y}\mathrm{v}^{\prime}-\mathrm{Y}|/\mathrm{Y}\geq\beta\), then the working state of transformer S = 'ERROR', which means the transformer has serious problems and should stop to overhaul. Step 3. Algorithm ends and outputs C, w, and S.
```  
**Algorithm 2** Abnormal Detection of the Transformer's Working State. The above oil temperature prediction and abnormal detection model based on bagging ensemble learning is the foundation of the oil temperature abnormal diagnosis method in this paper. However, if concept drift occurs in the working condition dataset of the transformer, then the prediction and abnormal detection model will have obvious bias and inevitably lead to the incorrect diagnosis result.
# Control Strategies for Gait Tele-Rehabilitation System Based on Parallel Robotics
## 3 Results  
Within the feasibility study described in Section 2.3, series of trials were conducted to explore the system performance, particularly with respect to overall system behavior, and both participants' perceptions as patient and therapist. The ability to be applied in tele-rehabilitation settings was also evaluated.  
In Figure 4, the results from the TMM trial are depicted. The data in the first and second row correspond to the right foot orientation and the displacement reference given to the six pistons of the right platform, respectively. Furthermore, the figures in the third row refer to two particular moments when the therapist is performing dorsiflexion (left picture) or plantar flexion (right picture).  
In Figure 5, sample results from the SMM trial are depicted. In this trial, the user has increased his trunk inclination until reaching maximum gait speed, and then reduced back until no robot movement was produced. We can observe that the trunk inclination is directly translated into the normalized gait speed (top), and also how this modulation affects the normalized stride duration (bottom).  
Regarding tele-rehabilitation validation, inter-connectivity between BSU and RU through internet was tested used the ping command. The mean time to transmit and receive a TCP/IP package (56 data bytes) between RU and BSU (Lima-Montpellier) is \(\approx\)206 milliseconds.
## 4 Experiments and Results  
### Experimental Conditions  
The experiment was conducted in two different locations, namely in a university and in a care facility in the Ibaraki prefecture where elderly individuals are allowed to stay for a maximum period of three months after hospitalisation and before becoming independent again and being able to return home. In both cases, the same experimental environment was recreated that is described in this section, and thus the experiment did not distinguish the results obtained in the two locations and a single analysis was performed.  
There were a total of 8 participants (three women among them) aged between 73 to 92 years old. They were cognitively healthy, and they consistently understood and fulfilled the task expected of them. During the preparation phase, the robot and the experimental environment were introduced to the participants. The participants were clearly told that they could walk once without the robot and once with the robot (Figure 3).  
In the second case, they could choose the preferred position either side-by-side by keeping the robot at their right (1) or their left (5), or behind [PERSON] by keeping the robot at their front-right (2), at their front (3) or at their front-left (4). Figure 4 shows the relative position of the user with respect to the robot. In all five cases, [PERSON] looked at an individual's eyes. The case of the robot following the user from behind was discarded because it was considered that elderly individuals must always look in front of them while walking due to safety reason. All the subjects signed the informed consent form and agreed to the video recording prior to participating in the experiment.  
A Wizard of Oz approach was preferred due to the hardware limitations of the current robot i.e., its inability to keep tracking the human user's position and measuring the gait data in real time. In the study, the robot moves and speaks based on the actions of elderly individuals. The robot was operated in a smooth manner due to the GUI and the 3d mouse.  
\begin{table}
\begin{tabular}{l l} \hline \hline  & Try to follow me \\ During the walk & You are doing great \\  & You are walking really well \\  & Just few meters until the end \\ \hline \hline \multirow{2}{*}{At the destination} & Bravo! You completed the path \\  & Let’s walk together again \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of the utterances of the robot to increase the users’ motivation based on intrinsic motivational theories. During the experiment, the robot speaks in Japanese since all the participants are Japanese. The English translation is reported.
## 3 Materials and Methods
### Evaluation Metrics and Feedback Adjustment Strategies
#### 3.4.3 Adaptive Strategy Refinement  
The feedback adjustment strategies are continuously refined based on performance evaluations and real-time data. By combining reward- and constraint-based feedback, the system learns to prioritize objectives effectively, maintaining a balance between environmental, economic, and social goals, and gradually decreases as the optimization process converges, allowing for finer adjustments as the system approaches its optimal state.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Challenges
#### 8.2.2 Integration with Legacy Systems  
Even though programmable networking paradigms such as KDN and SDN have been proposed, many systems still rely on traditional networking systems that may not be compatible with KDN systems. KDN involves the logical decoupling of knowledge, management, and control planes from forwarding elements, whereas in traditional networks there is no knowledge plane while management and control planes are not decoupled from the data plane. These legacy networks have not been designed with the same level of flexibility and interoperability inherited by KDNs. In order for the legacy systems to become compatible with KDNs, the organizations may need to invest in making them compatible. The organizations may need to change the underlying infrastructure in order to convert their legacy networks to be compatible with KDNs. For example, data should be collected in KDN for knowledge generation. Thus, it will be difficult to convert a legacy network to send data to management and control planes, as legacy systems may not support the collection of certain types of data. The legacy networks support manual configuration and management. On the other hand, KDN relies on the automation of network configuration dynamically adapting to changing network conditions, including the least amount of human intervention, which may be difficult to achieve with old legacy systems designed to be configured by humans. Furthermore, conventional networks may not support API compatibility for programmability. As the control is not logically centralized in legacy networks, they may not provide the network global level visibility and control required for the proper functioning of the KDN. Thus, a lack of visibility can act as a barrier to collecting the required data, which is essential for training or performing real-time predictions using machine learning models. Thus, because of the requirement for significant infrastructure changes, lack of flexibility, and difficulty in achieving the automation capability required for KDN, it is difficult to integrate KDN with legacy networks.  
However, there are alternative proposals to integrate legacy systems with KDN without changing the legacy networks using a hybrid control approach. As discussed above, pure KDN is difficult to integrate with legacy networks. As discussed in Section 5.3.1, a hybrid KDN switch can be implemented with a local control agent that can be used for legacy network routing by exchanging control information between the forwarding elements, in addition to a KDN module that can be used for communication related to KDN. Thus, in this architecture, KDN and legacy networks can coexist.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 3 Traditional Machine Learning Models
### Specific Methods  
Traditional machine learning methods applied in traffic prediction can be broadly categorized into three types: feature extraction-based methods, Gaussian process modeling methods, and state space modeling methods [13].  
1. Feature extraction-based methods are primarily employed to train regression models to solve practical traffic prediction problems. Their main advantage lies in their simplicity and ease of implementation. However, these methods also suffer from limitations, such as focusing only on time series data and neglecting the complexities of spatiotemporal relationships. [PERSON] et al. [14] proposed an adaptive K-Nearest Neighbors (KNN) algorithm that treats spatial features of the road network as adaptive spatial neighbor nodes, time intervals, and spatiotemporal iso-value functions. The algorithm was evaluated using speed data collected from highways in California and urban roads in Beijing.
2. Gaussian process methods utilize multiple kernel functions to capture the internal features of traffic data, while considering both spatial and temporal correlations. These methods are useful and practical for traffic forecasting but come with higher computational complexity and greater data storage demands when compared to feature extraction-based methods.
3. State space modeling methods assume that the observations are derived from a hidden Markov model, which is adept in capturing hidden data structures and can naturally model uncertainty within the system. This is an ideal characteristic for traffic flow prediction applications. However, these models can be challenging when it comes to establishing nonlinear relationships. Therefore, they are not always the best choice when modeling complex dynamic traffic data, particularly in long-term forecasting scenarios. [PERSON] et al. [15] introduced a congestion pattern prediction model, SG-CNN, based on a hidden Markov model and compared it with the well-known ARIMA baseline model for traffic prediction. Experimental results demonstrated that the SG-CNN model exhibited strong performance.
## 2 Measurement Setup
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Descriptions** & **Transceiver Site A** & **Transceiver Site B** & **Transceiver Site C** \\ \hline Station Name & KSJ-UTM & MENARA RAZAQ & UTM-RESIDENSI \\ Station Latitude (N) & 3\({}^{\circ}\)117\({}^{\prime\prime}\) & 3\({}^{\circ}\)10\({}^{\prime}\)21\({}^{\prime\prime}\) & 3\({}^{\circ}\)10\({}^{\prime}\)20\({}^{\prime\prime}\) \\ Station Longitude (E) & 101\({}^{\circ}\)43\({}^{\prime}\)46\({}^{\prime\prime}\) & 101\({}^{\circ}\)43\({}^{\prime}\)10\({}^{\prime}\) & 101\({}^{\circ}\)43\({}^{\prime}\)19\({}^{\prime\prime}\) \\ Site height (ASL) & 49 m & 43 m & 41 m \\ Frequency Tx/Rx & 73.5/83.5 GHz & 83.5/73.5 GHz & 73.5/83.5 GHz \\  & & 1.8 km (A-B link) & \\ Tx-Rx LOS distance & 1.8 km (B-A link) & 1.8 km (C-B link) & 300 m (B-C link) \\  & & 30 m (ANT2 0.3 80 HP) & 0.3 m (ANT2 0.3 80 HP) & \\ Antenna Type & 0.3 m (ANT2 0.3 80 HP) & 0.2 m (ANT2 0.2 80 HP) & 0.2m(ANT2 0.2 80 HP) \\ Antenna Height (AGL) & 20 m & 50 m & 70 m \\ Polarization & Vertical & Vertical & Vertical \\ Antenna Gain & 46.5 dBi & 46.5 dBi & \\  & & 43.5 dBi & 43.5 dBi \\ Antenna HPBW & 0.8\({}^{\circ}\) & 0.8\({}^{\circ}\) & \\  & & 1.1\({}^{\circ}\) & 1.1\({}^{\circ}\) \\ Max Tx Power & 15 dBm & 15 dBm & 15 dBm \\ Receiver Sensitivity & \(-\)75 dBm & \(-\)75 dBm & \(-\)75 dBm \\  & & 134.9 dB at 1.8 km & \\ Free space path loss & 135.9 dB & 119.3 dB at 300 m & 120.4 dB \\ Other losses & 6 dB & 7 dB & 7 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of experimental E-band links. Figure 1: Measurement links map in Kuala Lumpur, Malaysia for 1.8-km and 300-m line-of-sight (LOS) path at 73.5 and 83.5 GHz E-bands between Kolej Siswa Jaya (Site A) and Menara Razak, Universiti Teknologi Malaysia (Site B); and 300-m LOS path at E-band between Site B and Residensi UTM (Site C). Figure 2 presents the cumulative distribution function (CDF) of the measured rain rate for every minute along a one-year period together with the ITU-R PN.837-1 model [27]. The experimental CDF is also compared with the CDF of rainfall 1-h integration time data collected from the Malaysian Meteorological Station (MMS) over a 12-year period [28]. [PERSON] and [PERSON]'s model [28] was used to convert these rain-rate data to the equivalent 1-min integration time. Finally, the measured CDF in the link area is also compared with the CDF of l-minute rainfall data measured over three years (June 1992 to May 1995) in Kuala Lumpur, Malaysia (3\({}^{\prime\prime}\)08\({}^{\prime}\) N, 101\({}^{\prime\prime}\)39\({}^{\prime}\) E) using a tipping bucket rain gauge of 0.5 mm per tip [28]. It can be shown that the CDF of measured rain rate is very close to the ITU-R, MMS, and Chebil for any rain rates below 150 mm/h. The largest deviation between the CDF of the measured rain rate and the ITU-R model appears at low percentage of time (0.001%) with around 60 mm/h difference. However, the difference between the CDF of our measurements and the other measurement results--namely, MMS and Chebil--remains small (12 mm/h) even at these low percentages of time (0.001%).
## 5 Experiment Results
### Comparisons with Several Efficient Algorithms
All the compared algorithms were executed for 100 s on the aforementioned PC. From Figure 8, it can be observed that: (1) in most cases, the IIG algorithm provided the best initial values, demonstrating the effectiveness of the proposed heuristic initialization strategy in generating high-quality initial solutions. (2) As the runtime increases, the fitness values obtained by IIG are consistently the lowest, indicating that IIG effectively guides solutions toward better outcomes. (3) The final fitness values of the convergence curves confirm the effectiveness of the destruction-construction strategy, problem-specific local search strategy, and acceptance criterion. Additionally, Figure 9 illustrates the helicopter routing charts generated by the IIG for four selected instances. The red square in Figure 9 represents the rescue center, while the circle denotes the rescue site. For each instance, the blue solid line represents the route of the medical helicopter, and the red dashed line indicates the route of the transport helicopter. These results further validate that the IIG produces superior quality solutions when solving the collaborative helicopter rescue routing problem. Figure 8: Comparisons of the convergence curves for the compared algorithms. (**a**) Curves for instance rc105; (**b**) curves for instance rr103; (**c**) curves for instance rrc107; (**d**) curves for instance rrc208.
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 3 Experiments and Results
### Experimental Settings
#### 3.2.3 Evaluation Metrics  
To quantify the road extraction performance of our model, we employed a confusion matrix to assess the model's performance in binary classification problems. As shown in Table 1, the labels were categorized into positive and negative samples, while the prediction results were divided into true positives and true negatives (TPs and TNs), as well as false positives and false negatives (FPs and FNs) to represent accurate and inaccurate predictions, respectively. To thoroughly evaluate the model, this study utilized three evaluation metrics: precision (P), recall (R), and intersection over union (IOU). These metrics served to assess the road extraction performance of the network model.  
Precision: This is the proportion of true positive instances amongst all instances predicted by the model as the positive class. In other words, the precision reflects the proportion of actual roads in all instances predicted as roads by the model.  
\[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Real Category**} & \multicolumn{2}{c}{**Predictive Results**} \\ \cline{2-3}  & **Road** & **Non-Road** \\ \hline Road & True positive (TP) & False negative (FN) \\ \hline Non -road & False positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between positive and negative samples.  
Recall: This is the proportion of actual positive samples that were correctly identified by the model, indicating the proportion of correctly labeled roads by the model.  
\[Recall=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
Intersection over Union (IoU): IoU is a commonly used evaluation metric in semantic segmentation and object detection. It measures the accuracy of detecting ground objects from a dataset and quantifies the degree of fit between the extracted results and the ground truth labels. The higher the IoU value, the bigger the overlapping region between the results and the ground truth.  
\[IOU=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{8}\]  
In this experiment, we evaluated the road extraction performance of the HRU-Net model by calculating the precision, recall, and intersection over union (IOU), and compared the experimental results with other advanced remote sensing image road extraction methods.
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 2 Materials and Methods
### Sensor Performance
#### 2.4.4 Noise Sensors  
The noise sensors were tested in a laboratory protocol based on a NIOSH evaluation of smartphone noise measurement applications [43]. Measurements from the low-cost noise sensors were compared to a reference sound level meter (NTI Audio, SLM, Schaan, Liechtenstein). Pink noise was generated using a TalkBox (NTI Audio, Schaan, Liechtenstein) and an amplifier (Fender Musical Instruments Corp., Scottsdale, AZ, USA). Side-by-side measurements were conducted for each low-cost noise sensor and the reference instrument. The noise sensors were positioned 15 cm away from the TalkBox and amplifier. Pink noise was generated between 65 and 95 dB in 5-dB increments, and for 30 s at each sound level. Average slope, intercept, R-value, bias and precision were calculated between the low-cost noise sensors and the reference instrument, with an acceptance criterion of \(\pm\)2 dB [44].
## 3 Expression for Effective FRP Strain of Interior Joints Derivation
### The FRP and Joint Assembly Parameters  
The crucial parameters related to the FRP and joint assembly that can affect FRP strain need to be specified. In terms of the parameters, the equation of the effective FRP strain will be expressed. Similar parameters as those chosen by [19], including the parameters selected by [20], are selected for this purpose. The following parameters are selected based on the relation with the effective FRP strain of an assembly:  
1. Concrete compressive strength,\(f_{c}\).
2. Joint panel equivalent FRP area, \(A_{f,eq}\)
3. Elastic modulus of the FRP fibers, \(E_{f}\).