esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Validation of a Camera-Based Safety System for Fenceless Robotic Work Cells
## 4 An Industrial Case Study and Experimental Verification
### Test Results and Discussion
At this level, the detection time was at the highest level. The low-resolution camera showed that the detection time became shorter, and the performance increased as the illumination rose to 120 lux. The fluctuation seen after this level was related to the stability of the system itself. In terms of illuminance, the lowest level available was 120 lux. The high-resolution camera observed that the system worked stably even at low illumination levels. Commercial production areas are known to require at least 300-400 lux light [34]. To understand the illuminance levels more clearly, it is measured as 100 lux on a dark or cloudy day [35]. The illuminance of a residential living room at evening time is measured as 50 lux [36]. Considering the values obtained, the experimental setup was sufficient. The desired system can be installed by considering the camera's high aperture, which is expected to detect in systems where it is desired to operate at a lower light intensity. As a result, the hypothesis outlined in the experimental setup carried out was validated. Improvement suggestions are presented for large-scale industrial applications. In addition, cost comparisons for the commercial product and the designed safety system are shown in Table 4.  
Figure 17: System response time versus illuminance as a function of camera resolution using two graphics processors. Another comparison between the experimental setup and the commercial product is how the system set up in different images will react. As a result of these comparisons, the effects of the following features were sought:  
* Object size;
* Object brightness;
* Object geometry;
* Defective object corners;
* Camera resolution;
* The closeness of object and background colors to each other;
* Shadows on the object;
* Stray light reflections from the object. To compare the results of these factors, the aim was to learn the effect of the size, color, geometry, brightness, and shadings while classifying. As a result of the comparisons, it was seen that the response times of the proposed safety system were close to each other. Thus, by this experimental setup, it was proven that a realistic and cost-effective alternative option to commercial ones was assured. To validate the proposed safety system in the work cell with an industrial quality robot arm, the safety system was
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 3 Disruption Tolerant Distributed Routing Algorithm
### Global Topology Generation  
In the previous section, we introduced the constellation configuration of the LEO satellite network. According to the motion law of the constellation, the change of the space segment network topology is mainly the change of the length of the inter-plane ISLs, which affects the propagation delay in the network. The length of the inter-plane ISL depends on the latitude of the two satellites that it is connected to. Therefore, the change of the latitudes of the satellites is directly related to the change of the network topology. Learning from the idea of virtual topology [5], we divide a period of satellite motion into \(K\) time intervals of equal length. When the adjacent time interval is switched, each satellite changes the latitude information \(latC\) of the local satellite and recalculates the route.
## 4 Wave-Current Force on Pile  
### Calculation Theory of Wave-Current Force and Hydrodynamic Coefficient of Pile  
According to the analysis in Section 3, it is assumed that (i) the current-affected wave retains linear wave characteristics and (ii) only the wave height \(H\) changes. In that case, the wave surface elevation \(\eta\) can be expressed as  
\[\eta=\frac{H}{2}\sin(kx-\omega t) \tag{6}\]  
where \(H\) and \(k\) are the current-affected wave height and wave number, respectively, and \(\omega=2\pi/T\) is the angular frequency. The horizontal and vertical velocities of a water particle affected by a uniform current are:  
\[u_{x}=U+\frac{H}{2}(\omega-kU)\frac{\cosh k(z+d)}{\sinh kd}\cos(kx-\omega t) \tag{7}\]  
\[u_{z}=\frac{H}{2}(\omega-kU)\frac{\sinh k(z+d)}{\sinh kd}\sin(kx-\omega t) \tag{8}\]  
To consider the impact of hydrodynamic loads, the modified Morison formula and Kutta-Joukowski formula proposed by [PERSON] and [PERSON] [27] were used to evaluate the inline and transverse forces on the pile, i.e.,  
\[F_{x}=\int\frac{1}{2}C_{D}\rho A(u_{w}+U)|u_{w}+U|dz+\int C_{M}\rho Vi_{w}dz \tag{9}\]  
\[F_{y}=\int\frac{1}{2}C_{L}\rho D(u_{w}+U)^{2}dz \tag{10}\]  
where \(F_{x}\) and \(F_{y}\) are the inline and transverse forces acting on the pile, respectively. \(C_{D}\), \(C_{M}\), and \(C_{L}\) are the drag, inertia, and lift coefficients, respectively. \(\rho\) is the fluid density, \(A\) is the projection area of the pile perpendicular to the wave propagation, \(V\) is the volume of the pile immersed in the fluid, \(D\) is the pile diameter, \(u_{w}\) is the wave-induced horizontal particle velocity, and \(i_{w}\) is the horizontal particle acceleration.  
Figure 6: Wave height change versus relative current velocity \(U/c_{0}\) for \(U=0\)–\(0.4\) m/s.  
The resultant force \(F_{H}\) on the pile is the vector sum of \(F_{x}\) and \(F_{y}\), i.e.,  
\[F_{H}=\left(F_{x}^{2}+F_{y}^{2}\right)^{0.5} \tag{11}\]  
The theoretical inline, transverse, and resultant forces on the pile can be calculated using Equations (7)-(11). Then the hydrodynamic coefficients \(C_{D}\) and \(C_{M}\) can be calculated by the least-squares method, with \(C_{L}\) determined using Equation (10).  
Previous research experience suggests that the hydrodynamic coefficients (\(C_{D}\), \(C_{M}\), and \(C_{L}\)) are closely related to \(KC\)[7; 8; 9; 11; 16]. Therefore, herein, the redefined \(KC^{*}\) for wave-current conditions is used to characterize the hydrodynamic coefficients, i.e.,  
\[\left\{\begin{array}{l}KC^{*}=\frac{u_{m}T}{D}[\sin\varphi+(\pi-\varphi)\cos \varphi]\,\ |U|<u_{m}\\ KC^{*}=\frac{\pi|U|T}{D}\,\ |U|\geq u_{m}\end{array}\right. \tag{12}\]  
where \(\varphi=\arccos\frac{|U|}{u_{m}}\), and the maximum horizontal velocity of particles induced by waves is \(u_{m}=\frac{\pi H}{I}\coth kd\).
## 2 Materials and Methods
### Proposed Method
#### 2.2.6 Model Specifications  
The real-time processing capability of the system is critical in the proposed methodology. A frame rate of 30 fps and a window size of 3 s were used. This time frame is optimal for capturing the transient characteristics of the drowsiness indicators. To ensure accuracy in PERCLOS and POM calculations, thresholds were set at 0.2 and 0.5, respectively. Machine learning models employing SVM, Random Forest, and XGBoost were configured with a balance of specificity and computational demand. While SVM and Random Forest models utilized RBF kernels and 100 estimators, respectively, the XGBoost model was finely tuned with an objective of 'binary:logistic', 100 estimators, and a learning rate of 0.05. When the hyperparameters were not explicitly modified, the default values were adopted to maintain model simplicity. The fusion of these parameters with real-time data serves as the foundational design for our drowsiness detection framework. While it is crafted with the intent of achieving accuracy and efficiency, especially on resource-constrained devices such as the Jetson Nano, we recognize that this design is a starting point. Actual deployment may necessitate further tuning to ensure the robustness and reliability of the model in varied and unpredictable conditions of real-world use.
## 2 Development of Sensor Module  
Time-of-Flight (ToF) sensors are optical distance-measuring devices that operate by emitting a light pulse, typically infrared, and calculating the time it takes for the light to reflect back from an object to the sensor [24; 25; 26], as illustrated in Figure 1. The measured distance is represented by Equation (1).  
\[\text{Distance}=\frac{\text{Speed of Light}\times\text{Time of Flight}}{2} \tag{1}\]  
This technology is widely utilized across various fields where accurate, reliable, and compact distance measurements are essential. These applications include robotics for navigation and interaction, automotive systems for parking assistance and collision avoidance, consumer electronics like smartphones (for facial recognition and augmented reality), and industrial automation for presence detection and volume measurement.  
ToF sensors offer several inherent advantages that make them suitable for diverse applications. First, they provide highly precise and consistent distance measurements, largely unaffected by target surface properties such as texture, color, or ambient lighting conditions [27]. This robustness is critical for reliable performance in both indoor and outdoor environments. Second, their compact size and remarkably low power consumption make them ideal for integration into space-constrained and battery-sensitive applications, such as drones, where weight and energy efficiency are paramount.
# EEG Signals Based Internet Addiction Diagnosis Using Convolutional Neural Networks
## 2 Materials and Methods  
### Dataset Preparation  
The EEG data from the LEMON dataset are used for extracting features, and a functional connectome phenotyping dataset is the label in our algorithm. Both datasets constitute parts of the Leipzig Mind-Brain-Body interaction research database (MPILMBB), and some of the participants in these two datasets are overlapped [31; 32]. The LEMON dataset includes 203 valid resting-state EEG recordings, and each EEG session includes 16 separate parts (lasting 60 s: 8 closed-eyes (EC) parts and 8 opened-eyes (EO) parts) from each participant. The data are recorded by using a 62-channel (61 scalp electrodes plus 1 vertical electrooculogram (VEOG) electrode) EEG with a sampling rate of 2500 Hz. The EEG datasets were preprocessed by [PERSON] et al. [31]. They excluded data from 13 participants, considering the lack of event information, different sampling rates, mismatched header files, and insufficient data quality. The raw EEG data from 203 participants were down-sampled from 2500 Hz to 250 Hz, and band-pass filtered within 1-45 Hz by using a Butterworth filter with 8 th order which can be further improved [33; 34]. Then, it is divided into EO and EC conditions and to eliminate the influence of visual activity on EEG signals, EC EEG signals were used for this work. The functional connectome phenotyping dataset can be used to explore advanced cognitive abilities related to the internal functional structure of the brain, self-generated psychological experiences, and personality characteristics in the form of different questionnaires. In our algorithm, we use internet addiction test (IAT) results for classification based on different scores from individuals with or without IA. According to [PERSON]'s Alpha coefficient (\(\alpha\)) of 0.91, this IAT shows high reliability, and it reflects the severity of IA. Finally, 214 valid IAT results can be obtained in the MPILMBB database.  
After comparing 203 EEG recordings with 214 IAT results, 107 participants not only provided EEG signals but also submitted valid IAT results. The results of the IAT are defined as valid if they show a total score of at least 19 points. Considering the possible misclassification due to the inaccuracy of the self-assessment, 24 results with the highest scores (37-59, including 37 and 59) and the lowest 25 scores (19-23, including 19 and 23) are used. In this way, the difference between the two groups of labeled people is as large as possible based on the results of the IAT. In the algorithm, individuals without IA are labeled as 0 and individuals with IA are labeled as 1. The total datasets used in this paper consist of 49 individuals, including 25 healthy control individuals and 24 online users. In the subsequent classification training, around 80% (39 subjects) and around 20% (10 subjects) are respectively chosen as the training set and test set.
In the mentioned study, a hybrid model based on CNNs and BiLSTM was compared with standalone CNNs, LSTM, and BiLSTM. CNN-BILSTM models performed better than all standalone models with a precision, recall, and accuracy of 94.3%, 94.6%, and 94.2%, respectively. A bidirectional dilated LSTM (BiDLSTM)-based emotion classification method was presented for two datasets of tweets: WASSA Implicit Emotion Shared Tasks (IEST) and a new dataset Ekman's Emotion keyword (EEK). Both methods' accuracy was 72.83% and 80.79%, respectively [29]. A BiLSTM-based model was presented for sentiment classifications of a SemEval 2013 and IMDB movie review dataset with an accuracy of 85.02% and 95%, respectively [30]. A vision-based action recognition framework based on R-CNN and DLSTM was presented to classify actions based on the sequential pattern of earthmoving excavators [6]. In this study, R-CNN extracted the visual features and provided them to the first layer of LSTM to analyze the sequential pattern. The interim results of the first LSTM layer were analyzed again in the second layer to classify the activities of the excavator. The average precision and recall for all 3 classes, CNN, CNN_LSTM, and CNN-DLSTM, were 77.5% and 71.6%, 87.35% and 86.1%, and 90.9% and 89.2%, respectively. Additionally, the accuracy of these classes was 79.8%, 90.9% and 93.8%. The benefit of using BiLSTM over LSTM was the long-term bidirectional dependencies. It considered pre-and post-frames for the categorization of the frame. A convolution neural network (CNN) and deep bidirectional long short-term memory (DB-LSTM)-based method was presented to classify different actions on the UCF-101, HMDB51, and YouTube action video datasets [20]. Convolution neural networks extracted the features and the sequenced features were fed into the long short-term memory (LSTM). The average accuracy for the classification of the UCF-101, HMDB51, and YouTube action video datasets was 91.21%, 87.64%, and 92.84%, respectively. The different algorithms and techniques used in previous studies have been summarized briefly in Table 1.  
Several studies have demonstrated the use of bidirectional LSTM for speech, tweet, and human action recognition. However, there has been no attempt to examine the impact of bidirectional LSTM to automate earthwork operations. Furthermore, the application of Googlenet, standalone or integrated with other RNNs, was also not reported well in the construction industry. The goal of this study is to practically demonstrate the application of Googlenet for feature extraction in the field of the construction industry. Additionally, the application of BiLSTM for different activities of excavators and its response is observed. Thirdly, the integrated model of Googlenet and BiLSTM have yet to be adopted for the activity recognition of excavators in the earthworking field. Therefore, this paper considers the potential implications of the integrated CNN-based bidirectional long short-term memory (CNN-BiLSTM) model for the activity recognition of the excavator.
## 2 Receiving and Evaluation Methodology
Since the signal is processed off-line, the time when GNSS Receiver acquires the satellites cannot be obtained. Therefore, the duration of satellites signals transmission cannot be calculated by GNSS Receiver. So the pseudorange is obtained by the relative measurement between satellites. GNSS Receiver takes the reception time of the common frame as a reference edge without considering the satellite clock error. \[\Delta\rho_{n}=\text{c}\cdot\left(t_{n}-\underset{n}{\min}\left(t_{n}\right)\right) \tag{2}\]  
where the meaning of all symbols is shown in Table 10.  
Considering the satellite clock error decoded from the message, the pseudorange equation of a single satellite in a single frequency SDR [36] is written as  
\[\rho=\Delta\rho+\text{c}\cdot\left(t+\delta t\right)=r+I_{p}+T_{p}+\varepsilon_ {\rho}+MP_{\rho}+SDM_{\rho}+c\cdot\delta t \tag{3}\]  
where the meaning of all symbols is shown in Table 11.  
For a single satellite, single frequency, zero baseline differential receiver, the pseudorange difference equation is  
\[{\rho^{\prime}}_{n}=\rho_{n}-\rho_{ref}=\varepsilon_{\rho,n}-\varepsilon_{\rho,ref}+MP_{\rho,n}-MP_{\rho,ref}+SDM_{\rho,n}-SDM_{\rho,ref} \tag{4}\]  
where the meaning of all symbols is shown in Table 12.  
For each channel where the correlator spacing is fixed, the clock error, ionosphere and tropospheric error are a common mode in the pseudorange difference equation and are therefore removed in the equation. The noise can be effectively removed with an average. In the experimental situation below, the main residual multipath, signal nominal deformation and DLL noise are all relevant to the correlation spacing. The user receivers use different correlator spacing, including 0.125 chips, 0.15 chips, 0.175 chips, 0.2 chips, 0.25 chips, 0.3 chips, 0.35 chips, 0.4 chips, which are differentiated from reference receiver with a correlator spacing of 0.05 chips. The correlator spacing dependent errors were obtained for all the satellites. We use zero baseline proposed by [PERSON] et al. [37] difference to illustrate the positioning errors of the user receivers from the reference receiver. Figure 5.0  
Figure 5.1  
where the meaning of all symbols is shown in Figure 5.2  
Under some preconditions, the zero baseline difference can reflect the error caused by the user receiver using different correlator spacing. First, the user receivers and the reference receiver use the same satellite constellation. Second, they must use the same satellites signals. Third, their positioning algorithm must be the same. Provided that the above necessary conditions are met, the zero baseline difference can reflect the positioning error associated with the correlator spacing as same as the ranging difference. The quantitative expression of ranging difference and positioning error is as follows  
Figure 5.3  
where the meaning of all symbols is shown in Figure 5.4
## 5 Conclusions  
This paper presents a unified formulation of the multilateration localization problem involving fixed sensors (anchors) and mobile agents (drones) based on ultra-wideband-range measurements. Different gradient descent methods have been studied, using a constant rate, a variable rate, and a new formulation introducing a damping parameter in the descent step. We have proposed a detailed study of the influence of various parameters such as the number of neighboring nodes for multilateration, the initialization of the algorithm, and the choice of the cost function, in addition to considering the positioning errors of the nodes and the sensor measurement noise. This leads, depending on the localization scenario, to parameter settings being chosen which allow for the best results according to the rate of convergence of the system, the localization error, and the computation time. In particular, the fixed-step gradient parameter can be set based on the number of neighboring nodes and produced good convergence and accuracy results in most scenarios. A thorough discussion is given based on extensive simulation results. The results have clearly shown good efficiency in locating anchors without prior knowledge of their positioning and, subsequently, the use of multilateration to perform drone tracking, thus improving its position given by the inertial unit. Trends of interest for future work include merging distributed estimates from multiple drones to further improve localization using global optimization, as well as carrying out experimental tests in real environments.  
Conceptualization, O.L.-I.; Methodology, F.M., O.L.-I. and H.B.; Software, F.M.; Validation, F.M.; Writing--original draft, F.M.; Writing--review & editing, O.L.-I. and H.B.; Supervision, O.L.-I. and H.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: Comparative localization results of one drone of the fleet. (**Top:**): The trajectories estimated using only inertial odometry (**green** curve) and using inertial measurements and distance measurements to the anchors located by the drone (**blue** curve). The ground truth trajectory is represented by the **dark** curve. (**Bottom**): The Euclidean error of the localization in each case. Points marked ‘+’ on the blue curve indicate when multilateration is used in the estimation algorithm.
# A Local Information Perception Enhancement-Based Method for Chinese NER
## 4 Experimental Results and Comparison  
### Experimental Settings  
#### 4.1.1 Datasets  
Four commonly used Chinese datasets served as the basis for our experiments: Weibo [22], Resume [7], OntoNotes [36], and E-commerce [37]. Weibo is from Sina Weibo, and Resume is from Sina Finance. Weibo is labeled with four entities: personal names, places, organizations, and geopolitics, and contains a certain amount of noisy data; Resume is labeled with eight entities, such as educational institution, occupation, and title, and datasets have a limited number of entities but a high range of types. OntoNotes is a dataset derived from newswires and broadcasts and contains four named entity categories: personal names, places, organizations, and geopolitics. The E-commerce dataset is a dataset from the manually labeled e-commerce domain and includes both brand and product, two types of entities. Table 1 shows the details of these datasets.