esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Deep-Learning-Based Detection Approaches  
From the perspective of computer vision, the surface defect detection task is essentially object detection for defect images. With the rapid development of CNN [11], generic object detection technology has achieved outstanding success [6]. Generally, an object detector mainly consists of three parts: a backbone, a neck and a head [24]. The backbone is employed to extract features from an input image, such as VGG [19] and ResNet [21].  
Then, the neck part is often utilized to combine feature maps from different stages of the backbone, such as feature pyramid network (FPN) [34]. Finally, the head part is used for predicting classes and bounding boxes of objects. The head of object detection can be categorized into two kinds, i.e., one-stage method and two-stage method. Generally, two-stage detectors are more accurate, but have lower inference speeds. While, one-stage detectors are much faster, but have a relatively poor performance [6]. The R-CNN series method is the most representative two-stage object detection method, it includes Faster R-CNN [35], GA-Faster R-CNN [25], and TridentNet [26]. As for the one-stage object detection method, the most representative models mainly include you only look once (YOLO) [36], Single Shot MultiBox Detector (SSD) [37], and RetinaNet [38].  
With the development of computer vision, many works [12; 13] demonstrate that generic object detection methods based in deep learning could be transferred as defect detection methods. Recently, most of these state-of-the-art surface defect detectors rely on an anchoring scheme. For instance, [PERSON] et al. [39] adapt SSD as the meta structure to achieve real-time and accurate detection of surface defects. A cascaded autoencoder (CASAE) architecture is designed in [40] to detect metallic surface defects. Faster R-CNN [35] is improved in [13] to detect workpiece surface defects. [PERSON] et al. [41] propose a defect detectors-based YOLOv3 model for detecting identification of the railway. [PERSON] et al. [12] improve RetinaNet with difference channel attention and spatial feature fusion for steel surface defect detection. Despite the outstanding success, there are many challenges in the surface defect detection field. For instance, the receptive field of a convolution unit cannot be adjusted according to different defects. In addition, a set of anchor boxes are uniformly predefined with fixed scales and shapes, which increases the difficulties of bounding box regression.
## 4 Conclusions  
Based on the designed torque loading system and the proposed measurement method, four specimens subjected to torque load were tested, and the following conclusions were obtained.  
1. The method of applying template paint and lubricant on the sidewall of the foundation ring can effectively reduce the adhesion between the two parts, thus providing technical conditions for a single-factor stress analysis of the foundation under torque load.
2. The ratio of the torque load borne by the foundation ring increases with the growing torque load, which can be divided into the growth, mitigation, and stationary stages.
3. When the torque load is small, it is mainly borne by the adhesion force of the sidewall of the foundation ring and the top radial reinforcement. When it is large, it is gradually borne by the anchor bottom flange of the foundation ring.
4. At the middle and late stages of loading, the radial reinforcement on the top of the foundation shares about 20% of the torque load, while the foundation ring bottom flange shares about 80%.  
When the width and thickness of the bottom flange were both reduced by 13%, the proportion of torque load borne by the foundation ring decreased most obviously. In comparison, the reduction in the embedded depth of the foundation ring and the concrete strength had less effect.  
Finally, it needs to be clarified that the main purpose of this article is to study the transmission mechanism of the torque load in the wind turbine foundation, take certain loading measures, and reduce friction measures. However, this method can only minimize the influence of other loads on the torque load, and cannot completely eliminate the influence of the bending moment load.  
Conceptualization, H.D., P.Z. and Y.G.; experiment validation, Y.G. and P.Z.; analysis, Y.G., P.Z. and C.L.; writing, Y.G. and C.L. All authors have read and agreed to the published version of the manuscript. This research was founded by the National Science Foundation of China (Grant No. 51909188).  
We want to thank [PERSON] for his support in carrying out the experiments, and [PERSON] for his guidance during the writing of the paper.  
The authors declare no conflict of interest.
## 2 Downlink Beamforming Design and Uplink Power Allocation
### Methodology: Convex Optimization-Based Downlink Beamforming Design and Uplink Power Allocation
#### 2.1.2 Uplink Scenario  
Similarly to in problem (P1), in problem (P2), the SINR constraints become  
\[\text{SINR}_{\text{u},k}[n]\geq\gamma_{\text{u},k}\Rightarrow\mathbf{H}[n] \mathbf{p}[n]\preccurlyeq-\sigma^{2}\cdot\mathbf{1}_{K\times 1},\quad\forall k \tag{12}\]  
where \(\mathbf{H}[n]\in\mathbb{C}^{K\times K}\) and \(\mathbf{p}[n]\in\mathbb{R}^{K\times 1}\) are, respectively, defined as  
\[\mathbf{H}[n] =\begin{bmatrix}-\frac{1}{\gamma_{\text{u},1}}\|\mathbf{h}_{ \text{u},1}\|^{2},&\|\mathbf{h}_{\text{u},2}[n]\|^{2},&\cdots&\|\mathbf{h}_{ \text{u},K}[n]\|^{2}\\ \|\mathbf{h}_{\text{u},1}[n]\|^{2},&-\frac{1}{\gamma_{\text{u},2}}\|\mathbf{h }_{\text{u},2}[n]\|^{2},&\cdots&\|\mathbf{h}_{\text{u},K}[n]\|^{2}\\ \vdots&\vdots&\ddots&\vdots\\ \|\mathbf{h}_{\text{u},1}[n]\|^{2},&\|\mathbf{h}_{\text{u},2}[n]\|^{2},&\cdots&- \frac{1}{\gamma_{\text{u},K}}\|\mathbf{h}_{\text{u},K}[n]\|^{2}\end{bmatrix} \tag{13}\] \[\mathbf{p}[n] =[p_{1}[n],\ \ p_{2}[n],\ \ \ \cdots\ \ p_{K}[n]]^{T}.\]  
From (12), the problem (P2) can be converted into an LP as follows:  
\[\begin{split}\text{(P2^{\prime})}:\min_{p_{k}[n]}&\mathbf{1}_{K \times 1}^{T}\mathbf{p}[n]\\ \text{s.t.}&\mathbf{H}[n]\mathbf{p}[n]\preccurlyeq-\sigma^{2} \cdot\mathbf{1}_{K\times 1}.\end{split} \tag{14}\]  
As a result, (P2\({}^{\prime}\)) can be numerically solved via the convex or linear optimization method.
## 4 Discussion
### Availability in Closed Cabins  
To comprehensively evaluate the performance of our study in closed-cabin environments, we consulted human factor experts and compared our method with three existing emotion recognition methods across three dimensions: invasiveness, system complexity, and deployment cost. The comparison results are presented in Figure 15.  
Figure 15 illustrates these comparisons and highlights the strengths and weaknesses of each approach. Our noncontact method, with its balance of low invasiveness, moderate complexity, and cost-effectiveness, shows great promise for practical deployment in closed-cabin environments.
###### Abstract  
When a robotic manipulator interacts with its environment, the end-effector forces need to be measured to assess if a task has been completed successfully and for safety reasons. Traditionally, these forces are either measured directly by a 6-dimensional (6D) force-torque sensor (mounted on a robot's wrist) or by estimation methods based on observers, which require knowledge of the robot's exact model. Contrary to this, the proposed approach is based on using an array of low-cost 1-dimensional (1D) strain gauge sensors mounted beneath the robot's base in conjunction with time series neural networks, to estimate both the end-effector 3-dimensional (3D) interaction forces as well as robot joint torques. The method does not require knowledge of robot dynamics. For comparison reasons, the same approach was used but with 6D force sensor measurements mounted beneath the robot's base. The trained networks showed reasonably good performance, using the long-short term memory (LSTM) architecture, with a root mean squared error (RMSE) of 1.945 N (vs. 2.004 N; 6D force-torque sensor-based) for end-effector force estimation and 3.006 Nm (vs. 3.043 Nm; 6D force-torque sensor-based) for robot joint torque estimation. The obtained results for an array of 1D strain gauges were comparable with those obtained with a robot's built-in sensor, demonstrating the validity of the proposed approach.  
robot manipulator; force estimation; force control; strain gauge; deep learning; machine learning; neural networks +
Footnote †: journal: _Applied_
## 3 Experiments and Analysis
### Experimental Results  
To demonstrate the effectiveness of the sea ice classification method and the application of the DF-UHRNet network therein, two comparison experiments were designed: (1) using the Grad-CAM algorithm to implement the output of the deep semantic segmentation network (we also designed DF-UHRNet ablation experiments) and (2) using the comparison of different models employed under the same experimental conditions with the same dataset against more mainstream semantic segmentation networks, such as the U-Net, HRNet, and U-HRNet, in order to verify the advantages of our algorithm.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 2 Models
### DC Motor  
A DC motor is usually modeled as a resistor (\(R\)), a coil (\(L\)), and a voltage source (\(v_{b}(t)\)) in series [7; 8], as presented in Equation (7).  
\[v_{s}(t)=R\times i(t)+L\times\frac{di(t)}{dt}+v_{b}(t) \tag{7}\]  
where \(v_{s}(t)\) is the voltage applied to the motor and \(i(t)\) is the current that goes through it.  
This voltage source is a counter-electromotive force produced by the rotor, and therefore, it is proportional to the angular velocity with which it spins. Given that the power delivered at \(v_{b}(t)\) is useful, then it is equal to the mechanical power delivered at the rotor. Since the useful electric power is equal to \(v_{b}(t)i(t)\) and the mechanical power is given by the product of the torque \(T(t)\) and the angular velocity of the rotor \(\omega(t)\), the torque must be proportional to the current that goes through the stator.  
\[v_{b}(t) =k\times\omega(t) \tag{8}\] \[T(t) =k\times i(t) \tag{9}\]  
where \(k\) is a constant with the same value for both equations. This information can be combined with the mechanical equation of the motor to define a continuous time model of the motor.  
The mechanical equation relates the angular acceleration with all the torques applied to it.  
\[J\times\frac{d\omega(t)}{dt}=T(t)-T_{e}(t)-B\times\omega(t) \tag{10}\]  
In this equation, \(J\) is the mass moment of inertia, \(T(t)\) is the torque produced by the motor, \(T_{e}(t)\) is the set of opposing torques produced outside of the motor, and \(B\) is the drag coefficient (making \(B\times\omega(t)\) the drag induced torque).  
Combining the electric Equation (7) with the mechanical one (10), the following can be written.  
\[L\times\frac{di(t)}{dt}=v_{s}(t)-R\times i(t)-k\times\omega(t) \tag{11}\]  
\[J\times\frac{d\omega(t)}{dt}=k\times i(t)-T_{e}(t)-B\times\omega(t) \tag{12}\]  
With these equations, the resulting continuous time model of the DC motor is:  
\[\dot{x}(t) =A\times x(t)+B\times u(t) \tag{13}\] \[y(t) =C\times x(t) \tag{14}\]  
These can be presented in matrix form, as presented below:\[x(t) =\begin{bmatrix}i(t)\\ \omega(t)\end{bmatrix} u(t) =\begin{bmatrix}v_{\text{s}}(t)\\ T_{\text{e}}(t)\end{bmatrix} y(t) =\omega(t)\] \[A =\begin{bmatrix}-R/L&-k/L\\ k/J&-B/l\end{bmatrix} B =\begin{bmatrix}1/L&0\\ 0&-1/l\end{bmatrix} C =\begin{bmatrix}0&1\end{bmatrix}\]
# Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data
## 2 Data Acquisition and Processing  
### Data Acquisition  
The study utilized the LiMobile XT2 for the 3D scanning of the pavement environment, a high-precision device with an integrated inertial navigation and encoder. The device has a point cloud accuracy of \(\pm 1\) cm, a ranging error of \(\pm 1\) mm, a 360\({}^{\circ}\) horizontal perspective, and a vertical perspective of approximately 30\({}^{\circ}\). When mounted on a vehicle, the longitudinal line density interval on the pavement is 220 mm, which meets the sampling requirement of 250 mm specified in the relevant standard. Additionally, the effective measurement range of the device was 600 m. To ensure the accuracy of the acquired point cloud data, only point cloud results within a radius of 25 m around the device were selected for this study. The single-line scanning step was 0.009\({}^{\circ}\), which satisfied the density requirements of data acquisition. Figure 1 presents the LiDAR device used in this study.  
Field test roads were located in the Jiading District, Shanghai, with a total length of approximately 26 km, including ten road sections, namely the Bao'an Highway section, the Boyuan Road section, the Cao'an Highway Section, the Jiasong North Road section, the Lianqun Road section, the Lvhuan Road section, the Lvyuan Road section, the Xiangfang Road section, the Yining Road section, and the Zhongbai Road section, respectively. Field tests were conducted on the road section without stagnant water and height limitations to ensure the reliability of the obtained results and avoid the effects of the surrounding vehicles. Data collection was performed between 23:00 to 03:00 while maintaining a  
Figure 1: Three-dimensional road data acquisition based on the LiDAR device. (**a**) The 3D LiDAR device. (**b**) Vehicle-mounted LiDAR detection system.  
detection speed ranging from 30 to 40 km/h, respectively. The vehicle remained in the middle or right lane of each road section during the test period.
# Performance Analysis of Cache Based on Popularity and Class in Named Data Network
## 5 Simulation Models and Results  
This research built a comprehensive simulation environment using Matlab to analyze the performance of Static-CAPIC and Dynamic-CAPIC. Furthermore, it also compares both techniques with the combination of two existing techniques, LCD+sharing scheme. The network uses best path routing as a common routing algorithm in NDN. The LCD+sharing scheme carried out the LCD scheme as a first step. Whenever consumers request the content, the router sends data to the consumer and store its copy in the one-hop downstream router. There is no partition in the content store. Every content class is cached as long as it does not exist in the content store when requested, and the content store is not full yet. However, when the content store is full, a cache replacement mechanism is obtained.  
The network containing the consumer nodes, producers, and router nodes was modeled in this study to evaluate Static-CAPIC and Dynamic-CAPIC schemes performance. Each router node has a content store with the same total capacity. Contents on the network are grouped into different classes, as in Table 1. For Static-CAPIC, each class has a statically pre-determined proportion in each router. In contrast, for Dynamic-CAPIC, it is changing dynamically using the Dynamic CAPIC formula as in Equation (12).  
The study assumed that the data have the same size. Every request with an interest package is responded with a data/content package, either by routers or producer nodes. These assumptions simplify the model and make it easier to analyze. One slot in the content store is used to save one packet of content. Therefore, the content store's capacity is equal to the number of packets that can be saved.
## 4 System Model
### DMPF Scheduler  
The DMPF scheduler (DS) is at the core of the DMPF scheduler Module (DSM) because it is responsible to run algorithms. DS collects the buffered requests from the Request Analyzer component  
Figure 5: Dynamic Multi Path Finder (DMPF) Scheduler Module in SDN three layered architecture.  
(RAC) and current network status from the Resource Monitor Component (RMC). The DS selects a mode for processing and forwarding of requests. When processing requests, DS can process requests in _allLayer_ or _layerWise_ mode. The DS can forward requests on a _singlePath_ or _multiPath_ in the request forwarding mode. First, we explain the DS request processing modes. During _allLayer_ request processing mode, the scheduler accommodates all SVC layers of the current client request and then accommodates all SVC layers of the next client request. In _layerWise_ request processing mode, the scheduler incrementally accommodates the SVC layers of each client request. We explain the DS request processing modes, _allLayer_, and _layerWise_ with the help of Figure 6.
## 3 Results and Discussion
### Antioxidant Activity (AOA)
Figure 1: Antioxidant activity of lupin protein hydrolyses according to (**A**) ABTS, and (**B**) DPPH, FRAP, and CUPRAC. Values are means \(\pm\) SEM, n = 3 per treatment group. Different letters (a–f) within each assay chart columns indicate significant differences (\(p\) < 0.05) between treatments as analyzed by one-way ANOVA and TUKEY test. typically have high hydrophobic amino acid content or hydrophobicity [66]. The sample preparation in our study included only aqueous solution, which could be the reason for the lower values of activities. Food-derived antioxidant peptides typically range in length from 2 to 15 amino acids [67]. It is important to point out that antioxidant peptides usually contain hydrophobic amino acids including leucine, isoleucine, and proline [68], as well as aromatic amino residues such as tyrosine [69]. The major factor of the size, content, and amino acid sequence of the peptides is the type of peptidase, which has an impact on the antioxidant activity of protein hydrolyses [70]. The optimization of the enzymatic conditions, such as temperature, pH, E.S, and time, as well as the molecular weight, are also important determining factors in obtaining antioxidant peptides. The molecular weight of peptides of 500-1500 Daltons is considered to have higher antioxidant activity than peptides with a molecular weight of 1500 Daltons or less than 500 Daltons [71]. This type of peptide can be isolated and identified from different sources: beans [72], nuts [73], seeds [74], and other food products [67]. A study from 2020 [70] focused on the production, separation, and purification of different antioxidant peptides generated from plant proteins over the last 20 years and it summarizes them along with possible mechanisms and evaluation approaches. However, there is not necessarily a linear relationship between the antioxidant activity and the degree of hydrolysis of protein hydrolyses. On one side, favorable pH can have a considerable impact on the efficiency of enzymatic hydrolysis; on the other side, caution should be taken because a percentage of peptide fragments with antioxidant properties could be converted into passive amino acids or peptides by excessive hydrolysis. Numerous studies have demonstrated the possible use of antioxidant peptides produced from plant proteins in the food systems [67; 70].
## 4 Discussion
### Vibrational Spectroscopy in Biological Material  
Vibrational spectroscopy is both a tool used to study the microstructure of biological material and a diagnostic method that allows the analysis of the structure and processes occurring in tissue and in the course of genetic mutations [6; 30]. Infrared spectroscopy and Raman spectroscopy make it possible to determine changes in bone tissue as subtle as remineralization processes or the disorganization of collagen fibers. Starting from the research by [PERSON] and [PERSON] in 2000, the use of vibrational spectroscopy to analyze the structure and changes within bones, cartilage, or enamel began to play a significant role in research regarding the quantitative and qualitative composition of tissues [7; 8].  
This technique plays an increasingly important role as a diagnostic tool and supplements biochemical, imaging, and histological studies. Spectroscopy makes it possible to create maps, taking into account the chemical composition of healthy and pathologically changed tissue, as well as changes occurring in tissues between both above-mentioned states. Tissue changes result in new spectra, band shifts, or intensity changes.  
The applied Raman spectroscopy method has an advantage over infrared spectroscopy, associated with only possibly minimal preparation of samples and lower sensitivity to the degree of tissue hydration, which is extremely important in the case of biological material, as it does not disturb the image of the obtained scatter spectra and makes it possible to conduct measurements at a higher spatial resolution [13; 31]. As a result of the above-mentioned advantages, a considerable advantage of Raman spectroscopy is the possibility of using it in both in vivo and ex vivo models. The usefulness of spectroscopic examination, with particular emphasis on biological tissues, has already been described by researchers, and, at the same time, it allows the expansion of current research with qualitative and quantitative data regarding bone and cartilage composition [7]. This feature offers a new perspective on the possibility of conducting intraoperative assessments of cartilage and bone tissue, such as in osteoarthritis, as well as the diagnosis of pathological fractures or pathological changes in the skeleton at the developmental age.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 6 Discussion
The red channel presents the greatest power and the blue channel the lowest received power according to the spectral response of the wall's reflectance. For Configuration A, as illustrated in Figure 5a, displacement of the photodetector by 1 meter with respect to the center of the receiver plane results in a reduction of received power by more than 20% across each color detector channel (red, green, and blue). The lighting parameters shown in Table 5 reveal three critical problems in the illumination quality: non-uniform illuminance over the receiver plane, low color rendering, and saturated perceived color. The non-uniform illuminance problem must be solved using more than one RGB-LED source; on the other hand, the problems regarding color rendering and perceived color depend on the CSK constellation design. These simulations were carried out using the constellation design proposed in IEEE 802.15.7. Consequently, with low luminous efficiency of the blue LED, users will perceive a saturated blue color with very low color rendering quality. The design of the CSK constellation must be studied as an optimization problem that improves the symbol error rate and ensures color rendering quality. The communication performance of the VLC indoor system was analyzed by simulating an AWGN channel model while incorporating the effects of shot noise and thermal noise observed in the photodetector. A simulation was conducted to estimate the luminous flux required to achieve an SER of approximately \(10^{-5}\) for IEEE-based CSK modulation with sixteen symbols. With the receiver positioned vertically below the light source (position P1 in Figure 5a), a luminous flux of approximately 4000 lm is required to achieve an SER of approximately \(10^{-5}\). At positions P2 and P3, the luminous flux increases to about 7000 lm and 12,500 lm, respectively. The final analysis demonstrates the degradation of communication quality as the light detector is moved away from the light source. The estimated luminous flux corresponds to the smaller active area of the photodetectors. Enhancingthe optical power on the active area can be achieved using lenses or optical concentrators, which allow for managing reduced luminous flux at the transmitter while maintaining an appropriate SER. Additionally, the observed SER curves are based on the IEEE-standard constellation design; however, the Python package also supports customized constellation designs. Consequently, the performance of an optimal constellation design tailored to the indoor channel characteristics can be demonstrated using our Python-based model.