esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The urban transportation energy supply system is one of the most important transportation infrastructure vehicles in the traffic network. At the same time, periodic vehicle energy supplement demand leads to vehicle path deviation, idle queuing and lane change disturbance, resulting in traffic congestion, time delay and other negative effects. This leads to additional negative effects such as carbon emissions and energy consumption, increasing the travel cost of drivers, and intensifying the load of the urban transportation energy supply network, especially during holidays. According to the big data report of 2021 China's National Day, there are about 2.32 million passengers entering Chongqing City during the holiday, ranking among the top ten tourist cities in China. Electric vehicles may be charged for one hour and queued for four hours. The refueling behavior can lead to additional negative effects such as carbon emissions and other pollutants. However, due to the long charging time and the insufficient number of charging piles, electric vehicles will have negative effects, such as long queuing or detour charging, which may aggravate road congestion.  
Analyzing the time-dependent trend of the negative effect can provide a theoretical basis for relieving the refueling load of supply stations and reducing the negative effect of the transportation network. A negative effect prediction system of urban traffic energy supply network is studied aiming at the refueling behavior of heterogeneous vehicles in the traffic network. The prediction system is beneficial to provide management insights for managers and promote the construction of modern energy systems and green intelligent transportation systems. To date, there is little research on urban vehicle energy supplement prediction, and most of the traffic flow prediction focuses on long-term traffic flow prediction and short-term daytime traffic flow prediction. This work presents a method to predict the traffic volume and negative effect cost, especially for the calculation of traffic flow and negative effect cost during holidays.  
The remainder of this paper is organized as follows: Section 2 summarizes the relevant research; In Section 3, the composition factors and calculation methods of negative effect cost are analyzed; In Section 4, the prediction model of energy supplement traffic flow in holidays is described; Section 5 includes an modified mixed network equilibrium model; The proposed prediction method and models are verified by using real traffic flow data in Section 6; Finally, the main contributions are concluded and discussed in Section 7.
## 3 System Architecture  
An OCC system's fundamental principle is to control the intensity with which optical signals transmit and receive data and improve the system's communication performance using promising modulation techniques. The simplest and most well-known modulation method for amplitude-shift keying modulation is OOK, which transmits data using two statuses, \"on\" and \"off,\" which are signified by \"1\" and \"0\" bits, respectively. In this study, we proposed the details for a MIMO C-OOK modulation scheme based on deep learning for data detection and decoding. Using deep learning, the performance of an OCC system was improved compared to that of existing techniques. Figure 1 shows the architecture of the MIMO C-OOK-based deep learning model. On the transmitter side, we used multiple LEDs to transmit data, and only a single camera received data from multiple LEDs based on Deep Learning detection and tracking.
# Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics
## 2 Background
### Dynamical Inverse Kinematics Optimization  
Differently from _instantaneous optimization_ methods, the dynamical optimization approach does not aim to satisfy the measurement Equations (4a) and (4b) at each time-step. Instead, the idea is to drive the state configuration (\(q(t),\
u(t)\)) to match the target measurements. In the language of control theory, the configuration velocity \(\
u(t)\) is the control input, and the objective is to converge to the given target measurements. The block diagram for dynamical inverse kinematics implementation is presented in Figure 2. The scheme is designed in order to achieve the following three main tasks:  
1. correction of the measured velocity according to the current error,
2. inversion of the model differential kinematics to compute the state velocity \(\
u(t)\),
3. integration of state velocity to compute the configuration \(q(t)\).  
Figure 2: Dynamical optimization scheme for real-time inverse kinematics solution.  
The implementation of these three parts and the definition of the error are not unique, and depends on user design choices. In literature, Euler angles or unit quaternion parametrization are used for modeling floating base systems and for defining the rotational displacements. The implementation presented in the next section exploits rotation matrix parametrization both for target orientations and modeling of floating base systems. Moreover, a joint limit avoidance strategy is introduced in the inverse differential kinematics computation in order to comply with the model constraints. To the best of authors' knowledge, the proposed scheme has not been used in literature before, with the presented implementation, for motion tracking applications.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 4 Proposed Scheme for Linear Computation Coding
### Codebook Design
#### 4.4.1 Binary Mailman Codebook  
In the binary mailman codebook, only the all zero column is obsolete. It is shown in Appendix B that the multiplication of the binary mailman matrix with an arbitrary vector requires at most \(2K-5\) and \(K-2\) additions for column vectors and row vectors, respectively. The main issue with the binary mailman codebook is its lack of flexibility: It requires the matrix dimensions to fulfill \(K=2^{N}\). This may restrict its application.
# Recent Advances in Tunable External Cavity Diode Lasers
## 3 Research Progress of ECDL
### External Cavity Quantum Well Laser
#### 3.1.1 Grating-Feedback Configuration
Utilizing a commercial diode laser with a center wavelength of 803 nm, the system demonstrated a tuning range of 10 nm. Additionally, the study highlighted that the laser beam maintained exceptional directional stability throughout the tuning process. The Littman ECDL is tunable by rotating the cavity mirror. The angle of the grating during tuning is determined, which makes the direction of the output light also determined. For conventional Littman ECDL, it is difficult to achieve wide-range mode-hop-free tuning. To overcome this limitation and enable broad, continuous mode-hop-free tuning, a straightforward approach involves matching the distance between the aspheric lens and the beam waist position to the external cavity length [37]. Using this method, a wide mode-hop-free continuous tuning range of 140 nm (from 1480 nm to 1620 nm) has been achieved. Based on the Littman external cavity structure, the researchers added other optical elements to suppress mode competition, thereby achieving further linewidth narrowing. [PERSON] et al. added an etalon to Littman external cavity structure [20]. The etalon could suppress the neighbor modes in lasing mode acted as a filter. The theoretical and experimental results showed that increasing the reflectance of the standard could increase the wavelength accuracy but decrease the tunable range. The optimal range of the etalon's reflectivity was identified, with results showing that a reflectivity of 0.65 minimizes the output linewidth to as low as 50 kHz under the combined effects of the etalon and the Littman external cavity structure. Researchers have also thought to optimize the spectral characteristics of the Littman external cavity structure by implementing design modifications. For example, replacing the flat mirror with a curved mirror featuring a defined radius of curvature has been proposed [38]. [PERSON] et al. simulated the spectral-broadness based on Littman external-cavity diode laser with dynamic-curvature end mirror [39]. The configuration is shown in Figure 4. The simulation showed that the spectral broadening can be tuned effectively by varying the radius of curvature of the mirror and adjusting the injection current. The tuning range reached 250 kHz for the single longitudinal-mode operation.
## 4 Conclusions  
The effects of plasma treatment on physico-chemical parameters was mainly observed as a slight acidification of the tissue, and as a reduction of browning after longer exposure time (30 min), which was most likely due to enzymatic activity inhibition.  
As a consequence of catechin polymerization products and of the increase in hydroxycinnamic acids and chalcones, the phenolic profile of fresh-cut apples was significantly affected by 10 min of treatment, both in quantitative (approximately a 20% increase) and qualitative terms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Extract**} & \multicolumn{5}{c}{**Time (min)**} \\ \cline{3-6}  & & **0** & **10** & **20** & **30** & **120** \\ \hline TPI \({}^{*}\) & amphiphilic & 12,633b & 13,702a & 12,460b & 11,383c & 8207d \\ TPI \({}^{*}\) & hydrophilic & 855b & 917 ab & 981 ab & 1050a & 623c \\ TPI \({}^{*}\) & total & 13,484b & 14,622a & 13,357b & 12,242c & 8790d \\ ABTS \({}^{**}\) & amphiphilic & 14,265b & 15,076a & 14,734 ab & 13,526b & 10,098c \\ ABTS \({}^{**}\) & hydrophilic & 855a & 644b & 840 ab & 686b & 803 ab \\ ABTS \({}^{**}\) & total & 15,120 ab & 15,720a & 15,574a & 14,212b & 10,900c \\ DPPH \({}^{**}\) & total & 22,543a & 23,008a & 21,311a & 21,868a & 16,608b \\ FRAP \({}^{**}\) & total & 26,900b & 30,123a & 27,781b & 25,913b & 17,024c \\ \hline \hline \end{tabular} Values followed by different letters within the same row are significantly different at a \(p<0.05\) level.  
\({}^{*}\) (umol GAE kg\({}^{-1}\) f.w.), \({}^{**}\) (umol TE kg\({}^{-1}\) f.w.) \({}^{***}\) (umol Fe\({}^{2+}\) kg\({}^{-1}\) f.w.).  
\end{table}
Table 3: Total phenolic content (TPI) and antioxidant activity (umol kg\({}^{-1}\) f.w.) of Pink Lady apples, as affected by plasma treatment time.  
After 30 min of treatment, when the effect of plasma on enzymatic browning inhibition was significant, flavan-3- oils content was lower compared to the untreated apples, while dihydrohalcones and flavonoids showed a slight increase.  
As expected, considering its strict connection with the amount of polyphenols, antioxidant activity evaluated by different in vitro methods followed a similar trend, increasing after 10 min of processing. The DPPH showed lower sensibility compared with ABTS assays, while FRAP showed similar values to the TPI assay.  
Results highlighted how plasma exposure promotes a metabolic response of the fresh tissue that can noticeably influence the quality of the final product. Hence, it is of paramount importance to carefully select the exposure time in order to minimize the loss of nutritional properties.  
Conceptualization: S.T. and P.R., methodology: I.R. and G.S., formal analysis: S.T. and I.R., supervision: L.R., data curation: S.T. and I.R., writing of original draft: S.T. and G.S., manuscript review and editing: P.R. and F.R.  
This research was funded by the INNOFRUVE project (grant number (CUP) J12F16000020009), co-funded by the Emilia-Romagna Region through the POR FESR 2014-2020 funds (European Regional Development Fund).  
The authors declare no conflict of interest.
###### Abstract  
Heart rate (HR) is the most frequently used variable to monitor athletes' internal load during training and competition. High-intensity effort and abrupt HR changes during exercise have presented measurement accuracy issues depending on the chosen device. Therefore, this study aimed to compare two chest straps (Garmin HRM-Dual and Coospo H6) and one armband (Coospo HW807) during intermittent exercise under controlled laboratory conditions. Thirty active young men performed an indoor cycling protocol consisting of seven intermittent efforts with a 2 min effort stage followed by a 2 min recovery stage. The results show no difference between the chest straps (Garmin vs. Coospo), with a high level of agreement between the two devices (Bias = \(-0.2\) bpm, LoA\({}_{\text{low}}\) = \(+2.5\) bpm, LoA\({}_{\text{low}}\) = \(-2.9\) bpm, ICC = \(0.6\)-\(1.0\)). Differences were found between the chest straps and the armband during effort stages (\(\pm 5\) bpm, \(p<0.05\)), with similar bias and LoA values in the Garmin Strap vs. Coospo Armband (Bias = \(-0.5\) bpm, LoA\({}_{\text{up}}\) = \(8.3\) bpm, LoA\({}_{\text{low}}\) = \(-9.3\) bpm) and Coospo Strap vs. Coospo Armband (Bias = \(-0.4\) bpm, LoA\({}_{\text{up}}\) = \(8.3\) bpm, LoA\({}_{\text{low}}\) = \(-9.0\) bpm) comparison. Chest straps (Garmin HRM-Dual and Coospo H6) accurately measure HR during intermittent exercise with abrupt HR changes. However, caution should be taken when using armbands (Coospo HW807) to monitor intermittent and high-intensity effort.  
technology; sports performance; internal load; wearables; sensors; precision
## 3 Malfunction Probability Models for Communication Delays and Bit Errors  
Existing research shows that relay protection has implied thresholds for communication delay and bit error, beyond which protection malfunctions may result [19]. The probability reflects the uncertainty of the whole process, including communication delays and bit errors, measurement errors and other factors. This section analyses the malfunction probability of relay protection considering communication problems.
## 3 Results and Discussion
### Analysis of Diversity Performance
#### 3.1.1 Envelope Correlation Coefficient  
The evaluation of the ECC serves as a crucial metric for quantifying the degree of isolation achieved between the different elements of the antenna system. One of these two methods mentioned below can be utilized in order to carry out the computation of ECC based on the parameters of the antenna. The initial approach entails employing the far field emission patterns of the antenna components [23]. The second method, which is comparatively less difficult and requires fewer computational resources, involves employing S parameters [9]. The second methodology is commonly employed for high-efficiency MIMO antennas, making the method based on S parameters the favored choice. The acceptable threshold for the value of ECC should be below the 0.05 level. Equation (3) utilizes S-parameter methodologies for MIMO systems consisting of two elements [9]. Figure 17 illustrates the simulations and measurable curves associated with the MIMO antenna system that is given in this research. The antenna's ECC consistently remains below 0.05 across its entire operational bandwidth.  
\[\text{ECC}=\frac{\left|\text{S}_{11}^{*}\text{S}_{12}+\text{S}_{21}^{*}\text{ S}_{22}\right|^{2}}{\Big{(}1-\left|\text{S}_{11}\right|^{2}-\left|\text{S}_{21} \right|^{2}\Big{)}\Big{(}1-\left|\text{S}_{12}\right|^{2}-\left|\text{S}_{22} \right|^{2}\Big{)}} \tag{3}\]
# An Improved Image Semantic Segmentation Method Based on Superpixels and Conditional Random Fields
## 2 Overview  
An improved image semantic segmentation method based on superpixel and CRFs is proposed to improve the performance of semantic segmentation. In our method, the process of semantic segmentation can be divided into two stages. The first stage is to extract semantic information from input image as much as possible. In the second stage (also treated as post-processing steps), we intend to optimize the coarse features generated during the first stage. As a widely used post-processing technique, CRFs are introduced into the image semantic segmentation. However, the performance of CRFs depends on the quality of feature maps, so it is necessary to optimize the first stage results. To address this problem, the boundary information of superpixels is combined with the output of the first stage for the boundary optimization, which helps CRFs recover the information of boundaries more accurately.  
Figure 1 illustrates the flow chart of the proposed method, in which red boxes denote two stages and blue boxes denote three processing steps. In the first step, we use the FCN model to extract  
the feature information from the input image to obtain pixel-level semantic labels. Although the trained FCN model has fine feature extraction ability, the result is still relatively rough. Meanwhile, the SLIC algorithm is employed to segment the input image and generate a large number of superpixels. In the second step, also the most important, we use coarse features to reassign semantic predictions within each superpixel. Benefiting from the good image boundary adherence of superpixel, we obtain the results of boundary optimization. In the third step, we employ CRFs to predict the semantic label of each pixel for further refining the segmentation boundaries. At this point, the final semantic segmentation result is obtained. Both the high-level semantic information and the low-level cues in image boundaries are fully utilized in our method.
# Remote Sensing of Sewing Work Levels Using a Power Monitoring System
## 4 Results and Discussion
### Determining the Weight Factors of \(L_{s}\) and \(L_{d}\)  
The weight factors of the different \(L_{S}\) and \(L_{D}\) levels are presented in Figure 4 based on expert opinion. This section compares the weights suggested by the experts with those derived from the experiments (Table 2).  
Figure 8a compares the \(L_{S}\) weight factors suggested by the experts with those calculated from the experiment. When the experts judged the intermediate \(L_{S}3\) to be 1, they suggested a difference of 0.2  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Level** & **1** & **2** & **3** & **4** & **5** & **Difference between Levels** \\ \hline \multirow{3}{*}{\(L_{S}\)} & Expert-proposed & 1.40 & 1.20 & 1.00 & 0.80 & 0.60 & 0.20 \\ \cline{2-7}  & Test-determined & 1.70 & 1.35 & 1.00 & 0.65 & 0.30 & 0.35 \\ \cline{2-7}  & Difference & 0.30 & 0.15 & - & 0.15 & 0.30 & - \\ \hline \multirow{3}{*}{\(L_{D}\)} & Expert-proposed & 0.50 & 0.75 & 1.00 & 1.50 & 2.00 & 0.25/0.50 \\ \cline{2-7}  & Test-determined & 0.80 & 0.90 & 1.00 & 1.10 & 1.20 & 0.10 \\ \cline{1-1} \cline{2-7}  & Difference & 0.30 & 0.15 & - & 0.40 & 0.80 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of \(L_{S}\) and \(L_{D}\) weight factors proposed by the experts with those determined from the test results.  
Figure 6: Relationships between \(L_{S}\) and \(L_{D}\) and working time and quality: (**a**) Relationship between working time (\(L_{S}\)-T) and work quality (\(L_{S}\)-Q) according to \(L_{S}\) level; (**b**) relationship between working time (\(L_{D}\)-T) and work quality (\(L_{D}\)-Q) according to \(L_{D}\) level.  
Figure 7: Correlation of \(L_{S}\) and \(L_{D}\) with working time and quality: (**a**) Relationship of \(L_{S}\) and \(L_{D}\) to working time; (**b**) relationship of \(L_{S}\) and \(L_{D}\) quality.  
for each level, but the experimental results indicate that this difference is closer to 0.35. It can therefore be stated that the \(L_{S}\) values for the operators in each level were greater than those estimated by the experts. Considering the differences between the levels, the experts' judgment appears to be about 15% lower than the experimental results. Figure 8b compares the \(L_{D}\) weight factors suggested by the experts with the experimental results. As in the case of \(L_{S}\), when the intermediate \(L_{S}3\) was considered to be 1, the expert suggested a level difference of 0.25 and 0.5, but the experimental results indicated a level difference of 0.1. In contrast to \(L_{S}\), in the case of \(L_{D}\), each task was found be less difficult than estimated by the expert: the \(L_{D}\) results show that expert judgment of difficulty was about 15% to 40% higher than that indicated by the experimental results.  
The results in Figure 8 are examples of the use of PMS measurements to provide a quantitative evaluation of otherwise subjective expert decisions. Results in this form can be applied to determine operator arrangement for process optimization.
# Applications of Water Jet Cutting Technology in Agricultural Engineering: A Review
## 3 Applications of Water Jet Cutting Technology on Different Agricultural Materials
### Comparison of the Capability of Water Jet Cutting the above Agricultural Materials  
By exploring applications of water jet cutting technology in agricultural engineering, it can be confirmed that jet media and cutting parameters have a significant influence on the cutting capability of various agricultural materials, as shown in Table 2.  
Figure 10: Simulated velocity contours in the model domain ([25], adopted from “Development of a liquid-jet nozzle for fertilizer injection in paddy fields using CFD” with permission from Elsevier, 2022). Figure 9: The commercial sprayer mounted with a high-pressure injection system ([36], adopted from “Injection methods to reduce ammonia emission from volatile liquid fertilisers applied to growing crops” with permission from Elsevier, 2022). Different jet media, including pure water, abrasive particles, and special liquids, are needed to complete various cutting purposes. Specifically, pure water jets can cut many agricultural materials, such as vegetables, fruits, meats, and stems. Abrasive water jets improve the cutting ability to cut woods, sugarcane, and meat with bone by adding traditional abrasives such as garnet or abrasives for cutting food such as ice particles, salt particles, and sugar particles [37]. When cutting vegetables and soils, different liquid jets can be used for disinfection and fertilization, respectively [36; 38]. Besides, using PEO solutions as liquid jets can improve the meat cutting ability [39]. There are four typical cutting parameters (i.e., jet pressure, nozzle diameter, target distance, and traverse speed) affecting the cutting effects in different ways. First, compared with other materials, cutting soil can complete fertilization operations for the required cutting depth with jet pressures lower than 20 MPa. Adding abrasives or liquids to the working fluid increases the cutting ability, so cutting some meats needs lower pressures [39; 60]. Second, nozzle diameters of pure water jets and liquid jets are smaller than abrasive jets mixed with solid abrasives. When the nozzle diameter is larger, the kerf width and water consumption will increase, resulting in more raw material loss and water carried, which affects economic benefits and the weight of agricultural machinery, respectively [42; 60]. Third, many vegetables, fruits, and boneless meats can be cut at high target distances because of soft materials. It is necessary to increase the distance between the nozzle and