esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Results Discussion
### COD Detection Compensation Based on Fusion of UV-Vis Spectroscopy and Environmental Factors
#### 3.2.4 Fusion Modeling of Spectra and Three Environmental Factors  
The actual COD detection does not come from the influence of a single environmental factor but from the combined influence of multiple environmental factors. Therefore, it is necessary to compensate for the influence of three environmental factors simultaneously. This paper proposes a fusion method based on the weighted superposition of spectra and three environmental factors in order to simultaneously compensate for the influence of three environmental factors. The weighted superposition method is shown in Formula (1). Here, \(\chi_{pH}\), \(\chi_{Temperature}\), and \(\chi_{Conductivity}\) are the three environmental factors, and \(f_{PHt}\), \(f_{Temperature}\), and \(f_{Conductivity}\) are their corresponding weight coefficients. The three coefficients have been normalized, and their sum is one. The environmental factors are assigned corresponding weights (\(f_{x}\)) according to the value of \(R_{pred}^{2}\) in Table 4. The weight coefficients of pH, temperature, and conductivity were calculated according to Formulas (2)-(4).  
\[\widetilde{x}=\chi_{pH}*f_{pH}+\chi_{Temperature}*f_{Temperature}+\chi_{Conductivity}*f_{ Conductivity} \tag{1}\]  
\[f_{pH}=\frac{R_{pH}^{2}}{R_{pH}^{2}+R_{Temperature}^{2}+R_{Conductivity}^{2}} \tag{2}\]  
\[f_{Temperature}=\frac{R_{Temperature}^{2}}{R_{pH}^{2}+R_{Temperature}^{2}+R_{ Conductivity}^{2}} \tag{3}\]  
\[f_{Conductivity}=\frac{R_{Conductivity}^{2}}{R_{pH}^{2}+R_{Temperature}^{2}+R_{ Conductivity}^{2}} \tag{4}\]  
The weighted superposition of three environmental factors (WSTEFs) was taken as one factor \(\widetilde{x}\). It was fused as the 15 th dimension feature with the 14 features extracted by SCARS. The PLS model was established by the fusion of spectral features and WSTEF, and better COD detection accuracy was obtained. The \(R_{cal}^{2}\) of the model reached 0.9714, and RMSEC reached 2.19. The \(R_{pred}^{2}\) reached 0.9602, and RMSEP reached 3.52. The prediction results and standard value scatter plot of the COD prediction model based on SCARS+ WSTEF +PLS for the prediction set are shown in Figure 12. There is good consistency between the predicted values and the standard values.  
Figure 12: COD prediction results based on SCARS+ WSTEF +PLS.
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
## 3 Results
### Variation of the Fried Parameter  
Figures 10 and 11 illustrate the variations in angle-of-arrival fluctuations in a downlink channel with varying coherence lengths at different wavelengths and diameters. A diameter of 0.1 m was used in Figure 10, while a diameter of 5 m was used in Figure 10. A diameter of 0.1 m was used in Figure 10, while a diameter of 5 m was used in Figure 10. The use of different diameters highlights the impact of the receiver aperture on angle-of-arrival fluctuations.  
Figure 10: Variation in angle-of-arrival fluctuations (diameter = 0.10 m).  
Figure 9: Variation in the Fried Parameter in a downlink channel.  
From the observations in Figures 10 and 11, the angle-of-arrival fluctuation at 1550-nanometer wavelength was determined to be less than 5 microradians for a receiver diameter of 5 m at a coherence length of 0.06 m, whereas it was slightly less than 10 microradians for a receiver diameter of 0.10 m at the same coherence length. The obtained results show that the increase in diameter of a receiver aperture on the ground station decreases the angle-of-arrival fluctuations. Hence, from a design point of view, receivers with longer diameters should be used at ground stations to minimize the angle-of-arrival fluctuations.
# Study on Head Stabilization Control Strategy of Non-Wheeled Snake Robot Based on Inertial Sensor
## 5 Head Stabilization Experiment  
It can be seen from the simulation experiment that the stabilization algorithm can effectively help the head of the snake robot to stabilize. On this basis, the head stabilization experiment of the real snake robot is carried out to verify the head stabilization effect.  
This experiment is carried out with a 16-DOF snake robot [21] with a head controller and a battery module. The ground uses rubber as the playground for the snake robot to increase static friction to ensure the operating efficiency of the snake robot. The experimental scene is shown in Figure 12.  
The experiment uses the motion capture system VICON to verify the head stabilization control. The head posture change of the snake robot is tracked through the pose of VICON's marker point coordinate system in the VICON coordinate system to verify the effect of head stabilization control. The experiment mainly involves the VICON coordinate  
Figure 11: Head stabilization simulation data.  
Figure 12: Experimental Scene.  
system {O\({}_{\rm{v}}\):x\({}_{\rm{v}}\),y\({}_{\rm{v}}\),z\({}_{\rm{sv}}\)}, the head coordinate system {O\({}_{\rm{h}}\):x\({}_{\rm{h}}\),y\({}_{\rm{h}}\),z\({}_{\rm{hsss}}\)} and the marker coordinate system {O\({}_{\rm{m}}\):x\({}_{\rm{m}}\),y\({}_{\rm{m}}\),z\({}_{\rm{m}}\)}. The following relationship is observed between the attitudes of the coordinate systems:  
\[R_{\rm{h}}^{\rm{v}}=R_{\rm{m}}^{\rm{v}}R_{\rm{h}}^{\rm{m}} \tag{10}\]  
Among them, \(R_{\rm{h}}^{\rm{v}}\), \(R_{\rm{m}}^{\rm{v}}\) and \(R_{\rm{h}}^{\rm{m}}\), respectively, represent the attitude rotation matrix of the head coordinate system relative to the VICON coordinate system, the marker coordinate system relative to the VICON coordinate system and the head coordinate system relative to the marker coordinate system. To simplify the calculation, the head coordinate system coincides with the marker point coordinate system during the experiment, as shown in Figure 13.  
Based on the robot's inchworm locomotion gait, experiments were carried out on the movement of the snake robot with and without the stabilization algorithm. The experimental procedure is shown in Figures 14 and 15 and the experimental results are shown in Figure 16.  
From Figures 14 and 15, it can be seen that when the stable algorithm is not applied, the swing amplitude of the robot's head is larger, while under the stable algorithm, the swing amplitude of the robot's head is significantly reduced.  
Through experimental data, it can be seen that the compensation algorithm can effectively help the head controller of the snake robot to stabilize, thereby helping the laser radar to work stably.
# Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
## 4 Safe Margin Control Space
### Control Obstacle  
Consider a mobile robot that shares its workspace with multiple other passive agents. Let \(A\) and \(B_{j}\) are the geometry of the robot and the \(jth\) passive agent respectively. The robot and passive agent geometries are considered as their bounding circles, similarly as in the original formulation of \(VO\)[18]. Let \(O_{j}\) is the Minkowski sum of robot's and \(jth\) passive agent's geometries, \(O_{j}=A\oplus B_{j}\). The current position of passive agent is \(c_{j}(0)\) and its position at \(t>0\) is given by \(c_{j}(t)\). To avoid collision with a passive agent within time horizon \(\tau\), their relative position should remain outside the Minkowski sum of their geometries.  
\[p_{a}(t)-c_{j}(t)\
otin O_{j},\forall t\in[0,\tau] \tag{6}\]  
Therefore, the temporary control obstacle induced due to the state of the \(jth\) passive agent at a future time instant \(t_{i+n}\in[0,\tau]\), denoted by \(UO_{j}(t_{i+n})\), is defined as follows:  
**Definition 1**.: _(Temporary Control Obstacle) It is a set of control inputs for which the relative position vector is inside \(O_{j}\) at time \(t_{i+n}\)_  
\[UO_{j}(t_{i+n})=\{u|f(h(x_{a},u,t_{i+n}))-c_{j}(t_{i+n})\in O_{j}\} \tag{7}\]  
_where \(f(h(x_{a},u,t_{i+n}))-c_{j}(t_{i+n})\) is a relative position vector at time \(t_{i+n}\), for control input \(u\) given to a robot._  
Now, the control obstacle induced for a robot due to the predicted motion of \(j\)th passive agent over the time horizon \([0,\tau]\) can be defined as follows:  
**Definition 2**.: _(Control Obstacle) The control obstacle induced due to the states of a passive agent over time horizon \([0,\tau]\) is the union of temporary control obstacles over that horizon._  
\[UO_{j}^{\tau}=\bigcup_{0<t_{i+n}\leq\tau}UO_{j}(t_{i+n}) \tag{8}\]  
\(UO_{j}^{\tau}\) is the set of control inputs to a robot, that will lead it towards collision with a passive agent in time horizon \([0,\tau]\) into the future. In another words, the collision will not occur between the robot and passive agent \(B_{j}\) with in time horizon \([0,\tau]\) into the future, if robot selects its control input such that \(u\
otin UO_{j}^{\tau}\).  
In case of avoiding collision from multiple passive agents, we can extend the given approach as follows. Let \(N=\{1,...,m\}\) is the index of passive agents to be avoided. Then the control obstacle can be given as:  
\[UO^{\tau}=\bigcup_{j\in N}UO_{j}^{\tau} \tag{9}\]  
Robot selecting its control input outside \(UO^{\tau}\) will lead it to move without collision with \(m\) passive agents within time horizon \([0,\tau]\) into the future.
## 4 Experimental Results  
### Data Description  
This study evaluates the proposed method using two publicly available motor imagery EEG signal datasets. Dataset I is the BCICIII IVa dataset from the 2008 International BCI Competition [25]; dataset II is the BCICIV IIb dataset from the 2005 International BCI Competition [26].  
* Dataset I: BCICIII Iva:  
The dataset contains EEG data from five healthy subjects, collected using 118 channels (10-20 system) with a sampling rate of 100 Hz. The data have been preprocessed with power line interference filtering and bandpass filtering (0.05-200 Hz). The subjects performed motor imagery tasks for the right hand and foot under no feedback conditions, with each task lasting 3.5 s, and a total of 280 trials were conducted. This study selects data from 0.5 s to 2.5 s after the start of the motor imagery task, using 18 channels (C1, C3, Cz, Cz, C4, CFC3, CFC4, CFC5, CFC6, CCP3, CCP4, CCP5, CCP6, T7, T8, P3, Pz, and P4). The sample size for each subject is 18 \(\times\) 200 [20];  
* Dataset II:BCICIV IIb:  
The dataset contains EEG data from nine healthy subjects, collected using three channels (C3, CZ, C4) and three electroocologram (EOG) channels, with a sampling rate of 250 Hz. The data have been preprocessed with power line notch filtering and bandpass filtering (0.5-100 Hz). The subjects performed motor imagery tasks for the left and right hands. The first two days were no-feedback experiments (240 trials), and the last three days were feedback experiments (480 trials). The data from the first two days were used as the training set, and the data from the last three days were used as the test set. This study uses data from 4 s after the start of motor imagery, with a sample size of 3 \(\times\) 1000 for each subject.
# Measurement Vertex Position of a Large Aspheric Mirror
## 2 Methods  
The method is actually an improvement of the rotating method. Instead of measuring the change in coma with the PMI, the method only uses the PMI for alignment and directly measures the displacement with the MAT.  
In this method, a PMI and a null corrector which generates an aspheric wavefront are used to build a virtual axis. The virtual axis will always pass through the mirror's vertex when the test optics are well aligned to the mirror surface at each rotating angle.  
A laser tracker is used to align a visible target for MAT to the mirror's mechanical center (often defined by the outer diameter). A laser tracker is a portable CMS that utilizes a distance measuring interferometer (DMI) and two rotary encoders to track and measure the location of a sphere-mounted retro-reflector (SMR).  
The MAT allows the user to set reference lines of sight for applications such as the alignment and measurement of bearings and bores. By use of two built in optical micrometers, two dimensional displacements can be measured with high accuracy from a reference line of sight. The MAT is moved together with the test optics so that it can maintain relative position and orientation with the virtual axis. If the vertex is not at mirror's mechanical center, the MAT could observe target displacement after rotating the mirror and re-aligned to the test optics. Thus, the vertex position can be determined with two dimensional displacements from the MAT.  
The measurement sequence is as follows (see Figure 1):  
1. Use a laser tracker to measure the mechanical center of the mirror and align a target (for MAT) to mirror's mechanical center, record the eccentricity xtarget, ytarget.
2. Deploy the PMI, null corrector such that the optical axis of test optics coincides as closely as possible with mirror's optical axis, read the coma terms cx0, cy0, and calculate alignment deviation xcoma0, ycoma0 with mirror's parameter.
3. Deploy the MAT, place it along with PMI and test optics (on the same 5-axis table), and aim at the target center, record the reading of two micrometers: xmat0, ymat0.
4. Rotate the mirror by 180 degrees, adjust 5-axis table to re-align optical path and calculate alignment deviation xcoma180, ycoma180 with coma terms.
5. Adjust the MAT's micrometers, aim at the target center, and record the reading of micrometers xmat180, ymat180.
6. The displacements between the vertex position and mirror's mechanical center can be calculated as:  
\[\begin{split}\text{x}_{\text{vertex}}=(\text{x}_{\text{mat\_180 }}-\text{x}_{\text{coma\_180}}+\text{x}_{\text{coma\_0}}-\text{x}_{\text{ mat\_0}})/2+\text{x}_{\text{target}}\\ \text{y}_{\text{vertex}}=\left(\text{y}_{\text{mat\_180}}-\text{ y}_{\text{coma\_180}}+\text{y}_{\text{coma\_0}}-\text{y}_{\text{mat\_0}}\right)/2+\text{y}_{\text{target}} \end{split} \tag{1}\]
# Improvement of Network Flow Using Multi-Commodity Flow Problem
## 1 Introduction  
In recent years, network traffic has been increasing with the increase in applications using the Internet. This can be attributed to the growth of social games and video distribution services, which are becoming higher quality. Traffic volume per subscription is expected to increase approximately 14 times by 2030 compared to current traffic volumes [1]. In addition to PCs and smartphones, the development of the Internet of Things (IoT) [2] has led to an era in which automobiles and home appliances are connected to the Internet, and various terminals are sending packets. By 2030, the number of IoT devices is expected to exceed about 29 billion [3]. Routing must be performed to ensure that these services are available even under conditions of packet growth.  
Currently, most routing protocols select a single optimal path, such as open shortest path first (OSPF) [4]. OSPF is a link-state routing protocol that belongs to the interior gateway protocol (IGP) and uses a cost set for each link. [PERSON]'s algorithm [5] is used for route calculation, and the path that minimizes the total cost to the destination node is taken as the optimal path. If there are multiple equal-cost paths, OSPF can distinguish between them, but it arbitrarily uses one path by default. OSPF is effective when the optimal path has a large bandwidth. However, when the optimal path has a narrow bandwidth and a large amount of traffic flows over it, the risk of congestion is high and the desired performance is not achieved. In such cases, the current OSPF is problematic.  
In addition to the optimal path, redundant paths lead to the network's destination node. Therefore, load balancing using multiple paths is effective in reducing congestion [6]. Load balancing using multiple equal-cost paths is referred to as equal-cost multipath (ECMP) [7]. A round-robin divides traffic equally among all equal-cost paths. However, its performance depends on the number of equal-cost paths. The paths are limited, and other redundant paths can be used in the network as a whole.  
In this study, we propose a routing method that uses multiple paths with the multi-commodity flow problem [8] based on the maximum flow problem [9]. The commodity in the multi-commodity flow problem is the combination of source and destination. Nosimplified augmenting path algorithm is currently known. Linear programming [5] and a full polynomial time approximation scheme [10] are used as solution methods. This improves the throughput of the entire network and suppresses congestion. For this purpose, we implement the necessary functions on OpenFlow [11] and evaluate its performance.
# Research Trends and Future Direction for Utilization of Woody Biomass in Japan
## 8 Discussion
### More Comprehensive Forest Projection Systems  
Although most of the timber supply in Japan comes from planted forests, natural forests still constitute approximately 57% of the country's total forest area and 36% of its  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case No.**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Heat**} & \multirow{2}{*}{**GHG Emission**} & \multirow{2}{*}{**Reference**} \\  & & & **Utilization** & & **Utilization** & & **Resil Fuel** \\  & & & **Facilities** & & **[\%]** & & **[kg-CO\({}_{\text{2 eq}}\)/G]** \\ \hline
7 & 2016 & LCA & DHS & 29 & LPG & 21.60 & [78] \\
8 & 2016 & LCA & DHS & 29 & Kerosene & 23.10 & [78] \\
9 & 2016 & LCA & DHS & 29 & Heavy oil & 25.70 & [78] \\
10 & 2016 & LCA & DHS & 75 & LPG & 65.30 & [78] \\
11 & 2022 & LCA & Hot spring & 90 & City gas & 60.00 & [56] \\
12 & 2022 & LCA & Hot spring & 90 & heavy oil & 76.30 & [56] \\
13 & 2022 & LCA & Hot spring & 90 & Kerosene & 69.60 & [56] \\
14 & 2014 & Substitution effects & Household & 63 & Kerosene & 68.25 & [71] \\
15 & 2019 & Substitution effects & Hot spring & N.A. & heavy oil & 62.47 & [55] \\
16 & 2019 & Substitution effects & Hot spring & N.A. & Diesel & 60.49 & [55] \\
17 & 2020 & Substitution effects & Residence & 80 & LPG & 59.22 & [64] \\
18 & 2021 & Substitution effects & DHS & 80 & City gas & 46.11 & [75] \\ \hline \hline \end{tabular}
\end{table}
Table 11: Cont.  
total forest stock. From the perspective of resource existence, their significance cannot be overlooked [87]. However, owing to the lack of projection systems, only a few studies have been conducted on natural forests, with the majority focusing on planted forests. Furthermore, most studies utilize yield tables as projection tools. These yield tables were developed in the 1950s or 1960s and often cover forests less than 100 years old [88]. However, following the postwar period, the Japanese government initiated a massive reforestation program, resulting in many forests between 60 and 80 years old, with expectations to exceed 100 years old in the next few decades [87]. Existing projection systems cannot adequately address this evolving situation.  
In Europe, where woody biomass is widely utilized, many countries have established national-level projection systems [81]. Prediction systems for the entire EU have also undergone rapid development in recent years [89; 90; 91]. Some prediction systems have incorporated new functions, such as addressing climate change [92], forest disasters [93], and carbon sequestration [94], in addition to traditional resource estimations. In Japan, existing forest prediction tools are outdated, and a more comprehensive prediction system needs to be developed at the national level.
## 3 Mobility Support Scheme Based on Decision Tree-Based Pattern Classification and Regression
### Regression-Based Mobility Pattern Prediction
However, while a prediction accuracy of 86% is not low, it might not be sufficient for real-world industrial scenarios. To address this, the proposed scheme allocates resources not only to the location predicted by the regression model but also to devices located at the predicted device's previous and subsequent positions in time. (See Section 3.3 for more information). When the trajectory is classified as \"Random\", predicting the location of a mobile device becomes difficult based on polynomial regression as previously described. Alternatively, time-series prediction models such as RNNs could be employed; however, the prediction accuracy for randomly moving devices would significantly decrease. Therefore, in cases where the trajectory is determined to be \"Random\", we exploit the existing resource allocation scheme for mobility support [15].
# 1D Barcode Detection via Integrated Deep-Learning and Geometric Approach
## 3 Model Overview  
The 1D barcode is usually located on the package surface of a product which can have complex textures, making it difficult for the pure geometric-based methods to locate the region of the barcode. Regions outside 1D barcodes also take unnecessary computational cost, especially when dealing with high-resolution input images, which makes detection hard in real-time applications when using geometric methods, although they perform better in terms of accuracy. Deep-learning-based detection methods can locate the barcode fast using the object detection neural network technique, but the barcode in the bounding box region is still rotated randomly and mixed with background pixels, which makes it hard to decode.  
We consider that by incorporating deep-learning and geometric approaches, these problems will become easier to process. Through the fast parallel processing in deep-learning neural networks, geometric approaches help to improve the output accuracy. We propose a novel method in two-stages: barcode location and region extraction. Each stage is composed of a neural network and many geometric processes. The barcode location stage locates the bounding box of the 1D barcode using the YOLO object detection network and detects all line segments in the bounding box region using the LSD algorithm. Because the bars can be considered as a set of parallel line segments in a close distance, the rotation angle of the 1D barcode can be predicted by clustering all of the line segment angles and selecting the angle with the most line segments. Considering any line segments in an image also not belong to the 1D barcode, the region extraction stage processes the images cut from bounding boxes and proposes a selecting range for the line segments using the region estimation network. The final result is generated as a convex hull from the endpoints of the selected line segments. See an overview in Figure 1.  
During the entire process, there is no need to tune any manual parameters. All our approach requires is a standard dataset or some 1D barcode images for network training which can easily be gathered.
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 3 Voice Maps in Practice
### Subtle Voice Changes  
Voice maps can reveal changes that are local to a small sub-range of the voice, which might otherwise have eluded a more conventional assessment. This can be of particular relevance to singers [5], with voice problems or performance targets that can be very subtle. To the practicing singer, voice maps provide a useful personal frame of reference for monitoring one's own vocal status and progress [50; 51], even in a small part of the voice range.