esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Semantic Service Description and Context-Based Orchestration
### Construction of Ontologies
#### 3.2.6 Parameter Ontology  
The parameter ontology provides means for the semantic annotation of input and output parameters of a service (e.g., physical quantities like _Angle_, _Velocity etc._), but also for the description of non-functional properties. These non-functional properties can be divided into three categories: quality of service parameters (e.g., reliability, performance, safety), characteristics which are intrinsic for a device (e.g., the kind of consumed resource, its average resource consumption or its operating hours) and context-dependent information (e.g., location in the plant, current state of the device). Concerning the usage of quality of service parameters for the semantic description of services, one major issue is the definition and measurement of concrete metrics (e.g., \"reliability high\" or \"performance\" <1 ms). In order to perform a reasonable matching based on such metrics, experiments must be performed to deliver statistical values. However, this goes beyond the scope of the work described in this article. Nevertheless we modeled different quality of service attributes for future extensions of the system. Figure 4 depicts a part of the parameter ontology.
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
## 5 Performance Evaluation
### Scenario 2: String Topology, \(N=4\)  
Scenario 2 includes four linear networks. Figure 6a-c show the network throughput, frame-existence probability, carrier-sense airtime, as functions of offered load, respectively. We can observe from Figure 6a that the throughputs increase in proportion to the offered load in the range of \(0\leq O\leq 13.3\) Mbps. The reason is similar to the one in the previous section (i.e., networks being in the non-saturation state). We also have same confirmation as in Figure 6b, where within the range all the probabilities are less than one. However, the frame-existence probabilities of Network 2 and 3 reach one at \(O=13.2\) Mbps. For \(O\geq 13.2\) Mbps, meanwhile the frame-existence probabilities of the other networks still increase. Therefore, carrier-sensing probabilities increase as seen in Figure 6c. As a result, the throughputs of Network 2 and 3 decrease as the offered load increases in the saturation condition. It is also seen from Figure 6b that all networks are saturated at \(O=20.5\) Mbps. Therefore, the network throughputs keep the constant value in the range of \(O\geq 20.5\) Mbps. From Figure 6c, it is confirmed that the degradation of throughput in Network 2 and 3 is also caused by the bias of carrier-sensing.
# Near to Mid-Infrared (1.3-5 um) Widely Tunable, High Power Picosecond Pulsed Laser
## 1 Introduction
MgO:PPLN (magnesium-doped periodically poled lithium niobate) has also attracted significant attention as a non-linear material for use in OPOs due to its multiple properties of high nonlinear coefficient (\(\sim\)17 pm/V), large transparency range (0.5-5 \(\upmu\)m), and its resistance to optical damage. In recent years, there have been numerous reports investigating the generation of near- to mid-infrared picosecond laser emission using periodically polled crystals with synchronously pumped X-shaped cavities [25; 26; 27; 28; 29; 30; 31; 32]. For instance, the team of [PERSON] reported a 1064 nm, 81.1 MHz, picosecond Yb-fiber laser pumped MgOs:PPLT crystal mid-infrared wavelength tunable OPO. The wavelength tunability of the OPO is obtained in the range of 1530-1640 nm (signal beam) and 3022-3488 nm (idler beam), obtaining the maximum tuning length of 577 nm. The high signal output power of 4.3 W at wavelength of 1595 nm and mid -infrared idler output power of 2 W at wavelength of 3200 nm were achieved by adjusting the cavity mirrors, corresponding a total (signal and idler) conversion efficiency of 47%. In 2013, the team reported a 160 Mhz, 532 nm frequency-doubled Yb-fiber laser synchronously pumped picosecond OPO, which consisted of a fan-shape grating designed PPKTP crystal. Resulting signal and idler outputs were widely tuned in the tuning range of 726-955 nm and 1201-1998 nm, respectively. The maximum tuning length of this OPO was 1026 nm. While the aforementioned results from the literature show the potential for generating wavelength tunable emissions from OPOs, they serve to highlight the complexity and difficulty associated with tuning and optimizing X-shaped cavity designs. Typically, the wavelength tunability of X-shaped cavity OPOs is in the range of 1.43-1.63 \(\upmu\)m (signal field) and 3.06-4.16 \(\upmu\)m (idler field), with a maximum conversion efficiency of \(\sim\)73%. Compared to X-shaped ring cavity OPOs, Z-shaped cavity OPOs have a simpler design, are easier to adjust and optimize, and are more broadly tunable. In this research, we investigate the operation of a high-power, widely tunable near- to mid-infrared picosecond pulsed, singly resonant Z-shaped OPO using a MgO:PPLN crystal. The OPO is pumped using a Nd:YVO\({}_{4}\) picosecond pulsed laser and widely tunable signal, and the idler field emission is generated by tuning the grating period and temperature of the MgO:PPLN crystal.
## 2 Imaging Studies  
In order to monitor growth and to describe the dimensions of the basal and antero-posterior diameter, as well as the margins, of iris melanoma, there is agreement in the literature regarding the use of sequential photographs with a slit lamp and ultrasound biomicroscope, obviously before dilation. Ultrasound Bio Microscopy (UBM) is a high-resolution ultrasound technique that allows noninvasive in vivo imaging of structural details of the anterior ocular segment at near-light microscopic resolution and provides a detailed assessment of anterior segment structures, helping to differentiate solid iris lesions from iris cysts (acoustically empty), as well as to detect the involvement of the anterior chamber angle and ciliary body. Moreover, this examination can show a \"lion's paw\" appearance, defined as a posterior extension of the iris delimited by the lens [24].  
Anterior Segment Optical Coherence Tomography (AS-OCT) is a relatively new imaging technique, without direct contact with the eye, based on low-coherence interferometry, measuring the time delay and intensity of light back-reflected from tissue structures at various depths compared with a reference standard [25]. In the literature, AS-OCT is compared with UBM in imaging anterior segment lesions. [PERSON] et al. suggest that Anterior Segment Optical Coherence Tomography can be used in minor iris tumors with a low degree of pigmentation, whereas UBM is suggested for tumors of the ciliary body and those with heavy pigmentation [26]. They also suggest that AS-OCT is better than UBM for imaging iris lesions measuring \(\leq\)2 mm at the base and 0.6 mm in elevation, whereas UBM is superior for lesions larger than 3 mm, heavily pigmented lesions and those with posterior or ciliary body invasion [26]. Fluorescein angiography, which is usually less performed in clinical practice for posterior uveal melanomas, is rarely helpful to establish diagnosis [7; 22]. The vascular pattern of the tumor is irregular compared with the normal vascular pattern of the iris, and it can be hyperfluorescent inside and around the tumor. If fluorescein leakage is noted at a site that is distant from the tumor, occult or multifocal tumors should be suspected. In general, a disorganized vasculature showing a wide leakage of dye is suggestive of malignancy [7]. The use of computerized tomography and magnetic resonance imaging has also been described [27].
## 2 Battery ECM and Extended Battery ECM  
### Battery ECM  
The considered battery ECM is shown in Figure 2. It is one of the most widely used ECMs [6], and it includes a double RC network, which is a good trade-off between the error and the complexity of the model compared with single and triple RC structures [25].  
The resistor R\({}_{0}\) stands for ohmic resistance, which includes the resistance of contacts, electrodes, as well as electrolytes [26]. The double pair RC captures the transient battery dynamics such as the charge transfer kinetics, the lithium ion diffusion, and solid/electrolyte interface (SEI) dynamics [8].  
The voltage source \(V_{\text{OC}}\) represents the OCV, which mainly depends on the battery SOC [8]. As an example shown in Figure 3, its average value, \(V_{\text{OC}}(soc)\), is usually a monotonically increasing function of SOC [27]. In each SOC interval, it can be approximated by \(V_{\text{OC}i}(soc)=a_{i}\cdot soc+b_{i}\) (\(a_{i}\) and \(b_{i}\) are constant in the \(i\)th SOC interval). The nonlinearity of this curve is inevitable.  
Figure 2: Battery equivalent circuit model (ECM) with the double RC network.  
Furthermore, the battery SOC \(\in[0\%,100\%]\) can be modeled by the classical Coulomb counting method [28]:  
\[soc(t)=soc(t_{0})-\eta\int\limits_{t_{0}}^{t}\frac{I_{\text{batt}}(\tau)}{\text{C }_{\text{n}}}d\tau \tag{1}\]  
where \(soc\) is the operator of SOC; \(\eta\) is the Coulombic efficiency; C\({}_{\text{n}}\) (Ah) is the battery usable capacity; \(soc(t)\) is the required SOC at time point \(t\) based on its initial value \(soc(t_{0})\).  
Therefore, the battery dynamic behavior can be described by the following state-space representation:  
\[\left[\begin{array}{c}\frac{\text{d}V_{1}(t)}{\text{d}t}\\ \frac{\text{d}V_{2}(t)}{\text{d}t}\\ \frac{\text{d}soc(t)}{\text{d}t}\end{array}\right]=M\left[\begin{array}{c}V _{1}(t)\\ V_{2}(t)\\ soc(t)\end{array}\right]+NI_{\text{batt}}(t) \tag{2}\]  
\[V_{\text{batt}}(t)=V_{\text{OC}}(soc)-V_{1}(t)-V_{2}(t)-\text{R }_{0}I_{\text{batt}}(t)\]  
where,  
\[\mathbf{M}=\left[\begin{array}{ccc}\frac{-1}{\text{R}_{1}\text{C}_{1}}&0&0\\ 0&\frac{-1}{\text{R}_{2}\text{C}_{2}}&0\\ 0&0&0\end{array}\right],\quad\mathbf{N}=\left[\begin{array}{c}\frac{1}{\text{C}_ {1}}\\ \frac{1}{\text{C}_{2}}\\ \frac{-\eta}{\text{C}_{\text{n}}}\end{array}\right]\]  
and \(V_{\text{batt}}\) is the battery output voltage. \(V_{1}\) and \(V_{2}\) are the voltages across the capacitors C\({}_{1}\) and C\({}_{2}\), respectively; \(I_{\text{batt}}\) is the battery input current, according to its reference direction in Figure 2; \"+\" means discharging process, while \"-\" means charging process.
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 2 Proposed Methods
### Multi-Granularity Partition of the Multi-Agent Negotiation Model
#### 2.2.2 Middle-Level Granularity Model  
Middle-level granularity corresponding to a normal communication environment is designed to balance bandwidth usage and the negotiation effect.  
Similar to low-level granularity, the satellites are divided into one _Leader_ and several _Followers_. The process can be divided into the following steps shown in Figure 3:  
* **Step 1:** Preprocessing the missions. Same as the **Step 1** in low-level granularity.
* **Step 2:** Select a set of mission stages. Differently from the **Step 2** in low-level granularity, the set of mission stages contains both desired and candidate mission stages.
* **Step 3:** Transmit the mission set. Differently from the **Step 3** in low-level granularity, these mission stages broadcast include their execution costs and time windows, and the _Leader_ records the set.
* **Step 4:** Leader process. Same as **Step 4** in low-level granularity.  
Figure 2: Low-level granularity negotiation process.  
* **Step 5:** Announce temporary result. Same as **Step 5** in low-level granularity
* **Step 6:** Result check and vote. The _Followers_ working in the middle-level granularity are responsible for their own mission set. The assigned missions \(MS_{actual}\) must all be in the submitted mission set. The total cost of the mission set is less than satellite's capability \(c_{m}ax\). Besides, the assigned mission set must contain enough missions. The _Follower_ votes in favor if the above two constraints are satisfied. If not, the _Follower_ votes against. The vote result \(V\) is expressed as: \[V=\left\{\begin{array}{ll}1&\quad MS_{actual}\subseteq MS_{ desire}\;and\;c_{max}/2\leq\sum c_{i,j}\leq c_{max}\\ 0&\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \
## 4 Results and Analysis
### Imaging Results
In the process of detection, a large transmission voltage and time\(-\)gain compensation are required due to the large defect with a deeply buried depth, and a strong noise is introduced by a large transmission voltage and time\(-\)gain compensation. However, as shown in Figure 7c, the noise in the image can be eliminated basically after VCF weighting due to the value of the VCF factor of noise being low. Figure 8: Imaging results of groove with depth 0.1 mm and buried depth 90 mm. (**a**) DAS, (**b**) VCF, and (**c**) DAS_VCF with focus depth 30 mm; (**d**) DAS, (**e**) VCF, and (**f**) DAS_VCF with focus depth 40 mm; (**g**) DAS, (**h**) VCF, and (**i**) DAS_VCF with focus depth 50 mm; (**j**) DAS, (**k**) VCF, and (**l**) DAS_VCF with focus depth 60 mm; (**m**) DAS, (**n**) VCF, and (**o**) DAS_VCF with focus depth 70 mm.
## 4 Results
### Yield Forecasting Performance
#### 4.2.2 LAU Level  
At the LAU level, the overall quality of the crop yield predictions is lower than for NUTS-2. The RRMSE is around 14%, 19%, and 28% for winter wheat, winter rapeseed, and maize, respectively (Table 4).  
Figure 13 presents the quality metrics for LAU units for which crop yields forecasts were generated. Some LAU units lack predictions (marked with black) due to insufficient reference statistics used to train the XGBoost models or because a given crop is not cultivated there.  
Quality metrics of crop yield forecasts differ from year to year (Figure 14). For winter wheat and rapeseed they are in line with the results for NUTS-2, i.e., overestimation in 2011 (rapeseed) and 2018 (winter wheat). However, overestimated maize yields are strongly related to the amount of data available to train the models (see lowest panel in Figure 14). It can be seen that for the years 2015-2019, for which the amount of data is sufficient, the quality of the forecasts is already close to the NUTS-2 ones. In addition, the years when the forecast quality is lower are consistent with the NUTS-2 statistics, e.g., for 2015.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Crop Type**} & **MBE** & **RMSE** & **RMSE** & **RRMSE** & **R\({}^{2}\)** & **Correlation (r)** \\  & **(dt)** & **(\%)** & **(dt)** & **(\%)** & **(–)** & **(–)** \\ \hline Winter wheat & \(-\)0.01 & \(-\)0.03 & 5.20 & 13.77 & 0.75 & 0.87 \\ Winter rapeseed & \(-\)0.02 & \(-\)0.07 & 5.09 & 18.80 & 0.45 & 0.67 \\ Maize & 0.07 & 0.12 & 14.89 & 27.36 & 0.48 & 0.69 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall performance of crop yield forecasts at LAU level.  
Figure 13: Map of crop yield forecasts performance at the LAU level. Black areas denote LAU units where the crop yield forecast was not possible due to data availability issues.  
Variable length of time series of official statistics (used to train the models) among LAU units allows the assessment of how the size of the training dataset impacts the model performance. Figure 15 shows that for all three crops, the larger the training dataset (more years), the higher the accuracy of the crop yield forecasts. This is particularly evident for maize, for which a long data series corresponds to a more accurate prediction than for the other two crops.  
Figure 14: Time series of quality metrics of crop yield predictions for LAU units after the vegetation season.  
Figure 15: Correlation coefficient (\(r\)) between reference crop yield statistics (used to train the models) and crop yield forecast as a function of the available time span in years (color bars).
## 3 Numerical Solution Procedure
### Stress Boundary Condition  
Based on the assumptions in Section 2, the stress boundary conditions shown in Figure 8 are considered. The stress condition at the boundary of the failure zone is considered as an equivalent frictional contact, and the equivalent frictional strength is assumed to be fully mobilized.  
Without considering the effect of the pile shaft resistance on the stress state at the boundary of the failure zone, assuming that the vertical load acting on the boundary of the failure zone is equivalent to the weight of the overlying soil, the vertical load can be written as follows:  
\[P_{Y}=\sum_{i=1}^{n-1}\gamma_{i}^{\prime}H_{i}+\gamma_{n}^{\prime}\left(L- \sum_{i=1}^{n-1}H_{i}\right), \tag{11}\]  
where \(\gamma_{i}^{\prime}\) is the effective unit weight of each soil layer above the pile end, \(H_{i}\) is the thickness of each soil layer above the pile end, and \(L\) is the depth of computing node at the boundary.  
The assumption that the equivalent frictional strength is fully mobilized requires that the shear stress \(\sigma_{\tau}\) and the normal stress \(\sigma_{n}\) of the computing node at the boundary meet the following conditions:  
\[\frac{\sigma_{\tau}}{\sigma_{n}}=\tan\phi, \tag{12}\]  
Between the external load \((P_{Y},P_{X})\) and the stress components \((\sigma_{\tau},\sigma_{n})\), Equation (13) holds:  
\[\left\{\begin{array}{l}\sigma_{\tau}=-P_{Y}\cos\alpha_{SA1}+P_{X}\sin\alpha_ {SA1}\\ \sigma_{n}=P_{Y}\sin\alpha_{SA1}+P_{X}\cos\alpha_{SA1}\end{array}\right., \tag{13}\]  
where \(\alpha_{SA1}\) is the angle between the boundary of the failure zone and the vertical direction.  
Figure 8: The stress boundary condition at the ultimate state.  
Figure 7: The direction of the major principal stress at the contact boundary at the ultimate state.  
Combining (11), (12) and (13), the horizontal load \(P_{X}\) acting on the boundary of the failure zone can be obtained as follows:  
\[P_{X}=\frac{(\tan\phi\sin\alpha_{SA1}+\cos\alpha_{SA1})P_{Y}+\tan\phi\cdot(c/ \tan\phi_{\text{max}})}{\sin\alpha_{SA1}-\tan\phi\cos\alpha_{SA1}}, \tag{14}\]
# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 2 Materials and Methods
### Model of the Problem
#### 2.1.3 PnP Based Pose Estimation  
Corollary 1 states that, for a given camera (the intrinsic \(K\) is fixed), the object's projection on the image can be inferred, given the prior model of the object and the camera's external parameter. Considering the equivalence of the camera's external parameter and the object's pose, it is the inverse that needs to be concerned in pose estimation: given the picture of the object, the camera's intrinsic parameter and the object's prior model, find the camera's external parameter.  
Noting that \(P_{m}\) equals \(P_{o}\) and that OCS coincides with WCS, this problem becomes a typical PnP problem. There have been many well-developed solutions on how to solve PnP: Direct Linear Transform (DLT) [33], Perspective-3-Point (P3P) [34], Efficient PnP (EPnP) [2], etc. Thus, the key to solving the pose estimation is to find exactly where the keypoints of the object are projected in the image.
## 5 Results and Analysis
As shown in Table 6, in the urban road cycle and highway cycle, the MG average efficiency of configuration II increases by 3.38% and 5.47%, respectively compared with the reference configuration. Combined with Tables 5 and 6, the overall operating efficiency of power resource components in configuration II has been effectively improved, which confirms that the SMHPC-2 PG possesses excellent energy-saving performance. Figure 15 shows the energy loss of the configuration II and the reference configuration in different test cycles. In the urban road cycle, the improvement of fuel economy for configuration II is mainly due to the raising of operating efficiency of power resource components, as shown in Figure 15a. Compared with the reference configuration, the energy loss of engine and MG in configuration II decreased by 10.6% and 26.5%, respectively. In the highway cycle, because of the high efficiency of the engine in configuration II and reference configuration, the reduction of energy loss for the transmission system and MG becomes the main factor to improve the fuel economy of configuration II. As shown in Figure 15b, compared with the reference configuration, the energy loss of the transmission system and MG in configuration II are reduced by 1140 kJ and 930 kJ, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **UDDS** & **HWFET** \\ \hline Configuration II & 61.20\% & 79.05\% \\ Reference configuration & 53.45\% & 78.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average efficiency of MG.  
Figure 14: SOC (state of charge) curves of configuration II. **(a)** UDDS; **(b)** HWFET. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **UDDS** & **HWFET** \\ \hline Configuration II & 61.20\% & 79.05\% \\ Reference configuration & 53.45\% & 78.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Proportion of high-efficiency region of engine working points.
## 3 Modelling of the Bus Transportation Network
### Bus Consumption Modelling  
EB energy consumption varies according to several parameters: bus speed, weight, traffic, number of passengers, road slope, etc. Bus consumption also depends on heating, ventilation, and air-conditioning (HVAC), and other auxiliaries, as well as on the implementation of a deceleration and braking energy recovery system. Therefore, a bus consumption model is needed to assess the energy demand of each EB in the fleet. Physical (also called white-box) models rely on the analysis of physical and chemical processes in the  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Service n\({}^{\circ}\)** & **1** & **2** & **1** & **2** & **1** & **2** & **2** & **2** & **1** & **2** & **1** \\ \hline
**Trip n\({}^{\circ}\)** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline Bus stops & & & & & & & & & & & & \\ Gare & 06:42 & 07:54 & 08:36 & 09:36 & 10:00 & 12:10 & 14:10 & 15:36 & 16:00 & 16:49 & 17:36 \\ Couttolenc & 06:47 & 07:59 & 08:44 & 09:41 & 10:05 & 12:15 & 14:15 & 15:41 & 16:05 & 16:54 & 17:41 \\ Res. Univ. & 06:51 & 08:03 & 08:46 & 09:45 & 10:09 & 12:20 & 14:19 & 15:44 & 16:08 & 16:58 & 17:46 \\ Denielou & 06:55 & 08:07 & 08:51 & 09:49 & 10:13 & 12:25 & 14:23 & 15:48 & 16:12 & 17:04 & 17:51 \\ Mercieres & 06:58 & 08:12 & 08:55 & 09:52 & 10:16 & 12:28 & 14:26 & 15:51 & 16:16 & 17:08 & 17:55 \\ Parc Tertiaire & 07:00 & 08:15 & 08:58 & 09:55 & 10:19 & 12:30 & 14:29 & 15:53 & 16:19 & 17:11 & 17:58 \\ Longues & 07:04 & 08:18 & 09:02 & & & 12:33 & 15:57 & & 17:15 & 18:02 \\ Lecuru & 07:10 & 08:25 & 09:10 & & & 12:40 & & 16:05 & & 17:23 & 18:10 \\ Z.A. & 07:18 & 08:34 & 09:18 & & & 12:49 & & & 17:31 & 18:18 \\ Automne & 07:21 & 08:36 & 09:21 & & & 12:52 & & & 17:34 & 18:23 \\ Eglise & 07:25 & 08:40 & 09:25 & & & 12:56 & & & 16:33 & 17:38 & 18:25 \\ Aramont & 07:29 & 08:42 & 09:29 & & & 13:00 & & 16:37 & 17:42 & 18:29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Timetable of bus line “ARC Express” in Compiegne, France [34].  
Figure 2: Scheme of a bus line with three terminals (in red), intermediate bus stops (in green), two routes (in blue and purple), and the time schedule of one trip on the purple route.  
energy transmission and storage components of the vehicle [35]. Data-driven (also called black-box) models deduce the bus consumption based on large amounts of experimental or real-world operation data [35]. Eventually, intermediate grey-box models combine experimental data with mechanical insight [36].