esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 3 Optimal Control for Expected Total Rewards
### Total Reward Maximization  
Figure 5 shows the total rewards changes with the time slots. Compared with the PCA, the JPLA adds a link selection mechanism. Therefore, the JPLA can transmit data through the direct link when the battery is very low, which can increase the total rewards. The BIBAoSE takes the future system states into account, which makes a more efficient green energy allocation over N slots than the JPLA. However, all the algorithms can only alleviate the system performance degradation caused by insufficient energy and cannot replace the green energy supply. Therefore, the JPLA-F and the BIBAoSE-F always have the highest total rewards.  
Figure 4: The total rewards versus \(d_{SD}\), \(\omega_{t}=1\), \(\lambda=4\).  
Figure 6 displays the total rewards vary as energy harvesting intensity increases. The system is in a green energy-deficient state, when the energy intensity is low. In this case, the relay can deliver more bits as the intensity increases, which leads to a higher reward. However, the rewards will be constant once the energy intensity reaches a certain threshold, because the battery capacity is limited. It should be noted that the rewards of the BIABoSE are lower than the other algorithms when the green energy is enough due to the discretization of states. However, the BIABoSE achieves better performance in our main application scenario, which is a lack of green energy.
# Experimental Demonstration of MmWave Vehicle-to-Vehicle Communications Using IEEE 802.11 ad
## 5 Experimental Result
### City Experiment  
To investigate the GiV2V connectivity during high mobility, we performed experiments on normal city roads using the same configuration that was applied in the campus case. Figure 10b,d shows the results of the throughput and SNR while driving through the city, respectively. Here, we observed that disconnectivity occurred during the entire period of measurement because of mobility, while the connectivity in the campus measurement achieved partially stable periods. Owing to the 80 km/h speed limit and the deceleration/acceleration of vehicles on the city roads, the inter-vehicle distance varied more when compared to the campus experiment. The corresponding SNR plot in Figure 10d shows that the average SNR fluctuated more aggressively than the campus results. The campus SNR results in Figure 10c show that the SNR was stable because the vehicle convoy consistently maintained a low vehicle speed. However, samples with peak SNR (approximately 10 dB) were demonstrated more in the city measurement compared to the campus measurement because the vehicles on the campus almost did not stop during the test (approximately 400 s in Figure 10c; vehicles only stopped to end the test), while vehicles stopped at several intersections on the city road. Therefore, the measurement time with the peak SNR shown in Figure 10d indicated that the test vehicles stopped at the intersection.  
Figure 11: Campus and city road disconnectivity.  
Further, as shown in Figure 11, the disconnectivity while traveling through the city roads was higher than that in campus owing to the variation in inter-vehicle distance. For instance, the longest disconnection period was approximately 20 s when compared to the 15 s in the campus case because the fast moving vehicles could not move closer once they were sufficiently separated to cause a loss of connection until the vehicle in front had slowed down to stop for traffic signals. Therefore, 90% of disconnection intervals was less than 10 s in Figure 11d, which was double the value of the campus result.  
**Observation 6**.: Ten percent of disconnection period was more than 10 s in IEEE 802.11 ad based V2V communications on city roads.
# Statistical Tolerance Analysis--A Survey on Awareness, Use and Need in German Industry
## 7 Need and Potential of Statistical Tolerance Analysis
Nevertheless, respondents wanted \"tools that are easy and quick to use\", since the required and available time is essential for the participants to decide on either performing or skipping a statistical tolerance analysis. This is even more relevant for most design engineers due to the fact that tolerance analysis is only a subordinate step and not the main activity of their daily work. In consequence, it is essential that a tolerance expert (or at least an experienced key user) is available in companies to support engineers from all domains in dealing with tolerances. Fortunately, over the last decade, more companies have already appointed and established an in-house expert whose work has since then proven its worth. Figure 7: (**a**) Response of 98 participants to the (single-choice) question: “How great is your interest in further expanding your knowledge and expertise on statistical tolerance analysis?”; (**b**) response of 101 participants to the (multiple-choice) question: “What aspects do you consider to cause a high scrap rate in production?”. Looking beyond the horizon, the effects of geometric deviations of components on the final product have already attracted the attention of design engineers and led to a pronounced awareness. Nevertheless, this is only the beginning, as one participant's demand for an \"increase in the quality consciousness of employees\" illustrated. A common understanding of the influences of deviations (some also call them uncertainties, errors, faults, variations, shifts, malfunctions, etc.) on the functionality and aesthetic quality of a product has not yet been internalized everywhere. One participant highlighted the importance of the \"creation of a uniform understanding\" for the company's success, which is also considered essential among researchers [44; 63]. This becomes even more obvious when analyzing the results in Figure 7b, which ranks the aspects that respondents would consider if the scrap rate during production were to exceed its given limit. The participants chose from [PERSON]'s \"5 Ms\". While 91 of 101 respondents (1 abstention) still considered \"man\" as a relevant influencing factor, the aspects \"medium\" (34 of 101) and \"method\" (29 of 101) were significantly less frequent. Consequently, engineers should take a holistic view of their product and all the interactions that arise during the product's entire life cycle. The approaches of robust design, as well as the perspectives and methods of total quality management and lean management are promising. Fortunately, current research already integrates tolerance engineering in superordinate philosophies (such as in uncertainty management [64; 65], robust design [43; 66] and Industry 4.0 [67; 68; 69; 70].
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### Communication Protocols and Interfaces
#### 3.2.3 GPS-Specific  
The GPS technology for vessels operates in the frequency bands of 1575.42 MHz, called L1, and 1227.60 MHz called L2 [60]. Additionally, the DGPS function operates in the 283.5 kHz to 315 kHz band in Region 1 and in the 285 kHz to 325 kHz band in Regions 2 and Region 3, as per the Recommendation ITU-R M.823 [21; 97]. The regions are shown in Figure 3 [98].
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 4 Hardware Implementation
### Hardware Architecture
#### 4.2.2 Training Module
In summary, the novelties of our architecture compared to the related work discussed in Section 2.2 are:  
* Separately adjustable DOPs for inference and train module allow to adapt to application requirements and objectives, which is exploited by our framework as described in Section 4.3;
* Fully pipelined, on-chip architecture achieves low-latency and high-throughput even for applications that rely on small batch size;
* Different weight-width for inference and training enables training with higher precision while minimizing resources for inference.
# Geotechnical Study of Raspadalica Cliff Rockfall, Croatia
## 4 Results
### Results of the Geological and Geotechnical Survey
In all zones, the traditional geological and geotechnical field surveys and remote sensing analysis indicated three main sets of mechanical discontinuities in the rock mass: one set representing bedding planes and two sets of joints approximately orthogonal to the bedding planes, forming almost parallelepiped rock blocks of different volumes. The following sections describe the results that follow from the traditional geological and geotechnical field survey and remote sensing analysis. In Situ Traditional Geological and Geotechnical Survey  
Geologic and geotechnical investigations were conducted in an accessible area at the foot of the cliff to identify all the discontinuities and to describe all discontinuity features, such as orientation, spacing, persistence, roughness, discontinuity wall strength, aperture, degree of discontinuity wall weathering, seepage conditions, and the presence and hardness of discontinuity filling [12]. The observed discontinuities were grouped into three main sets (bedding, Joint Sets 1 and 2) as follows. Bedding dips from the NE (dip direction 0-25 degrees) to the SW and NW (dip direction: 230-305 degrees), but these variations are caused by a relatively gentle dip (15-30 degrees) and are strongly dependent on the measurement location. Joint Sets 1 and 2 are mostly subvertical with dips from 75 to 85 degrees and are nearly perpendicular sets of discontinuities, with the Joint Set 1 nearly parallel to the cliff face (dip direction: 10-30 or 155-210 degrees), while Joint Set 2 is nearly perpendicular to the cliff face and only occasionally exposed (dip direction: 90-120 or 275-280 degrees). Table 3 presents the mean values of orientations, discontinuity sets spacing, and persistence determined by in situ traditional geological and geotechnical surveys. The other discontinuity features (separation, infilling, discontinuity wall roughness, and weathering grade) were collected and used in the following rock mass description as more reliable data about rock mass discontinuity information than the data obtained by the remote sensing survey.
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 2 Related Work  
In recent years, skeleton-based HAR has received tremendous attention in the research community. Compared with color images and depth images, skeleton data are more robust to changing conditions and occlusions [5]. Most studies based on skeleton data adopt different feature extraction techniques to preprocess the skeleton data, and then motion features are expressed as matrices or encoded into images to serve as the input to the CNN models for training purposes.  
[PERSON] et al. [16] selected three joints as reference points to obtain new joint coordinates and transformed them into RGB images for fine-tuning the pretrained VGG19 model. [PERSON] et al. [17] designed an image encoder to transform the original coordinate data to image-formed data. Nevertheless, both methods ignored the temporal information, which is important in describing human motions. To consider temporal dimension information, in [18; 19], the authors encoded spatial-temporal information to motion images based on joint trajectory maps (JTMs). Although the two methods have added temporal features and achieved competitive results, it inevitably loses important information due to the issue of overlapping joint trajectories.  
To represent human actions more accurately, various advanced features and image coding methods have been proposed. [PERSON] et al. [20] summarized eight geometric features involving joint-joint distance and joint-joint orientation. In [21], the authors introduced a novel image coding technique to transform joint distance and orientation into color pixels. Moreover, a data augmentation method was used for information enhancement. [PERSON] et al. [22] proposed a group of kinetic skeleton features to obtain the spatial-temporal characteristics, and a 1D CNN model was introduced to identify human actions.  
Although skeleton-based HAR has made great improvements in accuracy, there are still two major challenges. First, utilizing the original coordinates, or only the spatial information, to describe human motion is not an ideal approach. Second, insufficient training samples prevent deep neural networks from efficiently learning deep features. Consequently, in our method, several relative features are utilized to represent human motions and a data augmentation strategy is proposed for information enrichment and overfitting prevention.
## 4 Case Study
### Simulation Analysis
#### 4.2.3 User's Power Purchasing Strategy Analysis  
The power purchasing strategies of users are aimed at minimizing their own total cost of electricity. Taking user 2 as an example, the optimal power purchasing distribution of user 2 is shown in Figure 8.  
As can be seen from Figure 8, user 2 has more bilateral electricity transactions with new energy generators during the time when the wind is strong (02:00-11:00; 19:00-24:00) and the light is strong (13:00-18:00). In combination with Figure 7, it can be seen that the carbon intensity of user 2 in the pool market has maintained at a value of 0.9 or slightly lower, which means that the change in user 2's power purchasing strategy in the pool market has a slight impact on the carbon intensity. For this reason, it mitigates its carbon responsibility by purchasing large amounts of electricity in the bilateral market. In addition, there are bilateral transactions between users and thermal power generators. This is because in order to increase revenue, thermal power generators take the path of reducing the price to sign electricity with users who have failed to win the new energy power producers. Based on the above analysis, it can be seen that the trading mechanism of the hybrid electricity market can timely reflect the market clearance situation and the resulting carbon responsibility to users, so as to further encourage users to adjust the power purchasing strategies and play a positive role in guiding users to choose new energy.
# Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot
## 4 Experiment and Results
### Safety Collision Detection  
In the safety collision detection experiment, the external force was detected through the virtual force sensor, and the movement of robotic arm would stop when the external force exceeded the safe range of external force of the human body to ensure the safety of the human when operating the equipment. In Table 6, BG/BGIA Risk Assessment Recommendations According to Machinery Directive: Design of Workplaces with Collaborative Robots [25] suggest the minimum external force and pressure that the body region can withstand.  
Clamping/squeezing force (CSF) is the external force affecting the body region at more than 0.5s; impact force (IMF) is the difference between the maximum force and the forces before and after the maximum when it is more than 5 N over a time interval of 0.5 s or less; pressure/surface pressing (PSP) is the pressure generated under the contact area of the collision; and compression constant (CC) is the deformation constant of each region of the body.  
According to Table 6, the smallest external force that the front side of the human neck can withstand is about 35 N. Therefore, the safety collision detection mechanism of the experiment was defined through the table. The flowchart of the safety collision detection algorithm is shown in Figure 11; when the external force is higher than the threshold for 10 ms, it will be considered as a collision and the movement of the robotic arm will be stopped.  
Figure 12 shows the experiment of collision detection with safety control. According to the result, the external force was detected at 5.11 s and the system judged that the collision occurred at 5.31 s. The safety collision mechanism was triggered within 10 ms after the external force occurred, and the movement stopped immediately. The result shows that the system conformed to the safety specifications suggested by [25] and the virtual force sensor can be applied in safe collision detection.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Skull** & **Face** & \
\begin{tabular}{c} **Neck** \\ **(Slides)** \\ \end{tabular} &
\begin{tabular}{c} **Neck** \\ **(Front)** \\ \end{tabular} & **Shoulder** & **Chest** & **Arm** & **Leg** \\ \hline CSF (N) & 130 & 65 & 145 & 35 & 210 & 140 & 160 & 140 \\ IMF (N) & 175 & 90 & 190 & 35 & 250 & 210 & 220 & 170 \\ PSP (N/cm\({}^{2}\)) & 30 & 20 & 50 & 10 & 70 & 45 & 50 & 45 \\ CC (N/mm) & 150 & 75 & 50 & 10 & 35 & 25 & 40 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The minimum external force and pressure that the body region can withstand.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **MAE (N)** & **RMSE (N)** & \(\gamma\) \\ \hline \(F_{x}\) & 20.0838 & 24.6242 & 0.7107 \\ \(F_{y}\) & 20.8401 & 30.0805 & 0.7325 \\ \(F_{z}\) & 9.3788 & 12.9381 & 0.7542 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis of the virtual force sensor.
## 5 Results  
### Validation of the Forward Model  
The study area is a national high-standard farmland of 5000 \(\text{hm}^{2}\) in Qixian County, Henan Province (114.17\({}^{\circ}\) E, 35.6\({}^{\circ}\) N), where winter wheat was cultivated annually from October to June, approximately. The region has a warm temperate humid monsoon climate with warm and rainy summer and cold and dry winter. The measured data were derived from the FieldSpec Handheld (a handheld geophysical spectrometer, Analytica Spectra Devices, Inc., Boulder, CO, USA). The spectral range of the measuring instrument is 325-1075 nm, and the spectral resolution was 3 nm at wavelengths 325-700 nm. We collected the test data on 4 March 2021 at about 12:00 when the solar light intensity was stable and the weather was clear and cloudless. The measurement results can represent the true reflectance of the vegetation canopy because the instrument is closer to the target and less influenced by aerosols and water vapor. Figure 7 compares the results between the simulated data at a solar zenith angle of 5\({}^{\circ}\) and the measured data at a sampling test point.  
Figure 6: The DFS of crop parameters: (**a**) the DFS of different wavelengths for all parameters and (**b**) the DFS of five bands for each parameter.  
It can be seen from Figure 7 that the simulated values of band 3 and band 4 were lower than the ground-measured values. However, the error between the measured and simulated reflectance at the sampling point was within 2%. The results indicated that the simulation of crop reflectance can be achieved by the coupled model from solar radiation to vegetation, further reflecting that the forward modeling process is reasonable.
# Model-Based Fault Analysis and Diagnosis of PEM Fuel Cell Control System
###### Abstract
Relative residual fault sensitivity was suggested, and the residual threshold was easily determined, regardless of the magnitude of each signal. [PERSON] et al. developed an approach for deriving nonlinear analytical redundancy via parity space [11]. With this parity space approach, the authors overcame the difficulty of generating analytical redundancy of a nonlinear system compared to it being done in a linear system. [PERSON] et al. and [PERSON] et al. worked on the fault diagnosis of PEM fuel cells with a LPV (linear parameter varying) observer [12; 13]. PEM fuel cell models were considered as LPV systems in both studies, and the LPV observer helped overcome difficulties in online measurement parameters, which are an online measurable state dependent parameter and a time delay dependent parameter. [PERSON] et al. proposed a methodology for diagnosing the performance degradation of PEM fuel cells  
Figure 1: PEM fuel cell diagram. by making equivalent-circuit models of the PEM fuel cell impedance and proving the relationships between the parameters of the equivalent-circuit model and the electrochemical cell model [14]. In this study, we address model-based fault diagnosis and the development of fault detection and isolation for PEM fuel cell control systems. [PERSON] et al. and [PERSON] et al. conducted a model-based fault diagnosis on the PEM fuel cell system with a similar methodology, applying the Dulmage-Mendelsohn decomposition [15; 16]. [PERSON]'s research introduces the fault diagnosis of the PEM fuel cell system in a general case and proposes how fault diagnosis can be improved with different system models. In this paper, components of the PEM fuel cell control system are in consideration and assumed to have faults to be diagnosed. The work in this paper is distinguished from the work carried out by [PERSON] by counting in the fault of various sensors in the PEM fuel cell control system. Furthermore, this work not only applies the Dulmage-Mendelsohn decomposition technique on the system model for fault diagnosis but also investigates how variables effect the detectability of the fault diagnosis system, especially when it comes to the variables related with the air compressor map. The paper is organized as follows. First, we provide an overview of PEM fuel cell systems. Next, we discuss the fault analysis of the system and a strategy for developing FDI algorithms. Finally, FDI algorithms were implemented on simulation models and the analysis of simulation results.
## 1 Introduction  
Due to the excellent stable emission performance of a wide tunable range and low cost, external cavity diode lasers (ECDLs) have become an ideal lasing source in many fields, such as high-resolution spectroscopy, optical sensing, atomic clocks and gas detection [1, 2, 3, 4]. In particular, the narrow linewidth characteristic of ECDL is the key element for coherent optical communication systems, coherent detection, optical sensing and measurement systems [5], and has attracted an extensive amount of attention.  
To achieve a narrow linewidth output, optical elements with the function of mode selection are applied to an external cavity structure, such as the confocal Fabry-Perot cavity, diffraction grating, etalon, and interference filter [6, 7]. As the common external cavity structures, Littrow and Littman configurations use diffraction grating for optical feedback and mode selection [8, 9, 10]. A compact, tunable MEMS ECDL was designed, and a tuning range of about 40 nm in the C-band with a linewidth of less than 50 kHz was achieved [11]. Of course, the grating parameters such as grating reflectivity and grating resolution also have an effect on the tunable range and linewidth [12]. In addition, the interference filter shows a very good performance of wavelength selection [13]. An interference filter with a multimode bandwidth is employed in an external cavity diode laser, and the laser can be tuned over 14 nm with a narrow linewidth of 26 kHz [14]. In addition, the interference filter can be used alone in the external cavity system for the wavelength selection and can reduce the sensitivity of the wavelength [15,16]. Although narrow bandpass filters provide an alternative approach to the wavelength selection, they limit the tuning range of lasers.  
In this paper, we design and present a unique tunable external cavity structure. We employ diffraction grating and an interference filter to achieve a narrow linewidth output. The interference filter is placed between the lens and the grating. By adding the filter, the SMSR is improved due to filtering out the fluctuation of the noise floor. The interference filter can suppress the potential competition mode and improve the spectral purity of the main mode. In this way, the linewidth is narrowed. The diffraction grating is for the wavelength selection and optical feedback. Based on the external cavity structure of ECDL, it achieves an ultra-narrow linewidth output of 13.6 kHz. Meanwhile, the ECDL has a wide tunable range of 40 nm at an injection current of 200 mA by adjusting the diffraction grating. During the tuning process, there may be a mode-hopping phenomenon. Additionally, the SMSR throughout the tuning range is measured. The highest SMSR is 57 dB at a wavelength of 1555 nm.