esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault-Tolerant Tripod Gait Planning and Verification of a Hexopod Robot
## 3 Planning Method of F-TT Gait
### Phase Sequence Planning of F-TT Gait  
Based on the theory of the supporting triangle in Section 3.1, the gait phase sequence is planned in this section. As shown in Figure 4, because of the symmetry of the robot, the influence of a single leg fault on the leg distribution of a hexapod robot can be divided into two categories: one is that any one of the middle legs is damaged; the other is that any one of the front or rear legs is damaged. For these two kinds of faults, a unified gait description equation is established.  
Figure 4: Two forms of one leg failure of a hexapod robot.  
Figure 3: Six kinds of support triangles. (**a**) The triangle formed by legs on one side, (**b**) The triangle formed by two front legs and a middle leg, (**c**) The triangle formed by the front and rear leg on one side and the middle leg on the other side, (**d**) The triangle formed by two rear legs and a front leg, (**e**) The triangle formed by two middle legs and a front leg, (**f**) The triangle formed by the front and middle leg on one side and the rear leg on the other side.  
Some new symbols are defined in Table 2.  
The F-TT gait equation is based on the tripod gait of the non-fault hexapod robot. Gait function \(g\) for walking machines with \(n\) legs is defined as Equation (15):  
\[g=(\kappa_{1},\kappa_{2},\ldots,\kappa_{n};\phi_{1},\phi_{2},\ldots\phi_{n}) \tag{15}\]  
The F-TT gait phase equation is given by:  
\[\begin{array}{c}\phi_{\overline{Lf}}=\infty\quad\phi_{L1}=0\\ \phi_{\overline{L1}}=\frac{1}{3}\cdot\phi_{L2}=\frac{1}{3}\\ \phi_{\overline{L2}}=\frac{2}{3}\quad\phi_{L3}=0\\ \kappa_{\overline{Lf}}=0,\kappa_{L2}=\frac{1}{3},\kappa_{\overline{L1}}=\kappa _{Lj}=\frac{2}{3},\;i=1,2;j=1,3;\end{array} \tag{16}\]  
where \(\phi\) is the phase, \(\kappa\) is the duty factor, and the subscript is the symbol of the corresponding leg.  
The process of F-TT gait motion is divided into three stages. Five legs in good conditions are divided into three groups. Each time, one group is used in the transfer phase and the rest in the support phase. However, since one leg is broken, another leg needs to swing twice in the motion. The application of the F-TT gait planning equation in two kinds of fault situations will be elucidated.
# An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture
## 3 The Proposed 5G Procedure
### Data Delivery Procedure
#### 3.2.2 Mobile Host to Mobile Host  
As described in Figure 5, the data delivery procedure between the mobile host and the attached core network is the communication between two mobile hosts. First, UE sends data packets through a GTP tunnel to M-GW. A decryption process occurs in M-GW to identify the destination IP address. When the M-GW decides that the destination belongs to a different network, it sends the info request message to C.E. to acquire corresponding M-GW\({}_{\infty}\)IP address. Based on the C.E., the M-GW chooses a routing path to the target gateway based on its routing table. Once the M-GW\({}_{\infty}\) receives the data packet, a paging procedure occurs to identify the location of the corresponding U.E. After the paging procedure, a GTP tunnel is established to deliver data packets to the C.N.
## 4 Results and Discussion
### CGCW Performance Analysis
#### 4.2.2 Influence of the Hole Shape  
The influence of the hole shape on the breakthrough curve of the CGCW is shown in Figure 8. Two different hole shapes including circular and rectangular holes are analyzed. The \(H_{\rm G}\), \(r_{\rm G}\) and \(T_{\rm G}\) are assumed to be 3 m, 0.05 m and 0.0015 m, respectively. Compared with other hole shapes, the performance of the CGCW is better when the GMB holes are circular. For example, the breakthrough time for a circular hole is 1.1 times larger than that for a rectangular hole when \(k_{\rm G}\) = 10\({}^{-9}\) m/s. It is mainly because the leakage through a circular hole is lower than that through other shapes of holes as the shape factor for the circular hole is lowest. Additionally, it can be found that the influence of the hole radius and shape are similar. The same trend that the parameter impacts will be weakened when the impermeability of the CGCW is good. For example, the breakthrough time for the circular hole is only 0.9 years longer than that for the rectangular hole when \(k_{\rm G}\) = 10\({}^{-10}\) m/s.  
\begin{table}
\begin{tabular}{c c c} \hline
**Hole Shape** & **Empirical Equation** & \(\mathbf{R}^{2}\) \\ \hline Circular & \(Q=0.2769k_{\rm G}H_{\rm G}T_{\rm G}^{0.0462}T_{\rm G}^{0.0971\ln T_{\rm G}+0.8 235}\) & 0.99 \\ Triangular & \(Q=0.9572k_{\rm G}H_{\rm G}T_{\rm G}^{0.4854}T_{\rm G}^{0.0881\ln T_{\rm G}+0. 1419}\) & 0.91 \\ Rhombic & \(Q=0.1966k_{\rm G}H_{\rm G}T_{\rm G}^{0.2511}T_{\rm G}^{0.5161\ln T_{\rm G}+0. 4733}\) & 0.93 \\ Square & \(Q=0.3804k_{\rm G}H_{\rm G}T_{\rm G}^{0.5678\ln T_{\rm G}+0.054}\) & 0.91 \\ Rectangular & \(Q=0.7094k_{\rm G}H_{\rm G}T_{\rm G}^{0.7547}T_{\rm G}^{0.2761\ln T_{\rm G}+0. 6797}\) & 0.92 \\ \hline \end{tabular}
\end{table}
Table 2: Empirical equations to predict leakages for different shapes of holes.
## 2 Materials and Methods
### Application of the Attended Encoder-Decoder to the Short-Term Load Forecasting
#### 2.4.4 Online Training  
A Piecewise Learning of the Underlying Function  
The standard training approach as applied for example in natural language processing consists of a choice of representative training and validation data. The goal is to learn the approximation of the underlying function in one training procedure and to use the model for a longer period. The validation data are used to control the progress and the quality of the training. Such holistic training procedures can be quite a time-consuming one depending on the complexity of the function to be learned expressed in the network architecture and the number of training data required. Additional problems arise if there is not enough representative training data available or there is a sudden change in the underlying function which is not captured in the chosen training data. In load forecasting, holidays and abnormal days (very cold or very hot) are usually underrepresented and there is no simple method to create artificial data without prior knowledge of the underlying function. Due to the increasing number of renewables and prosumers, load patterns are subject to further unexpected changes. Therefore, there must be a possibility to train the network fast with a limited amount of training data.  
In the presented approach, transfer learning [25; 26] in combination with online training is being used to cope with time-consuming training procedure, the insufficient amount of training data especially for weekends and hot days, and changing load patterns. Figure 7 shows an overview of the training and inference procedure.  
Figure 7: Overview of the applied algorithm for training and inference.  
The preprocessed training data are loaded. If the forecast for the next day's load power shall be executed, the most similar days are picked. If available, the weights from the previous day are loaded into the model. The encoder-decoder model is trained for 50 epochs in one batch using the ADAM optimizer. Each training data related to the day \(day_{j}\) is weighted according to the Euclidean distance to the time series under prediction \(day_{i}\) as calculated by Equation (13) shown in (19).  
\[w_{j}^{train}=100*|\frac{max(d(day_{i},day_{1n}))-d(day_{i},day_{j}))}{max(d(day_ {i},day_{1n}))}| \tag{19}\]  
No validation data are used but an early stopping criterion is applied if the value of the loss function falls below the threshold of 0.0001. The approximated piece of the underlying function follows the data in the training data set. The prediction results are decoded and returned. The weights are stored so that they can be reused as a starting point for the next training. The data of the forecasted day is added to the training data set. Using this approach, the training time is distributed in small chunks on each day to be forecasted. Additionally, the most recent data can be easily included in the training procedure capturing the most recent changes in the load pattern.
## 5 Conclusions  
ATR-FTIR spectroscopy was successfully applied to study human bone samples from several burials of the Garamantian necropolis of the Fewet oasis in SW Libya. Spectral results suggest that the analyzed bone samples underwent mild diagenesis, which was confirmed by comparing the data with analogs of modern ruminant bones from the same area. In particular, human bones underwent a higher rate of carbonate loss relative to phosphate loss and were degraded mainly because of a loss of proteins and collagen. Among all the bones studied, samples H-716M and H-976M were more affected by diagenesis, as they were the most recrystallized, and showed the lowest amounts of organic components (amide I/PO\({}_{4}\) ratio), carbonate groups relative to phosphates (A\({}_{1412}\)/A\({}_{1024}\) ratio), and FWHM\({}_{603}\) indexes. Moreover, sample H-716M shows the highest MM (A\({}_{1024}\)/A\({}_{1091}\) ratio) value.  
Interestingly, a correlation was found with the burial site. In fact, the samples from the BA1 location showed low values of the IRSF index, whereas those from the BA2 location displayed high values of the A\({}_{1024}\)/A\({}_{1091}\) ratio, the FWHM\({}_{603}\), and IRSF indexes, and, lastly, the samples from the BA3 location exhibited high values of the A\({}_{1412}\)/A\({}_{1024}\) ratio and the FWHM\({}_{603}\) index. The relationship between most of the IR data of the human bones and burial areas showed that the bones from the lower levels of the necropolis generally exhibited a higher rate of hydroxyapatite recrystallization, i.e., a higher diagenetic impact. Moreover, the human bones displayed lower amounts of organic matter relative to phosphates and IRSF values. The FWHM\({}_{603}\) and MM indexes, along with the C/P ratios, did not distinguish between the human and modern ruminant bones, which suggested that both underwent early diagenesis. In contrast, the lower IRSF values of the human bones made it possible to distinguish them from those of modern ruminants.  
Lastly, the comparison of the IR data of cis-posed Fewet human bones with those of coeval Meroitic individuals from the Al-Khiday (Sudan) cemetery, who lived under similar climatic conditions but were inhumated after death, showed different patterns of bone diagenesis. In fact, Fewet bones displayed comparatively lower values of the IRSF, FWHM\({}_{603}\), and C/P ratios, likely because of the lack of significant ion exchange between the bone and the soil.  
**Author Contributions:** Conceptualization, F.C. and U.M.; formal analysis, E.G. and V.N.; resources, L.M. and M.A.T.; data curation, V.N.; writing--original draft preparation, F.C., E.G. and V.N.; writing--review and editing, F.C., E.G. and V.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Universita di Roma \"La Sapienza\", grants project \"Grandi Scavi\" through Prof. [PERSON].  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We thank [PERSON] for her precious support in ATR spectra collection.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Co-Efficient Vector Based Differential Distributed Quasi-Orthogonal Space Time Frequency Coding
## 3 Co-Efficient Vector Differential Procedure
### Quasi-Orthogonal Code Construction  
We now show how the elements of the co-efficient vector \(\overline{v}_{k+1}\) were rotated to ensure quasi-orthogonality. Given that the stream of \(\Gamma m\) input bits were mapped into \(\Gamma\) symbols denoted by \(v_{i},i=1,2,\ldots,\Gamma\), the symbols were then combined as \(v_{1}=v_{1}+\widetilde{\mathrm{v}_{3}}\), \(v_{2}=v_{2}+\widetilde{\mathrm{v}_{4}}\), \(v_{3}=v_{1}-\widetilde{\mathrm{v}_{3}},v_{4}=v_{2}-\widetilde{\mathrm{v}_{4}}\), and so on. Let \(\Phi=\mathbf{D}\cdot diag\left[1,e^{i\theta_{1}},\ldots,e^{i\theta_{(\Gamma/2 )-1}}\right]\), where \(\mathbf{D}\) was a \((\Gamma/2)\times(\Gamma/2)\) Hadamard matrix, the information symbols were then constructed as \([v_{-}(k+1)(1),v_{-}(k+1)(3),\ldots,v_{-}(k+1)(\Gamma-1)]T=\Phi\cdot[v_{-1},v _{-}3,\ldots,v_{-}(\Gamma-1)]T\) and \([v_{-}(k+1)(2),v_{-}(k+1)(4),\ldots,v_{-}(k+1)(\Gamma)]T=\Phi\cdot[v_{-2},v_{ -}4,\ldots,v_{-}\Gamma]T\). Thus, \([v_{k+1}(1),v_{k+1}(2),\ldots]^{T}\) were mapped onto a signal constellation \(\mathcal{A}\) of size \(2^{m}\) while \([v_{k+1}(3),\allowbreak v_{k+1}(4),\ldots]^{T}\) were mapped onto a signal constellation \(\mathcal{A}_{r}\), which was a rotated version of \(\mathcal{A}\). The rotation angles \(\theta\) of the information symbols ensured that the codes achieve full diversity, see Chapter 5 of [28] for further explanation on constellation rotation.
# Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches
## 2 Methodology
### Vein Image Acquisition
#### 2.2.2 Design Parameters  
Several design parameters were investigated to determine the optimum illumination source and structural factors in vein detection systems that optimize the visualization. The critical parameters must be fine-tuned to improve the visualization process of the vein to achieve the highest image quality. The parameters of interest are: (i) wavelength, (ii) LEDs arrangement, (iii) presence of diffuser, and (iv) square LED light source arrangement. Other parameters that are set to be constant in this experimental setup are average luminance intensity, exposure time, and distance of the light source. The experimental code name was assigned according to the following criteria:  
Method: Hessian (H), Contrast (C)  
Region of Interest (ROI): Palm (P), Arm (A)  
Wavelength: 720 nm (72), 760 nm (76), 850 nm (85), 900 nm (90), 940 nm (94)  
LEDs Arrangement: Square LED (M), Ring LED (R)  
Square LED Arrangement: 3 \(\times\) 3, 4 \(\times\) 4, 5 \(\times\) 5, 6 \(\times\) 6  
Number of LEDs: 8, 9, 16, 25, 36  
The inclusion of diffuser: Without diffuser (WO), With diffuser (W)  
Table 3 below presents some of the code-name for the sample image obtained:
## 2 Related Work
The authors in [18] utilized the formula of the spatial correlation coefficient by combining (i) the characteristics of a multi-beam satellite channel and (ii) the scatter distribution to set up a realistic random channel model and addressed the distribution of scatterers near the receiver antenna in mobile-satellite communication systems, which causes the multipath signals to correlate. Last,but not least, the authors in [19] derived a theoretical formulation to clarify the correlation characteristics differences in different fading settings to fade space diversity and adaptive array antennas. It is important to note that satellite communication operating at higher frequencies (i.e., above 10 GHz) is typically line-of-sight, as there are usually no nearby scatterers at the satellite or the Earth station to create multipath and, thus, achieve fully independent paths. As a result, the authors in [20] reported that they used the downlink channel as a reference for the transmit and receive spatial correlation, with the former referring to the space segment and the latter referring to the system's ground segment. The comprehensive literature review thus recognised the critical significance of incorporating spatial correlation in the signal model for satellite communication, which is often overlooked in simulations and analyses for simplicity. Despite its inherent complexity, considering spatial correlation is crucial as it enables the accurate capture of the real-world behavior of satellite communication systems, leading to more realistic and reliable DoA estimation results.
## 1 Introduction
Based on the proposed LDR-HDR framework, the RLUNet model features the Reverse Lossy Network, which includes four Image Pipeline Module (IPM) blocks and three Compression Artifact Adjustment Module (CAAM) blocks designed to reconstruct HDR content. The IPM block is divided into three components: Recovery of Overexposed Regions (ROR), Dequantization (DQ), and Reconstruction of Linearized Mappings (RLM), which are responsible for the recovery of overexposed areas, dequantization, and linearized mapping, respectively. Meanwhile, the CAAM block focuses on mitigating compression artifacts, with its output characteristics utilized to aid in the final reconstruction of HDR content. From the second and fourth columns in Figure 1, it is clear that the Reverse Lossy Network effectively extends the dynamic range of the image by inverting the lossy operation, obtaining a reconstruction closer to HDR images in the overexposed regions, and alleviating the artifacts and chromatic aberration problems. Besides, the Texture Filling Module (TFM) block has been integrated to support the Luminance Guidance Module (LGM) block in determining both the invalid and valid masks. The invalid mask directs the partial convolution process in the Recovery of the Overexposed Regions (ROR) module, thereby enhancing the visual performance and detail of the overexposed areas in the reconstructed HDR image. Meanwhile, the valid mask aids the Spatial Feature Transform (SFT) layer within the Dequantization (DQ) module to effectively expand the image's dynamic range. Figure 1 illustrates that our method recovers more detailed texture information within overexposed regions (e.g., the curtain edges and reflected sunlight details in the first and third columns), highlighting the effectiveness of the integration of the Texture Filling Module (TFM) and the Recovery of Overexposed Regions (ROR) module in the retrieval of lost details in overexposed regions. Our main contributions are summarized as follows:  
* The RLUNet is designed to balance dynamic range expansion and recover lost content by reversing the lossy operations inherent in the imaging pipeline principle. * The Texture Filling Module (TFM) is used in conjunction with the Recovery of Overexposed Regions (ROR) to enhance the detail texture of the overexposed areas. * The experiments demonstrate that the proposed RLUNet model outperforms various state-of-the-art methods on different testsets.
# A Novel Design Methodology and Numerical Simulation of BLDC Motor for Power Loss Reduction
## 2 Landsman Converter-Based Power Loss Reduction in BLDC Motor Drive  
The BLDC drive, based on the bridgeless landsman PFC converter, is built for PFC in low-power appliances. The BLDC motor speed can be directly tuned using the utilization of DC bus voltage. Electronic switching of the BLDC motor is used to minimize the switching-related loss in six IGBT inverters, enabling a small switching frequency. The single-phase AC supply to the bridgeless landsman converter. The built-in power factor enhancement of the AC supply is performed by the Landsman converter with a bridgeless PFC configuration designed for DCIM operation. Input inductor current during DCIM operation is discontinuous during the switching period.  
The Landsman converter operates in Modes 1, 2, and 3. Figure 1 shows the Landsman converter-based power loss reduction. In Mode 1, the supply input inductor energy and the energy stored is transferred to the second capacitor as soon as the switch is ON. The secondary capacitor value is available as a continuous voltage during operation. In Mode 2, the output inductor begins to discharge the supply current while the DC link side inductor and the second capacitor are charged during this operation as soon as the switch is OFF. In Mode 3, the converter operates in DCM, where the input inductor is fully discharged and the input current is zero. The voltage of the second capacitor is continuously reducing and the DC bus side inductor current is increasing. In this mode, the landsman converter output is a DC supply to the VSI. The input AC supply is given to the landsman converter. This landsman converter converts the AC voltage into DC voltage. This DC voltage is provided to the inverter of the source voltage. The VSI converts a DC supply into a 3-phase AC supply. The output of the voltage source inverter is then supplied to a balanced 3-phase load such as BLDC. The conducting period is 180 degrees. The inverter consists of two input and output terminal filters to remove the harmonics. A Hall Effect Sensor is the device used to measure BLDC motor speed by sensing the rotor position. The current and voltage sensors are the devices used to calculate the current and voltage. The output of the Hall Effect sensor, the voltage sensor, and the current sensor is provided to the analog for the digital converter. Then, the digital signal of the ADC is amplified and the amplified signal is given to the microcontroller using an embedded C coding program which is burned into the IC ATMEUTA 328 P by using the ARDUINO compiler. The 5V power supply is provided as a reference voltage to the controller. The MOSFET driver is used in the output of the controller. The 12V power supply is given to the MOSFET. The output of the MOSFET or feedback is supplied to the landsman converter and VSI, which is used to maintain the power factor at unity by controlling the MOSFET switches.
## 3 Results and Discussion
### Influence of Ambient Conditions--Surrounding Temperature and Ventilation (Parameter Sweep Study)
#### 3.3.1 Influence of Ambient Temperature  
The ambient temperature (AT) has a strong influence on the use/charge cycle, as highlighted in Figure 5a with the simulated ambient temperatures of 10, 20, 30, and 40 \({}^{\circ}\)C.  
Figure 5: Parameter variation study with heat-conductive polymer I highlights the strong influence of ambient temperature and wind speed: (**a**) Variation in ambient temperature (10, 20, 30, 40 \({}^{\circ}\)C). (**b**) Variation in convection coefficient (1, 5, 10, 20 W/m\({}^{2}\) K). Upper part: temperature of central cell, lower part: state of charge of cell.  
At all ambient temperatures, the cell temperature quickly rises during the use phase to 70.8 \({}^{\circ}\)C (10 \({}^{\circ}\)C AT), 71.6 \({}^{\circ}\)C (20 \({}^{\circ}\)C AT), 72.4 \({}^{\circ}\)C (30 \({}^{\circ}\)C AT), and 73.1 \({}^{\circ}\)C (40 \({}^{\circ}\)C AT), the maximum temperature.  
At low temperatures (10 \({}^{\circ}\)C AT), the cooling system can handle the created heat well and quickly cools down to the charging start temperature, which is reached after 68 min. During the charging process, the temperature slowly increases, but the cell stop temperature is not reached. The cells are fully charged without interruption after 119.5 min (with a charging time of 51.5 min). The maximum temperature after use was 70.8 \({}^{\circ}\)C.  
Moderate temperatures (20 \({}^{\circ}\)C AT) lead to a maximum temperature of 71.6 \({}^{\circ}\)C, and the charging start temperature is reached after 85 min. The configuration just misses the threshold to charge without interruption--with one interruption, the fully charged state is reached after 153 min (a 68-min charging time).  
Higher ambient temperatures of 30 and 40 \({}^{\circ}\)C significantly reduce the effectivity of the convective heat transport from the surface of the battery pack. As a consequence, the packs cool down slower, and the charging start temperature is reached after 87 and 135.5 min. During charging, the cells heat up quickly, and the stop temperature is reached once (30 \({}^{\circ}\)C, AT) and three times (40 \({}^{\circ}\)C AT) until the cells are fully charged after 159 min and 291 min (79.5 and 179 min charging times), respectively. Obviously, the charging process is significantly prolonged. However, the maximum temperature after use is only few degrees higher (72.4 and 73.1 \({}^{\circ}\)C) than that at lower ambient temperatures. This underlines the strong influence of the thermal mass of the cooling system on the maximum temperature. The ambient temperature is of minor significance, at least at low convection coefficients.