esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Integrated Electric Brake System  
The IEB system introduced in this study is a single box, as shown in Figure 1, and consists of a hydraulic control unit (HCU), electronic control unit (ECU), and motor assembly. It replaces the existing hydraulic booster with a motor and integrates the electronic stability control system and ABS module into one box [21; 22; 23; 24].  
Figure 2 presents the operating sequence of the electric brake system. The IEB system primarily contains a motor, worm gear, worm wheel, pinion gear assay, rack gear, and a pump. When the driver steps on the pedal and sends a signal to the stroke sensor, the ECU receives the stroke sensor signal and sends the operation signal to the motor. The braking force was transmitted to the vehicle caliper via the gear and pump.  
Figures 3 and 4 illustrate the topology and winding arrangement of the base motor used to operate the developed integrated electric brake system. It has an 8-pole 12-slot surface permanent magnet structure, and the winding is composed of four parallel concentrated windings.  
Figure 1: Integrated electric brake system.  
Figure 2: Operating sequence of the integrated electric brake system.  
The specifications of the base model are listed in Table 1. Maximum power at high speeds is crucial for ensuring rapid braking performance in electric brake systems. The characteristics of the torque constant are also an important design factor for the continuous braking operation of an electric brake system.  
If the motor fails, the vehicle cannot brake because the IEB system operates based on the motor. This is a problem that significantly affects driver safety, particularly for autonomous vehicles. Therefore, it is necessary to develop a dual drive system composed of two or more components so that the system can operate even if the motor or inverter fails. The DWM is a representative motor of a dual drive system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Unit** & **Base Model** \\ \hline Number of slots/poles & - & 12/8 \\ Outer diameter of stator & mm & 72 \\ Stack length of stator & mm & 34 \\ Mechanical airgap & mm & 0.5 \\ Steel sheet of stator/rotor & - & 50 PN470 \\ Line to line resistance & m\(\Omega\) & 21.5 \\ Max. voltage & V & 12 \\ Max. current & A & 120 \\ Min. torque constant & Nm/A & 0.03 \\ Max. power & W & 900 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the base model developed for integrated electric brake systems.  
Figure 4: Winding topology of the base model.  
Figure 3: Configuration of developed a base model.  
When designing a DWM based on a base model that has already been developed, it is advantageous in terms of price, as it can utilize existing manufacturing tools. In this study, to minimize the development cost, a DWM was implemented by changing only the number of winding series and parallel circuits from the existing developed base model.
# Ferrite-Loaded Spidron Fractal Loop VHF Antenna for UAV Applications
###### Abstract  
In this letter, an electrically small Spidron fractal loop antenna operating in the VHF band is proposed. The ferrite material, which consists of a nickel-zinc combination, is loaded into inside of the loop antenna to increase the gain of the antenna in the low frequency band. To minimize the magnetic loss of the ferrite in the high frequency band, the amount and configuration of the ferrite are optimized using a genetic algorithm. Through this optimization step, the amount of the ferrite is decreased to 37.5% and the gain of the antenna in the high frequency band is improved. The size of the proposed antenna is \(0.0242\times 0.0242\times 0.0051\,\lambda_{L}^{3}\) at the lowest operating frequency. The proposed antenna was fabricated to verify the performance, and the simulated and measured results are in good agreement. The measured peak gains varied from \(-31.6\) to \(-1.9\) dBi within the measured frequency band. To examine the performance of the proposed antenna mounted on an unmanned aerial vehicle model (UAV), the antenna on a UAV was also simulated and the results were discussed. The simulated realized peak gains of the antenna on the UAV and on flat ground are similar.  
f 2020; 10, 5392; doi:10.3390/app10155392
## 4 Network Communication
### Channel Characterization
#### 4.1.5 Routing Layer
Once it exceeds a predefined maximum hop count, ALTR will apply the shortest hop-routing protocol as an alternative protocol to forward packets to the destination as soon as possible. Reference [100] proposes the Least Total-Route-Temperature (LTRT) protocol. It calculates routes and transmits packets by the single-source shortest path algorithms, such as the Dijkstra algorithm. However, the weight in graph theory is denoted by temperature of nodes in order to detour the nodes of high temperature. A detailed comparison of BSN routing protocols is shown in Table 3.
## 4 Measurement Results
The results indicate that the proposed phase shifter achieved a highly competitive performance compared to the other works. This is attributed to the innovative technique employed in the design, which enabled the phase shifter to attain a figure of merit (FoM) that is surpassed only by [11]. This phase shifter has a much higher gain due to the inclusion of an active balun that dominates the gain. Despite this, our circuit displays the second-best RMS phase error and performs similarly to other works with respect to RMS gain error, even though no additional amplifiers were included in the design. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **This Work** & [33] & [19] & [11] & [36] & [37] & [10] \\ \hline \multirow{2}{*}{**Tech.**} & \(130\) nm & \(250\) nm & \(65\) nm & \(180\) nm & \(180\) nm & \(130\) nm & \(130\) nm \\  & SiGe & SiGe & CMOS & SiGe & SiGe & CMOS & CMOS \\ \hline \multirow{2}{*}{**Freq. (GHz)**} & \(17.8\)–\(20.2\) & \(8\)–\(12\) & \(15\)–\(38\) & \(6\)–\(18\) & \(8\)–\(12\) & \(12\)–\(18\) & \(6\)–\(18\) \\ \hline \multirow{2}{*}{**No. of bits**} & \(6\) & \(6\) & \(6\) & \(5\) & \(5\) & \(6\) & \(4\) \\ \hline \multirow{2}{*}{**Gain (dB)**} & \(0\) & \(-2.5\)* & \(-1.7\) & \(19.5\)* & \(1.75\)* & \(1\)* & \(-0.2\) \\ \hline \multirow{2}{*}{**RMS phase error (*)**} & \multirow{2}{*}{2.36} & \multirow{2}{*}{2} & \multirow{2}{*}{3.5} & \multirow{2}{*}{5.6} & \multirow{2}{*}{4.6} & \multirow{2}{*}{4} & \multirow{2}{*}{10} \\  & & & & & & & \\ \hline \multirow{2}{*}{**RMS gain error (dB)**} & \multirow{2}{*}{1.46} & \multirow{2}{*}{6.4} & \multirow{2}{*}{1} & \multirow{2}{*}{1.1} & \multirow{2}{*}{0.6} & \multirow{2}{*}{0.9} & \multirow{2}{*}{1.7} \\  & & & & & & & \\ \hline \multirow{2}{*}{**Pdc (mW)**} & \(82\) & \(110\) & \(19.2\) & \(61.7\) & \(73.9\) & \(37.5\) & \(8.7\) \\ \hline \multirow{2}{*}{**Core area (mm\({}^{2}\))**} & \multirow{2}{*}{1.23} & \multirow{2}{*}{1.65} & \multirow{2}{*}{0.16} & \multirow{2}{*}{0.27} & \multirow{2}{*}{0.6} & \multirow{2}{*}{0.24} & \multirow{2}{*}{0.14} \\  & & & & & & & \\ \hline \multirow{2}{*}{**FoM \({}^{\ddagger}\)**} & \multirow{2}{*}{1.82} & \multirow{2}{*}{0.56} & \multirow{2}{*}{1.26} & \multirow{2}{*}{7.43} & \multirow{2}{*}{1.42} & \multirow{2}{*}{1.53} & \multirow{2}{*}{0.32} \\  & & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Comparison of state-of-art active VSSPs. Figure 10: Measured frequency response: (**a**) phase-shift, (**b**) S21, (**c**) S11, and (**d**) S22.
# Failure Detection for Semantic Segmentation on Road Scenes Using Deep Learning
## 1 Introduction  
In recent years, with deep learning breakthroughs on vision applications [1; 2; 3; 4], autonomous driving vehicle technology has been commercialized. Especially, the convolutional neural network (CNN) [5; 6; 7; 8; 9; 10] shows an outstanding performance in several core technologies and has achieved novel performance in various computer vision tasks such as classification [11; 12; 13; 14; 15] and object detection [16; 17; 18; 19]. In particular, the semantic segmentation task [20; 21; 22; 23] is indispensable in autonomous driving systems which gives detection and identification information of objects.  
Based on vision methods, advanced driver assistance systems make autonomous driving possible. The National Highway Traffic Safety Administration [24] categorizes five levels of developmental stages of autonomous driving technology:  
* Level 0: The driver performs all operations.
* Level 1: Some functions are autonomous, but the driver's initiative is required.
* Level 2: Many of the essential functions are autonomous, but driving still requires attention.
* Level 3: This is an autonomous driving stage, but, when a signal is given in an unexpected situation, the driver must intervene.
* Level 4: This is an autonomous driving stage that does not require a driver to board.  
These levels can be divided into two main stages: Levels 0-2 represent the autonomous driving assistance stage and Levels 3-4 represent the fully autonomous driving stage. To achieve a Level 3 autonomous driving system, notifying the driver when an unexpected situation occurs is necessary, which needs failure detection system. In this paper,we focus on detecting failure case on semantic segmentation which is the main vision recognition system.
## 4 Experiments
### Few-Shot ASC Performance
In some cases, the absence of explicit prototypes might lead to less efficient representations, especially in limited labeled data. This simplicity and efficiency make ProtoNet SOTA in few-shot ASC and it is used in various challenges like few-shot animal sound event detection (DCASE). However, we found that ProtoNet might not capture fine-grained task-specific knowledge because ProtoNet's nearest-neighbor classification is based on class prototypes. To address this, we propose a novel task-adaptive MAML that transforms parameter initialization according to task-specific knowledge for more accurate adaptation to new tasks. This fine-grained task-specific learning improves classification accuracy, especially for classes with subtle differences. To sum up, by customizing parameter initialization according to task-specific knowledge, TAPT addresses key limitations of MAML and  
Figure 4: Accuracy comparison of the ASC schemes on the ANAFCC dataset in (**a**) 1-shot and (**b**) 5-shot settings. Figure 3: Accuracy comparison of the ASC schemes on the BirdVox-14 SD dataset in (**a**) 1-shot and (**b**) 5-shot settings. ProtoNet, offering enhanced adaptability, fine-grained task-specific learning, efficient knowledge transfer, improved generalization, and balancing between complexity and flexibility. Due to these properties, quantitative results demonstrate that TAPT significantly outperforms comparison schemes in all settings. Overall, TAPT performed the best in all cases on both datasets, achieving relative gains of up to 20.32% and 8.69% on the BirdVox-14 SD and ANAFCC datasets, respectively, when compared to the second-best scheme. In summary, the original MAML showed serious limitations in ASC, and TAPT was able to achieve outstanding ASC performance by transforming the initial parameters of DeepASC more adaptively than MAML.
## 3 Preliminary and Model
### Traffic Degradation Image Restoration Model
#### 3.4.4 Experiment Details  
The pretrained model is based on Stable Diffusion 1.5. The framework employs Python 3.11 + PyTorch 2.3.1 + CUDA 11.8, with training conducted on two V100-32G GPUs. The Adam optimizer is adopted with a batch size of 32 images per batch, and input images are normalized to \(512\times 512\) resolution. The training process consists of two parts:  
First, image semantics encoder is trained with a learning rate of \(2\times 10^{-5}\) for 500 epochs. After training, its parameters are frozen and incorporated into TDIRM for the overall model training. The second part involves training the ControlNet module and U-Net denoising module in TDIRM. In the ControlNet module, parameters are initialized using the pretrained encoder and middle blocks from the U-Net of SDM. In TCE attention module, parameters are initialized with self-attention module weights from SDM. Training is performed with a learning rate of \(3\times 10^{-5}\) for 5000 iterations. The DDPM sampling method is employed with the timestep set to 150.  
Evaluation Metrics: The study employs two supervised metrics--PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index)--to quantify the similarity between restored and degraded images. Higher PSNR and SSIM values indicate greater similarity. Additionally, NIQE (natural image quality evaluator) is adopted to assess the naturalness and perceptual quality of restored images, where a lower NIQE score signifies more natural-looking results.  
Beyond these three metrics, the evaluation also includes mAP, precision, and recall to measure improvements in vehicle detection accuracy before and after image restoration.
## 4 The Proposed Detection System
### Architecture and Design  
The proposed system was designed to be able to collect and process images generated from different data sources. In turn, a specific architecture was conceived to be flexible enough to allow replacing or adding new algorithms without significant effort. To assure efficiency, the concurrent processing of multiple data sources must be supported. The processes applied on the images are independent of each other and they adapt correctly to a chain pattern [15; 20]. Furthermore, all the applied algorithms are able to take as input the output of their previous immediate(s). The aforementioned schema is modeled by the pipes and filters architecture [28].  
Taking into account the previous comments and the review of related works, an architecture based on _pipes_ and _filters_ was proposed for the developed system. The system consists of two main modules. The _Recognition and Tracking_ module is responsible for detecting and monitoring pedestrians (objects of interest); the _Pattern Detection_ module analyzes the results of the first module to detect patterns based on (recent) historical information. Both modules support multiple concurrent executions using separate processes. The Pattern Detection module supports processing multiple unrelated data sources. The system also includes three auxiliary modules: _control panel_, _instance launcher_ and _events generator_. The Control Panel module graphically shows the received events and sends command orders (which refers to requested actions) to the Instance Launcher. The Instance Launcher is responsible for the creation of Recognition and Tracking module instances, as separate processes. Finally, the Events Generator receives the information of detected patterns and injects them back to the Control Panel. The system modules and the exchanged information are described in Figure 1.  
Advanced Message Queuing Protocol (AMQP) protocol is used for communications between modules. AMQP is an open and secure protocol that guarantees delivery on time (or the consequent expiration), uniqueness, and correct ordering of messages, and also data integrity. The following subsections describe each module of the system.
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 3 Approach
### Reference Pressure Extraction
#### 3.3.3 Magnetic Signal  
The magnetic sensor can detect disturbances of the ambient magnetic field, as a result of steel elements inside the walls of a building. Hence, the intensity of the magnetic field can be used as an indicator for identifying the OITransition [24]. In this section, we introduce a process for the identification of the OITransition by measuring the disturbances of the magnetic field. For the identification of OITransitions, we combine a Gaussian filter and the moving window standard deviation. In the following example, we selected a magnetic dataset from the collected data [20] from four of our buildings. The corresponding magnetic signal is shown in Figure 8:  
The route that corresponds to the signal shown in Figure 8 begins outdoors, followed by four indoor transitions and four outdoor transitions. Towards the end of the time interval, the third outdoor transition occurred when exiting the fourth building.  
In the first step, disturbances in the signal were found using the moving window standard deviation with a window size of 20 samples along the time axis. The resulting signal is shown in Figure 9 (orange line). Once disturbances were identified, a second moving standard deviation extraction was applied to the new generated signal. This time, the window size corresponded to 200 samples. The result is illustrated in Figure 9 (purple line).  
Figure 7: Frequency of GPS uncertainty from data collected from outdoors (a), indoors (b) and during an OITransition.  
In the third step, a Gaussian filter was applied to the resulting signal in order to smooth it with a kernel of 500 samples. As can be seen in Figure 9 (red line), this contributed to the identification of the four blobs that correspond to the duration--one by one--of the indoor walking activities. They then could be used to distinguish indoors from outdoors.  
Finally, in order to enable binary classification between indoor and outdoor areas, a moving STD was performed, followed by another Gaussian filtering step. The resulting signal was then used to determine the start and end of the indoor areas. The final classification can be seen in Figure 10 (black line), where the value \(1\) corresponds to indoors and 0 corresponds to outdoors.
## 4 APSP and Routing Tables
### Distributed Quantum Searches  
With this intuition in mind, we now state two useful theorems of [7] for distributed quantum searches. Let \(X\) denote a finite set throughout this subsection.  
**Theorem 3**.: _Let \(g:X\to\{0,1\}\), if node \(u\) can compute \(g(x)\) in \(r\) rounds in the CONGEST-CLIQUE model for any \(x\in X\), then there exists an algorithm in the quantum CONGEST-CLIQUE that has \(u\) output some \(x\in X\) with \(g(x)=1\) with high probability, using \(\tilde{\mathcal{O}}(r\sqrt{|X|})\) rounds._  
This basic theorem concerns only single searches; however, we need a framework that can perform multiple simultaneous searches. Let \(g_{1},\ldots,g_{m}:X\to\{0,1\}\) and  
\[A_{i}^{0}:=\{x\in X:g_{i}(x)=0\},A_{i}^{1}:=\{x\in X:g_{i}(x)=1\},\forall i\in [m].\]  
Assume that there exists an \(r\)-round classical distributed algorithm \(C_{m}\) that allows a node \(u\) upon an input \(\chi=(x_{1},\ldots,x_{m})\in X^{m}\) to determine and output \((g_{1}(x_{1}),\ldots,g_{m}(x_{m}))\). In our use of distributed searches, \(X\) will consist of nodes in the network, and searches will need to communicate with those nodes for which the functions \(g_{i}\) are evaluated. To avoid congestion, we will have to carefully consider those \(\chi\in X^{m}\) that have many repeated entries. We introduce a notation for this first. Define the quantity  
\[\alpha(\chi):=\max_{I\subset[m]}|\{\chi_{i}=\chi_{j}\quad\forall i,j\in I\}|,\]  
as the maximum number of entries in \(\chi\) that are all identical.  
Next, given some \(\beta\in\mathbb{N}\), assume that in place of \(C_{m}\), we now have a classical algorithm \(\tilde{C}_{m,\beta}\), such that upon input \(\chi=(x_{1},\ldots,x_{m})\in X^{m}\), a node \(u\) outputs \(g_{1}(x_{1}),\ldots,g_{m}(x_{m})\) if \(\alpha(\chi)\leq\beta\) and an arbitrary output otherwise. The following theorem summarizes that such a \(\tilde{C}_{m,\beta}\) with sufficiently large \(\beta\) is enough to maintain a quantum speedup as seen in the previous theorem:  
**Theorem 4**.: _For set \(X\) with \(|X|<m/(36\log n)\), suppose there exists such an evaluation algorithm \(C_{m,\beta}\) for some \(\beta>8m/|X|\) and that \(\alpha(\chi)\leq\beta\) for all \(\chi\in A_{1}^{1}\times\cdots\times A_{m}^{1}\). Then, there is a \(\tilde{\mathcal{O}}(r\sqrt{|X|})\)-round quantum algorithm that outputs an element of \(A_{1}^{1}\times\cdots\times A_{m}^{1}\) with a probability of at least \(1-2/m^{2}\)._  
The proof can be found in ([7] Theorem 3).
# Production of Biogas and Biomethane as Renewable Energy Sources: A Review
## 3 Results and Discussions
### Anaerobic Digestion and Co-Digestion
For example, the low concentration of organic load and ammonia production due to high nitrogen can cause the reduction in microbial activity, decreasing biogas production [113]. In addition, temperature and process type can be limitations for the net energy balance of biogas production; just as in the co-digestion of manure with lignocellulosic biomass, the degree of crystallinity of cellulose can slow down the hydrolysis step of DA and act as an inhibitor of anaerobic microorganisms [114,115]. Several technologies for raw biomass feedstock pretreatment have been proposed by researchers to overcome AD inconveniences and improve access to biomass for enzymes as well as bacteria [116]. Particularly, different techniques, such as thermal, chemical, biological and physical methods, are available to optimize the fermentation of animal manure. For example, it was found that cattle manure AD coupled with thermal hydrolysis pretreatment (THP) at 160 \({}^{\circ}\)C with 2% (dry wt.) sodium hydroxide (NaOH) addition produced a higher biochemical methane potential (BMP), 227.0 \(\pm\) 11.0 mL-CH4/g-volatile solid, with approximately 8.8% biodegradability, compared with the BMP of the intact cattle manure (CM) sample (182.2 \(\pm\) 2.5 mL-CH4/g-volatile solid) [117]. Furthermore, the CO2 emitted during the production of biomethane could represent a sustainable solution to the use of chemical solvents in the biomass preparation process, minimizing its emissions into the atmosphere. The use of this by-product can have advantages, such as the optimization of biogas production and the kinetics of the fermentation process in the bioreactor, resulting in an increase in the energy value of manure obtained from the raw material after CO2 pretreatment [118]. The potential of biogas produced through anaerobic digestion also depends on the type of manure used. In detail, the species, breed, age, body weight, feed of the animal can influence chemical and physical composition and thus the content of organic matter, carbohydrates and fats [107]. The ideal carbon-nitrogen balance for anaerobic digestion is in the range of 20:1 to 30:1 [119]. However, the use of animal waste (cattle, swine, poultry) as mono-substrate is not very convenient to produce biogas due to the low carbon ration and low efficacy [120]. Co-digestion is a preferable strategy to mono-digestion, because it improves the C/N ratio, nutrient supply and buffering capacity by overcoming the limitations of AD. By taking into account the synergistic effects of using various raw materials, process stabilization is positively influenced. Due to the positive synergies in the digestion medium and the required nutrient supply, the use of co-substrates increases biogas yields compared with the mono-digestion of the same substrates. Different co-digestion strategies are shown in Table 6.