esa-sceva/satcom-chunk-collection huggingface dataset:
# Hourglass 3D CNN for Stereo Disparity Estimation for Mobile Robots
## 3 Methodology
### Hourglass 3D CNN Architecture  
To aggregate the feature information from the disparity spatial dimensions, AAFS [12] includes a cascaded architecture for the 3D CNN to regularize the cost volume. Such a 3D CNN consists of five \(3\times 3\times 3\) convolutions followed by batch normalization and ReLU, except for the final layer. Since AAFS [12] aims to achieve a tradeoff between accuracy and computational cost, it attempts to not increase the number of feature maps in the cascaded  
Figure 2: Attention-aware feature extractor.  
3D CNN. In our work, an hourglass 3D CNN is proposed to replace the cascaded 3D CNN to improve the tradeoff. The architecture of the proposed 3D CNN is shown in Figure 3, with a more detailed definition of each layer proposed in Table 1. Our proposed architecture allows the model to effectively learn more contextual information, leading to accuracy improvements and maintaining a low computational cost for real-time applications on mobile devices.  
As shown in Table 1, **F** features that represent the number of additional dimensions are set to 8, 4, and 4 for stages 0, 1, and 2, respectively. In a cascaded 3D CNN, the additional dimension is kept the same, with a stride of one. Thus, to increase accuracy, the additional dimension needs to be increased, which leads to a large increase in the computational cost because the same resolution is maintained. An hourglass 3D CNN is based on an encoder-decoder architecture, which reduces the required amount of computation by compressing the spatial dimension of cost volume and learning more feature dimensions, then upsampling back to the original dimension of cost volume in the decoder. Specifically, we employ two sequential 3 \(\times\) 3 \(\times\) 3 convolutions with a stride of one followed by batch normalization and ReLU; the number of feature maps is **F** for the cost volume. Then, we compress the spatial dimension of volume by four times with two levels of the encoder. In each encoder level, two sequential 3 \(\times\) 3 \(\times\) 3 convolutions are applied, and the stride of the first layer is two. This results in the spatial dimension of the volume decreasing by a factor of two, whereas **F** is doubled. As a result, the size of the cost volume decreases by four times, and the number of feature maps is **4F**. In addition, resolution loss decreases spatial accuracy and fine-grained details. To tackle this problem, we use two residual connections, as shown in Figure 3, to retain higher-frequency information. Then, we apply two 3D transposed convolutions with a stride of two; and the number of feature maps is 2F and F, respectively. Finally, 3 \(\times\) 3 \(\times\) 3 convolutions in series with a stride of one and a single-feature output in the last layer are applied to predict densities. This results in a final, regularized cost volume with a size of _disparity_\(\times\)_height_\(\times\)_weight_.
## 3 Results
### Viridiflorol Has Antifungal Properties  
To study whether SiTPS is involved in a fungal defense mechanism, we performed an in vitro growth assay using viridiflorol, the main terpene produced by SiTPS. Spores from the plant pathogen _Colletotrichum truncatum_ (syn. _Colletotrichum capsici_) isolate CP2304 were added in the wells of a microtiter plate, containing liquid medium supplemented with viridiflorol in different concentrations. The compound inhibited germination of spores and consequently growth of _C. truncatum_ at a concentration of 100 \(\mathrm{\SIUnitSymbolMicro mL}\) and higher, whereas the respective methanol controls did not affect at all fungal growth (Figure 6).  
Figure 5: Proposed biosynthetic pathway leading to the production of viridiflorol. Viridiflorol biosynthesis starts with a 1,10-closure of either _E,E_-FPP (route a-Clade I, e.g., SiTPS) or (3_R_)-NPP (route b-Clade II, e.g., AaVS) to form a germacradienyl cation (_E,E_ or _Z,E_, respectively). Two cyclization events (e.g., 1,11 and 2,6 closure) and additional reactions (e.g., addition of a molecule of water) result to formation of viridiflorol.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 6 Conclusions  
Aerial remote sensing based on UAVs is a new field for inspection of equipment in electrical industry, as a result of the ergonomics and flexibility of the new platforms and their high capacity access in inhospitable areas. This work put forward some artificial vision techniques applied to the exploitation of information provided for the different sensors. In this study, the performance of a low cost positioning system for aerial platforms was analyzed. Results show that the location achieves a very high precision with respect to transmission lines, with 1.5 and 27 cm as the worst cases in laboratory and field tests, respectively. In addition, the system can operate under different lighting conditions and on GNSS-denied areas. The system was tested over distribution grid located in agricultural environments. There are still other tests to be performed to have a complete guarantee. Experiments showed very good performance in the platform positioning, behaving similarly in all experiments and within expected and suitable ranges for this application. Although the system hasbeen specially designed for UAVs, its operating system is flexible to add on to other platforms, since it does not depend on the platform type. Moreover, the computational cost was suitable for controlling a UAV. The absence of such commercially available technology will lead the authors future work to design, develop and test more efficient hardware and accurate processing algorithms.  
Conceptualization, O.M., M.P. and F.A.C.; Methodology, O.M.; Software, O.M.; Validation, O.M., R.G. and F.A.C.; Formal Analysis, O.M.; Investigation, O.M., R.G, M.P. and F.A.C.; Resources, M.P. and F.A.C.; Data Curation, O.M.; Writing--Original Draft Preparation, O.M. and F.A.C.; Writing--Review and Editing, F.A.C.; Visualization, O.M.; Supervision, M.P. and F.A.C.; Project Administration, F.A.C.; and Funding Acquisition, F.A.C. and O.M.  
This work was supported in part by the Advanced Center of Electrical and Electronic Engineering AC3E (CONICYT/FB0008), CONICYT-PFCHA/Doctorado Nacional/2015-21151338 and DGIIP-UTFSM Chile.  
We would like to thank the Universidad Tecnica Federico Santa Maria and the Advanced Center of Electrical and Electronic Engineering AC3E.  
The authors declare no conflict of interest.
## 3 Results  
ETS Factors Show a Wide Range of Expression in Thyroid Cancer Specimens but Remain Comparable across Thyroid Cancer Types  
To assess which ETS factors are relevant in the control of _TERT_ promoter in thyroid cancer specimens, we adopted an unbiased approach, evaluating their reported binding preferences and expression patterns in thyroid tumors and cell lines (Figure 1). We first compared the known consensus binding sequences for each of the 27 factors in the ETS family [25] against the de novo sites created by TPMs in the gene promoter (5'-CCGGAA-3'; underlined adenines are created by G > A mutations (reverse strand) at _TERT_-124 and-146). ETS factors from classes III and IV showed binding preferences (5'-GGA-3' and 5'-GGAT-3', respectively) that likely render them less dependent on de novo sites created by TPMs (Figure 1, top).  
We then leveraged transcriptomic data from The Cancer Genome Atlas (TCGA) analysis of PTC [9] as well as our own evaluation of PDTC, ATC, and thyroid cancer cell lines [8; 20]. As shown in Figure 1, seven ETS factors, including FEV, ETV2, ELF5, and all from classes III (SPI1, SPIB, and SPIC) and IV (SPDEF), were not expressed in thyroid specimens, ruling them out from having any role in _TERT_ promoter control in thyroid cells. Conversely, factors such as ETS1 and ETV5 showed high and consistent expression across thyroid cancer types, whereas most ETS proteins, including GABPA, were expressed at intermediate levels. Overall, these analyses narrowed the list of candidate ETS factors regulating _TERT_ transcription in thyroid tumors down to 20 members.
# Convective-Microwave-IR Hybrid Drying of Kaolin Clay--Kinetics of Process
## 1 Introduction
[16] used the microwave-infrared method to dry eggplant; [PERSON] et al. [14] used the convection-microwave-infrared method to dry green peppers; [PERSON] et al. [15], [PERSON] et al. [17] and [PERSON] et al. [18] used the microwave-infrared-vacuum method to dry red chili, raspberry and zucchini, respectively. All authors showed a positive effect of the hybrid combination of drying techniques on the process time and the quality of the product. The aim of this work is to investigate how the use of hybrid drying techniques affects the kinetics of the process and the quality of the tested ceramic materials. For this purpose, the drying of a kaolin cylinder using convection, convection-microwave, convection-infrared and convection-microwave-infrared heating was experimentally tested. Seven different processes were tested. As a result of the research, it was shown that the most effective method results from the combination of all three energy sources; meanwhile, due to the quality of the product, the energy should be supplied periodically. The novelty of the work is the use of a hybrid drying technique, which combines the classic convection technique with modern microwave and IR technology. As a result of the research, it was found that the best method of drying is the use of all three energy sources simultaneously, with microwaves and IF used periodically. This method of drying ensures shortening of the process time, reduction in energy consumption and appropriate product quality.
# Challenge-Response Pair Mechanisms and Multi-Factor Authentication Schemes to Protect Private Keys
## 5 Discussion: Recovery Protocols in Real-Life Implementation
### Secure Recovery When the Terminal Device Is Defective or Lost  
In real-life applications of PKI such as crypto wallets protecting a portfolio of cryptocurrencies, the loss of the crypto wallet can have catastrophic consequences when the private keys signing the transactions and blockchains are lost. The protocols presented in this paper enable the use of multiple terminals with the same set of multi-factor authentication schemes. Users can enroll several devices such as smartphone, laptop, and desktop with their password, token, biometric image, and virtual token. Users can decide whether to use the same password, and random numbers RN\({}_{1}\) and RN\({}_{2}\) in all terminal devices, or to use different passwords and random numbers for each terminal device. With such a setup, users can plug in their MFAs and enter their passwords on any of these terminal devices to sign a new crypto transaction. As long as at least one of the terminal devices is functional, users are able to have access to the private key. The user experience could be similar to that offered by a cold wallet that can operate offline, or a hot wallet directly interacting with the cloud.
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## 1 Introduction
### Paper Organization  
The remainder of this paper is organized as follows: Section 2 meticulously delineates the system model. The formulation of the successful transmission probability and average throughput for both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, utilizing WiFi and mmWave technologies, is elaborated in Section 3. Subsequent analytical performance evaluation, employing Matlab-based Monte Carlo simulations, is comprehensively discussed in Section 4. The paper concludes with key findings and insights in Section 5.
## 5 Hardware Deployment
### Software Framework PYNQ  
The criteria for choosing a software framework for FPGA is to reduce the design effort in hardware coding and to get easy access to reusable components in FPGA. PYNQ (Python Productivity for Zynq) is an open-source, Python-based framework that can be deployed on all Xilinx SoC devices. PYNQ features a software stack that resides on the PS portion of the Zynq RFSoC device and facilitates user interaction via a network connection and a standard web browser, e.g., chrome [32]. In addition, the PYNQ framework incorporates a monolithic pre-configured bitstream that contains built-in libraries and integrated Intellectual Properties (IPs) to support a range of hardware peripherals, easing the burden of non-FPGA experts and software programmers with little to no hardware expertise [33]. The key features of the PYNQ framework are listed below:  
* Linux or Ubuntu-based operating system that provides an easy-to-use platform for developers to harness the advantage of power and flexibility of FPGA acceleration.
* Pre-installed Python libraries and integrated drivers to develop embedded systems faster using high-level Python-based programming.
* Development tools such as 'Jupyter Notebook' and Pre-built overlays for far more accessible features. The notebook provides a user-friendly interface to code and testalgorithms. The pre-built overlays provide access to the overlay functions for common tasks with the hardware accelerators.  
Various FPGAs are configured with the PYNQ framework and a Yocto-based tool flow, like the low-cost Zynq architecture PYNQ-Z2 and PYNQ-Z1. Both are low-cost development boards designed explicitly for FPGA-based neural network deployment. After going through all the technical detail, we decided to use PYNQ-Z2 FPGA, shown in Figure 4, an attractive embedded platform for several reasons as below:  
* Higher Processing and Performance: It features a dual-core ARM Cortex-A9 processor, which provides high performance for complex pre-processing on the input data [34] and manages workload more efficiently
* Flexibility and Customizability: The PYNQ-Z2 board provides a highly flexible and customizable platform for neural network deployment to optimize performance, improve accuracy, shorten inference time at lower power consumption [35], and customize neural network architectures for specific applications compared to using off-the-shelf hardware.
* Larger memory: The PYNQ-Z2 has 1 GB DDR3 RAM, making it more versatile and capable than the PYNQ-Z1 with 512 MB DDR3 RAM. The more significant onboard memory can be beneficial to store the data while working with large-scale data sets before feeding it into the neural network model. In addition, it provides a highly low-latency performance, a critical requirement for real-time NN applications [36].
* I/O options: The PYNQ-Z2 provides more I/O options to connect a wide range of external peripherals, such as medical equipment and user interface devices, for communication and easier integration with the FPGA with additional interfaces like SATA, PCIe and FMC connectors.
# Collision Risk Evaluation and Verification of GNSS-Based Train Integrity Detection
## 2 GNSS-Based Train Integrity Detection  
A typical GNSS-based TIMS consists of a ground monitoring center, Head-of-Train (HoT), and End-of-Train (EoT) units. An integrated train positioning system (in Figure 1), including a BDS, GPS, and inertial measurement unit (IMU), is employed to monitor the dynamic state of trains. EoT-HoT and train-ground communication are achieved by The General Packet Radio Service (GPRS) wireless channel. Messages from EoTs and HoTs are gathered by the train's integrity detection software in the ground monitoring center, where the train's integrity decisions are made.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Cause of the Accident**} & \multicolumn{2}{c}{**Quantity**} & \multicolumn{4}{c}{**Type of Accident**} \\  & **Count** & **Percentage** & **Collision** & **Derial** & **Other** \\ \hline Knuckle broken or defective & 466 & 14 & 93 & 318 & 55 \\ Coupler mismatch & 387 & 11.6 & 41 & 302 & 44 \\ Coupler draw head broken or defective & 672 & 20.2 & 55 & 585 & 32 \\ Coupler retainer pin/cross key & 412 & 12.4 & 18 & 355 & 39 \\ missing & & & & & \\ Draft gear/mechanism & 423 & 12.7 & 19 & 371 & 33 \\ broke/defect & 209 & 6.3 & 24 & 175 & 10 \\ Coupler shank broken/defective & 125 & 3.7 & 5 & 110 & 10 \\ Coupler shank broken/defectivel & 11 & 0.3 & - & 10 & 1 \\ Other coupler/draft system & 622 & 18.7 & 97 & 446 & 79 \\ Total & 3327 & 100 & 352 & 2672 & 303 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accidents or events related to train integrity caused by train coupler and traction devices.  
Figure 1: Structure of a typical GNSS-based TIMS.  
The ground monitoring center receives the train's location messages, such as traveling mileage, velocity, and direction, and the train's integrity and potential collision will be detected. Any warning would be sent to the managers and drivers to take safety action to avoid accidents. The TIMS should locate the train position, detect loss of train integrity (accidental train parting) and also point out the potential collision. The detailed timeline of a train's integrity detection under potential collision risk is illustrated in Figure 2.  
In the GNSS-based TIMS, an undetected train parting is a dangerous failure. When a limited safe time is exceeded, the undetected train breakage will lead to a rear-end collision. For the development of the safety-relevant GNSS-based TIMS, the safety evaluation and verification should be done based on a simulation in the system design phase.  
TIMS is based on GPS/BDS and integrated with an IMU sensor to realize train localization. The GNSS-based train position error is the absolute difference between the estimated position and the actual position in two dimensional space [1,2], while the protection level (PL) is the uncertainty of location estimation, extending in a bounded domain under a certain confidence probability [13,14], bound to the horizontal PL with a probability derived from the integrity requirement (see Figure 3). Then we can form a safe train position description.
## 4 Results and Discussion
### Fuel Economic
The changes induced by the weighting factor are more distinct compared to those resulting from battery capacity, as evidenced in Figure 5. It is clear that, when the weighting factor is set to 0.1, as shown in Figure 4(a), he output power of the FCS closely matches the demand power. This indicates that, at this weighting factor, the EMS prioritizes durability, ensuring that the fuel cell remains active to provide stable power and avoid entering unfavorable operational conditions while minimizing battery degradation before such conditions arise. As the weighting factor increases to 0.3 and 0.5, as shown in Figure 4(b),, a noticeable reduction in fuel cell output power occurs. In these scenarios, the battery plays a more significant role in alleviating dynamic power demands. When the weighting factor reaches 0.7, as depicted in Figure 4(d), fuel cell shutdowns begin to occur. This is because the EMS increasingly emphasizes economic efficiency, determining that recharging the battery via the fuel cell results in economic losses. Finally, when the weighting factor is set to 0.9, as shown in Figure 4(e), fuel cell shutdowns become more frequent. At this point, the system only seeks to meet the required power demand, without regard for whether the fuel cell is operating under unfavorable conditions. Figure 5: _Cont._
## 5 Analog Domain Processing  
### Analog Signal Processing at Transceiver  
The signal flow for data transmission is depicted in Figure 5. As shown, the main objective of analog signal processing is amplification with high power. The output voltage of the MCU after DAC is limited to 3 volts, however, by passing through the amplifier, an analog signal having a maximum 200 volts peak-to-peak is generated and applied to the transducer. The amplifier module is made up of power amplifier ICs and a transformer.  
Figure 4: Physical layer frame format.  
Figure 3: Block diagram of MCU.  
The functional diagram of a receiving modem is shown in Figure 6. Once acoustic waves detected at the transducer are transformed into electric signals, analog signal processing is conducted by passing in order through a pre-amplifier, bandpass filter, variable amplifier and envelope detector. Since the electric signal output from the transducer has a very low signal level on the order of a few millivolts, it is primarily amplified at the pre-amplifier with tens of amplification gain. Then, as the realization of non-coherent detection, bandpass filtering and envelope detection are conducted about the resonant frequency. In between them, a variable amplifier which is implemented by an analog IC is inserted in order to maintain the signal level steady for the envelope detection to be properly performed. It is possible to change the amplifier gain into one of the four gain values (1, 2, 4 and 8) with the help of a controlling signal from the MCU. Finally, the analog signal is supplied to the MCU for ADC and the reconstruction of data.
## 2 Preliminaries
### Problem  
We assume that each process \(P_{i}\in V\) maintains a variable \(\mathit{state}_{i}\in\{\mathsf{nCS},\mathsf{OutCS}\}\). For each configuration \(C\), let \(\mathcal{CS}(C)\) (resp., \(\overline{\mathcal{CS}(C)}\)) be the set of processes \(P_{i}\) with \(\mathit{state}_{i}=\mathsf{nCS}\) (resp., \(\mathit{state}_{i}=\mathsf{OutCS}\)) in \(C\). For each configuration \(C\) and each process \(P_{i}\), let \(\mathcal{CS}_{i}(C)\) (resp., \(\overline{\mathcal{CS}_{i}(C)}\)) be the set \(\mathcal{CS}(C)\cap(N_{i}\cup\{P_{i}\})\) (resp., \(\overline{\mathcal{CS}(C)}\cap(N_{i}\cup\{P_{i}\})\)). The behavior of each process \(P_{i}\) is as follows, where we assume that \(P_{i}\) eventually invokes entry-sequence when it is in the \(\mathsf{OutCS}\) state, and \(P_{i}\) eventually invokes exit-sequence when it is in the \(\mathsf{InCS}\) state.  
\(\mathit{state}_{i}:=(\text{Initial state of $P_{i}$ in the initial configuration $C_{0}$})\);  
**while true**  
\(\mathbf{if}(\mathit{state}_{i}=\mathsf{OutCS})\{\)  
Entry-Sequence;  
\(\mathit{state}_{i}:=\mathsf{nCS}\);  
/* Critical Section */  
\(\}\)  
\(\mathbf{if}(\mathit{state}_{i}=\mathsf{nCS})\{\)  
Exit-Sequence;  
\(\mathit{state}_{i}:=\mathsf{OutCS}\);  
/* Remainder Section */  
\(\}\)  
**Definition 1**.: _(The generalized local critical section problem). Assume that a pair of numbers \(l_{i}\) and \(k_{i}\) (\(0\leq l_{i}<k_{i}\leq|N_{i}|+1\)) is given for each process \(P_{i}\in V\) on network \(G=(V,E)\). Then, a protocol solves the generalized local critical section problem on \(G\) if and only if the following two conditions hold in each configuration \(C\)._  
* _Safety: For each process_ \(P_{i}\in V\)_,_ \(l_{i}\leq|\mathcal{CS}_{i}(C)|\leq k_{i}\) _at any time._
* _Liveness: Each process_ \(P_{i}\in V\) _changes_ \(\mathsf{OutCS}\) _and_ \(\mathsf{InCS}\) _states alternately infinitely often._  
We call the generalized local CS problem when \(l_{i}\) and \(k_{i}\) are given for each process \(P_{i}\) \"the generalized local \((l_{i},k_{i})\)-CS problem\".  
We assume that the initial configuration \(C_{0}\) is safe, that is each process \(P_{i}\) satisfies \(l_{i}\leq|\mathcal{CS}_{i}(C_{0})|\leq k_{i}\). In the case of \((l_{i},k_{i})=(0,1)\) (resp., \((1,|N_{i}|+1)\)), the initial state of each process can be \(\mathsf{OutCS}\) (resp., \(\mathsf{nCS}\)) because it satisfies the condition for the initial configuration. In the case of \((l_{i},k_{i})=(1,|N_{i}|)\), the initial state of each process is obtained from a maximal independent set \(I\) as follows; a process is in the \(\mathsf{OutCS}\) state if and only if it is in \(I\). Note that existing works for CS problems assume that their initial configurations are safe. For example, for the mutual exclusion problem, most algorithms assume that each process is in the \(\mathsf{OutCS}\) state initially, and some algorithms (e.g., token-based algorithms) assume that exactly one process is in the \(\mathsf{InCS}\) state and other processes are in the \(\mathsf{OutCS}\) state initially. Hence our assumption for the initial configuration is common for existing algorithms.