esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Implemented Micro-Modem  
The building blocks of our micro-modem developed by the abovementioned design methodology are shown in Figure 8. The subfigures represent the digital board, analog transmission board and analog reception board, respectively.  
The three boards are assembled and stacked to constitute the modem hardware as shown in Figure 9(a). The bottom layer is the digital board which is responsible for digital signal processing. It is equipped with SPI and UART ports to interwork with other devices and a regulator module to generate 3.3 volts.  
Figure 8: (**a**) Digital board; (**b**) Analog transmission board; (**c**) Analog reception board.  
Figure 7: Symbol structure for the transmission of 1.  
Figure 9: (**a**) Modem hardware; (**b**) Omni-directional transducer.  
The middle layer is the analog transmission board. Besides the circuitry of analog signal processing for transmission, it is also equipped with a regulator producing 12 volts in order to supply power to several analog elements. The top layer is the analog reception board in charge of analog signal processing for reception.  
Figure 9(b) shows the spherical transducer to be connected to the modem hardware shown in Figure 9(a). The transducer has a resonant frequency of 70 kHz and a diameter of 34 mm. Owing to its spherical structure, it provides an omni-directional beam pattern tridimensionally. Its \(TRV\) is 147 dB re 1 uPa/V at 1 m and \(RR\) is \(-200\) dB re 1 V/uPa. The maximum input power is restricted to 190 W for normal operation.  
The implemented micro-modem is operated by an external general-purpose lithium-ion battery of 14.8 volts, consuming 4.5 watts. As mentioned before, the supply voltage is converted to 12 volts and 3.3 volts through regulators for the operation of two analog boards and the digital board, respectively.
# Application of Machine Learning Models for Baseball Outcome Prediction
## 2 Method
### Machine Learning Methods
The search space for each model was designed based on domain expertise and commonly recommended values from prior studies (Table 2). For logistic regression, we applied L2 regularization (penalty = 'l2') with inverse regularization strength C \(\in\) {0.001, 0.01, 0.1, 1, 10}, using the lbfgs solver and max_iter = 1000. The decision tree model explored max_depth \(\in\) {3, 5, 7, 10} and min_samples_split \(\in\) {2, 5, 10, 20}. The Random Forest classifier was tuned with n_estimators \(\in\) {100, 200}, max_depth \(\in\) {5, 10, 15}, and min_samples_split \(\in\) {2, 10}, with bootstrapping enabled. For XGBoost, the tuning space included n_estimators \(\in\) {100, 200}, learning_rate \(\in\) {0.01, 0.1, 0.2}, max_depth \(\in\) {3, 6, 9}, subsample and colsample_bytree \(\in\) {0.6, 0.8, 1.0}, and regularization parameters reg_alpha and reg_lambda \(\in\) {0, 0.1, 1}. Early stopping was applied based on validation performance. LightGBM adopted a similar tuning scheme with num_leaves \(\in\) {31, 63}, max_depth \(\in\) {-1, 5, 10}, and early stopping enabled to prevent overfitting. The Artificial Neural Network (ANN) comprised one hidden layer with 64 neurons using ReLU activation and a dropout rate of 0.3. It was optimized with the Adam optimizer, learning_rate \(\in\) {0.001, 0.01}, and batch_size \(\in\) {32, 64} and trained for up to 50 epochs with early stopping (patience = 10). All models were evaluated using the same five-fold cross-validation splits to ensure fair and consistent comparison.
# miRNA-Gene Association Prediction Method Based on Deep Learning Model
## 4 Discussion  
### Dataset  
In bioinformatics, biological information is often used to create networks and sequences. While sequence information on several miRNAs and genes is plentiful, there is a lack of network-related information. Our research goal was to improve the prediction performance of deep learning models, so instead of reducing the dataset, we tried to generate as much relevant data as possible. Therefore, we used only sequence information in this study, not network information. We hypothesized that a model trained on extensive miRNA-gene relationship data would have superior generalization performance and would be able to capture the characteristics of each miRNA and gene.  
We also conducted additional experiments to determine the impact of a negative dataset on the performance of deep learning models. Keeping the 358,864 positive data points constant, we substituted the negative data points with random pairs that were not present in the positive set in equal numbers. The AUC for the model trained with random negatives was 0.91, which is 0.07 lower than that achieved when using our established criteria for negative data (see Table 9). This suggests that our method of labeling negatives based on the three distance criteria improves model performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rank** & **miRNA** & **Gene** & **Support** \\ \hline
1 & hsa-miR-503-5p & _BRCA2_ & \\
2 & hsa-miR-6499-3p & _BRCA2_ & \\
3 & hsa-miR-125a-3p & _BRCA2_ & \\
4 & hsa-miR-665 & _BRCA2_ & \\
5 & hsa-miR-27b-3p & _BRCA2_ & \\
6 & hsa-miR-4442 & _BRCA2_ & \\
7 & hsa-miR-6081 & _BRCA2_ & \\
8 & hsa-miR-4478 & _BRCA2_ & \\
9 & hsa-let-7e-5p & _BRCA2_ & TargetScan \\
10 & hsa-miR-6836-5p & _BRCA2_ & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Top 10 candidate miRNAs associated with _BRCA2_ from the Unknown Data Pool.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Negative Set Method** &
\begin{tabular}{c} **Three Distance-Based** \\ **Filtered Negative** \\ \end{tabular} & **Random Negative** \\ \hline AUC & 0.98 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance changes due to negative dataset differences.  
Additionally, we present the distribution of the positive and negative data used in this study in a PCA (principal component analysis) three-dimensional space, as shown in Figure 7.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 9 Results and Future Work  
### Results  
An initial protocol test-bed was constructed, and an example showing the use of an update message was demonstrated at the 2016 3 rd IEEE World Forum on the Internet of Things. This implementation consisted of an IoT C2 system: using the Mosquito broker; a Raspberry Pi 3 as an example device, alongside an example device-side daemon; and a C2 server. Both the daemon and server were implemented using the SRUP library, C++ components and Python scripts, with the interprocess communication handled using Apache Thrift [31]. Although not formally measured, the performance of this system was more than adequate. A short video demonstrating the update messages and the C2 interface was produced. [32]  
A packet capture showed that the most complex message-type measured (an update initiate message) consisted of 424 bytes of SRUP data (consisting of a 33-byte URL, a 64-byte SHA-256 digest and an 8-byte target; plus the message signature, transaction token and other protocol overheads), sent as 493 bytes of MQTT traffic. Even this message type (the most complex, consisting of more fields than any other message type) could easily be delivered over even an unreliable or slow Internet connection. The adoption of MQTT QoS 1 (to provide guaranteed delivery of the message) adds an additional  
Figure 6: An example of an identity phrase generated using a word-list.  
MQTT PUBACK message (consisting of just 60 bytes total, when using Internet Protocol Version 4 (IPv4)).  
The cryptographic functions used to support the SRUP protocol are also highly efficient in terms of performance, even on relatively low-power hardware. The implementation tested thus far, on a Raspberry Pi 3 and using OpenSSL to provide both the SHA-256 digests and the RSA signatures, was more than adequately executed. In a test case, signing and verifying a representative SRUP initiate message, using a Raspberry Pi 3 and averaged over five runs, signing took an average of 56.68 ms, and verifying the signature took just 9.91 ms.  
Work continues to provide a complete reference implementation of the SRUP protocol, as described in this paper, in the form of a C++ library.
## 3 Results and Discussion
### Detection of IGF-1 in Bovine Colostrum Powders  
After the multi-step pretreatment of bovine colostrum powders, real samples were measured by both the traditional and fluorescent sandwich ELISA methods. Since the LOD of the fluorescent sandwich ELISA method is much lower than that of the traditional ELISA method, real samples were diluted five times and two times, respectively, to obtain similar results, which further proved the high sensitivity of the fluorescent sandwich ELISA method. As shown in Table 2, an IGF-1 content of about 1125.00 ng/g (CV = 3.81%) was detected via the fluorescent sandwich ELISA method, and 1026.56 ng/g (CV = 3.12%) of IGF-1 was detected by the traditional ELISA method in comparison. The very similar values reveal the accuracy of the proposed fluorescent sandwich ELISA method. Recovery tests were also carried out to further demonstrate the availability of the proposed fluorescent sandwich ELISA method. We added 1, 5, and 15 ng/mL of IGF-1 standards to five-fold-diluted bovine colostrum samples and the solutions were measured by the fluorescent sandwich ELISA method. The recovery rates were in the range of 91.71-102.32% (CV < 5.54%), and the final calculated IGF-1 content was about 1096.68 ng/g. CV values could be further decreased when maintaining a constant environmental temperature and humidity and, moreover, avoiding photobleaching caused by long-term exposure to light. For comparison, a similar standard addition method was used to detect IGF-1 concentration by the traditional ELISA method. Since the linear range and LOD are much higher in traditional ELISA than fluorescent sandwich ELISA, lower dilutions (two-fold-diluted bovine colostrum samples) and larger amounts of standards (25, 60, and 80 ng/mL of IGF-1 standards) were applied in traditional ELISA. The average recoveries were in the range of 100.69% to 109.75% with a CV of 2.50% to 5.89%, and the IGF-1 content was calculated as about 1087.72 ng/g. The similar contents between the two methods demonstrate that the proposed fluorescent sandwich ELISA method had high accuracy, precision, and reproducibility in the detection of IGF-1. Furthermore, the good recovery results further prove  
Figure 6: OD values of IGF-1 in bovine colostrum powders of three pretreatments based on traditional ELISA method.  
that the pretreatment of bovine colostrum with Method III effectively avoided the influence of the matrix in the colostrum powder, thus ensuring that IGF-1 was specifically recognized. However, the specific detection of IGF-1 in other milk foods still remains challenging due to differences in bioactive component profiles compared to bovine colostrum and potential amino acid sequence variations in IGF-1 from different sources [40]. These factors limit the use of the proposed multi-step pretreatment and fluorescent sandwich ELISA.
## 5 Experimental Signal Analysis
### Ring Gear Fault--Experimental Signal Analysis  
The effect of ring gear wear on the gearbox is determined using the theoretical derivation. Figures 26-28 show time domain waveform and the frequency domain spectra of the frequency domain for the health and wear signals for different input speeds and output loads. In all the cases, the frequency component \(f_{m}\) is dominant, so the frequency of planetary gear wear is observed in the sidebands in the enlarged Fourier spectrum for the health signal.  
Figure 26: Waveform for a broken ring gear, Fourier spectrum and enlarged Fourier spectrum for an input speed of 1200 rpm and a load of: (**a**) 32 N-m, (**b**) 56 N-m and (**c**) 80 N-m.  
Figure 25: Waveform for a broken planet gear, Fourier spectrum and enlarged Fourier spectrum at an input speed of 3000 rpm and a load of: (**a**) 32 N-m, (**b**) 56 N-m and (**c**) 80 N-m.  
Figure 28: Waveform for a broken ring gear, Fourier spectrum and enlarged Fourier spectrum for an input speed of 3000 rpm and a load of: (**a**) 32 N-m, (**b**) 56 N-m and (**c**) 80 N-m.  
Figure 27: Waveform for a broken ring gear, Fourier spectrum and enlarged Fourier spectrum for an input speed of 2100 rpm and a load of: (**a**) 32 N-m, (**b**) 56 N-m and (**c**) 80 N-m.  
Compared to the health signal for 1200 rpm and loads of 32 N-m, 56 N-m and 80 N-m, the meshing frequency (\(f_{m}\)) is 216 Hz and the rotating frequency for the planet carrier (\(f_{H}\)) is 2 Hz. For a vibration signal for 2100 rpm and loads of 32 N-m, 56 N-m and 80 N-m, the meshing frequency (\(f_{m}\)) is 378 Hz and the rotating frequency for the planet carrier (\(f_{H}\)) is 3.5 Hz. For the vibration signal for 3000 rpm and loads of 32 N-m, 56 N-m and 80 N-m, the meshing frequency (\(f_{m}\)) is 540 Hz and the rotating frequency for the planet carrier (\(f_{H}\)) is 5 Hz.  
The vibration signal for ring gear wear has different frequency domain spectra, as shown in Figures 26-28. The spectral peaks occur at the same frequencies of \(f_{m}\pm kf_{H}\) (where \(k\ \in\ Z\)) as those for the health signal. In the enlarged Fourier spectrum for the wear signal, the sidebands at the frequencies of \(f_{m}\pm kf_{H}\) (where \(k\ \in\ Z\)) have significantly larger amplitudes than those for the health signal, so the gearbox is faulty at the ring gear.  
For the experimental signal analysis for faulty gears, there are 9 working conditions, which correspond to different speeds and loads. The results show that the speed has a higher sensitivity to vibration. For the fault frequency, the amplitude of the signals is larger, which validates the theoretical derivations for the vibration signal models and the characteristic frequency for faulty gear in a planetary gearbox.
## 5 Evaluation Results
### Results of System Feature Analysis  
Comparing system features on each of the services is associated with service configuration, pricing, and relevant system features such as memory and CPU. For the posed question RQ1, we successfully identified the key differences in system features between the serverless computing services. Table 2 compares the service configurations, pricing, and memory and CPU of the three services. These services offer different configurations and pricing models, making them suitable for different types of applications and use cases. While the application deployment used default resources on each of the three serverless platforms, the pricing model may vary if deployed in a different tier or with GPU resources.  
Cloud Run is Google's serverless computing platform that allows for the easy deployment of containerized applications. It is priced based on the number of requests and compute time used. The first 120 min of build time per day is free. This experiment used an instance with 0.5 GB of memory per container and a single CPU. The application was able to handle a high volume of requests without any performance degradation.  
AWS App Runner, on the other hand, does not offer free quotas. It costs around $0.064 per vCPU-hour and $0.007 per GB-hour. It supports two pricing models: a provisioned plan and an active container instance plan. The maximum number of instances for the service  
Figure 1: Visual representation of request latency, CPU usage, and API response time concepts.  
is limited to 25, but the maximum concurrency is 200, compared to 100 for Cloud Run. The maximum concurrency for an instance of the AWS App Runner can be found during the configuration stage of the service. We tried setting a large value on the configuration page and found the system suggesting 200 as the maximum value allowed for the selected instance. Thus, the value 200 was added. The maximum memory size available for a container is 12 GB, while Cloud Run has a capacity of 34 GB. App Runner also has a maximum CPU capacity of 4, while Cloud Run has 8.  
Microsoft Container Apps is a relatively new service launched in May 2022 [47]. It offers two pricing options: one based on resource utilization and one based on a fixed pricing plan. The free-tier quotas are the same for both Cloud Run and Container Apps. Container Apps support scaling to zero if the scaling setting is not based on CPU utilization. The maximum number of instances supported is 300, which is higher than the other two services. In this test, a configuration of 1 CPU core and 2 GB of memory was used for the container. There was a good amount of documentation available about getting started with the hosting service, but due to the recent release of the service, there were not many external resources available about the service.
# A Truly Second-Order and Unconditionally Stable Thermal Lattice Boltzmann Method
## 4 Numerical Tests
### 2D Natural Convection in a Square Cavity
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameters** & **Grid** & \(\mathbf{11^{2}}\) & \(\mathbf{301^{2}}\) & \(\mathbf{351^{2}}\) & \(\mathbf{401^{2}}\) & \(\mathbf{451^{2}}\) & **Reference**[23; 24; 25] \\ \hline \multirow{2}{*}{\(Nu_{1/2}\)} & STLBM & 305.29 & 26.737 & 29.610 & 29.774 & 29.875 & \\  & USTLBM & 322.15 & 29.744 & 29.874 & 29.958 & 30.014 & 30.225–30.301 \\ \hline \multirow{2}{*}{\(u_{\max}\)} & STLBM & 1730.3 & 551.59 & 294.05 & 297.02 & 302.28 & \\  & USTLBM & 1799.6 & 312.71 & 317.23 & 319.76 & 321.27 & 321.37–323.65 \\ \hline \multirow{2}{*}{\(v_{\max}\)} & STLBM & 1919.2 & 2503.2 & 2199.3 & 2206.7 & 2212.3 & \\  & USTLBM & 2010.4 & 2219.6 & 2220.1 & 2221.6 & 2222.0 & 2222.0–2222.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of flow parameters in \(Ra=10^{8}\) at different mesh sizes. 301 \(\times\) 301 is within 0.02%, while the relative error of STLBM at the same mesh size is 12.66%. These results evidently prove that, although in the same order of accuracy, USTLBM is more accurate than STLBM at the same mesh size.
# A Web-Based BIM-AR Quality Management System for Structural Elements
###### Abstract  
This paper investigates quality management (QM) during the execution phase of structural elements by proposing, developing, and testing a complete framework by integrating building information modeling (BIM) and augmented reality (AR) technology. QM during execution is boosted by BIM-AR integration through a dedicated web-based system aimed at reducing the occurrence of omissions and negligence. With such a system, efficiency is improved by allowing the entering of inspection data directly in a shared digital environment, where people involved in QM have permanent access to updated information and inspection results, clearly organized, and entered in real time. The system has been developed in the asp.net framework using C# language where, by generating a web-based checklist and establishing its link to AR, it can enhance the process of information extraction from industry foundation class (IFC) 4D BIM models and the recording of inspection data. A test has been performed on a real case study in Budapest, to assess the effectiveness of the system in the field. Results demonstrate the following benefits brought by such a type of QM system: improved understanding of the design, access to information, and overview of the quality status of the project, leading to reductions in defects and reworking, as well as improved and quicker response and decision-making.  
Keywords:Building Information Modeling; process improvement; construction management; information and communication technologies; Augmented Reality +
Footnote †: journal: Applied Sciences
# Source Symbol Purging-Based Distributed Conditional Arithmetic Coding
###### Abstract  
A distributed arithmetic coding algorithm based on source symbol purging and using the context model is proposed to solve the asymmetric Slepian-Wolf problem. The proposed scheme is to make better use of both the correlation between adjacent symbols in the source sequence and the correlation between the corresponding symbols of the source and the side information sequences to improve the coding performance of the source. Since the encoder purges a part of symbols from the source sequence, a shorter codeword length can be obtained. Those purged symbols are still used as the context of the subsequent symbols to be encoded. An improved calculation method for the posterior probability is also proposed based on the purging feature, such that the decoder can utilize the correlation within the source sequence to improve the decoding performance. In addition, this scheme achieves better error performance at the decoder by adding a forbidden symbol in the encoding process. The simulation results show that the encoding complexity and the minimum code rate required for lossless decoding are lower than that of the traditional distributed arithmetic coding. When the internal correlation strength of the source is strong, compared with other DSC schemes, the proposed scheme exhibits a better decoding performance under the same code rate.  
distributed source coding; arithmetic coding; context model; purging Article
# An Effective Algorithm for Finding Shortest Paths in Tubular Spaces
## Appendix A
### Proof of Remark 1
As \(\mathbf{Y_{m}}\
ot\in\partial\Omega\), we infer that \(S_{f}\) is not the farthest visible cross-section by \(\mathbf{X}\) (\(\mathbf{X}\) can see farther with the line of sight through \(\mathbf{Y_{m}}\)). Thus, there is only one visible point \(\mathbf{Y}\) in \(S_{f}\) that can be seen by \(\mathbf{X}\), and \(\mathbf{XY}\) is the correct direction of the tendon according to Corollary 2. Moreover, we can demonstrate that \(\mathbf{XY}\) is also the longest length of sight from \(\mathbf{X}\). One can easily confirm that \(\mathbf{XY}\) must be tangent to \(\partial\Omega\) at a point \(\mathbf{T}\) of the cross-section \(S_{T}\). Let \((\mathbf{\alpha})\) be the corresponding tangent plane. Let \(\Omega_{v}\) be the space enclosed by \(\partial\Omega\), \((\mathbf{\alpha})\), and the cross-section containing \(\mathbf{X}\) as illustrated in Figure 11. Then, \(\Omega_{v}\) contains all the visible points by \(\mathbf{X}\) of \(\Omega\) located behind the cross-section \(S_{T}\). The problem now is to prove that \(\overline{XY}\) is the longest length of sight in \(\Omega_{v}\). As the tube does not overlap itself, we obtain: \(\mathbf{XY}\geq\mathbf{TY}\geq 2R\). Thus, one can confirm that \(\Omega_{v}\) is totally contained by the sphere \((\mathbf{\chi})\) center \(\mathbf{X}\) and the radius \(\overline{\mathbf{XY}}\). We then have \(\overline{XY}\) as the longest length of sight from \(\mathbf{X}\). It is evident that every point on the ESP must belong to one of the three partitions (\(\mathbf{P1}\): see \(\mathbf{\Omega}\); \(\mathbf{P2}\): see \(S_{end}\), but not see \(\mathbf{\Omega}\); and \(\mathbf{P3}\): not see \(S_{end}\)) and as the correct direction is unique for each position, if the VP follows the proposed correct direction throughout its journey, its moving path will describe the ESP. (Q.E.D).