esa-sceva/satcom-chunk-collection huggingface dataset:
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 4 Experimental Setup
### Transmission  
In an attempt to reach the conditions of a real-world application, the implemented transmitter (TX) is build around the CILAOS LED panel from Holight [24]. The latter is specifically designed for indoor lightning and consists of 4 modules of 20 Surface-Mounted Device (SMD) LEDs connected in parallel to emit white light while a 30 \(\times\) 30 cm polycarbonate sheet ensures uniform light diffusion. In the case of LOS transmission, the center of the LED panel is positioned at a height of 120 cm, facing the camera; in the NLOS scenario, the light source is situated 130 cm above the ground, facing a white wall with an orientation of 30\({}^{\circ}\) relative to the wall's normal and at a distance of 110 cm from it. The  
Figure 14: Representation of the demodulated signal.  
Figure 15: Experimental setup in different configurations.  
OOK modulation of the emitted light is managed by the bulk controller for LED LM3409 from Texas Instruments [25], offering both analog and Pulse-Width Modulation (PWM) dimming to control the current powering the LED.  
In order to facilitate the distinction between high and low logical levels emitted by the source, and therefore enhancing the bright and dark strips in the image, PWM dimming (consisting of successively turning on and off the LED panel) was implemented here to drive the LED. The STM32H7 microcontroller controls the LM3409 switching state by generating the message that needs to be transmitted at a data rate of 4 kbps, consisting of a 10-bit header and a 40-bit payload. The different parameters of the transmitter are summarized in Table 1.
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 2 Related Work
### Dct  
The DCT, first proposed by [PERSON] in 1972, is a widely used transformation technique in signal processing and data compression. A DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT). It transforms a signal or image from the spatial domain to the frequency domain, but only with real numbers.  
In the 1D case, the forward cosine transform for a signal \(g(u)\) of length M is defined as:  
\[G(m)=\sqrt{\frac{1}{2}}\sum_{u=0}^{M-1}g(u)c_{m}\cos(\pi\frac{m(2u+1)}{2M}) \tag{24}\]  
For \(0\leq m<M\), and the inverse transform is:  
\[g(u)=\sqrt{\frac{2}{M}}\sum_{m=0}^{M-1}G(m)c_{m}\cos(\pi\frac{m(2u+1)}{2M}) \tag{25}\]  
For \(0\leq u<M\), with:  
\[c_{m}=\begin{cases}\frac{1}{\sqrt{2}}&for\ m=0,\\ 1&otherwise.\end{cases} \tag{26}\]  
The index variables (u, m) are used differently in the forward and inverse transforms. DCT is akin to the fast Fourier transform (FFT) but can approximate lines well with fewer coefficients [46]. The 2D DCT transform is equivalent to applying a 1D DCT vertically, then performing a 1D DCT horizontally based on the vertical DCT above.
## 5 Results and Discussion
### Trends to Be Further Explored
#### 5.3.3 Data Hierarchy and Ancillary Data  
Hierarchical classification approaches can also be developed in order to obtain maps at different thematic levels, enabling, for example, the evaluation of the cropping practices and crop types independently of the use of a priori cropland mask, discriminating more vegetation species [68; 83; 195]. This approach can potentialize the suitability of Red-edge bands for the decomposition of vegetation types [146; 162].  
LULC classifications performance can also be improved by including additional input variables [11], especially those above concerning the pixel level. Pixel enrichment with geographic objects is a promising application in this case [10]. However, providing analysis across a broad geographic scale is a great challenge, and multi-temporal segmentation methods are in development to solve it and to support LULCC analysis. Meanwhile, different methods that arrange different datasets about human activities (i.e., crop management practices) and geographic conditions are being considered to provide data beyond the pixel information, improving LULCC detection.  
Several studies have shown the importance of contextual ancillary variables such as topographic, textural, and climatic data to be used as metrics or features to provide information about the structure and morphology of landscape context [80; 196], mitigating spectral confusion among spectrally similar classes [197; 198]. [PERSON] et al. [197] remark the importance of topographic data to map LULC in Chile, a complex topographic region. In addition, for Chile, [PERSON] et al. [198] showed that a Digital Elevation Model and its derivatives (aspect, position index, and slope), water, snow, and cloud probabilities improved LULC classification. Incorporating texture metrics to increase the separability among different LULC types was highlighted by [121]. [PERSON] and [PERSON] [199] showed that incorporating textural metrics within an OBIA approach improved crop classification accuracies. Discriminating between different forest stages, [PERSON]. [150] demonstrated the importance of textural metrics of Red-edge and SWIR bands and associated VIs. [PERSON] et al. [17] reinforced the importance of texture as a subtle feature for differentiating cultivated lands from natural vegetation. [PERSON] et al. [21] showed the importance of downscaling to extract more accurate texture information. [PERSON] [116] employed textural and spectral homogeneity metrics in the FORCE framework approach. Texture metrics are applied in the OBIA approach because the traditional per-pixel based methods are not completely suitable for understanding landscape shape and context [80]. [PERSON] et al. [196] demonstrated that classification accuracies of heterogeneous areas, as Eastern Amazon, Brazil, can be improved by incorporating environmental variables (e.g., geological, pedological, climatic and topographic) in a model based on RF algorithm and L8/OLI spectral data.
# Off-Cloud Anchor Sharing Framework for Multi-User and Multi-Platform Mixed Reality Applications
## 3 Design and Implementation of the System
### System Design
#### 3.3.2 Anchor Synchronization Module  
A brief description of the algorithm designed to perform the synchronization and sharing of anchors between HoloLens 2 devices is presented in the following paragraphs and is depicted in the flow diagram in Figure 4.  
First, when a new device is connected to the system, if it is merely acting as a server, no actions would be taken, since it has no User Interface (UI) and no anchor (virtual attachment to the real world) is needed. When an MR client or MR host (i.e., a device acting as client and server at the same time) is connected, the following procedure is carried out:  
1. Load local anchor (either from the HoloLens anchor store or from a file in local memory). If this anchor exists and is loaded successfully, the process ends.
2. If there is no local anchor, two situations may occur: 1. If the device is acting as a host (server+client), a new anchor is created and stored locally (either in the anchor store or in memory in a file), and the process ends. 2. If the device acts as a client, a request is made to the server asking to send an anchor.
3. Once the request is received by the server, it prepares the anchor to be sent (if the size of the anchor is too large, the NetworkTransmitter component is used to divide it into smaller pieces and transmit them one at a time).
4. Finally, when the client receives the whole anchor, it is saved in an in-memory file and loaded into the scene (it is imported into Unity's internal anchor management system and stored in the local anchor store). The format in which the anchor is stored in local memory is a plain text file containing the serialized byte array that is generated after exporting the anchor using the MRTK libraries. After the anchor is loaded, the 3D model is moved to the anchor position in order to synchronize the scenes of two devices (server and client), so they can both see the 3D objects on the same physical position.  
Figure 4: Flow diagram shows the designed algorithm for when a new device connects to the system.  
To optimize performance and avoid unnecessary synchronization overhead, the system was intentionally designed to prioritize the use of local anchors stored on the HoloLens device. If a valid local anchor is found on the device, it is automatically loaded on the client, ignoring the need to request another version from the server. Thus, in cases where spatial inconsistencies are observed, users can manually trigger the synchronization process through an in-app menu option. This action would initiate the full synchronization protocol previously described, allowing for realignment across MR devices.
## 4 Experiments and Results
### Results and Analysis
#### 4.4.2 Experimental Results of a Knowledge-Based QA Dataset  
In order to further validate the effectiveness of the proposed model in this paper, three comparison models were selected to compare with our model in knowledge-based QA datasets. The experimental results are shown in Table 6. For rows A to D, the learning rate was 0.3 and the hidden size was 130. The results indicate that the proposed method improves 6% on accuracy and BLEU declines to 3.10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Row** & **Method** & **Accuracy** & **BLEU** \\ \hline A & Bi-LSTM & 59.6\% & 5.35 \\ B & Double Bi-LSTM & 57.4\% & 5.60 \\ C & Attentive Bi-LSTM & 62.7\% & 3.22 \\ D & Multi-scale CNN & 64.7\% & – \\ E & Ours & 71.4\% & 3.10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results on Knowledge-based QA dataset.  
Figure 7: Accuracy changes with the number of self-attention layers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Row** & **Learning Rate** & **Hidden Size** & **Accuracy** & **BLEU** \\ \hline A & 0.7 & 90 & 63.6\% & 5.70 \\ B & 0.6 & 110 & 69.4\% & 4.80 \\ C & 0.3 & 120 & 70.6\% & 3.00 \\ D & 0.3 & 130 & 71.4\% & 3.10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results with different parameters.  
The Bi-LSTM model received the state of the previous layer and maintained the sequential features to make semantic features more accurate. The double Bi-LSTM model adopted Bi-LSTM to extract the semantic feature. Then, it used Bi-LSTM to extract contextual information. The results are slightly improved over the single experiment. The results of the attentive Bi-LSTM are significantly improved than that of rows A and B. This shows that the calculation of the weight of each word is helpful to improve the question-answer matching. Row D uses multiple CNNs to extract the semantic information from the text in medical QA pairs.  
On the basis of row C, the proposed method adopts self-attention to extract global features of a sentence before continuing to next phase. It can better capture the semantic association between answers and questions. The experiments illustrate that self-attention outperforms basic neural networks in answer selection tasks.
# Convolutional Graph Network-Based Feature Extraction to Detect Phishing Attacks
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref.** & **Authors** & **Year** & **Detection Algorithm** & **Performance** \\ \hline
[29] & [PERSON], et al. & 2018 & Machine learning & ACC = 97.58 \\
[26] & [PERSON], & 2019 & Phishing Website Association & ACC = 95.20 \\
[32] & [PERSON] & 2019 & Classification (PWCAC) & ACC = 92.80 \\
[32] & [PERSON], et al. & 2019 & Heuristic nonlinear regression strategy & ACC = 96.25 \\
[35] & [PERSON], [PERSON], & 2019 & Effective classifiers and feature & Precision = 97.1 \\  & et al. & & selection techniques & Recall = 96.3 \\
[27] & [PERSON], et al. & 2020 & Intelligent association & Precision = 85.8 \\  & & classification technique & Recall = 85.7 \\  & & & F-score = 85.7 \\
[36] & [PERSON], et al. & 2020 & Gravitational search-based feature selection & ACC = 95.53 \\
[34] & [PERSON], [PERSON], et al. & 2021 & Bayesian optimization and gradient boosting & TPR = 94.87 \\  & [PERSON], & 2021 & Bayesian optimization and gradient boosting & ACC = 97.08 \\  & [PERSON], et al. & 2021 & Optimized stacking ensemble model & Precision = 96.58 \\  & & & Recall = 98.08 \\  & & & F-score = 97.49 \\
[30] & [PERSON], et al. & 2021 & Novel machine learning fusion approach & ACC = 97 \\
[31] & [PERSON], [PERSON], et al. & 2022 & DL-based models and hyperparameter optimization & ACC = 94.5 \\  & [PERSON], & & ACC = 97.76 \\
[33] & [PERSON], et al. & 2022 & Multilayer stacked ensemble learning model & Precision = 97.34 \\  & et al. & & Recall = 98.07 \\  & et al. & & F-score = 97.70 \\  & & & ACC = 63.25 \\
[41] & [PERSON], et al. & 2022 & Graph convolutional network & Precision = 75.23 \\  & & & Recall = 16.25 \\
[39] & [PERSON] AD. & 2023 & Convolution Neural Networks & ACC = 86.5 \\
[38] & [PERSON], et al. & 2023 & Binary bat algorithm and neural network & ACC = 94.8 \\  & & & ACC = 63.25 \\
[40] & [PERSON], et al. & 2023 & Community-enhanced GCN-based detection model & Precision = 75.23 \\  & & & Recall = 16.25 \\  & & & F-score = 26.73 \\  & & & ACC = 87.3 \\
[42] & [PERSON], et al. & 2024 & Graph convolutional network & Precision = 87.8 \\  & & & Recall = 89.0 \\  & & & F-score = 88.4 \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of previous works in the field of phishing detection.
### Occurrence of PVA  
The PVA occurs during both invasive and noninvasive ventilation and can be categorized into four general types: flow asynchrony, trigger asynchrony, cycle asynchrony and mode asynchrony [11; 13]. Common manifestations are cases of autotriggering (ventilator breath occurs without patient effort), double-triggering (patients inspiratory effort continues beyond the ventilator inspiratory time), ineffective breaths (inspiratory effort is not followed by ventilator breath), premature cycling (the ventilator terminates the inspiratory flow prior to the patient's need) and late cycling (ventilator inspiratory time exceeds the patient's inspiratory time) [19; 20]. Noninvasive ventilation techniques are prone to PVA due to leakages [19]. A framework for evaluating the clinical impact of PVA and attempts to better structure such efforts was presented by [PERSON] et al. [21].  
Patient-ventilator asynchronies occur frequently and in the most common ventilation modes [18; 20; 22]. A severe PVA can be defined by proportion (PVA events in \(\geq\)10% of breathing cycles) or by clustering in 3 min period (PVA in 50% of breaths, assuming  
Figure 1: Basic structure and main functional building blocks of a mechanical ventilator (taken and adapted from [8]).  
a breathing rate of 20 per minute) [20; 23]. A recently suggested method estimate PVA severity based on recurrence (PVA is observed twice a day for at least two minutes) [24]. However, the proposed concept requires further validation. Negative physical and mental outcomes such as excessive load on respiratory muscles, lung injury, prolonged ICU stay, discomfort and anxiety have been linked with severe PVA [13; 22; 25; 26; 27]. A higher frequency of asynchronies has been associated with higher mortality as well [13; 20]. Some studies report a lower incidence of severe asynchronies when modern ventilation modes such as neurally adjusted ventilation assist (NAVA) is used in comparison to conventional ones (PSV, V/A-C) [11; 18; 28; 29; 30; 31]. However, conventional modes of mechanical ventilation stand a test of time and remain frequently used. This is in part due to the increased complexity of new ventilation modes. Current emerging solutions focus on shortening the weaning process and increasing lung protective ventilation techniques [32], mainly supported by AI software solutions with advanced in vivo monitoring [33; 34]. In some cases, this can lead to the ventilation device becoming a \"black-box\" for an attending physician. Furthermore, there is no strict taxonomy in the naming of ventilation modes and manufacturers often introduce different names for similar modes, which can lead to confusion. Further advances in the complex field of mechanical ventilation could benefit from a truly interdisciplinary approach combining the knowledge of medical professionals and biomedical engineers [8; 35].
# An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs
## 3 Existing Equations Used for Two-Way Flat Slabs
### ACI 318-14 Building Code Equations  
A set of simple equations were proposed in the ACI 318-14 code to calculate the shear strength provided by concrete. The control perimeter is half of the effective depth of the slab (0.5\(d\)) from the loaded area for punching shear stress. ACI 318-14 requires that the nominal shear resistance for slabs without shear reinforcement be approximated as the smallest value of \(V_{n}\) calculated from the following expressions:  
\[V_{n}=\ 0.083\bigg{(}2+\frac{4}{\beta_{c}}\bigg{)}\lambda\ \sqrt{f_{c}}\ b_{o}\ d\, \tag{1}\]\[V_{n}=\ 0.083\left(\alpha_{s}\frac{d}{b_{o}}+2\right)\lambda\sqrt{f_{c}^{ \prime}}\ b_{o}\ d\, \tag{2}\]  
\[V_{n}=\ 0.33\ \lambda\ \sqrt{f_{c}^{\prime}}\ b_{o}\ d\, \tag{3}\]  
where \(V_{n}\) is the shear strength in N, \(b_{o}\) is the perimeter of the critical section in mm, \(d\) is the effective depth of slab in mm, and \(\lambda=1.0\) for normal weight concrete and 0.75 for all lightweight concrete. Otherwise, \(\lambda\) is determined based on volumetric proportions of lightweight and normal weight aggregates, but does not exceed 0.85. Here, \(\alpha_{s}=40\) for interior columns, 30 for edge columns, and 20 for corner columns; \(\beta_{c}\) is the ratio of the longer to the shorter dimension of the loaded area; and \(f_{c}^{\prime}\) is the cylinder compressive strength of concrete in MPa.
###### Abstract  
Scalmalloy(r) is a novel alloy designed to work with the unique processing inherent in additive manufacturing (AM). This alloy is post-processed using a single artificial aging treatment rather than a multistep heat treatment, as often noted in traditional manufacturing processes. Much of the literature details the impact of direct aging treatments around the temperature and time recommended by the manufacturer, 325 \({}^{\circ}\)C for 4 h; however, few studies have explored the impact of delayed artificial aging on the resulting mechanical and microstructural behavior. This study explored this missing link and determined the impact that the time between the fabrication of the component and artificial aging has on the final properties. For this study, directed energy deposition (DED) was utilized to fabricate Scalmalloy(r) components. Post-processing via artificial aging was performed after fabrication to yield samples naturally aged for 0, 2, 4, 6, 8, 10, 12, and 14 days. Samples were subsequently aged at 325 \({}^{\circ}\)C for 4 h. Tensile testing determined that natural aging negatively impacts tensile strengths. After 6 days of delayed artificial aging (6 NA), the yield strength (YS) and tensile strength (TS) relative to day 0 (0 NA) case were diminished by 10% and 8.6%, respectively. After 12 days (12 NA), even greater property decreases were noted, with a 23% decrease in YS and an 18% decrease in TS. Microstructural characterization was performed and an increase in precipitate size and decrease in precipitate number coupled with increased grain sizes were theorized to be linked to the decrease in tensile properties. The negative impact of delayed artificial aging after AM fabrication for Scalmalloy(r) highlights the importance of fast transitions from deposition to heat treatment.  
Keywords:Scalmalloy(r); additive manufacturing; aluminum; directed energy deposition; laser-based processing; natural aging; delayed artificial aging; heat treatment +
Footnote †: journal: Applied Sciences
# A Novel Plug-in Board for Remote Insect Monitoring
## 2 Materials and Methods
### Communication Modules
The EMB-LR12768 can communicate with the controller device over the UART interface. The default settings of the UART interface are 9600 baud/sec, 8 data bits, no parity, and no hardware flow control. The controller can send simple \"AT-like\" commands to the module, packed into small packets. The double communication units may seem to be redundant, but both may be required depending on the short- and long-term goals. It is well known that deep machine learning models require a huge amount of input data (trap images) to fine-tune the network's parameters. To increase the size of the available dataset, it is worth periodically taking images of trapped insects and sending them to the server side. With the SIM7600E-H, this task can be accomplished easily with the HTTPS protocol. On the other hand, if we want to transmit only the number of cached insects with some additional information (e.g., timestamp), it is sufficient to use only the LoRaWAN communication channel because it has a lower monthly fee (a smaller number of agreements with the network service provider) and power requirement. Since the Raspberry Pi has only one UART interface and both communication modules use the UART, we must enable the control of the shared interface access. In our design, it is fixed with an HTC125 quadruple bus buffer gate IC that incorporates four 3-state buffers. Utilizing the enable lines of the 3-state buffers, we can easily control access to the Raspberry Pi's UART interface. Figure 3: Voltage-matching circuit. The SIM_1V8 label refers to the 1.8 V voltage line that comes from the SIM7600E-H module.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 4 Node Classification
### Estimating Neighbor Counts
#### 4.2.3 Trend Detection  
To extract the trend from \(Q\) and \(R\), a linear regression model is used. This model finds the best-fitting straight line for all data in a data sequence and decides the trend of the sequence in accordance with the slope of the fitting line. The trend detection used here is the regression of neighbor counts over time and that of ratios of boundary nodes over time. Consider a regression model, where a dependent variable \(y_{i}\) is linked to an independent variable \(x_{i}\) through the following equation:  
\[y_{i}=ax_{i}+b \tag{5}\]  
The equation above describes the relationship between \(y\) and \(x\) and represents a line with an intercept of \(b\) on the \(y\)-axis and a slope of \(a\). The two values, \(a\) and \(b\), for which the sum of the squares of the estimated errors is the minimum, have to be estimated. The estimation equation is given as follows:  
\[\text{Sum of Square Error}(SSE)=\sum_{i=1}^{n}{(ax_{i}+b-y_{i})^{2}} \tag{6}\]  
and:  
\[\frac{\partial SSE}{\partial a}=2\times\sum_{i=1}^{n}{x_{i}(ax_{i}+b-y_{i})} \tag{7}\]  
\[\frac{\partial SSE}{\partial b}=2\times\sum_{i=1}^{n}(ax_{i}+b-y_{i}) \tag{8}\]  
Setting these expressions equal to zero and solving for \(a\) and \(b\) produce:  
\[a=\frac{n\sum_{i=1}^{n}{(x_{i}y_{i})}-\sum_{i=1}^{n}{x_{i}\sum_{i=1}^{n}{y_{i}}}} {D} \tag{9}\]  
\[b=\frac{\sum_{i=1}^{n}{x_{i}^{2}\sum_{i=1}^{n}{y_{i}}}-\sum_{i=1}^{n}{x_{i} \sum_{i=1}^{n}{(x_{i})(y_{i})}}}{D} \tag{10}\]  
where \(D=n\sum_{i=1}^{n}{x_{i}^{2}}-(\sum_{i=1}^{n}{x_{i}})^{2}\); \(a\) represents the slope, and \(b\) represents the \(y\)-axis intercept. This slope represents the direction of the trend. A negative slope represents a decreasing trend; a positive slope represents an increasing trend; and zero represents that there is no change in the data sequence. Therefore, a left node from a monitoring area has a decreasing trend in \(Q\) and an increasing trend in \(R\) simultaneously.