esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results and Discussion
### Environment Classifier  
For environment classification we selected the set of {_restaurant/pub/cafe, office, home, street/road, nature_} as the categories to be recognized. The list includes environments that would be useful for many mobile applications, but it is short enough for reasonable accuracy analysis. In environment classification all the features from the GPS, WLAN and Bluetooth were selected. In addition, the likelihoods output by the audio classifier were used as features. Features from the accelerometer were not used in this case as they did not improve the recognition accuracy.  
Figure 5 shows the scatter plot of the first two features after the compression method presented in Section 4 was done. Some of the most important features were audio features and GPS TTFF and maximum WLAN signal strength. The confusion matrix of leave-one-user-out cross-validation method is presented in Table 7.  
DT and SVM results for the classes _office_, _nature_, _street/road_, _home_ and _restaurant/pub/cafe_ are presented in Table 8. As the some classes  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{true}\backslash\mathbf{decision}\) & \(\mathbf{In}\) & \(\mathbf{out}\) \\ \hline \(\mathbf{In}\) & \(86\) & \(14\) \\ \(\mathbf{Out}\) & \(6\) & \(94\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix of the classes _indoor_ and _outdoor_ using SVM. The values denote percentages.  
Figure 5: Two main features after the feature compression.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{true}\backslash\mathbf{decision}\) & \(\mathbf{In}\) & \(\mathbf{out}\) \\ \hline \(\mathbf{In}\) & \(96\) & \(4\) \\ \(\mathbf{Out}\) & \(6\) & \(94\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix of the classes _indoor_ and _outdoor_ using decision trees. The values denote percentages.  
Class average classification accuracy for MAP classifier is 70%, for DT 68% and for SVM 63%. Thus, there are no great differences between these classifiers. It should be noted that default parameters from MatLab's _ClassificationTree class_ were used for DT and SVM, in addition, the class imbalance problem was not taken into account for these classifiers, thus by taking these aspects into account, accuracies may slightly increase. However, the main idea in the paper was not to show whether the Bayesian classifier can beat DT or SVM, but to show that our Bayesian classifier is not dramatically inferior to state of art methods and thus it is feasible to propose adaptation algorithm for this Bayesian approach. Comprehensive comparative between classifiers is thus left out.
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 4 Discussion and Analysis
### Flexibility and Other Issues  
Some researchers proposed to attach fingerprints on the surface of a model or to slightly alter the vertex coordinates or mesh connectivity of the model surface to prevent copyright violation [31, 32, 33, 34]. Nonetheless, these protection mechanisms may be unintentionally damaged in the G-code generation, 3D printing, and post-processing stages. As a result, the decoder may fail to accept the watermark in the verification process [4, 5]. Hiding the watermark inside the model and printing it by using different materials, layer thickness, or filling patterns alleviate this problem [34, 35]. Using the intrinsic characteristics of the 3D printer can offer some help too [36]. However, detecting and evaluating hidden and subtly arranged watermarks require special facilities, for example high-resolution scanners and powerful reconstruction software [4, 5]. In this research, we employ the SOM procedure to embed watermarks. This procedure allows us to plant watermarks inside the input model or to create embossed and engraved patterns on the model surface. Thus, our approach is flexible and can accommodate different 3D printing platforms and applications.  
In the presented test results, we utilized strong light sources to reveal watermarks hidden in printed parts, since the raw materials are semi-transparent. This strategy fails if the raw materials are opaque. Using thermal photography and radiation imaging methods may enhance the detection process [16, 17]. In the work of [31], watermarks are created by using magnetic materials or RFID chips and can be captured by sensors regardless of their positions. These methods were pragmatic if the slicers had been extended to generate G-codes for multi-material printing and chip insertion.  
Besides the watermark embedding methodology, watermark-design deserves some attentions too. This issue is seldom discussed in literatures, except the survey papers of [4, 5]. A regular and large watermark is easier to verify, but it is prone to be uncovered. On the other hand, a small and irregular watermark is more imperceptible. Nonetheless, it may suffer from damages caused by the slicer and the 3D printer, especially when the underlying 3D printer is a low-end machine. As a result, the watermark could fail to be accepted by the decoder. By performing watermarking in a voxel-space, it is relatively easier to adjust the sizes, patterns, and locations of watermarks. Thus, we can gain extra flexibility in designing watermarks to fit the 3D printers and applications.
## 3 Simulations and Measurements  
### Stimuli  
The Oldenburg Sentence Test (OLSA) [24] corpus with the male speaker is used as the speech material to evaluate the proposed algorithm. The OLSA corpus consists of 120 German sentences for each speaker. A sentence includes name, verb, number, adjective, and noun and there are 10 alternatives for each word. The corpus is downsampled to 16 kHz. Speech shaped noise (SSN) and pink noise (PN) are used as the interferers which are convolved with binaural room impulse response (BRIR) and presented at an average level of 65 dB-SPL for left and right ears. The SSN was generated by a summation of all 120 OLSA sentences followed by phase randomization, creating noise with a similar long-term spectrum as the speech corpus. In addition to SNN, also pink noise is used which has an energy distribution similar to environmental noise. It covers the frequency range between 100 Hz to 8 kHz, approximately corresponding to the spectral range of the speech material.
## 1 Introduction
Most of the above studies on RFA mainly focused on gas flow distribution and internal structure efficiency. There are limited experimental verification on PSA oxygen production. [PERSON] et al. [19] verified a new PSA system with radial flow bed on oxygen production experimentally under a cycle time less than 30 s. It has been showed that enriched oxygen could be produced when air was fed inward. [PERSON] et al. [20] established a radial flow rapid pressure swing adsorption (RPSA), and found  
Figure 1: Schematic of the Skarstrom cycle. that small adsorbent particles and long effective lengths would be better for RPSA. [PERSON] et al. [21] designed a radial flow PSA unit with two-tower configuration for oxygen production in consideration of the gas flow pattern, outer flow channel width and channel structure on oxygen production. It is very effective to study gas separation by numerical simulation [22; 23; 24; 25; 26]. However, most of the studies are based on the 1-D or 2-D RFA [27], and few studies have been reported in research of PSA oxygen production based on 3-D RFA. In actual PSA processes, the gas separation in the adsorption bed is a coupling process of multiple complicated processes such as gas flow, heat transfer and mass transfer. In order to investigate the characteristic of gas flow during PSA for oxygen production and improve oxygen production efficiency, a 3-D gas-solid two-phase PSA model was established for the \(\pi\)-shaped centripetal RFA (CP-\(\pi\) RFA). The flow and heat transfer characteristics, oxygen distributions, effect of product flow rate and cycle times were comparatively studied using this model. This model can better reflect the actual PSA oxygen production process. The laws of the pressure, velocity, temperature and oxygen distribution in the adsorber could provide an important technical reference for CP-\(\pi\) RFA in the PSA for oxygen production.
# Honesty-Based Social Technique to Enhance Cooperation in Social Internet of Things
## 3 Proposed Model
### Preliminaries and Assumptions:
#### 3.1.4 Classification of Nodes  
The network's nodes can be classified as friend, mutual friend, selfish, or honest nodes.  
A friend of node \(n_{i}\) can be denoted as \(F(n_{i})\) that is present in the community and has active liking and trust (\(ALT\)) with the node \(n_{i}\). A mutual friend of nodes \(n_{i}\) and \(n_{j}\), \(M(n_{i},n_{j})\), refers to the common friend of two interacting friends in a community. A well-behaved node is an honest node in the community, which means node \(Hn_{i}\) transmits the data or messages correctly to its fellow nodes in a network. The neighbor's node, \(n_{k}\), denotes all those nodes that have direct interaction with nodes in the community. Additionally, the node does not try to modify history. Any node in the community that uses the resources of the other nodes for its purpose but does not serve the community with its resources, such as energy, bandwidth, link sharing, and storage space, can be described as a selfish node \(Sn_{i}\).  
Equations (2)-(5) are used to define these nodes.  
\[F(n_{i})\in N:\ \left\{n_{j}\in N_{alive}\wedge n_{j}\in ALT(n_{i})\ \right\} \tag{2}\]  
\[M\big{(}n_{i},n_{j}\big{)}\in N:\ \left\{n_{k}\in N_{alive}\wedge n_{k}\in ALT(n_{ i})\ \wedge n_{k}\in ALT(n_{j})\right\} \tag{3}\]  
\[Hn_{i}\ \in\ N:\ \left\{n_{i}\ \in\ \tau_{cr}\ \vee\ \tau_{Mn(com)}\ \epsilon\ N_{alive}\ \wedge\ n_{i}\ is\ trustful\ \right\} \tag{4}\]  
\[Sn_{i}\in N:\ \left\{n_{i}\in N_{alive}\wedge n_{i}\ is\ not\ in\ Hn_{i}\right\} \tag{5}\]  
where \(N\) is the total number of nodes which is between \((0,\ 1]\).
###### Abstract  
Deep excavation and shield tunneling processes often induce significant deformation in adjacent existing pile foundations, posing risks to the safety of superstructures. To address this issue, capsule grouting expansion technology has emerged as an efficient and economical technology to actively control pile deformation during such construction activities. This technology has been applied in some engineering projects aimed at safeguarding nearby buildings or facilities. However, theoretical research on capsule grouting expansion technology is limited and lags behind its practical engineering application. This paper proposed a simplified theoretical approach for estimating the effect of capsule grouting expansion technology on the deformation of neighboring pile foundations. Initially, virtual image technology was employed to obtain an analytical solution for the lateral displacement of soil resulting from the expansion of a cylindrical cavity with infinite height. The pile was considered as an Euler-Bernoulli beam on a Winkler foundation, allowing for the calculation of the loading response of the existing pile foundation. Utilizing the two-stage method, the theoretical analytical method of the lateral deformation of the pile shaft caused by capsule expansion was obtained. The reliability of the proposed theoretical method was verified by comparing the measured and calculated results. Further investigation into the controlling effect of the capsule grouting expansion technology on the deformation of the pile foundation was conducted through parametric analyses. These analyses encompass capsule radius, the distance between the capsule and pile foundation, the depth of the capsule, the length of the capsule, and the pile base constraints.  
capsule grouting; virtual image technology; pile deformation; two-stage method +
Footnote †: journal: Applied Sciences  
Article  
A Simplified Theoretical Method for Estimating the Effect of Capsule Grouting Expansion Technology on Controlling Pile Deformation  
J. Sci. **2024**, _14_, 5449. [[https://doi.org/10.3390/app14135449](https://doi.org/10.3390/app14135449)]([https://doi.org/10.3390/app14135449](https://doi.org/10.3390/app14135449))
## 1 Introduction
### Technologies for Modernizing Workplaces  
A necessity for modernizing the work environment and to help in preventing diseases and accidents has been recognized. In recent decades, the terms Industry 4.0, Industry 5.0, and Internet of Things (IoT) have been introduced.  
The term Industry 4.0 defines a new level of organization and control over the entire value chain, aiming for continuous improvement and avoiding waste [7; 8]. Industry 4.0 has not been largely developed in all countries. However, as discussed in [9; 10], business and technological innovators are already looking ahead to Industry 5.0. In [PERSON] et al. [9], Industry 5.0 is defined as the era of the socially intelligent factory which will involve autonomous manufacturing with human intelligence and AI as a backbone technology. In addition, the concept of Industry 5.0 is focused on environmental sustainability and more skilled jobs compared to Industry 4.0 [11]. Nevertheless, it is important to highlight that there is still little literature on this topic.  
Concerning IoT, its main goal is to provide a network infrastructure with interoperable communication protocols and software to allow for the connection of a variety of objects to the Internet. IoT can be applied, for example, to monitor machines in factories and farms, as well as workers' movements to prevent and identify accidents [12].  
In this context, various technologies have been added to work environments to enable, for example, the automatic verification of the use of personal protective equipment (PPE), the continuous monitoring of exposure to risks, and the automatic identification of emergencies. It must be highlighted that not only industry requires this kind of system, but also other sectors such as agriculture, mining, and services, among others. For example, smart PPE and monitoring systems were proposed by [13; 14; 15; 16; 17; 18; 19; 20]. A review of technologies and trends of environmental risk assessment and management in the Industry 4.0 context is shown by [PERSON] et al. [21]. [PERSON] et al. [22] developed a systematic review of the literature to discuss useful sensors for occupational risk assessment.  
The solutions mentioned above can help companies to reduce accidents and work-related diseases, and an analysis of the large amount of data collected by these smart devices has the potential to guide companies to improve OSH programs.
## 4 Results and Discussion
### Crankshaft Power Control  
Delayed ignition means delayed combustion, resulting in a drop in crankshaft power. Figures 15 and 16 are the comparisons of the flow and crankshaft power before and after improvements. As observed from the figure, since the improved exhaust system allows more compressed air to enter the exhaust port for cooling, increasing the draft pipe increases the intake air flow, thereby increasing the crankshaft's power. Therefore, in order to ensure the stable output of crankshaft power, in this paper, a throttle controller is added to the model after improving the intake system to adjust the throttle opening to ensure the required crankshaft's output power. The controller is connected with two sensors and a driver. The sensor's sensing signals are the crankshaft speed signal and the average effective pressure signal. The driver drives the throttle valve and controls the throttle's  
Figure 14: Drainage tube flow.  
Figure 13: The improved intake system.  
valve opening to make the average effective pressure meet the set requirements in order to achieve the purpose of power control.  
With the delay of ignition, in order to ensure that the average effective pressure meets the requirements, the throttle valve controller must increase the throttle valve opening so that the intake air volume increases and more fuel is provided for the cylinder to make up for the loss of power. Figure 17 shows the power performance of the engine under constant power operation after adding the throttle controller. It can be seen that when the ignition advance angle is greater than 12 \({}^{\circ}\)CA BTDC, the crankshaft power in the power range of 300-600 kW can be well maintained at the control power level. When the power is 600 kW and when the ignition is later than 12 \({}^{\circ}\)CA BTDC, the crankshaft power begins to decrease significantly; when the power is 500 kW and when the ignition is later than 10 \({}^{\circ}\)CA BTDC, the crankshaft power decreases to a certain extent; crankshaft power has been good at 400 kW and 300 kW. It can be seen that when the ignition advance angle is changed and the throttle's valve opening correspondingly increased or decreased, the crankshaft output power of the engine can be controlled at a certain power level.  
Figure 16: Comparison of crankshaft power before and after improvement.  
Figure 15: Comparison of intake pipe flow before and after improvement.
## 5 The Hardware Implementation of the Dual-Axis-Based Solar Tracking System
### Main and Modified Versions of the BILBO Architecture
As can be seen in Table 2, the original BILBO variant allowed sufficient headroom for additional gates and hardware changes to satisfy all modes of operation according to the testing needs, therefore proving extremely adaptable for practical applications. Due to the BILBO's flexibility and being aware of the limitations that are imposed by the two earlier described architectures, we propose a novel and efficient fault diagnosis implementation based on a modified BILBO architecture. The hardware design, with minimal hardware \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Original BILBO Modes of Operation**} & \multicolumn{3}{c}{**Expanded BILBO Modes of Operation**} \\ \hline
**B2** & **B1** & **Modes of Operation** & **B3** & **B2** & **B1** & **Modes of Operation** \\ \hline
0 & 0 & Shift (scan) mode (\(\text{Qt}\leq\text{Qt}-1\)) & 0 & 0 & 0 & Shift (scan) mode ((\(\text{Qt}\leq\text{Qt}-1\)) \\
0. & 1 & MISR (BIST) mode (\(\text{Qt}\leq\text{Zt}\oplus\text{Qt}-1\)) & 1 & 0 & 1 & MISR (BIST) mode (\(\text{Qt}\leq\text{Zt}\oplus\text{Qt}-1\)) \\
1 & 0 & Initialization mode (\(\text{Qt}\leq 0\)) & 1 & 0 & 0 & TPG (BIST) mode (\(\text{Qt}\leq 0\oplus\text{Qt}-1\)) \\
1 & 1 & System mode (\(\text{Qt}\leq\text{Zt}\)) & X & 1 & 0 & Initialization mode (\(\text{Qt}\leq\text{0}\)) \\  & & X & 1 & 1 & System mode (\(\text{Qt}\leq\text{Zt}\)) \\ \hline \hline \multicolumn{5}{c}{**Modified BILBO Flip-Flop**} & \multicolumn{3}{c}{**Modified BILBO Modes of Operation**} \\ \hline
**B2** & **B1** & **Xi** & **Mode of Operation** & **B2** & **B1** & **Modes of Operation** \\ \hline
0 & 0 & 0 & Initialization (\(\text{Qt}\leq 0\)) & 0 & 0 & Shift (scan) mode (\(\text{Qt}\leq\text{Qt}-1\)) \\
0 & 1 & Zt & System mode (\(\text{Qt}\leq\text{Zt}\)) & 0 & 1 & System mode (\(\text{Qt}\leq\text{Zt}\)) \\
1 & 0 & \(\text{Qt}-1\) & Scan mode (\(\text{Qt}\leq\text{Qt}-1\)) & 1 & 0 & LFSR (BIST TPG) mode (\(\text{Qt}\leq\text{Qt}-1\)) \\
1 & 1 & \(\text{Zt}\oplus\text{Qt}-1\) & BIST (\(\text{Qt}\leq\text{Zt}\oplus\text{Qt}-1\)) & 1 & 1 & MISR (BIST ORA) mode (\(\text{Qt}\leq\text{Zt}\oplus\text{Qt}-1\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Truth table of operational modes for the original and modified BILBO architectures. Figure 8: The conventional BILBO architecture is implemented with NAND blocks and a multiplexer. area overhead, will employ a reduced number of combinational and sequential circuits to identify stuck-at faults (SaFs) on the MCU's critical path signals which control the motor driver's behavior.
## 5 Discussion  
The methodology aimed to determine irrigation dates and amounts at the plot level by integrating surface soil moisture (SSM) data obtained from Sentinel-1 (SSM\({}_{\text{S1}}\)) into Optirrig using an inversion technique. To achieve this, we compared the change in SSM\({}_{\text{S1}}\) between two Sentinel-1 SAR acquisition dates, namely, t\({}_{\text{(I)}}\) and the previous date t\({}_{\text{(I)}}\), with the change in SSM simulated by Optirrig under various combinations of irrigation dates and amounts (I\({}_{\text{(I)}k}\)), where j represents the date and k represents the amount) injected into the model.  
The analysis that produced the results presented in the previous sections focused on determining irrigation dates and amounts for which the disparity between the change in SSM predictions and simulations between two SAR observations (i) was minimized, aiming for a value as close to zero as possible, and (ii) met specific criteria by demonstrating significant changes between the SSM simulated for different irrigation scenarios in Optirrig and its difference with the SSM obtained from Sentinel-1 data. These criteria were applied using a change detection approach commonly used in signal analysis and remote sensing applications. For a more detailed understanding of the process of inversion and the specific selection criteria utilized to detect irrigation events, please refer to Figure 6 and its accompanying explanations.  
The method yielded significant results, highlighting the value and interest in combining SSM derived from Sentinel-1 SAR data and Optirrig SSM simulations. This combination demonstrated the potential benefits of detecting irrigation events over maize plots. However, there were limitations to detecting irrigation events through the combined use of SSM\({}_{\text{S1}}\) and Optirrig SSM simulations, which could result in both missed (false negative) and false positive irrigation detections, between two SAR observations. These limitations primarily stemmed from four factors: (1) the 6-day temporal resolution of the Sentinel-1 SAR being insufficient to detect all the irrigation events, (2) heavy rainfall and soil saturation, (3) the limit of penetration of the C-band SAR signal in dense vegetation cover, and (4) farmer's irrigation practices.
## 4 Biological Applications of EUV/SXR Microscopes Based on a Double Stream Gas-Puff Target Source
### Angular Projection SXR Tomography for Biological Nanoimaging
#### 4.2.1 Hep-2 Cells Crystals  
A sample of liver cancer cells HEp-2 cells (human larynx epidermoid carcinoma cell line) was imaged. The cells were fixed in 4% paraformaldehydehe in 0.5 mL of PBS medium. A 3 \(\upmu\)L drop of solution was deposited on top of a 30 nm Si\({}_{3}\)N\({}_{4}\) membrane and dried in an N\({}_{2}\) atmosphere. In this case, due to the low density of cells in the sample drop, it was possible to observe only the crystals formed from the PBS medium, as shown in Figure 22a, which was acquired with an optical microscope (40\(\times\) objective). The image chosen for SXR tomography, marked with a red rectangle (Figure 22b), was obtained at a magnification of 100\(\times\) with a FOV of 100 \(\times\) 60 \(\upmu\)m\({}^{2}\). Some acquired projections (in an angular range of 0\({}^{\circ}\)-54\({}^{\circ}\)) are shown in Figure 22c. The images were acquired using the internal camera. The presented images were obtained cropping the full-frame CCD image to FOV of 100 \(\times\) 60 \(\upmu\)m\({}^{2}\). It is possible to observe that starting from 42\({}^{\circ}\) the FOV results darker, because the edge of the membrane supporting the sample becomes visible (from the right side to the left) and, in other words, thicker,  
Figure 21: Comparison of the mouse hippocampal neuron acquired (**a**) with an optical microscope (40\(\times\) objective, 400\(\times\)) and (**b**) SXR microscope (in false colors), showing a single neuron with dendrites branching out. Image acquired after 200 SXR pulses, 20 s exposure. Figure (**a**) from [76].  
increasing the SXR light path length in the membrane. From the 12\({}^{\circ}\) projection, it is observable damage (crack) of the membrane, which becomes more visible at larger angles, where part of the membrane goes behind the intact part and the darker region (i.e. where the SXR radiation propagates), which is perpendicularly deformed from the image plane and is visible only for larger angle projections [59; 92].