esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Liquid Smoke Treatment on Fiber Functional Groups  
Changes in fiber functional groups with liquid smoke treatment can be analyzed by performing FTIR testing. Figure 9 shows the result of fiber FTIR testing on TP, P1J, P2J, and P3J.  
The graph of the FTIR test results shows differences in the composition of fiber compounds where the functional groups O-H, C-H, C=O, C=C, and CH\({}_{3}\) look different; this indicates that the treatment of fiber with liquid smoke affects the change in wave crest.  
Figure 10 is a combination of the FTIR BSFs transmittance pattern TP, P1J, P2J, and P3J, where the shape of the peak on the graph changes due to the liquid smoke treatment. The constituent molecules in the FTIR Spectrum show that the wave peaks numbered from 2500-4000 cm\({}^{-1}\) are in the O-H and C-H spectra and caused a significant addition of P2J BSFs composition. The peak wave number 1500-2000 cm\({}^{-1}\) is in the spectrum C=O and C=C, which indicates that the BSFs compound increased in intensity after treatment, and the compound content was more significant than BSFs without treatment. Therefore the liquid smoke can add C=O and C=C compounds to the chain of BSFs compounds such as lignin and hemicellulose, and the high carbon composition of BSFs can increase their strength of BSFs. While at the peak of the wave number 500-1500 cm\({}^{-1}\) is in the CH\({}_{3}\) spectrum, which indicates the addition of CH\({}_{3}\) composition, P2J BSFs become the most significant increase in functional groups.  
Figure 9: FTIR test of BSFs (TP, P1J, P2J, and P3J).  
As the result of liquid smoke treatment from this study, P2] fiber was the most optimal fiber with a significant increase in tensile strength, crystalline intensity, functional groups of BSFs, and changes in morphology, This result is in line with previous research, which states that liquid smoke can change the functional groups of fiber [8,17]. Therefore, BSF fiber with P2J liquid smoke treatment becomes new knowledge about the characteristics and properties of BSF and the latter becomes one of the solutions as lightweight and environmentally friendly composite reinforcement for the future.
# DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index
## 5 Conclusions and Outlook  
The Baltic Dry Index (BDI) is a parameter representative of international shipping activities. It is an essential tool with which ship-owners and stakeholders plan their maritime businesses and avoid market risk. Unlike common time-series data, the BDI index is characterized by volatility, non-stationarity, and complexity; therefore, its prediction is very challenging indeed. In previous work, most researchers have used the Artificial Neural Networks (ANN) and statistical models. In keeping with the popularity of deep learning in this decade, in this paper, we propose a deep-learning approach whereby the deep sequential models (RNN, LSTM, and GRU) are combined in an ensemble called Deep Ensemble Recurrent Networks (DERN) for accurate prediction of short- and long-term BDI. In short-term prediction using the RMSE indicator, DERN had an error rate roughly a half of GRU, LSTM, Deep RNN and MLP, and approximately a third of ARIMA. However, in long-term prediction, the error rate was not as good as the short-term prediction. Specifically, the results showed that with increasing prediction steps, the error rate grew. Therefore, the long-term prediction is more challenging than short-term prediction. Nonetheless, DERN still outperforms the conventional methods in long-term prediction. Ship-owners and stakeholders, not to mention investors, prevalently are more interested in long-term prediction. In future work, we will propose a more fine-grained approach entailing sequence-to-sequence learning for more accurate long-term prediction.  
Conceptualization, I.M.K. and H.B.; Data curation, I.M.K., S.S. and H.Y.; Formal analysis, I.M.K., H.B., S.S. and H.S.; Methodology, I.M.K.; Funding acquisition, H.Y.; Investigation, S.S.; Supervision, H.B.  
Figure 8: Long-term prediction: seven-steps-ahead prediction of BDI: (**a**) Deep RNN; (**b**) LSTM; (**c**) GRU; (**d**) DERN.  
Figure 9: Averages of RMSE, MAE and MAPE for one-to-seven-steps-ahead prediction of BDI using MLP, Deep RNN, LSTM, GRU, and DERN, respectively.  
and H.Y.; writing--original draft, I.M.K.; Writing--review & editing, H.B. and H.Y.; Project administrator, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by principal cooperative research of KMI (the Korea Maritime Institute) and the project titled 'Development of IoT Infrastructure Technology for Smart Port' funded by the Ministry of Oceans and Fisheries, Korea.  
The authors declare no conflict of interest.
## 3 Experiments and Analysis of Results
Reducing the tolerance threshold: Currently, the MSE threshold for classifying an interval as anomalous is set at 0.5. Lowering this threshold (e.g., to 0.4) would increase the rigor of the selection criteria and may reduce the number of false positives. However, this approach requires caution, as too strict a threshold may lead to missing real failures. Improving the quality of signal recognition by increasing the number of iterations, as described in (1), will allow for lowering the threshold without losing true positives. 3. Increasing the level of detail of the wavelet transform: A higher level of detail can be used for wavelet analysis. This will detect more subtle features of the signal, which can help to distinguish between normal areas and anomalies more accurately. 4. Using different wavelets: Different wavelets have different sensitivities to different types of anomalies. Experimenting with different wavelet families (e.g., Morlet, Mexican Hat, Dobeshi, etc.) can lead to better recognition performance for particular types of signals and faults. Thus, despite the presence of false positives, the experimental results generally demonstrate the effectiveness of the proposed approach for detecting haar- and algb-type failures in a noisy signal with a sinusoidal component. \begin{table}
\begin{tabular}{c c c} \hline
**Interval** & **Fault Type** & **Error (MSE)** \\ \hline
18 & Invexp & 1.922 \\
19 & Bad signal & Nan \\
20 & Invexp & 0.481 \\
21 & -exp & 1.902 \\
22 & Invexp & 1.696 \\
23 & Bad signal & Nan \\
24 & algb & 0.209 \\
25 & algb & \(3.187\times 10^{-13}\) \\
26 & algb & 0.251 \\
27 & algb & \(2.174\times 10^{-13}\) \\
28 & Bad signal & Nan \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 2: Signal with faults.
# Auditing Accessibility of Pavements and Points of Interest in Urban Areas: The 'Seek & Go' Tool +
## 6 User Experience
To enhance the user experience, individuals can directly insert specific questions and photos from their camera or photo library, eliminating the need to switch between screens. The survey provides a comprehensive map overview, highlighting the selected entity for ease of reference. This allows users to quickly locate the relevant element among segments, pedestrian roads, crossings, pavements, obstacles, entrances, and POIs. From the UX perspective, the filled surveys are stored locally, enabling instantaneous saves without waiting times for uploading everything on the server. Uploading is triggered on demand when users have fast internet access. This offline-first design allows for better flexibility as it enables users to identify missing input, provides better performance, and promotes data resiliency because it reduces the risk of data loss. Additionally, the 'Seek & Go' application offers a key feature whereby the various entities are interconnected, forming a graph for calculating alternative accessible routes from point X to point Y on a map (Figure 3). Users can define multiple entrances to POIs and connect different segments by pavement or pedestrian road through an intuitive and user-friendly UI, utilizing icons and visual aids. This enables the calculation of alternative paths, such as utilizing a nearby pedestrian crossing when an obstacle blocks the entrance of a POI for wheelchair users on one side of the road. Figure 3: Tap-friendly UI interaction for connecting segments using self-explanatory icons such as sockets and plugs (map pin #1). POI containing two entrances connected to different segments (map pin #2) and pedestrian crossing connecting two opposite pavements (map pin #3) are also displayed graphically on the screenshot. The \"Seek & Go' application does not incorporate routing calculations in its functions. However, it enables data sharing through its API to third-party systems, which can utilize the collected information to propose alternative access routes and determine the most optimal one based on the user's specific requirements, priorities, or preferences. Furthermore, ongoing application developments encompass advanced analysis and programming methods for creating maps with higher spatial resolution [59].
## 2 Materials and Methods
### Effects of Co-Registration  
In Section 2.1, the time lag between two SAR channels is neglected, which is true for the along-track DCC, since the distance between the two channels is usually less than 1 m, and such an along-track distance would cause a time lag that is much less than that of a sample interval. However, for cross-track DCC, the channel distance is usually in meters for airborne platforms and can even reach hundreds of meters for spaceborne platforms. The two channels are distributed in the cross-track direction, which makes it possible to cause a time lag of tens to thousands of sample intervals. Thus, range co-registration for interference signals will be needed for cross-track DCC.  
Assuming that IPSR estimation is precise, the jamming residual after DCC suffers from a co-registration error. The residual is dependent on the jamming bandwidth to signal bandwidth ratio since the narrowband signal phase changes slower than that of the wideband signal.  
Figure 9 shows the jamming residual with the co-registration error of different jamming bandwidth to signal bandwidth ratios. The curves show that the jamming residual suffers from the jamming bandwidth to signal bandwidth ratio and co-registration error, which will grow with either the jamming bandwidth to signal bandwidth ratio or the co-registration error. As we can see from Figure 9, when the jamming bandwidth is equal to the signal  
Figure 8: IPSR estimation under different JSRs.  
Figure 7: Cosine similarity with IPSR.  
bandwidth, a 0.2 resolution cell co-registration error will approximately cause \(-12\) dB jamming residual, which is commonly considered to be acceptable.
### Analysis of the Evolution Law of the Impact of Upper Boundary Buried Depth on Fault Activation
#### 2.2.2 Statement of the Tress Change of Fault Plane Caused by Mining  
Figure 11 depicts the shear stress distribution on the fault plane for various fault upper boundary burial depths. It can be seen that the influence of the upper boundary burial depth on the fault activation is mainly manifested in: 1 When the upper boundary burial depth of the fault is different, the shear stress value that is different on the same fault plane. With the rising of burial depth, the shear stress value also increases, and the peak value of shear stress also increases. For instance, when the burial depth increases from \(300\) to \(700\) m, the peak value of shear stress increases from \(2.48\) to \(4.75\) MPa. This law shows that the deeper the mining goes to the stratum, the shear stress on the fault plane increases with mining influence, making the fault easier for it to become activated. 2 The change in mining depth will not change the shear stress distribution law on the fault plane (Figure 11). When other parameters are fixed, the shear stress distribution and peak value position are the same under varied mining depths, that is, the shear stress increases to its greatest between \(0\) and \(-50\) m of depth, progressively declines between \(-50\) and \(-300\) m, and is influenced by mining between \(-300\) and \(-400\) m of depth because it corresponds to the orebody mining height. Within the depth range of \(-400\) to \(-500\) m, the shear stress progressively reduces.  
The principal stress difference distribution of fault hanging wall rock at the intersection of the monitoring line and the fault under the conditions of different upper boundary burial depths of the fault, as shown in Figure 12. The maximum and minimum principal stresses in the hanging wall of the fault are larger than those in the football, and the difference between the maximum and minimum principal stresses grows with the upper boundary burial depth of the fault, showing that the fault contributes to a greater barrier effect on stress field propagation.  
Figure 11: The shear stress distribution of the fault plane under various buried depths of the upper boundary of the fault.  
Figure 12: The distribution of stress difference between the hanging wall and football of surrounding rock on the monitoring line under different upper boundary burial depths of fault, they should be listed as: (**a**) Maximum principal stress difference; (**b**) Minimum principal stress difference.
## 4 Outliers in IoT Context
### Main Sources for Sensor Outliers that are Relevant to the IoT Context  
There are three main causes of sensor outliers that are relevant to the IoT as shown in Figure 4.  
* Intrinsic Sensor Errors: This kind of error is associated with impaired readings or measurements coming from a faulty sensor which is embedded within an IoT device. As the sensors are electronic  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Reference** & **Definitions** \\ \hline
[23] & _“An outlier is an observation which deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism”_ \\
[24] & _“An outlier is a data point which is significantly different from other data points, or does not conform to the expected normal behaviour, or conforms well to a defined abnormal behaviour.”_ \\
[25] & _“A spatial-temporal point whose non-spatial attribute values are significantly different from those of other spatially and temporally referenced points in its spatial or/and temporal neighbourhoods is considered as a spatial-temporal outlier.”_ \\
[26] & _“An outlier is an observation or subset of observations that appears to be inconsistent with the rest of the set of data”_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definitions of sensor outliers in the IoT context.  
Figure 3: Etymological relationship/differences between IoT and WSN.  
components, they often fail suddenly and stop working without any indication of degrading performance [26; 27; 28]. This kind of sensor failure feeds in either no readings or null readings to the data processing algorithm within the IoT system [23]. Some literature identified this kind of sensor failure as \"Binary failure\".
* Sensor Events: As the sensor is deployed to collect the data in real-world scenarios or events, there is a probability of unprecedented change in the event caused by unlikely situations that severely affects the sensor thereby causing outliers. For example, an IoT system with multiple sensors monitoring temperature and humidity levels in a farm, if a worm crawls on one of the sensors, the farmer will be getting readings on how moist and warm the worm is, these readings will be ineffectual for the system and hinders the performance of the whole monitoring IoT system.
* Intermittent Sensor Errors: The last category of sensor failure is the intermittent errors that mare majorly caused due to sporadic events like theft, malicious attack and tampering with the sensor [29; 30; 31]. A situation where a loose connector in the sensor or elsewhere within the sensing hardware could also cause the sensor to produce intermittent sparse data to the data processing algorithms [32].
# Application of Additive Manufacturing Technology for Chair Parts Connections
###### Abstract  
Additive manufacturing (AM) is a potential technology to be applied for replacing wood joints in the furniture industry. Overall, 3D-printed connectors have a lot of advantages in chair construction: the possibility of improving the design by using colored materials and new concepts with no limits of shape and size, avoiding the use of synthetic materials such as adhesives and finishing materials. The present research shows the results of the research conducted on 3D-printed connectors for a chair leg-stretchers joint. Larch (_Larix decidua_ Mill.) wood was used for chair components and selective laser sintering (SLS) technology was employed for the 3D-printing connector designed to assemble the chair elements. Diagonal tensile and compression tests were performed on the L-type joints with 3D-printed connectors and the bending moments were determined and compared to the results obtained after testing the leg-stretchers mortise-tenon joints made of larch wood, taken as a reference. Micrograph investigation was finally performed on the broken areas of the 3D-printed connectors as a result of mechanical testing failures. The results show that the SLS printing technology applied to build the connector does not ensure enough strength to the assembly, and changes in printing parameters are needed for further research, in terms of laser power and scan speed.  
additive manufacturing; 3D printing; chair parts; connector +
Footnote †: journal: Applied Sciences
## 4 Experimental Results and Analysis
### Test 1: Powdery Mildew vs. Healthy  
In this test, 45 powdery mildew and 45 healthy mango leaf images were classified. Table 2 shows that an accuracy of 96.6% was attained through cubic SVM. It proved to be the highest amongst all the other competing classifiers. Furthermore, 0.16, 0.97, 0.97, and 3.4 were the obtained values of sensitivity, specificity, AUC, and FNR, respectively. Sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a condition. The confusion matrix of this test is also given in Table 3.
## 3 Results and Discussion
### Investigation of the Theoretical Maximum Bandwidth  
#### 3.2.1 Equidistant Gap \(\Delta f\)  
In the preceding subsection, Figure 5 only showed the simulation of bandwidth up to a maximum of five cantilevers. However, it is possible to further increase the number of cantilevers and thus to increase the bandwidth. Figure 7 shows the theoretical maximum power of 12 cantilevers with \(\Delta f\) of 1.5 Hz and \(f_{0}\) of the first PEC equal to 40 Hz. It is shown that the last cantilevers do not significantly increase the bandwidth. The reason for this behavior is the smaller amplitude of the last PECs due the smaller beam deflection with the reduced tip mass. Figure 8 shows the increase in bandwidth dependence on the number of cantilevers. It can be seen that beginning from 10 cantilevers, the bandwidth does not significantly increase. Therefore, an efficient number of cantilevers can be estimated as 10 with a maximum bandwidth of 13 Hz.  
The above observation can be made for various \(\Delta f\). Figure 9a shows the dependence of the \"effective\" number of cantilevers (this means that the bandwidth does not significantly increase with an additional cantilever) on \(\Delta f\). It can be seen that if \(\Delta f\) is decreasing, more cantilevers can be used, but the total bandwidth (see Figure 9b) improves only slowly. A further interesting aspect is that, although the number of cantilevers is increased and \(\Delta f\) is reduced, the bandwidth does not exceed a value of about 15 Hz. This means that the mass tuning is rather limited and a wide frequency spectrum cannot be achieved by mass tuning alone.  
Figure 6: Simulated bandwidth of the array with large detuning \(\Delta f\) = 2.4 Hz.  
Figure 7: Simulation of a cantilever beam array with 12 cantilevers and \(\Delta f\) = 1.5 Hz.
## 2 Numerical Simulation
### Simulation Matrix  
The upscale and downscale simulations were conducted over 80 s, during which the top RPM was varied. This strategic approach enables comprehensive monitoring of torsional vibrations at three specific depths along the drill string, providing insights into how these vibrations propagate under differing torque conditions, as outlined in Table 1. By focusing on these key depths, the performance of the drill string in critical zones can be analyzed, enhancing understanding of the torsional dynamics involved. The decision to use steel as the material for each setup was pivotal. Steel's inherent properties, such as its high tensile strength and durability, ensure that every component can withstand the extreme stresses, vibrations, and potential corrosive effects encountered in the challenging environments of drilling operations. This material choice not only contributes to the structural integrity of the drill string but also plays a vital role in minimizing fatigue and failure under cyclic loading conditions. Additionally, the careful selection of essential parameters such as RPM, torque, density, and static and dynamic friction coefficients is crucial for accurately modeling the behavior of drill strings. These parameters were carefully chosen to align with the specific characteristics of each string configuration,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Length** & **OD** & **ID** & **Gauge** & **Total Length** & **Mass** & **Total Mass** \\ \cline{2-7}  & **(m)** & **(inch)** & **(inch)** & **(inch)** & **(m)** & **(kg)** & **(kg)** \\ \hline Bit & 0.03 & - & - & 0.20 & 0.03 & 0.001 & 0.001 \\ \hline Drill collar & 0.15 & 0.25 & 0.20 & - & 0.18 & 0.01 & 0.011 \\ \hline HWDP & 5.32 & 0.50 & 0.15 & - & 5.50 & 0.20 & 0.211 \\ \hline Drill pipe & 9.50 & 0.12 & 0.10 & - & 15.00 & 0.15 & 0.361 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modeled downscale string.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Length** & **OD** & **ID** & **Gauge** & **Total Length** & **Mass** & **Total Mass** \\ \cline{2-7}  & **(m)** & **(inch)** & **(inch)** & **(inch)** & **(m)** & **(kg)** & **(kg)** \\ \hline Bit & 0.5 & - & - & 5 5/8 & 0.5 & 28.16 & 28.16 \\ \hline DC \(\times\) 8 & 72 & 4 1/2 & 2 1/8 & - & 73 & 4506.18 & 4534.34 \\ \hline HWDP \(\times\) 10 & 111 & 3 1/2 & 2 1/16 & - & 184 & 3170.08 & 7704.42 \\ \hline DP \(\times\) 28 & 266 & 3 1/2 & 2 5/8 & - & 450 & 6416.2 & 14,120.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modeled upscale string.  
thereby enhancing the fidelity of the simulations. By rigorously calibrating these variables, the simulations capture the complex interactions and responses within the drill string during operational conditions. This not only leads to more reliable results regarding torsional behaviors but also provides deeper insights into optimizing drilling parameters.