esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Structure Identification
However, there are still some doubts about the valence state of the Fe atoms in it. For this reason, XPS tests were performed on La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\). The test results are shown in Figure 4, where Figure 4A is the survey spectrum, from which the presence of each element of La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) can be seen. Figure 4B is a fine graph of Fe 2p orbitals. From this figure, the binding energies of electrons in Fe 2p3/2 and 2p3/2 orbitals are 710.67 eV and 723.98 eV, respectively. By comparing these with the binding energies of electrons in 2p3/2 and 2p3/2 orbitals of Fe\({}^{3+}\) and Fe\({}^{2+}\) in the previous literature, it was concluded that Fe was coexisting in La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) as 2+ and 3+ [43]. However, the binding energy of Fe2p3/2 in La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) was closer to that of Fe\({}^{3+}\). Moreover, the distance of 2p3/2 from the satellite peak was 8.01 eV, which is closer to the 9 eV of Fe\({}^{3+}\), while it is farther from the 6 eV of Fe\({}^{2+}\)[44]. From these two phenomena, it can be concluded that more Fe in La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) was present as 3+, where the presence of Fe\({}^{2+}\) was due to the substitution of La and insufficient oxygen treatment. The comparison of Figure 4C,D with previous literature on La 3d and Ba 3d can yield evidence for the presence of La and Ba in the tested samples [45; 46]. Figure 3: The energy dispersive spectrum (EDS) of La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) ceramic specimen. Figure 2: SEM images of a La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) ceramic specimen, which was sintered at 1400 \({}^{\circ}\)C for 3 h and subsequently annealed in an O\({}_{2}\) atmosphere at 800 \({}^{\circ}\)C for 15 h in 3 steps.
# Pinion Failure Analysis of a Helical Reduction Gearbox in a Kraft Process
## 3 Investigation on Gear Design and Alignment  
Based on the visual evidence, the type of failure (plastic deformation) that took place in the teeth of the pinion (refer to Figure 3b) revealed that it might have been due to either design error or misalignment. Thus, it was decided to check the safety factor of the pinion and gear from the strength perspective (in bending and contact stress modes) at both loads (3 MW and 3.75 MW) using American Gear Manufacturers Association (AGMA) design methodology [25; 26; 27]. Because the failure of pinion teeth occurred at a localised position near the thrust disc (refer to Figure 3b), it was decided to check the misalignment between the pinion and gear at high load (3.75 MW), employing finite element analysis (using ANSYS software). The design and alignment (deformation) investigations are discussed below.
# Ground Stability Analysis in Non-Open-Cut Tunneling Method Using Small-Diameter Steel Pipe Piles
## 1 Introduction  
Owing to an increase in the urban population, traffic has become a major issue in the megacities of the world. To overcome this issue, road networks in large cities are becoming increasingly complex. The more complex a road network becomes, the more often a new road is constructed under the existing road. When such a construction has to be undertaken without interrupting the traffic, the non-open-cut method of tunneling is used. Several non-open-cut methods, such as the pipe roof structure (PRS), tubular roof construction method (TRCM), new tubular roof (NTR), roof panel shield (RPS), and front jacking methods, are used for constructing tunnels under existing roads.  
In the PRS, TRcM, NTR, and RPS methods, the tunnel is constructed by placing concrete under the roof that is formed by each method of arranging the steel pipes. During tunnel construction using medium- and large-sized (800-2800 mm) steel pipes, ground settlement or heave may occur duringthe process of pressing steel pipes or digging up soil inside steel pipes. Front jacking is a method of installing precast structures by towing them on the other side to a certain location after installing the pipe roof. In this method, a completely formed concrete box is used, and the tunnel structure is comparatively more stable than other non-open-cut methods. However, settlement is possible because of the gap between the structure and the pipe roof [1].  
As the ultimate goal of tunnel construction under existing roads is safety as well as minimum displacement during the construction, the non-open-cut method of tunneling using small-diameter steel pipe piles (o 114 mm, 6 T) to avoid problem that using medium- and large-sized is described in this paper.  
In this study, the steps of construction, which are impacted by the stability of the piles, were simplified and divided into two stages: model testing and numerical modeling. The first step of construction (Stage 1) involves pulling back the pressure panel, and the second step involves propelling the precast structure (Stage 2). A model test was conducted to analyze the tendency of the piles to remain stable based on the cover depth, tunnel size, and existence of shoe structure. A numerical analysis was conducted to verify the model test results and analyze the impact of varying the relative location between the pile and the tunnel face on the construction of the tunnel. Abaqus 2019 was used for the numerical analysis because it is capable of considering the interaction between the ground and the tunnel during large deformation analysis [2].
## 5 Noise Interference and Outlier Measurements  
During the organization of the dataset, an outlier measurement by the NT-SF1 microphone was identified at the receiver coordinate \((x=0.75,y=5.25,z=1.7)\) m labeled as \(00x04y\). The irregularity in the W-channel RIR at \(00x04y\), compared to the neighboring receiver point at \(00x03y\), is shown in Figure 11.  
Upon closer investigation, it became apparent that the anomaly was likely caused by air conditioner noise, given the proximity of a duct directly above this receiver point. Measurements repeated at and around this receiver point confirmed that the issue persisted at different heights at \((x=0.75,y=5.25)\) m. However, the distortion diminished when the receiver was shifted by approximately 50 cm in the \(x\)-\(y\) plane.  
Figure 11: RIRs measured by the NT-SF1 microphone in the wood panel environment with source at S2 and receiver at the coordinates (**a**) \(00x04y\) and (**b**) \(00x03y\).  
Figure 10: Center time (\(T_{s}\)) across frequency in octave bands for the felt and wood environments calculated from the RIRs measured by (**a**) Rode NT-SF1 microphone, (**b**) em32 Eigenmike.  
In the recording studio, there are four air conditioner ducts, but only the duct above 04x00y appeared to influence the measurements. The impact of the other ducts was either obscured by furnishings or mitigated by felt surfaces. It is noteworthy that the measurements taken by the em32 Eigenmike at this position did not exhibit any noticeable distortions.  
Increasing the SNR with respect to the receiver at 00x04y can mitigate the effects of noise interference. However, for this dataset, the loudspeaker volume was set to emulate typical studio performance levels and was kept unchanged while measuring at all receiver positions, reflecting real performance scenarios. Moreover, the large receiver region considered in our dataset makes SNR optimization impractical.  
Despite the acknowledged impact of air conditioner noise, it is essential to highlight that the RIR measurements were intentionally conducted without switching off the air conditioners to capture real recording scenarios. Therefore, the RIRs corresponding to the receiver code 00x04y measured by the NT-SF1 microphone are still included in the dataset but separated into an 'Outlier' folder, warranting consideration for their specific nature in any application.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 7 Challenges and Future Research Directions
### Feedback Based Retransmissions  
Conventional feedback-based LTE retransmission techniques like automatic repeat request (ARQ) or hybrid ARQ (HARQ) might not be efficient in FANET since the channel conditions are mostly hostile and poor due to rapid variations in the network topology [171]. Indeed, these techniques are applicable when the channel variation is slow [172]. Likewise, the performance of traditional modulation schemes such as M-QAM begins to decay as the channel variation increases in FANET which affects the data transmission rates. Hence, having a type of adaptive modulation and coding (AMC) scheme for different channel conditions is profitable. Indeed, AMC can be considered a solution to the performance degradation of the system in poor channel conditions. Furthermore, compared to the conventional feedback-based approaches of ARQ/HARQ, AMC can help as an alternative to estimate the CSI and send feedback to the transmitter. This can also be a great help for power control on the transmitter side, since power management is an important issue in FANETs.
# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 3 Methodology
### Image Segmentation  
In this study, we used the SegNet semantic image segmentation network, which is a modern and effective deep learning-based segmentation technique. It is a flexible, general approach and the current state-of-the-art for image segmentation [20]. This network can learn the data and be trained on those data to identify patterns and segment the important parts [21]. Here, there are reasons that lead us to use a modern CNN segmentation technique that learns and segments different images based on their characteristics. The first reason is that some types of sesame disease may reduce the size and change the structure of the leaves. The other is that since the images are field images, the color and intensity of the background, which is the surrounding field, are similar to those of the leaf part. Semantic segmentation (SegNet) is a CNN segmentation technique that associates each pixel of an image with a class label. It is currently utilized as a highly flexible technique. It has the ability to learn new features.  
Figure 4: (**a**) Original grayscale label image; (**b**) median filtered and contrast-enhanced image.  
Segmentation is performed using SegNet, a deep convolutional encoder-decoder network. The segmentation output S is obtained as follows:  
\[S(x,y)=\underset{k}{arg\ max}P(C_{k}\mid\boldsymbol{I^{\prime\prime}}) \tag{12}\]  
where \(P(C_{k}|l^{\prime\prime})\) is the probability of class \(C_{k}\) (leaf or background) for each pixel.  
To use a SegNet semantic segmentation network in our experiment, we followed these steps:  
1. Create pixel label images of the original sesame leaf images using the ground truth labeler;
2. Load the training data and create a pixel label data store and an original image data store;
3. Add the pre-trained VGG16 CNN model;
4. Load the pre-trained SegNet semantic segmentation network;
5. Train the SegNet network with our dataset images;
6. Display and store the segmentation results.  
After training the SegNet model using pixel-labeled sesame leaf images with the original sesame images, the trained network segmented the input images. Figure 5 shows the sample segmentation process and results.
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 3 Related Work
The approach employed deep learning, namely CNNs, to extract  
characteristics and categorize stroke events. The study found that LeNet achieved 96-97% accuracy for classification and 85-87% accuracy for segmentation; however, segmentation accuracy and high computational demand prevented real-time clinical applications. [PERSON] et al. [6] suggested a hybrid deep transfer learning system for improving stroke risk prediction. Their process includes fine-tuning a pre-trained model utilizing a hybrid approach while adhering to ethical norms and data protection protocols. The hybrid deep transfer learning-based stroke risk prediction (HDTL-SRP) approach outperformed earlier models and showed potential for 5G/B5G hospital integration; nonetheless, data privacy and heterogeneity issues may limit scalability and generalizability. [PERSON] et al. [7] reviewed neuroimaging and DL advances in stroke diagnosis. Their strategy included examining existing approaches, finding research gaps, and investigating the potential of deep learning in stroke detection. CNNs and fully convolutional networks (FCNs) performed well in stroke detection and segmentation but required large, annotated datasets, limiting their use. [PERSON] et al. [8] thoroughly assessed deep learning approaches for early ischemic stroke prediction, noting both strengths and drawbacks in current models. The process includes reviewing the literature, evaluating model efficiency, and recommending changes. The study found that the ConvNeXt Base model detected ischemic strokes with 84% accuracy using MRI scans, indicating the possibility for early intervention. Obtaining big, labeled datasets remains a significant difficulty. [PERSON] et al. [9] studied deep learning algorithms for ischemic stroke imaging analysis. The technique entailed examining current advances in stroke imaging and measuring the performance of deep learning models. While deep learning models greatly improved ischemic stroke diagnosis and clinical outcomes, their reliance on big, high-quality datasets and difficulty generalizing across clinical contexts limited their usefulness. Table 1 provides a summary of various other literature carried out in the field of ML and DL applications to brain stroke detection and prediction, highlighting the outcomes from each work.
## 3 Results and Discussion
### Adsorption Performances of DE and DE-LDH
#### 3.2.7 Mechanism of CR Adsorption on DE-LDH  
The possible mechanisms and the surface interactions of DE-LDH with the CR dye molecules were investigated by XRD, FTIR and BET. Figure 3a shows the XRD patterns of adsorbent DE-LDH before and after CR adsorption. There were few changes observed in the XRD pattern after adsorption, which indicates that the crystallinity of DE-LDH is stable even after dye adsorption; only a slight shift of the peaks due to crystallinity was observed after dye adsorption. Figure 3b shows FTIR spectra of DE-LDH before and after dye adsorption: it was noted that many new peaks (400-1000 cm\({}^{-1}\)) were observed after CR adsorption on the DE-LDH surface (Figure S2, Supplementary Materials) and the major hydroxyl group (-OH) shifted from 3471 to 3448 cm\({}^{-1}\) due to hydrogen bonding between one of the functional groups of CR (-N=N-) and the -OH of DE-LDH. After the dye adsorption, the peak at 1020 cm\({}^{-1}\) was found to be due to the symmetric stretching of the sulfonate group (-SO\({}_{3}\)\({}^{-}\)) of CR [28]. The FTIR spectra indicates that the high CR adsorption on DE-LDH is strongly dependent on the hydrogen bonds and electrostatic interaction. Furthermore, Table 1 evidently shows that the surface area of DE-LDH (51 to 44 m\({}^{2}\)/g) decreases after adsorption. Thus, the above results suggest that CR was strongly adsorbed on the pores of DE-LDH. Figure 9 shows a schematic representation of the adsorption mechanism of CR onto DE-LDH.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & & \multicolumn{3}{c}{**Pseudo-First-Order**} & \multicolumn{3}{c}{**Pseudo-Second-Order**} \\ \hline \multirow{3}{*}{**Adsorbents**} & **C0** & **qe, exp** & **K1** & **qe, cal** & & **K2** & **qe, cal** & \\  & **(mg/L)** & **(mg/g)** & **(1/min)** & **(mg/g)** & **R2** & **(g/mg/min)** & **(mg/g)** & **R2** \\  & **(\(\pm\)5)** & **(\(\pm\)3)** & **(\(\pm\)0.05)** & **(\(\pm\)1.5)** & & **(\(\pm\)0.05)** & **(\(\pm\)2)** & \\ \hline DE & 50 & 2.52 & \(-\)0.015 & 0.21 & 0.640 & \(-\)0.056 & 1.89 & 0.987 \\  & 100 & 4.50 & \(-\)0.013 & 0
## 3 Results
### Spatial Variability
#### 3.2.3 TRIM  
The daily precipitation estimates by the TRMM had the highest correlation and Willmott coefficients in SJRC (r = 0.51 and d = 0.69), and the lowest in PXR (r = 0.35 and d = 0.57). The lowest error in daily precipitation estimates was obtained in CCR (RMSE = 10.69 mm day\({}^{-1}\)) and the largest was obtained in MTP (RMSE = 13.72 mm day\({}^{-1}\)) (Figure 8A). The monthly precipitation estimates had the highest performance in DMT (r = 0.96; d = 0.98; and RMSE = 36.89 mm month\({}^{-1}\)) and the lowest (i.e., highest error) in  
Figure 7: Taylor diagrams showing the comparisons of (**A**) daily (mm day\({}^{-1}\)), (**B**) monthly (mm month\({}^{-1}\)), and (**C**) annual (mm year\({}^{-1}\)) measured and estimated precipitation, as well as (**D**) the performance diagram of the **MERRA** product at each of the weather stations in the state of Mato Grosso, Brazil. In panels (**A**–**C**), the Pearson correlation coefficient (r) is represented by the azimuth angle (black outlines), the RMSE was represented by the red dashed outlines, and the standard deviation (SD) was indicated by on the x and y axes, respectively. In panel (**D**), the probability of detection (POD) is represented on the x and y axes; the BIAS was indicated by the dashed black lines; and the critical success index (CSI) was represented by continuous black lines. For (**A**), the daily values for the entire study period were averaged, while for (**B**), the total monthly values for all months during the study period were averaged, and the annual values in (**C**) were the annual values averaged for all years during the study period.  
PRR (RMSE = 54.84 mm month\({}^{-1}\)) (Figure 8B). The annual precipitation estimates had the highest correlation and Willmott coefficients and the lowest error in GBC (r = 0.91; d = 0.94; and RMSE = 136.26 mm year\({}^{-1}\)), the lowest correlations and highest errors in RDP (r = 0.42; d = 0.59; and RMSE = 259.37 mm year\({}^{-1}\)) and PRR (r = 0.79; d = 0.85; and RMSE = 341.44 mm year\({}^{-1}\)) (Figure 8C).  
The daily precipitation estimates showed the highest performance in detecting the occurrence of daily events in MTP as indicated by the categorical indices (CSI = 0.58; POD = 0.68; and FAR = 0.19), and the lowest performance in CCR (CSI = 0.37; POD = 0.43; and FAR = 0.26). In addition, the BIAS indicated that the TRMM underestimated the occurrence of precipitation events with BIAS values ranged from 0.59 to 0.85 (Figure 8D).
## Underlying Theories  
### Ieee802.15.4 Standards  
IEEE802.15.4 is a standard for personal area networks (PAN) that is low-rate, low-power, and low-cost. In this standard, devices are divided into two types: full function device (FFD) and reduced function device (RFD). An FFD can function as a PAN coordinator, coordinator for part of the network, or ordinary device, whereas an RFD only functions as an ordinary device to send data to the PAN coordinator. The FFD, which acts as a coordinator, besides sending its own data, also functions to forward data sent by the RFD. Network topologies supported by the standard are star (single-hop), cluster tree, and mesh (multihop). In the star topology, all communication can be done only through the PAN coordinator, while in other topologies, communication can be done through several coordinators on a multihop basis. The 802.15.4 standard defines two types of channel access methods: beacon enabled (BE) mode and non-beacon enabled (NBE) mode, where the PAN coordinator ensures which one will be selected as the access method. BE mode uses the superframe structure (seeFigure 2), where the slotframe format is determined by the coordinator. Each slotframe is limited by beacons that are generated periodically by the PAN coordinator and divided into 16 slots of the same size. The time between two consecutive beacons is called the beacon interval (BI). The duty cycle principle used in the BE mode can be used for power management [24; 25].  
Optionally, each superframe can consist of active and inactive periods. In the active period, the nodes communicate with their coordinator, while during the inactive period the nodes enter low-power mode to save energy. The active period can then be divided into the contention access period (CAP) and contention free period (CFP). During the CAP, the CSMA-CA slotted algorithm is used to access the channel, whereas CFP communication uses the TDMA method with a number of guaranteed time slots (GTSs), which are predetermined for each node. There is no superframe in the NBE mode. Thus, the node is always active (energy conservation is delegated to the layer above the MAC protocol) and it uses an unslotted CSMA-CA algorithm to access the channel.  
A number of studies have examined the performance of the MAC 802.15.4 protocol, in both BE and NBE modes, so that limitations and deficiencies in the standard have been identified. Some of the limitations and disadvantages of the 802.15.4 standard are explained in [24]. These include unlimited delay, limited communication reliability, no protection against interference or fading, and relay nodes that always require energy. For this reason, the 802.15.4 standard is not suitable for many important scenarios that require stringent requirements in terms of timeliness and/or reliability.
# On the Association between Dental Implants, Osteoporosis and Bone Modulating Therapy
## 3 Results  
### Study Population  
The cohort (n = 792) comprised 37.2% males and 62.8% females. A total of 58% were aged \(\leq\)65, 33.4% were 66-79.9 years, and the remaining 8.6% were \(\geq\)80. Smokers comprised 5.1% of the cohort.  
At patient level--40.8% implants were placed in pristine and 59.2% in augmented bone. At least one implant failed in 14.4% of the patients.  
Most of cohort (90.6%) did not have osteoporosis (EIF 14.6%) while 9.4% had osteoporosis (5.2% received BMT (EIF 15.5%); 4.2% did not take medications (EIF 7.4%)).  
No cases of ONJ were reported up to one year post-implant-supported prosthesis delivery.  
All data were likewise measured at the implant level (n = 2971). The age distribution- was 57.7% \(\leq\)65, 34% 66-79.9 years and the remaining 8.3% were \(\geq\)80. Smokers' prevalence was 6.6%. Augmented implants comprised 61.2% and 38.8% were placed in pristine bone. EIF accounted for 3.8% of the implants. Most implants (90.9%) were placed in patients without osteoporosis (EIF 3.9%), 5.8% in patients with osteoporosis (EIF 4.5%) and used BMT, while the remaining 3.3% (EIF 1.6%) were in those who did not take medication despite being diagnosed with osteoporosis. For a complete description see Table 1 for implant level descriptive statistics and Table 2 for patient level descriptive statistics.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **M** & **SD** & **N** & **\%** \\ \hline \multicolumn{4}{l}{Demographic characteristics} \\ \hline \multicolumn{4}{l}{Age group (years)} \\ \hline \(\bullet\) & \(\leq\)65 & & 1715 & 57.7 \\ \hline \(\bullet\) & 66–79.9 & 1009 & 34 \\ \hline \(\bullet\) & \(\geq\)80 & 247 & 8.3 \\ \hline \multicolumn{4}{l}{Smoking} & 195 & 6.6 \\ \hline \multicolumn{4}{l}{Clinical Characteristics} \\ \hline \multicolumn{4}{l}{Augmentation} \\ \hline \(\bullet\) & Pristine & & 1153 & 38.8 \\ \hline \(\bullet\) & Augmented & & 1818 & 61.2 \\ \hline \multicolumn{4}{l}{Implant length (mm)} & 11.38 & 1.60 \\ \hline \multicolumn{4}{l}{Implant diameter (mm)} & 3.84 & 0.31 \\ \hline \multicolumn{4}{l}{EIF} \\ \hline \multicolumn{4}{l}{Osteoporosis} & 279 & 9.4 \\ \hline \(\bullet\) & No osteoporosis & & 2692 & 90.6 \\ \hline \(\bullet\) & Osteoporosis with BMT & & 155 & 5.2 \\ \hline \(\bullet\) & Osteoporosis without BMT & & 124 & 4.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic and clinical characteristics of the cohort at implant level (n = 2971).