esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Classification of Routing Protocols
### Temperature-Aware Routing Protocols
#### 4.2.6 Routing Algorithm for Network of Homogeneous and ID-Less Bio-Medical Sensor Nodes (RAIN)  
RAIN [57] is a routing protocol that aims to reduce the average temperature rise and average power consumption of bio-medical sensor nodes. The three phases of RAIN are: setup phase, routing phase and status update phase. The term ID-less does not mean that the sensor nodes do not use IDs at all. Instead of using static global IDs, RAIN uses temporary IDs. These temporary IDs are randomly generated during the setup phase between one and (2\({}^{16}\)-1), valid for its operation life. ID 'zero' is reserved for the sink node. All nodes including the sink node, broadcast their IDs through hello packets.  
In the routing phase each generated data packet having a unique packet ID [N,T,R], is routed towards the destination node using the multi-hop technique, where 'N' is the node's ID, 'T' is the time at which the data packet is generated at the source node, and 'R' is a random number. A hop-count is associated with each packet to stop the infinitely routing loops. If the hop-count exceeds the threshold value of HOP_THRESH, then it is discarded. Each node maintains the list of the packets IDs to prevent the duplicate transmission of packets and a data packet is dropped if its ID already exists in that list. Each node also maintains the estimated temperatures of the neighbor nodes by monitoring their communication activities. If destination node is among the neighbor nodes, the packet is forwarded to it; otherwise it is forwarded to the neighbor node using probability which is inversely proportional to its temperature. In status update phase, the sink informs all its neighbor nodes if it receives any data packet by sending the packet's ID to reduce the power consumption of the node.
# Emission of Terpenoid Compounds from Rice Plants
## 3 Results  
### Flow-Through Chamber Measurement in the Field  
One of the monoterepene alcohols, linalool, was detected, and its highest emission rate was observed at the first or second (2.5 h later) sampling session (panels a\(\sim\)c in Figure 1). The linalool emission rate decreased in the late afternoon. Similar time courses of linalool emission were observed in four replications for each of the three cultivars. No other monoterepenes or isoprene were detected in these emissions. Two typical mass spectra of sesquiterpenes were observed, but were not identified because of the limited number of available sesquiterpene standards. The peak heights of the total ion chromatograms of the two sesquiterpenes were less than 1/20 of that of linalool and were not considered for further analysis. The emission rate of \"Koshihikari\", \"Nipponbare\", and \"IR72\" ranged between 0.055 and 0.93 \(\upmu\)g gDW\({}^{-1}\) h\({}^{-1}\), 0.35 and 1.26 \(\upmu\)g gDW\({}^{-1}\) h\({}^{-1}\), and 0.01 and 0.53 \(\upmu\)g gDW\({}^{-1}\) h\({}^{-1}\), respectively (Table 1).  
Figure 1: Time course of the emission rate of linalool (**a–c**), and its relationships with photosynthetic photon flux density (PPFD) (**d–f**) and leaf temperature (**g–i**). Black arrow indicates the time when the aboveground parts of the rice plant were enclosed in the bag.  
The linalool emission rate was plotted against PPFD (panels d-f in Figure 1) and leaf temperature (panels g-i in Figure 1). They seemed to increase with leaf temperature in some cases, but no clear relationships were found in other cases. This is also the case for the relationship between the emission rate and PPFD.  
In the enclosing procedure, manipulating rice stems and leaves by hand might cause slight damage to the rice plants and trigger an emission burst of oxidation products [15]. However, the emission of linalool seems to be affected by the temperature and light intensity in some cases (Figure 1). Because the measurements started at 8-9 a.m., it was difficult to separately evaluate individual factors (wounding damage or environmental conditions). To check if the emission was caused by the wounding effect, the emission data were collected under constant temperature and light intensity in the laboratory.
## 3 Results and Discussion
### SI Characteristics
#### 3.2.2 Roughness  
The average value of the Ra roughness parameter obtained via turning (i.e., before burnishing) was 0.471 \(\upmu\)m. The specimens' 2D roughness height, shape, and Rk group parameters obtained in different DB configurations are listed in Table 5. Figure 5 shows a comparison of the respective roughness profiles, while Figure 6 shows a comparison of the most common Ra values. The four DB processes significantly reduce the height roughness parameters compared to turning, resulting in mirror-like surfaces. The Ra parameter is reduced between 9.6 and 11.5 times, with a difference of 8 nm between the maximum and minimum Ra values. The DB processes produce surface textures with negative skewness (Rsk) and kurtosis (Rku) values greater than three. The Rsk and Rku parameters directly influence the operational behavior of the corresponding component [57; 58]. More negative skewness values and kurtosis values greater than 3 are interpreted to improve a specimen's tribological behavior, especially in the boundary friction and lubrication friction regimes [46; 59]. From this perspective, the dry DB process (specimen D) produces a suitable surface texture. The better oil-holding capacity of specimen D is also confirmed by the largest value of the functional parameter Rvk (see Table 5) [57].  
Figure 5: Roughness profiles of the diamond-burnished specimens: (a) F\({}_{7}\)(b) A; (c) N; (d) D.
# Sensing and Device Neighborhood-Based Slot Assignment Approach for the Internet of Things
## 3 Motivation  
Simultaneous communication is among the common issues associated with resource constraint networks, such as the IoT and wireless sensor networks. To resolve this issues, TDMA-enabled communication approaches, which were specifically designed for the resource-limited networks, were reported in the literature. These approaches have resolved the aforementioned issue, i.e., the simultaneous communication of multiple devices with a common destination device, preferably with available resources; however, existing approaches were either designed for a particular application area or were very complex. As per our understanding and knowledge, these benchmark approaches (i.e., either flat or hierarchical networks) for available slots are either assigned in a dedicated or in a preemptive nature to numerous requesting or member devices \(C_{i}\) in an operational IoT network. However,these approaches are not realistic, as devices that usually belong to the IoT or WSNs are deployed randomly. Apart from it, the majority of the existing approaches are based on an unrealistic assumption, i.e., that member devices \(C_{i}\) in an IoT should be similar; that is not always possible in these networks. Moreover, existing schemes do not have a clear policy for scenarios where a member device \(C_{i}\) is eager to move into the coverage area of another server or CH \(S_{j}\) module in the operational IoT networks. Therefore, a reliable TDMA-enabled schemes needs to be developed that has the capacity to resolve the aforementioned issues (preferably, the mobility of member devices \(C_{i}\)without changing the available technological infrastructure of the IoT networks. Generally, every member device \(C_{i}\) and server \(S_{j}\) has a specific neighborhood, as depicted in Figure 1, at a particular time interval, irrespective of the device mobility. If a uniform slot allocation policy (static or dynamic), as prescribed by the existing approaches, is utilized throughout the IoT network where member devices \(C_{i}\) are allowed to move from one server to another, then it is high likely that a portion of device will be in a constant waiting state while highest priority devices \(C_{i}\) are served first. However, if this slot allotment policy is refined according to the neighborhood of either a member device \(C_{i}\) or a server \(S_{j}\), then the above-mentioned problems will be resolved.
## 2 Materials and Methods
### Study Selection  
The initial literature search was conducted following predefined inclusion criteria. Studies were considered eligible if they were empirical research (excluding reviews, single-case studies, books, or manuals) and focused on the general population or individuals with stress-related conditions, obesity, or specific dietary patterns. Additionally, selected studies had to employ validated stress measures, such as cortisol levels or psychological assessments of stress and anxiety; in the latter case, studies on anxiety were included in the review, as several papers examined stress-related phenomena while utilizing anxiety measures. The review included research examining psychobiotics or adaptogens within the fields of psychology, medicine, or neuroscience, published between 2014 and 2024, with full-text availability in open access. Only studies published in English or Spanish were considered.  
Studies that were excluded were reviews, single-case studies, books, and manuals; studies focusing exclusively on clinical populations unrelated to stress (e.g., severe psychiatric or neurodegenerative disorders) without clear relevance to stress modulation; interventions not involving psychobiotics or adaptogens as primary components; studies lacking validated stress-related outcome measures; publications in languages other than English or Spanish; and studies without full-text open-access availability.  
Two independent reviewers ([PERSON] and [PERSON]) assessed the search results from each database separately, selecting studies based on their abstracts and full texts. Articles were included only if both reviewers reached an agreement. If any discrepancies arose, a third reviewer (N.S.-M.) was involved in resolving the disagreement and establishing a consensus.
## 4 Characterization and Performance Analysis
### SQM Variation Profiles
For BPSK(1) with the narrow correlator scheme, [31] showed that for short-delay multipath, the in-phase tracking range error envelope linearly increases from zero to \((1+\alpha)d_{trk}/2\) (\(\alpha\) is the ratio of reflected and LOS signal amplitudes and \(d_{trk}\) is the early-late tracking correlator spacing) with respect to the multipath signal delay (\(\tau\)). Considering this, it can be shown that for \(\tau\leq(1+\alpha)d_{trk}/2\), when \(d_{trk}<d_{mon}\leq 1\) is hold (\(d_{mon}\) is the monitoring correlator spacing), both early monitoring and tracking correlators are located in Region 2 and late tracking and monitoring correlators are both located in Region 4. Under these circumstances, since the correlators located in Regions 2 and 4 are affected by multipath symmetrically, the early-minus-late value for monitoring pairs of correlators will be equal to the tracking ones, resulting in zero output for the double Delta SQM metric. Even for the Delta metric, since DLL keeps the tracking early-minus-late value around  
Figure 4: SQM variation envelopes of Double-Delta metric for **(a)** High Resolution Correlator (HRC) strategy and BPSK(1) signaling; **(b)** High Resolution Correlator (HRC) strategy and BOC(1,1) signaling. zero, monitoring early-minus-late values will be kept around zero as well. For \((1+\alpha)d_{trk}/2<\tau\), depending on multipath delay and correlator spacing, the early correlators will mutually be in different regions or late correlators will be in different regions or early late correlators will be in different regions or early late correlators will be affected by the multipath correlation curve asymmetrically, resulting in non-zero SQM envelopes. This will occur until \((1+d_{mon}/2)<\tau\) when the multipath correlation curve has no overlap with tracking and monitoring correlators anymore. Using a similar argument, it can be shown that the out-of-phase envelope takes a zero value for \(\tau\leq(1-\alpha)d_{mon}/2\). A similar argument can be made for the HRC discriminator and BOC(1,1) signaling schemes.
## 5 Comparison between Predictive Analysis and Test Results
### Surface Resistivity  
As shown in Figure 17a,b, under normal scale coordinates and semi-logarithmic scale coordinates and the exponential, the hyperbolic function model is used to predict the resistivity of the CLSM considering the curing age and fixed water-binder ratio. The trend of the different desulfurization slag contents was compared with the test results. The analysis results show that the resistivity prediction model based on the hyperbolic model is based on the relationship between the resistivity and curing age and can accurately evaluate the trend of resistivity change under different desulfurization slag contents and curing ages. The analysis shows that the MAPE value is 11.64% under different desulfurization slag contents and curing ages when the water-binder ratio is 1.5. Thus, the surface resistivity prediction model proposed by [PERSON] (2017) [PERSON] (2017) cannot accurately be simulated for the later resistivity increase trend, but the analysis result is also good. The MAPE value is only about 10%, as [PERSON] (2017) proposed, but if the MAPE is less than 10%, the model has an excellent predictive capability. Thus, the surface resistivity prediction model set up in this study has a good predictive ability.
###### Abstract  
Robust localization and mapping are crucial for autonomous systems, but traditional handcrafted feature-based visual SLAM often struggles in challenging, textureless environments. Additionally, monocular SLAM lacks scale-aware depth perception, making accurate scene scale estimation difficult. To address these issues, we propose D3L-SLAM, a novel monocular SLAM system that integrates deep keypoints, deep depth estimates, deep pose priors, and a line detector. By leveraging deep keypoints, which are more resilient to lighting variations, our system improves the robustness of visual SLAM. We further enhance perception in low-texture areas by incorporating line features in the front-end and mitigate scale degradation with learned depth estimates. Additionally, point-line feature constraints optimize pose estimation and mapping through a tightly coupled point-line bundle adjustment (BA). The learned pose estimates refine the feature matching process during tracking, leading to more accurate localization and mapping. Experimental results on public and self-collected datasets show that D3L-SLAM significantly outperforms both traditional and learning-based visual SLAM methods in localization accuracy.  
learned local features; visual SLAM; line segment; pose prior; virtual stereo +
Footnote †: journal: Medical Image Computing  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] D3L-SLAM: A Comprehensive Hybrid Simultaneous Location and Mapping System with Deep Keypoint, Deep Depth, Deep Pose, and Line Detection.  
_Appl. Sci._**2024**, _14_, 9748. [[https://doi.org/10.3390/app14219748](https://doi.org/10.3390/app14219748)]([https://doi.org/10.3390/app14219748](https://doi.org/10.3390/app14219748))
## 2 Extraction Techniques
### Solid-Phase Microextraction (SPME)
#### 2.1.1 Extraction Mode  
For the SPME extraction, fused silica coated fiber can be introduced into the sample in three different ways: (i) direct extraction; (ii) headspace (HS-SPME); and (iii) extraction with membrane protection. Obviously, there are many factors affecting both of these sampling procedures, and some of these will be discussed in the current manuscript [PERSON] et al. (2010); [PERSON] et al. (2010); [PERSON] et al. (2010). The extraction efficiency of each mode depends on the analytes properties and the sample matrix (reviewed in [PERSON] et al. (2010)).  
(i) Direct extraction (DI-SPME)  
The coated fiber is directly immersed in the aqueous samples, and the analytes are transported directly from the sample matrix into the extracting phase. The sample agitation is often carried out with a small stirring bar to decrease the time necessary for equilibration time and to improve the analyte transportation from the sample bulk to the fiber vicinity [28,29].  
(ii) Headspace (HS-SPME)  
In headspace mode, the analytes are extracted from the gas phase above a gaseous, aqueous or solid sample. The primary reason for this modification is to protect the fiber from adverse effects caused by non-volatile, high molecular-weight substances present in the sample matrix (e.g., proteins). The headspace mode also allows for matrix modifications (including pH adjustment) without affecting the fiber. In a system consisting of a liquid sample and its headspace, the amount of an analyte extracted by the fiber coating does not depend on the location of the fiber (in the liquid or gas phase) [28]. The analyte amount sorbed on the fiber, and the resulting sensitivity, are determined both by sorption kinetics and the distribution coefficient of the compound between the coating fiber, the headspace and the sample (reviewed in [30]).  
(iii) Extraction with membrane protection  
A selective membrane separated the sample from the fiber, which lets the analytes through, while blocking the interferences. The main purpose for the use of the membrane barrier is to protect the fiber against adverse effects caused by high molecular-weight compounds when very complex samples are analyzed [28].
## 3 Results and Discussion
### Effects of Processing on 5-HMF and Acrylamide Content  
The results indicate that the puffing process of wheat grain led to a significant increase in the content of 5-HMF, with nearly a 1300-fold rise observed in comparison to the initial value (Table 4). The increase in the content of 5-HMF as an intermediate in the Maillard reaction is linked to the increased temperatures that grains encounter during the processes of puffing and the direct dehydration of sugars, notably fructose and glucose, as part of caramelization [60]. The results showed a 5-HMF content of 622.98 \(\pm\) 0.02 mg/kg in puffed wheat grains, which is significantly higher than the content of 66.6 mg/kg reported by [PERSON] et al. [5]. The difference could be linked to the differences in puffing process parameters. Research by [PERSON] et al. [61] found that wheat-based breakfast cereals tend to have higher 5-HMF content than rice-based breakfast cereals. The formation of 5-HMF depends on many factors such as temperature, water activity, the type of sugars present in the matrix, and protein content [18; 53].  
Similarly, acrylamide, a known carcinogen, forms in carbohydrate- and protein-rich foods during heating at high temperatures (above 120 \({}^{\circ}\)C). The formation of acrylamide involves a reaction between reducing sugars (glucose, fructose) and the amino acid asparagine [55]. The study determined that the puffed wheat grains contained acrylamide at a concentration of 240 \(\pm\) 60 \(\upmu\)g/kg. The determined acrylamide concentration complies with the requirements of European Commission Regulation 2017/2158 [62], which states that the acceptable acrylamide concentration in wheat-based breakfast cereals should not exceed 300 \(\upmu\)g/kg. As shown in Table 4, the acrylamide concentration in untreated wheat samples was below 20 \(\upmu\)g/kg. The concentration of free asparagine in grains affects the concentration of acrylamide in breakfast cereals. The asparagine concentration in grains according to various studies [23; 61] varies depending on the harvest year, fertilizer application, wheat variety, and storage conditions.  
Both 5-HMF and acrylamide are byproducts of thermal processing, raising safety concerns. The acceptable daily doses of 5-HMF range from 80 to 100 mg/kg of body weight [22]. If a person weighs 70 kg, the acceptable daily dose should not exceed 5600-7000 mg of 5-HMF.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Variable** & **Wheat** & **Puffed Wheat Grains** \\ \hline
5-HMF, mg/kg & 0.47 \(\pm\) 0.03 & 622.98 \(\pm\) 0.02 \\ Acrylamide, \(\upmu\)g/kg & \(<\)20 & 240 \(\pm\) 60 \\ \hline \hline \end{tabular} Note. Average (\(n\) = 3) \(\pm\) standard deviation. 5-HMF—5-hydroxymethylfurfurural.  
\end{table}
Table 4: Content of 5-HMF and acrylamide in whole and puffed wheat grains.
## Materials and Methods
### Auto-Regressive and Moving Average Model (ARMA)  
The auto-regressive moving average model is an important method for studying time series. It uses an auto-regressive model (referred to as the AR model) and a moving average model (referred to as the MA model) as a basis for \"mixing\". It uses modern statistics and information-processing techniques to investigate time series law, which is a group of powerful tools for solving practical problems. Time series laws have been widely used in many fields such as finance, economy, meteorology, hydrology, and signal processing. Based on the historical data of the sequence, this reveals the structure and regularity of the dynamic data, and quantitatively understands the linear correlation between observable data. Time laws use mathematical statistics to process and predict its future value. The ARMA model is used as a predictive model and its basic principles are as follows [37; 38; 39]:  
Let \(X(t)=\{X_{t},t=0,\pm 1,\cdots\}\) be a 0-mean stationary random sequence and satisfy for any \(t\)  
\[X_{t}-\phi_{1}X_{t-1}-\cdots-\phi_{p}X_{t-p}=\varepsilon_{t}+\theta_{1} \varepsilon_{t-1}+\cdots+\theta_{q}\varepsilon_{t-q}, \tag{15}\]  
where \(p,q\) is the ARMA model order; \(\varepsilon=\{\varepsilon_{t},t=0,\pm 1,\cdots\}\) is the white noise sequence with variance \(\sigma^{2}\); \(\phi,\theta\) is a non-zero undetermined coefficient; and \(\{X_{t}\}\) is the ARMA \((p,q)\) process with mean u [40].
# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 3 Methodology  
In this section, we demonstrate the approaches adopted in our study for machine translation. In addition, we demonstrate the evaluation metrics in our experiments. Notably, for MT, we study the effect of model design and back-translation on the quality of MT models. Our focus is on low-resource English-Bengali translation.  
As stated earlier, for our study, we proceed with LSTM (without attention mechanism) and Transformer-based architecture, as demonstrated in Section 4. As stated earlier, this is our baseline study to evaluate whether Transformer models can perform better in our study of English-Bengali machine translation. In our further study, we use the baseline Transformer as the basis and employ back-translation with the decoding strategies mentioned earlier. Moreover, we exploit the OpenNMT [67; 68; 69] toolkit to build the framework from input text in the source language to output text in the target language. OpenNMT is an open-source platform that provides various pre-processing and post-processing methods as well as encoder and decoder model structures for sequence transduction tasks. By using OpenNMT, we can explore the performance of customized LSTM and Transformer models in the English-Bengali translation task. Besides the selection of model structure, the training data is also critical for NMT tasks. However, as a low-resource language pair, the English-Bengali translation lacks parallel data. Therefore, we employ back-translation to augment the training data.