esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Path Establishment (Route Discovery)
#### 3.5.1 Proactive (Table-Driven)  
In proactive or table-driven routing protocols, the route is known prior to the requirement by dint of the maintenance of forwarding and an advertising table in the memory of every sensor node. The forwarding table contains a list of destinations, combined with one or more next-hop neighbors that lead toward these destinations in addition to the costs associated with each next-hop option and the advertised one records links and their variations as long as their status change using different methods to update them. Consequently, packets can be sent out to their destination immediately, without the delay imposed by route discovery. However, proactive protocols may be overly aggressive because a certain amount of control traffic, independently of data traffic on the network, is needed and must always be present to keep routing tables up to date and consistent over the whole network, which may consume large amounts of bandwidth and energy although many established routes may never be needed. Among recent proactive protocols used in WSNs, we find:  
* **SinkTrail**  
SinkTrail is a proactive routing protocol [58]. It uses logical coordinates to infer distances and selects the best route establishes for data reporting routes. It reduces energy consumption and supports mobile sinks in large-scale networks.
# Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition
###### Abstract  
Amed entity recognition (NER) is a fundamental task in many natural language processing (NLP) applications, such as text summarization and semantic information retrieval. Recently, deep neural networks (NNs) with the attention mechanism yield excellent performance in NER by taking advantage of character-level and word-level representation learning. In this paper, we propose a deep context-aware bidirectional long short-term memory (CaBiLSTM) model for the Sindhi NER task. The model relies upon contextual representation learning (CRL), bidirectional encoder, self-attention, and sequential conditional random field (CRF). The CaBiLSTM model incorporates task-oriented CRL based on joint character-level and word-level representations. It takes character-level input to learn the character representations. Afterwards, the character representations are transformed into word features, and the bidirectional encoder learns the word representations. The output of the final encoder is fed into the self-attention through a hidden layer before decoding. Finally, we employ the CRF for the prediction of label sequences. The baselines and the proposed CaBiLSTM model are compared by exploiting pretrained Sindhi GloVe (SdGoVe), Sindhi fastText (SdfasText), task-oriented, and CRL-based word representations on the recently proposed SiNER dataset. Our proposed CaBiLSTM model achieved a high F1-score of 91.25% on the SiNER dataset with CRL without relying on additional handmade features, such as hand-crafted rules, gazetteers, or dictionaries.  
Sindhi named entity recognition; recurrent neural networks; CaBiLSTM; self-attention mechanism; contextual representation learning +
Footnote †: journal: Medical Image and Vision
## 4 Noise Control
### Optimization of Structural Parameters
\begin{table}
\begin{tabular}{c c c c c} \hline
**Structural Parameters** & **Before Improvement** & **Lower Limit** & **Upper Limit** & **After Improvement** \\ \hline Blade outlet angle of the first stage impeller [\(\lx@math@degree\)] & 52.0 & 50 & 54 & 52.5 \\ \hline Blade outlet angle of the second stage impeller [\(\lx@math@degree\)] & 51.0 & 50 & 54 & 53.4 \\ \hline Outlet radius of the first stage impeller [mm] & 33.1 & 26 & 40 & 37.1 \\ \hline Outlet radius of the second stage impeller [mm] & 33.5 & 26 & 40 & 35.2 \\ \hline Width of the first stage diffuser [mm] & 4.0 & 2 & 6 & 4.9 \\ \hline Width of the second stage diffuser [mm] & 3.0 & 2 & 6 & 3.2 \\ \hline Outlet diameter of the first stage volutic [mm] & 36.8 & 30 & 44 & 41.6 \\ \hline Outlet diameter of the second stage volutic [mm] & 39.4 & 30 & 46 & 39.4 \\ \hline \end{tabular}
\end{table} 
Table 6: Ranges and changes in structural parameters before and after improvement. Figure 18: Static entropy contours of 90% heights of impellers. (**a**) First stage. (**b**) Second stage. The aerodynamic acoustic analysis of the improved air compressor was carried out. After the centrifugal impeller is improved, the aerodynamic noise improvement results at the near-field Monitoring Points SP1 and SP2 under the idle condition are shown in Figure 21.  
After the improvement of the structure, the \"buzz-saw\" noise at the rotating fundamental frequency and its doubling frequency noise are improved. Tables 7-9 lists the noise improvement effects of the rotating fundamental frequency and the doubling frequency of the impeller under different working conditions in detail. Figure 19: Static entropy contours of 50% heights of impellers. (**a**) First stage. (**b**) Second stage. Figure 20: Static pressure contours of diffusers and solutes after improvement. (**a**) First stage. (**b**) Second stage. After the structure of the centrifuge is improved, the aerodynamic noise of the compressor at the fundamental frequency and the second harmonic frequency of the impeller rotation is improved under different operating conditions, and the difference reaches 12.6 dBA.
## 3 Methods
### Study Phases
#### 3.4.3 Prototype Evaluation  
All eight co-design participants were contacted via email to participate in the evaluation phase. Participants were sent a link to an online video demonstrating how the prototype application works, and they were also sent the URL of the working prototype hosted on the web server. After viewing and interacting with the prototype, participants were required to evaluate the prototype by completing the mHealth App Usability Questionnaire (MAUQ) [39]. The MAUQ is a validated questionnaire with different versions available for patients as well as healthcare providers. We chose the healthcare provider version, with a slight modification in wording to fit into the context of the prototype. The MAUQ for healthcare providers is an 18-item questionnaire categorized into three subscales--ease of use, interface and satisfaction, and usefulness. One item related to poor internet connection was not relevant to this study and was, therefore, excluded. Of the remaining three Diffusion of Innovation attributes, complexity was tested via the ease-of-use items, and trialability was tested via the items from the interface and satisfaction, and usefulness categories. Positive and negative opinions about the prototype were measured using a 5-point Likert scale, with 1 indicating participants strongly disagreeing, and 5 indicating participants strongly agreeing with the statements. An additional open-ended question was added to the usability questionnaire asking about any additional features that participants felt would be valuable to incorporate in future iterations of the prototype.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 3 Trajectory Data Sparsity and Anomaly Detection
### Trajectory Preliminary Restoration  
Trajectory restoration aims to enhance the integrity and reliability of trajectory data. Among the many methods for trajectory restoration, the interpolation method is widely applied, especially the cubic spline interpolation method. This method constructs a smooth continuous function curve by fitting the given scatter data, and its advantage is that it can retain the trend features of the original data to more accurately restore the real change trend of the trajectory data. In addition, it is characterized by high interpolation accuracy and good smoothness, making it suitable for processing complex trajectory data with preliminary restoration.  
In this study, based on identifying the location and number of trajectory restoration points, the preliminary trajectory restoration is carried out by using the cubic spline interpolation method. The restoration process is as follows: assume that a vessel trajectory is \(\text{Traj}=\{\text{point}_{1},\cdots,\overline{\text{point}_{i}},\cdots, \text{point}_{i+l},\cdots,\overline{\text{point}_{i+m}},\cdots,\text{point}_ {n}\}\), where \(\text{point}_{i+l}\) represents a certain trajectory point in the vessel trajectory and \(\overline{\text{point}_{i}}\) is the restoration point after the trajectory abnormality judgment. Through the cubic spline interpolation method, the marked trajectory restoration point is restored as \(\widehat{\text{point}_{i}}\). Finally, the preliminary restoration of the vessel trajectory, \(\widehat{\text{Traj}}=\Bigl{\{}\text{point}_{1},\cdots,\widehat{\text{point }_{i}},\cdots,\text{point}_{i+l},\cdots,\widehat{\text{point}_{i+m}}, \cdots,\text{point}_{n}\Bigr{\}}\), is obtained.
## 2 The Improved Gas Leakage Model of Pressure Pipeline  
### The Hole Models for Gas Leakage  
The refrigerant leakage characteristics in refrigeration systems are usually analyzed by means of the hole model in many studies. In the hole model, the leakage process of gas in the pipeline is shown in Figure 1. The process zero to one is the gas transportation process from the steady state point to the leak hole, and the process one to two is the gas leakage process from the inside of the pipeline to the external space through the leak hole. The flow state of the gas at the leak hole is considered, which is divided into sonic flow and subsonic flow based on the critical pressure ratio (CPR) [9]. For the different flow states of the gas in the leakage section, the corresponding solution methods are given in the hole model, and their expression are as follows:  
\[\left\{\begin{array}{l}CPR=\frac{p_{a}}{p_{\sigma}}=\left(\frac{2}{k+1} \right)^{\frac{k}{k-1}}\\ Q=\xi A_{\sigma\
u}p_{1}\sqrt{\frac{kM}{K1}\left(\frac{2}{k+1}\right)^{\frac{k +1}{k-1}}}\\ Q=\xi A_{\sigma\
u}p_{1}\sqrt{\frac{2k}{K-1}\frac{M}{K1}\left[\left(\frac{p_{a }}{p_{1}}\right)^{\frac{2}{2}}-\left(\frac{p_{a}}{p_{1}}\right)^{\frac{k+1}{k }}\right]}\\ \end{array}\right.\quad p_{1}\geq p_{\sigma}\text{ The gas is in sonic flow state} \tag{1}\]  
where \(p_{a}\) is the atmospheric pressure of the surrounding environment, MPa; \(p_{\sigma}\) is the critical pressure, that is, the corresponding pressure at the point 1 when the gas in the leakage section changes from subsonic flow to sonic flow, MPa; \(p_{1}\) is the pressure inside the pipeline, MPa; \(Q\) is the mass flow rate of the leaking gas, kg/s; \(A_{\sigma\
u}\) is the leak hole area, m\({}^{2}\); \(\zeta\) is the orifice loss coefficient; \(k\) is the isentropic index; \(M\) is the kilomolar mass of the gas, kg/kmol; \(R\) is the gas constant, kJ/(kmol\(\cdot\)K).  
The hole model reveals the leakage mechanism of the gas in the pipeline to a certain extent, but there are still limitations: (1) the change of the gas state parameters in the interval from the leakage port in the pipeline to the outer wall of the pipeline during the gas leakage process is ignored in the modeling; (2) the gas flow state in the leakage section is directly determined according to the given critical pressure ratio formula, and the pressure of the gas at the outlet of the leakage section is assumed. For example, when \(p_{1}<p_{\sigma}\), the pressure of the gas at the leak hole is equal to the atmospheric pressure, \(p_{2}=p_{a}\); when \(p_{1}\geq p_{\sigma}\), the pressure of the gas at the leak hole is equal to \(p_{1}\cdot CPR\), \(p_{2}=p_{1}\cdot CPR\). The above limitations will lead to certain deviations in the calculated leakage mass flow rate and state parameters of the leaked gas.
## 4 Results
### Response of SIF to Drought
More comprehensively, [PERSON] _et al._[44] reported that both water and heat stress lead to a decline in chlorophyll fluorescence. In this study, the positive correlations between GOME-2 SIF and PDSI indicate that plants emit weaker SIF under the combined stress of heat and water. The main conclusion that can be drawn from the correlations between SIF and the various drought indices is that GOME-2 SIF responds significantly to drought over seasons and years, and positive correlations between SIF and drought indices were found. This provides the basis for the use of satellite-measured SIF to monitor large-scale droughts. Figure 6: Correlation coefficients of short-term standard precipitation indices (SPIs) and solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) from June to August. Results for CD 1401, CD1404, CD2501, and CD3901 are indicated by (**a–d**), respectively. The “\(n\)” in the subfigure represents the sample number of the regression analysis while the “\(p\)” represents the lowest significance level of the linear relationship between SIF and SPI.
## 1 Introduction
It is worth emphasizing that the content of bioactive compounds in garlic, and thus its antimicrobial properties, depend on many factors, such as the plant variety, growing factors, as well as storage and processing after harvesting [13]. Due to the high moisture content, which affects its germination and the degradation of its nutritional and pharmacological values, garlic has a relatively short storage period. Storing garlic at lower temperatures can extend this period to 9 months at a temperature of \(-1.5\,^{\circ}\mathrm{C}\)[15], but low-temperature storage significantly increases the costs incurred by producers; therefore, to enhance the shelf life of harvested garlic, a drying process is often employed. This method effectively protects garlic from the growth of microorganisms and inhibits many degradation reactions that occur with increased water availability while preserving its nutritional properties. Moreover, reduced water content facilitates the storage and distribution of this food crop. Various drying techniques, including hot air drying, vacuum freeze drying, air impingement drying, pulsed vacuum drying, microwave-vacuum drying, and infrared radiation drying, each with distinct advantages and drawbacks, are utilized to produce dried garlic [16; 17]. Understanding these methods enables the optimization of drying processes, ensuring the production of high-quality dried garlic for various applications. The choice of drying technique often depends on factors such as desired product features, cost, and available equipment. Hot air drying is commonly used due to its relatively simple and cost-effective operation [16]. However, there are disadvantages with this technology, such as high temperature, which can cause degradation of some of the bioactive compounds, affecting their quality and nutritional value as well as therapeutic activity. Hence, understanding the optimal drying conditions (a combination of temperature and drying time) that preserve or enhance garlic's antimicrobial activity is crucial for ensuring the long-term effectiveness of the resulting products. This knowledge may lead to the production of more effective dietary supplements and medicinal preparations. Moreover, comprehending the impact of drying conditions on the bioactive compounds and antimicrobial properties of garlic could also expand the application of garlic in medicine, dietetics, and the food industry. Considering the nutritional and medicinal value of garlic, as well as the need for effective processing to ensure high-quality products, this research focuses on studying the growth dynamics of a model strain of _E. coli_ in response to the antimicrobial activity of garlic subjected to convective drying at various combinations of time and drying temperatures. Additionally, the total thiosulfinate content (TTC), including allicin, the predominant antimicrobial component of garlic, and total phenolics content (TPC) were evaluated in the examined garlic products.
## 3 Results
### Confirmation of Experiment Results  
The purpose of the confirmation experiments was to determine whether the biomimetic effect of the optimized disc could be extended to a variety of soil conditions. The drag force data for all treatments are summarized and analyzed in Table 6. The results showed that, as compared to the flat disc, the optimized biomimetic disc could improve drag force by 9 to 25%, depending on the soil condition. According to the paired samples t-test with a 95% confidence level, there were significant differences in drag force between the flat disc and the optimized biomimetic disc in all treatments (\(p<0.05\)). In addition, the statistics indicated that variations in soil moisture content could affect the soil-to-disc drag force by up to 35% in the range of this experiment. Based on the least significant difference (LSD) test results, statistically different treatments are highlighted in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Condition** & **Disc Type** & **Mean \(\tau\) (N)** & **Std. Dev** & **t Value** & \(p\) **Value** & \(\Delta\) **(\%)** \\ \hline \multirow{2}{*}{23\% MC, S1} & Flat & 8.3 & 0.37 & \multirow{2}{*}{9.67} & \multirow{2}{*}{0.001} & \multirow{2}{*}{18.1} \\  & Biomimetic & 6.8 & 0.43 & & & \\ \hline \multirow{2}{*}{30\% MC, S1} & Flat & 9.4 & 0.50 & \multirow{2}{*}{5.67} & \multirow{2}{*}{0.005} & \multirow{2}{*}{24.5} \\  & Biomimetic & 7.1 & 0.37 & & & \\ \hline \multirow{2}{*}{37\% MC, S1} & Flat & 10.5 & 0.57 & \multirow{2}{*}{16.65} & \multirow{2}{*}{<0.001} & \multirow{2}{*}{21.9} \\  & Biomimetic & 8.2 & 0.53 & & & \\ \hline \multirow{2}{*}{23\% MC, S2} & Flat & 6.9 & 0.40 & \multirow{2}{*}{8.45} & \multirow{2}{*}{0.001} & \multirow{2}{*}{23.2} \\  & Biomimetic & 5.3 & 0.20 & & & \\ \hline \multirow{2}{*}{30\% MC, S2} & Flat & 8.8 & 0.37 & \multirow{2}{*}{26.70} & \multirow{2}{*}{<0.001} & \multirow{2}{*}{25.0} \\  & Biomimetic & 6.6 & 0.43 & & & \\ \hline \multirow{2}{*}{37\% MC, S2} & Flat & 6.5 & 0.40 & \multirow{2}{*}{3.36} & \multirow{2}{*}{0.028} & \multirow{2}{*}{9.2} \\  & Biomimetic & 5.9 & 0.17 & & & \\ \hline \hline \end{tabular}  
* S1: silty loam soil, S2: sandy clay loam soil, optimized biomimetic disc: Disc 14, Std. Dev: standard deviation, \(\Delta\): change in drag force of optimized disc with respect to flat disc in similar soil conditions. The paired-samples \(t\)-test was conducted using five runs for each soil condition.  
\end{table}
Table 6: Statistical analysis on the drag force of the flat disc against that of optimized biomimetic disc.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Level**} & \multicolumn{4}{c}{**Control Parameters**} \\ \cline{2-4}  & **CR (\%)** & **HDR (\%)** & **DBD (mm)** \\ \hline
1 & \(-\)17.95 & \(-\)18.14 & \(-\)18.38 \\
2 & \(-\)17.56 & \(-\)17.07 & \(-\)18.02 \\
3 & \(-\)19.15 & \(-\)19.46 & \(-\)18.27 \\ Delta & 1.59 & 2.39 & 0.36 \\ Order & & HDR \(>\) CR \(>\) DBD \\ \hline \hline \end{tabular}
\end{table}
Table 5: Signal-to-noise response table for drag force.
## 4 Discussion
### The Cerebellar Transcriptome Profile of ATM-Null Mice at 12 Months of Age
Overall, excitation and growth stimuli in the ATM-null mouse cerebellum appeared to elicit deficient nuclear responses, in view of the downregulation of immediate-early genes _Nr4a3/Nr4a2/Nr4a1_, _Dusp1_, _Fos/Fos12_, _Npas4_, _Per1/Per2/Per3_, _Fox3o/Fox0_, and _Homer1_. A parallel downregulation of the NPAS4 protein interactor _Art_ and its binding partner _Hif3a_ mRNA were observed, as well as reduced transcript levels of downstream factors _Slc2a12_ and _Rora_ (which is responsible for ataxia and intellectual deficits [178; 179]), contrasting with upregulation of the alternative interactor _Arnt2_ mRNA [180; 181; 182; 183; 184]. With relevance to the osmotic homeostasis in ATM-null cerebellum, a strong downregulation was observed for _Dbp_ as the transcription factor that controls the expression of alpha-fetoprotein and albumin (whose dysregulation is characteristic for A-T [4; 5]), and is crucial for circadian regulation of synaptic plasticity [185; 186; 187]. The notion of changed nutrient and osmotic regulation was also supported by the downregulation of amino acid-sensing _Rragd_[104]. Downregulation was prominent for immune-regulating and damage-responsive protein kinase transcripts _Smg1_, _Sik1_ and _SgkT_[188; 189; 190], findings that also implicate altered RNA surveillance, osmotic and nutrient homeostasis in A-T pathology. A deficit in inflammatory responses was also evident from the downregulated transcripts of _Ccl27a_, _Sid1_, _Ill16_, _Rnf122_ and _Serinc2_[191; 192; 193; 194; 195]. The deficiency of immunoglobulin/fibronectin-domain-containing _Boc_ may contribute to the observed upregulation of many protocadherin, cadherin, and contactn pathway members (_Cdh6_, _Cdh9_, _Cdh10_, _Cdh19_, _Pcdh17_, _Pcdh10_, _Pcdh11x_, _Pcdh17_, _Pcdh18_, _Pcdh19_, _Cntn4_, _Cntn5_ and _Cntnap5a_) [196]. In conclusion, pathway enrichment analyses of the transcriptome profile supported the novel concept that failure of ATM-mediated adaptation to osmotic/nutrient and perhaps oxidative stress, via altered USP2/ATMIN signals, leads to a generalised abnormality in neurotransmitter-neuropeptide signals from synaptic and dense core vesicles, with reduced immediate-early signals and impaired synaptic adhesion.
## 5 Results
### FP16 Quantization  
The FP16 strategy is a quantization technique that applies a conversion from FP32 to FP16. When compared with 8-bit dynamic range quantization, more bits have to be stored after quantization. The weights are then represented in FP16. Tables 5-8 show the FP16 results for S-UNIWARD and WOW for both 0.2 and 0.4 bpp. The GBRAS-Net results are also presented in the aforementioned tables for comparison purposes.  
Table 5 shows the results of the simulations for S-UNIWARD at 0.4 bpp. The quantized weights model achieved 87.29% accuracy, which represents an increase of 0.13% over the original model. The results for S-UNIWARD at 0.2 bpp, presented in Table 6, show that the accuracy increased by 0.75% when compared with GBRAS-Net, with 74.35%. The results for WOW at 0.4 and 0.2 bpp are shown in Tables 7 and 8, respectively. The accuracy of the model increased slightly, by 0.04% for WOW at 0.4 bpp and 0.05% for WOW at 0.2 bpp, when compared with the original model.  
Although the memory requirements are higher than those obtained with dynamic range quantization, they are reduced by approximately half compared to the original GBRAS-Net, with 0.31 MB.