esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 2 Steam Trap Study  
### Steam Processes  
Steam is widely used in major manufacturing industries such as the oil and gas, chemical, and petrochemical sectors due to its versatility and efficiency. It serves as a highly effective heat source for heating reactors and distillation columns, while also powering turbines to generate electricity for pumps and compressors. Steam is essential in refining processes like distillation and thermal cracking, as well as for cleaning and purifying equipment. Additionally, it provides a safer alternative to direct combustion heating, reducing risks of fire and explosions. In oil extraction, steam injection helps lower the viscosity of heavy oil, improving its extraction and transport. In summary, steam plays a critical role in transferring energy, driving machinery, enabling refining processes, and ensuring safety in these sectors.  
The major elements involved in steam circuits are as follows:  
* Steam boiler. This is the element that converts clean water into steam thanks to several heating elements (electric resistors, fuel, gas, etc.).
* Steam pipelines. Their mission is the transportation of steam in all production processes.
* Pumps. They are used for pumping condensed water to the boiler.
* Steam traps. In a steam pipeline, they are typically installed every 30 to 50 m depending on factors like pipe size and operating conditions. Additionally, steam traps are placed at critical points such as the following:
* Low points in the piping: to prevent condensate buildup.
* Changes in elevation: where condensate can accumulate.
* Before processing equipment: like heat exchangers or control valves.
* End of steam pipelines: to purge condensate before it re-enters the system.  
Steam traps can be classified into three main types based on their operating principles. Mechanical steam traps use a float that rises with the condensate level, opening the valve to discharge water, and then closing once the condensate is removed. Thermodynamic steam traps operate by leveraging the speed difference between steam and condensate; slower condensate flow keeps the valve open, while the higher speed of steam causes it to close. Thermostatic steam traps respond to temperature changes, opening the valve when cooler condensate is present and closing it when hot steam arrives. Figure 2 shows all the steam production and condensed water evacuation processes.
# Multi-Beam Focusing and Deflecting Characteristics of Liquid Crystal Optical Phased Array
###### Abstract  
As a programmable optical device, a liquid crystal optical phased array (LCOPA) has many unique applications. Multi-beam fan-out is one of the novel application cases. However, currently commercially available liquid crystal optical phased array devices typically use reflective structures and are operated on desktop software. This can be inconvenient for the construction of the optical path and the manipulation of the device. On the other hand, multi-beam fan-out capability alone may not be sufficient in some application scenarios. The additional function of focusing might bring some new advantages. Thus, we developed a transmissive liquid crystal optical phased array device based on a fully embedded human-computer interaction control method. It does not require the installation of computer desktop software and only needs a touch screen for easy human-computer interaction. This not only brings convenience to control, but also reduces the cost of devices. Meanwhile, based on the Gerchberg-Saxton algorithm (GS algorithm) and the method of lens phase superposition, we achieved a composite function of multiple-beam fan-out with focusing function by simulation and experiment. This composite function may have unique application value for some special application scenarios.  
liquid crystal optical phase array device; multi-beam control; human-computer interaction; GS algorithm; focusing +
Footnote †: journal: _photonics_  
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Footnote 0: _Email:_ [EMAIL_ADDRESS]
## 3 System Framework and Algorithm Procedure
### Improved DMAC (I-DMAC) Algorithm  
DMAC decouples the channel usage as data transmission and contention process, which can significantly improve channel utilization. However, in multi-rate network, the channel is often over-occupied by some low-rate nodes and resulted in the performance anomaly problem. Thus, in this section, we also present two-time fairness algorithms: I-DMAC1 and I-DMAC2. I-DMAC1 solves the performance anomaly problem by giving more contention opportunity to the high-rate nodes during contention processing, which is demonstrated by the following pseudo code.  
Figure 2: The procedure of basic decoupling Medium Access Control mechanism (DMAC).  
```
0: The number of nodes
0: The transmission order stored in Contention Queue Buffer
0: AP broadcasts Beacon
1: If the node receives a Beacon frame
2: -Initializes the Contention Window value
3: Data Transmission Processing
4: If idle_time == DIFS and the node's back-off_value!= 0
5: -Performs the back-off process
6: If back-off_value == 0 and the channel is idle
7: -Transmits a DTR
8: -Increment the competition counter (NumComp++)
9: If NumComp <= \(R/R_{min}\) /* does not reach the maximum number of competitions*/
10: -Chooses a random back-off value
11: -Go to step 4
```  
**Algorithm 1** Contention Processing for I-DMAC1  
In Algorithm 1, we assume that the date rate of the lowest rate node is \(R_{min}\). Subsequently, within a CP, a node can send several DTR frames by triggering its back-off counter and its value depends on the ratio of its data rate to \(R_{min}\). Therefore, a high-rate node can obtain more transmission opportunities in a super-frame time. Contrarily, I-DMAC2 directly provides more transmission opportunity to the high-rate nodes during the data transmission period. Thus, it can also solve the performance anomaly problem. Accordingly, we present the partial pseudo code of Algorithm 2.  
```
0: A Beacon piggybacking the first ID of Contention Queue Buffer, \(R\), \(R_{min}\)
0: The node in Contention Queue Buffer transmits data
0: AP broadcasts Beacon
1: If a node receives a Beacon frame and its ID is piggybacked in the beacon
2: -Transmits a data frame
3: -AP transmits ACK frame
4: If a node receives an ACK frame and its ID is piggybacked in this ACK frame
5: -Transmits a data frame
6: -Increment the transmission counter (NumTran ++)
7: If NumTran <= \(R/R_{min}\) /* does not reach the maximum number of transmissions*/
8: -Go to step 5
```  
**Algorithm 2** Data Transmission Processing for I-DMAC2
# A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
## 2 Materials and Methods
### Proposed Feature Extraction Method  
The flowchart of VMD-Information Gain for the pipe blockage detection process is illustrated in Figure 1.  
Step 1: Each 300 sets of sampling are performed on the 9 statuses of buried pipelines to obtain 2700 sets of sample data in total.  
Step 2: The original acoustic signals in various statuses are denoised through the Butterworth filter, thereby obtaining acoustic signals with a [0-6000] Hz frequency range.  
Step 3: The denoised signals in various statuses are decomposed by the VMD, and the number of optimal decomposition layers K is decided by determining whether over-decomposition is produced or not.  
Step 4: VMD operation is performed on the 100 sets of sampled data in 9 statuses, thereby deriving K components. By utilizing information gain, M effective components are filtered from the K components. Information gain values of the components are selected as per the principle of decision tree selection. A threshold is set up, which is then compared with the filtered information gain values. Filtering is terminated if the information gain value of a component signal is less than the threshold.  
Step 5: Sound pressure level conversion is performed on the filtered M effective components, and then the RMS entropy values are computed.  
Step 6: The RMS entropy in various statuses are constituted separately into eigenvectors, which are then input into four different classifiers SVM, KNN, ELM, and RF, for effective identification of the pipeline operating conditions.
## 3 Clinical Applications of Artificial Intelligence
### Therapy  
ML can be useful to carry out many of the activities during the whole workflow of radiotherapy, starting with the choice of the optimal radiation approach, e.g., choice of proton vs. photon [76]. A convolutional neural network (CNN) can automatically segment targets and organs at risk in radiotherapy [77]. ML-based auto-planning [78,79] mimics the iterative plan design, evaluation, and adjustments made by experienced operators with the goal of improving quality and efficiency and reducing inter-user variability [46]. Knowledge-based approaches leverage a large database of prior treatment plans (up to thousands) to develop associations between geometric and dosimetric parameters from a selection of previous plans in order to determine achievable dose constraints or dose distributions that can be used for benchmarking the quality of plans [9,80]. ML-based auto planning was also developed for brachytherapy [81].  
The dose distribution from radiation therapy treatment can be predicted by DL in order to speed up the optimization [82] or determine the best achievable dose distribution from the patient image [83]. ML was applied to predict dose in brachytherapy [84] and in vivo measured dose in intraoperative radiotherapy [85].  
Recently, dosomics, the application of radiomics or DL to the analysis of the dose distribution, eventually corrected into biologically effective dose to account for diverse fractionation, was investigated for the ability to predict side effects of radiation therapy [86,87]. Radiomics can also be applied to cone-beam CT (CBCTs) acquired for image-guidance of the radiotherapy treatment, making these images useful for data mining [88].  
A major concern of radiotherapy is the change in the anatomy of the patient during therapy, which could result in unwanted dose changes. In this case, re-planning of the treatment is warranted. ML can identify significant changes in patient anatomy during radiotherapy [19] and predict patients who would benefit from adaptive radiotherapy (ART) [89]. Eventually, by using information extracted from radiomics voxel-based analyses, sensitive/resistant tumor sub-volumes might be identified, requiring higher (or lower) dose, thus enabling dose painting according to a \"radiomic target volume\" (RTV) [90].  
In nuclear medicine, radiometabolic therapy with unsealed (radiopharmaceutical) or sealed sources (microspheres, etc.) is of growing importance. The application of AI in this area can improve dosimetry by accounting for patients' anatomy, activity distribution, and tissue density, and planning, in order to administer the highest dose to the target while sparing critical organs, as well as for predicting treatment response [91]. Methodological studies have been performed to investigate the robustness of dosomic approaches [92].
## 9 Prototype Testing and Results  
One of the geospatial locations' boundaries was selected, defined by the National Census Bureau, to collect census data. Three local NGOs--Saylani [60], Pakistan Bait-ul-Mal [61], and Customs Healthcare Society [62]--were onboard, with consent to support this research work.  
Two out of three NGOs helped by providing one volunteer each to conduct door-to-door data collection, leveraging their familiarity with the community. They also shared pre-existing data from their records. The third NGO, with a healthcare facility in the area, gathered information from patients during checkups.  
The volunteers were equipped with tablets installed with our solution for information collection. Since the volunteers were not tech-savvy, a training session lasting three hours was conducted before fieldwork. The training covered application usage and addressed any queries that the volunteers had.  
We surveyed 265 units, encompassing both residential houses and homeless individuals in one selected geospatial location. Within this group, we identified 53 homeless individuals possessing national ID cards and 23 without any form of identification. For the latter group, we collected thumbprints, totaling 76 homeless individuals (refer to Figure 7--By Residential Nature section).  
In the context of this proof-of-concept, it is crucial to emphasize the living conditions of the homeless community. The chosen urban location attracts individuals migrating from rural areas in search of employment opportunities. The labor community often migrates individually and resides in shared accommodations to minimize rental costs, support their families, or sustain themselves. Our interactions with them typically occurred after official working hours. Their living arrangements varied, with some utilizing workshops, placing basic beds in corners, residing in under-construction buildings, beneath bridges, on footpaths, or in open-space tents. Unlike housing owners/renters, the homeless community was cooperative, providing maximum information and granting permission for us to capture photos of their living arrangements, as depicted in Figure 8 (verbal permission obtained).  
Figure 7: Analytics.  
The system evaluation was accomplished in four stages: (i) architecture design, (ii) prototype solution, (iii) software usability, and (iv) blockchain performance.
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 4 Annotation Scheme
### Therapist Utterance Attributes
#### 4.1.3 Input  
The primary manner of communicating knowledge to the client is informing. Based on MISC coding [3] and insights from a professional therapist regarding the patterns of informing in the AnnoMI transcripts, we use the term Input to include a wide range of conveyed knowledge and consider 4 subtypes: providing information, giving advice, presenting options, and setting goals (negotiation). Some examples are given in Table 6. When an utterance contains more than one type of Input, the annotators choose the main type of Input to make the labels mutually exclusive and facilitate utterance-level NLP applications.
# 3D Ear Normalization and Recognition Based on Local Surface Variation
## 3 Technical Approach
### Normalization of the Ear Data
#### 3.2.2 Eliminate the Face Data near the Ear  
Based on Equation (4), the LSV value of a vertex will change with the scope of the neighborhood. So, if the \(N\)-ring neighborhood of a vertex is selected, then the LSV value of each vertex can be calculated as a local surface feature value. Figure 3 shows an ear region range image and its 3-ring LSV value map. The colors of pixels correspond to the LSV values of vertices which are normalized to 0-1.0.  
As can be observed in Figure 3, most of the LSV values of ear data are higher than face skin data. As such, the face skin data will be eliminated based on the principle of \(\sigma_{n}(p)>\sigma_{t}\). The results obtained from processing with different thresholds are shown in Figure 4.  
It is illustrated in Figure 4 that most of the face skin data were effectively eliminated with three different thresholds. However, lots of useful ear regions are removed in Figure 4c,d at the same time. The motivation behind this procedure is to remove redundant data leaving only ear data for the recognition, so based on these results it can be seen that \(\sigma_{t}=0.01\) should be selected as the  
Figure 4: The results of processing with different thresholds: (**a**) The original ear data; (**b**) The results of \(\sigma_{t}=0.01\); (**c**) The results of \(\sigma_{t}=0.02\); (**d**) The results of \(\sigma_{t}=0.03\).  
Figure 3: The LSV values of human ear: (**a**) The range image; (**b**) The LSV value map.  
parameter to subsequently apply on hundreds of individuals to separate face and ear data within the ear regions satisfactorily.
## 5 Evaluation Results
### Effects of the Packet Generation Interval on Routing Protocol Performance  
In Figure 6, network performance for various packet generation intervals is shown. The results for the \(PDR\) are illustrated in Figure 6a. In general, increasing the packet generation interval will reduce network traffic. Thus, the \(PDR\) tends to increase as the packet generation interval increases. By rapidly spreading packets and selecting optimal relay nodes based on the predicted information and social relationships, the HLPRP achieves a better \(PDR\) than the others when the packet generation interval is between 25 s and 45 s.  
Figure 6b shows the results of the \(PDL\). The latency from the HLPRP is lower than from other routing protocols when the packet generation interval varies between 5 s and 25 s. When network traffic is light (i.e., the packet generation interval is between 35 s and 45 s), using the flooding strategy, epidemic routing obtains the shorter delivery latency. However, the \(DC\) from epidemic routing is huge, as shown in Figure 6c. Figure 6c also indicates that the HLPRP has a lower \(DC\) than CORP, PRoPHET, BUBBLE Rap, and epidemic routing.  
Figure 5: The network performance for various buffer sizes. (**a**) \(PDR\). (**b**) \(PDL\). (**c**) \(DC\).  
Figure 6: The network performance for various values of the packet generation interval. (**a**) \(PDR\). (**b**) \(PDL\). (**c**) \(DC\).
## 2 Reliability Analysis Based on the AdaBoost Surrogate Model  
### Reliability Modeling of Complex Structures under Multi-Failure Mode  
There are two primary reliability models for complex structures exhibiting multi-failure mode: the series system and the parallel system. Nevertheless, in the realm of practical engineering applications, complex structural systems frequently exhibit multiple failure modes, with the overall failure mode typically arising from the interactions among these failures. Particularly within intricate systems, the interrelationships between failure modes become progressively more complex, as each mode engages in reciprocal interactions with others.  
Suppose that a problem contains \(m\) failure modes, the corresponding limit state equation is denoted by \(g^{(k)}(\mathbf{x})=0(k=1,2,\ldots,m)\), and the corresponding failure domain is denoted by \(D_{f}^{(k)}=\left\{g^{(k)}(\mathbf{x})\leq 0\right\}(k=1,2,\ldots,m)\), where \(\mathbf{x}=\left\{x_{1},x_{2},\ldots,x_{n}\right\}\) represents an \(n\)-dimensional random variable.  
When \(m\) failure modes are present in series, the relationship between the system's failure domain, \(D_{f}\), and the failure domain, \(D_{f}^{(k)}\), of each individual mode can be expressed as follows:  
\[D_{f}=\mathop{\cap}\limits_{k=1}^{m}D_{f}^{(k)} \tag{1}\]  
When \(m\) failure modes occur simultaneously, it is a parallel situation, and the relationship between the system's failure domain, \(D_{f}\), and failure domain, \(D_{f}^{(k)}\), of each individual mode can be expressed as follows:  
\[D_{f}=\mathop{\cap}\limits_{k=1}^{m}D_{f}^{(k)} \tag{2}\]
## 2 Materials and Methods
### The Analysis of Variance
\begin{split}\sum\limits_{i=1}^{\alpha}\sum\limits_{j=1}^{n} \big{(}y_{ij}-\overline{y}_{\cdot\cdot}\big{)}^{2}=n\sum\limits_{i=1}^{\alpha }\big{(}y_{ij}-\overline{y}_{\cdot\cdot}\big{)}^{2}+\sum\limits_{i=1}^{\alpha }\sum\limits_{j=1}^{n}\big{(}y_{ij}-\overline{y}_{i\cdot}\big{)}^{2}\\ SS_{Total}=SS_{Level}+SS_{E}=\sum\limits_{i=1}^{\alpha}\sum\limits_{j=1}^ {n}{y_{ij}}^{2}-\frac{y_{\cdot\cdot}{2}^{2}}{N}\\ SS_{Level}=(\alpha-1)\sigma^{2}+n\sum\limits_{i=1}^{\alpha}\tau_{i}^{2}= \sum\limits_{i=1}^{\alpha}\frac{y_{\cdot\cdot}^{2}}{n}-\frac{y_{\cdot\cdot} {2}^{2}}{N}\\ SS_{E}=\alpha(n-1)\sigma^{2}\end{split}  \tag{5}\]  
Similarly, the total degrees of freedom are the sum of the degrees of freedom (DF) of factors and errors:  
\[\alpha*n-1=(\alpha-1)+\alpha*(\alpha-1) \tag{6}\]  
\[DF_{T}=DF_{Level}+DF_{E}\]  
The mean square of the factors is given as follows:  
\[ \begin{split} MS_{Level}=\frac{SS_{Level}}{\alpha-1}\\ MS_{E}=\frac{SS_{E}}{\alpha*(n-1)}\end{split}  \tag{7}\]  
Finally, the division between the mean squares results in the calculated F. The appropriate test statistic is as specified below. The critical F value is calculated to determine the significance of the results. This value is calculated from the significance function and degrees of freedom in the F tables. Since the confidence interval in this study is 95%, the significance function is 0.05. If the calculated F is greater than the critical F, the null hypothesis is rejected. This means that the results of the study are statistically significant. \[F=\frac{SS_{Level}/(\alpha-1)}{SS_{E}/[\alpha(n-1)]}=\frac{MS_{Level}}{MS_{E}} \tag{8}\]  
We can also find a \(p\)-value for this test statistic, as specified below. If the \(p\)-value is small (\(<\)0.05), it indicates a piece of strong evidence against the null hypothesis. As a result, the null hypothesis is rejected. Thus, for a hypothesis with a \(p\)-value less than 0.05, the null hypothesis is rejected, and the alternative hypothesis is accepted. This means that the results of the study are statistically significant. \[P\cong P\Big{(}F_{a-1,\alpha*(n-1)}>F\Big{)} \tag{9}\]  
The ANOVA is summarized in Table 4. This table contains the source of variation, sum of squares (SS), degrees of freedom (DF), mean square (MS), F-value, critical F, and \(p\)-value. In this study, there is one factor (nozzle temperature) with six levels: 220 \({}^{\circ}\)C, 230 \({}^{\circ}\)C, 240 \({}^{\circ}\)C, 250 \({}^{\circ}\)C, 260 \({}^{\circ}\)C, and 270 \({}^{\circ}\)C. Six observations were made for each level.
## 2 Materials and Methods
### Selection of the Simulation Sites  
Firstly, we analyzed seven (which are the black and yellow triangles) cities located within the six NEB desertification hotspots (Figure 1). We included Ibimirim town in the analysis due to the proximity to the CAB desertification hotspots. From the Gilbues hotspot, we selected the Bom Jesus do Piaui town, located at the southern Piaui state, in a transition region between the Caatinga and Cerrado biomes. In the Iraucuba hotspot, the Sobral city was selected, located 20 km from the hotspot boundaries. The Patos town was selected for the Serido hotspot, which is also 20 km in distance from its boundaries. For the Jaguaribe, Inhamuns, and Cabrobo desertification hotspots, the towns of Jaguaribe, Taua, and Cabrobo were selected, which are located inside the hotspots. The coordinates, elevation, and percentage of data gaps for each selected station are presented in Table 1. Mean annual accumulated rainfall, temperature, and relative humidity are shown in Table 2. Cabrobo and Ibimirim feature the lowest mean annual precipitation values, with 404.6 and 380.9 mm/year, respectively. However, air temperature is lower in these two locations due to nighttime cooling, which is a typical phenomenon in arid regions, particularly during the dri  
Figure 1: Spatial distribution of INMET stations and location of desertification hotspots.  
These seven stations were primarily analyzed regarding (i) data quality, prioritizing stations with fewer data gaps; (ii) proximity to the desertification hotspot, prioritizing stations located within hotspots; (iii) proximity of other nearby stations in the surroundings of the hotspots that could be used as comparison regarding the response of each observed and simulated data. By adopting these criteria, the Cabrobo site was selected for the simulations and meteorological data analysis, with the Ibimirim station being used for comparison (Table 1). Both sites are located in the Pernambuco state; the Cabrobo site is located within the Cabrobo desertification hotspot (Figure 1), and the Ibimirim site is located approximately 40 km from the boundaries of the hotspot and 190 km from the Cabrobo site. The selected hotspot and sites are located south of the Pernambuco state, in a region highly dependent on agriculture, which can be directly affected by the consequences of desertification, economically impacting the entire region [33].