esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter Optimization of Orbital TIG Welding on Stainless Steel Pipe
###### Abstract  
Orbital TIG welding is widely applied to weld pipes to pipes in many fields, such as food, chemicals, oil, gas, and transportation. Optimizing welding parameters such as voltage, current, and travel speed is critical to achieve a good-quality weld. This study investigated the impacts of orbital welding parameters and filler wire diameters on the tensile strength of 304 stainless steel pipes. The 304 stainless steel pipe has an outer diameter of 76 mm and a thickness of 2 mm. Filler wire is used with the workpiece, and is available in three diameters of 0.8 mm, 1 mm, and 1.2 mm, wire feed speed from 3.8 mm/s to 5.6 mm/s, current from 90 A to 110 A, and travel speed fixed at 5.5 mm/s. The highest tensile strength of 562 MPa was achieved with heat input of 0.32 kJ/mm and wire feed speed of 3.8 mm/s. In addition, the best parameters via the Taguchi method were found. The parameters' influence trends on the weld quality were also revealed.  
f  
filer wire speed; heat input; tensile strength; microstructure
# Voice Assistant Application for Avoiding Sedentarism in Elderly People Based on IoT Technologies
## 4 Case Study and Results
### Hardware Used
#### 4.1.3 Bluetooth BLE Smart Bracelet  
There are different smart bracelets for this purpose used in the literature by [PERSON] et al. [58], [PERSON] et al. [59], and [PERSON] et al. [9] in their works. Some of these bracelets include the Lively watch, Apple Watch, Amazon Fitbit, and the Garmin Vivofit 2. The first bracelet has a higher price than others on the market, as it comes with smart pill dispensers. An added functionality for use by the elderly is the receipt of medication reminders. However, the main drawback of this smart bracelet is the lack of a detailed documentation to access the data it collects. As for the Apple Watch, it is one of the most expensive bracelets on the market, and there are no manuals or packages explaining how to connect and collect data from the device. If we add to this the tightness with which Apple usually protects its products, it is not a good choice for the case study. Something similar happens with the Garmin Vivofit 2, which is significantly cheaper than the Apple Watch, but still more expensive than some of its direct competitors, and there is no manual explaining how to connect to it and get its data. On the other hand, bracelets such as the Amazon Fitbit and the Xiaomi Mi Bands have the best prices on the market in terms of value for money, and there are also various libraries that allow you to connect and obtain data from these smart bracelets through a communication interface.  
After evaluating these options, the Xiaomi Mi Band 2 and the smartwatch Amazfit Bip have been used as the smart bracelet devices for the elderly, due to their low cost, low power consumption, and long battery life (up to 30 days of battery life), and, above all, because of the possibility to access the data through a well-defined communication interface, for which several software libraries are available. In the case of the Xiaomi Mi Band, the second version has been used over the others, since it is simpler, cheaper, has a longer battery life, and it is easier to understand its operation, in addition to the fact that from the fourth model onwards a key is needed to carry out the connection process and obtain the data. These new models could be included in the system but would involve additional configuration steps for the user, and we have preferred to keep the process as simple as possible.  
In this respect, the idea of developing an ad hoc smart bracelet for this purpose was considered, but a commercially available option that would suit the requirements of the case study was finally chosen.
## 3 Results
### Case Study: Faults in the Lithuanian Distribution Grid
#### 3.2.2 Probabilistic Analysis of Primary Reasons
Two significance levels are used in Table 16. In our opinion, the 0.10 level is a more suitable significance level for the Lithuanian distribution grid (due to errors and uncertainties during the collection and classification of data), however, 0.05 is the most widely used. When the significance level is lower, the confidence interval is broader. It can be noticed that the confidence interval of the Poisson distribution is asymmetrical: for discrete distributions, it is often difficult (impossible) to obtain the interval with an exact confidence level \((1-\alpha)\), due to the discrete nature of the statistics [62]. Misinterpretation of confidence intervals and levels is a common problem even among highly qualified scientists. This topic has been studied by [64; 65]. The confidence level is connected with the reliability of the estimation procedure: once the interval is calculated, it either covers the value of a (respective) population parameter or not; thus, the confidence level is not a probability of this coverage. Figure 69: Probabilistic analysis of the most common primary fault cause frequency in the Lithuanian 10 kV grid. Both Poisson and Gaussian models are given (when confidence level of one-tail is 90%), except cable lines due to unknown sample standard deviation. Please note that all events and their distributions are separate and independent.
## 3 Research Steps and Methodology  
Before getting through the proposed methods' details and their implementations on the OPSC problem, a comprehensive overview of the main research methodology, including the whole steps followed in this paper, are illustrated in Figure 1.  
As is shown in this figure, first, the mathematical model of the OPSC problem was built as presented in Section 2. Then, the proposed PODESCA was established by the unique hybridization between DE, SCA, and QOT, where the performance analysis of that algorithm was tested by solving benchmark functions and comparing the results with those from some other novel algorithms. After that, the proposed SBDMT was also constructed by utilizing four SBIs to be the inputs of TOPSIS, while the output was the best candidate buses for SC placement, so that the search space of the OPSC problem is extensively and effectively reduced. The OPSC problem was then solved for three RDSs; 33-bus, 69-bus, and 118-bus, where two cases were considered for each system; without reducing the search space, and with reducing the search space by SBDMT. For the best validation of the proposed  
Figure 1: Main research methodology.  
methods' effectiveness and robustness, the results obtained by only applying PODESCA on the OPSC problem were first compared with those from several previous methods in the literature, where those comparisons proved the superiority of the proposed algorithm over most of the other methods. Then, the results obtained by applying PODESCA without and with reducing the search space by SBDMT were also compared, where the achieved results for the three RDSs clearly verified the effectiveness of both proposed methods, especially for large-scale systems, where the SBDMT increases the consistency of PODESCA in obtaining the optimal solutions.
# Review on Fast Pyrolysis of Biomass for Biofuel Production from [PERSON]
## 3 Biomass Generalities
### Influence of the Parameters on the Products Used during Fast Pyrolysis
#### 3.5.3 Influence of Particle Size  
[PERSON], L [41] studied the effect of particle size (0.10 mm to 1.2 mm) on the formation of products of the fast pyrolysis of pine sawdust and apricot kernels at 1073 K. Mass and heat transfer have little influence on the formation of pyrolysis products in the case of particles with diameters less than 0.20 mm [41].  
This is in accordance with the results in Figure 3. In contrast, for particles larger than 0.20 mm in diameter, pyrolysis is primarily controlled by mass and heat transfer within the particle [41]. This is in disagreement with the results in Figure 3, which indicate a transition regime, not a thermal regime, for particles with diameters between 0.2 and 1.2 mm.  
In the literature, one finds many works concerning the influence of the size of the particles on the formation of the pyrolysis products. These works are generally carried out with particles whose size is between 0.1 and 2 mm. The results show that the smaller the particle size [28; 42; 43]:  
* The higher the gas yield.
* The higher the H\({}_{2}\) and CO yield
* The more the quantity of CO + H\({}_{2}\) synthesis gas increases
* The larger the H\({}_{2}\)/CO molar ratio is
* The more the yield of CO\({}_{2}\) decreases
* The more the hydrocarbon yield decreases. The gases are quickly expelled from a small particle, and therefore their residence time in the reactor is higher.
* The more the yields of condensable gases and char decrease.  
Several pyrolysis regimes are to be considered depending on particle size and temperature. In his work on biomass stratification, [PERSON] determined the boundaries between pyrolysis regimes as a function of temperature and particle size [35].  
Figure 3 shows the pyrolysis regimes as a function of temperature and particle size [44]. The limits are not lines but thick hatched areas because they take into account uncertainties. This shows that the chemical reaction of pyrolysis is limiting for particles smaller than 0.1 mm at temperatures between 973 and 1100 K. In this case, the reaction is in the intrinsic regime.  
Between 1173 and 1323 K and for particles between 0.1 mm and 1 mm, the reaction is in an intermediate situation, called the transition regime, which is neither a chemical nor a thermal regime. We are in the thermal regime for particles of several millimeters, with limitations caused by internal heat transfer.
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 4 Matrix-Based Data Relay Algorithm
### Basic Matrix Model  
Before introducing these three matrices, we first need one data structure \(N_{i}\) to store the sensors known by \(v_{i}\), and it can be updated by the path of messages it receives. Since sensors have different local information, we take sensor \(v_{i}\) as an example to describe these three matrices.  
\(\mathbf{C}:1\times|N_{i}|\rightarrow[0,1]\) is the connection matrix to illustrate the connections between \(v_{i}\) and other sensors recorded in \(N_{i}\). Each element \(\mathbf{C}_{v_{i},v_{k}}\) represents the connection probability between \(v_{i}\) and \(v_{k}\) estimated by \(v_{i}\). Especially, \(\forall v_{i}\in V\), \(\ C_{v_{i},v_{i}}=0\).  
\(\mathbf{D}:|N_{i}|\times|L_{i}|\rightarrow[0,1]\) is the local data distribution matrix, and each element \(\mathbf{D}_{v_{k},d_{j}}\) shows the probability of \(d_{j}\) in \(v_{k}\)'s local data set by \(v_{i}\)'s estimation.  
Figure 2: The frame of matrix-based Data Relay algorithm.  
\(\mathbf{U}:|L_{i}|\times|L_{i}|\rightarrow[0,c]\) is the utility matrix of data known by \(v_{i}\), and each element \(\mathbf{U}_{d_{l},d_{k}}=U(d_{l},d_{k})\) shows the incremental utility when adding \(d_{l}\) meets \(\{d_{k}\}\).
# BioShare: An Open Framework for Trusted Biometric Authentication under User Control
## 4 Sample Implementation
### User Application (UA)  
The BioShare User Application is implemented as an Android app that is deployed on mobile terminals. The key challenge in the implementation of UA is to maintain a reliable connection between the terminals and the server in a changing environment.  
**TCP Connections:** The user application either creates a short TCP/IP connection to the server for each call, or keeps a long connection with the server for a certain period. The former requires lower resource costs, while the latter shows better performance.  
**Notification Mechanism:** Because Android or iOS kills the application running in the background automatically at irregular intervals, the User Application may be unreachable to the server from time to time. We need a notification mechanism through which the service application can notify the terminal to relaunch the user application at any time. Apple Push Notification Service [53], Google Firebase Cloud Messaging [54], and other third-party products provide reliable notification services for iOS and Android devices.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 5 Conclusions
### Methodological Contributions  
Our primary methodological contribution lies in the development of a theoretically grounded evaluation index system that quantifies regional forecast reliability through a hierarchical structure, encompassing forecast reliability, provincial impact, and forecasting complexity. This multidimensional framework effectively captures the heterogeneity across different forecasting systems and provides a mathematical foundation for optimal weight determination that previous approaches lacked.  
The PCA-based weighted fusion algorithm introduces a novel approach to integrating multi-source forecasting data, systematically determining optimal weights based on the evaluated trustworthiness of each provincial forecast. Our time-varying weighting mechanism further enhances this approach by adapting to temporal load patterns and changing regional forecasting capabilities throughout the day. This dynamic adaptability represents a significant advancement over existing approaches in the literature, which typically employ static weighting schemes.
## 3 Results
### Accuracy  
The recalls and precisions of the six extractions are shown in Figure 7, and the F1 scores are shown in Figure 8. The NDBI, IBI, NDISI, NDII, and CBI gave high recalls and low precisions, especially in Case 3 which had a large amount of bare soil, where the F1 scores were all less than 65%. The extraction accuracy by the proposed method was higher than others in our three case images, and the average values of the recall, precision, and F1 score were 95%, 91%, and 93%, respectively.  
to the urban heat island effect. Impervious surfaces are artificial surfaces that water cannot infiltrate to reach the soil, such as parking lots, streets, and highways. They often have a low specific heat capacity and heat up fast, which often leads to the higher temperature in urban areas than rural areas, the uneven distribution of heat, the abnormal hydrothermal cycle, and the harmful urban heat island effect [35]. It is important to build accurate and fast methods to extract impervious surface distribution which is helpful for detecting regional environmental changes in urban areas and achieving sustainable urban development.  
The existing methods for the extraction of impervious surface distribution increased the difference between impervious surfaces and other ground objects and achieved high precisions in some cases [7; 8; 9; 10; 11]. However, differences between sensors, regions, and seasons often lead to inconsistencies in the spectral features of ground objects in images. Many methods have poor abilities to distinguish impervious surfaces from bare soil because of their spectral similarity [36], and it is also not easy to distinguish withered vegetation in winter from impervious surfaces.
## 1 Introduction  
Inflammation of the dental pulp can occur as a result of severe caries, periodontal diseases, or traumatic events. Root canal therapy and other adjunct treatment options aim to remove the inflamed tissues or suppress their inflammatory response based on the stage and reversibility of inflammation [1; 2; 3]. A successful endodontic treatment needs thorough debridement and complete sealing of the root canal system. Failure in endodontic treatment manifests itself by clinical signs and symptoms or on radiographic examinations. A myriad of factors can lead to the failure of root canal treatment, which can be classified as preoperative, intraoperative, or postoperative factors [4; 5]. Separation of endodontic files and strip perforation are among the procedural intraoperative errors which ultimately may lead to the failure of root canal treatment [6; 7]. The possibility of separation of rotary nickel-titanium (NiTi) files is shown to range from 0.4% to 4.4% [8]. Several etiologic factors such as operator proficiency, root canal anatomy, torque, reuse of NiTi files, autoclaving cycles, and materials used in combination with the rotary files are among the factors leading to the separation of endodontic files [9; 10; 11]. In addition, mesial roots of the mandibular molars are susceptible to strip perforations due to their thin dentinal walls [12].  
Radiographic examinations play an important role in diagnosing the etiology of endodontic failures and procedural errors [13; 14]. Several imaging modalities can be used for evaluating the outcome of root canal treatments. Digital periapical radiographs are readily available, as they have a low radiation dose and are easy to obtain [15]. However, they are limited by their two-dimensional projection. Cone beam computed tomography (CBCT), as the most commonly used three-dimensional imaging modality in dentistry, is widely used for endodontic purposes [16; 17; 18]. The presence of metallic artifacts and a higher radiation dose are some of the issues which need to be considered when CBCT is being planned to evaluate the root canal system before, during, and after root canal treatments [19]. Therefore, imaging modalities for monitoring the outcome of endodontic treatments must be selected based on a cost-benefit approach.  
Limited studies have compared the accuracy of periapical radiography and CBCT in detecting separated rotary files in root canals. Additionally, some studies have reported a better performance of periapical radiography in detecting strip perforations, while others concluded that perforations can be detected more accurately on CBCT images. Accordingly, the comparison of digital periapical radiography and CBCT for detecting separated rotary instruments and strip perforation in filled canals would be valuable for clinical diagnosis and decision-making from a cost-benefit approach. Therefore, the aim of the present study was to compare the diagnostic potential of CBCT and digital periapical radiographs in detecting separated rotary files and strip perforation in filled canals.
## 3 Method  
### DenseNet  
In a standard CNN, the output of the \(l\)th layer \(\mathbf{x}_{l}\) is calculated by applying a non-linear transformation to the output of the previous layer \(\mathbf{x}_{l-1}\)  
\[\mathbf{x}_{l}=L_{l}(\mathbf{x}_{l-1}) \tag{1}\]  
where \(L_{l}\) is a convolution followed by a non-linearity activation function. Conventional CNNs consist of a stack of convolutional layers. However, deeper CNNs are more difficult to train due to vanishing gradients. In ResNet [5], residual blocks are used to train deeply structured neural networks. A residual block sums the identity mapping of the input to the output of the layer. The output \(\mathbf{x}_{l}\) of a residual block is given by  
\[\mathbf{x}_{l}=H_{l}(\mathbf{x}_{l-1})+\mathbf{x}_{l-1} \tag{2}\]  
where \(H_{l}\) is a non-linear transformation, which usually consists of a single layer or a stack of multiple layers. The identity mapping acts as a skip connection from a lower layer to the upper layer, which enables input features to be reused and the gradient to flow directly from the upper layer to the lower layer.  
DenseNet [6] is built from stacks of dense blocks. To improve the information flow between layers, DenseNet uses skip connections from any layer to all subsequent layers in each dense block. Each layer in dense blocks can be expressed as  
\[\mathbf{x}_{l}=L_{l}([\mathbf{x}_{l-1},...,\mathbf{x}_{0}]) \tag{3}\]  
where [ ] represents the concatenation of feature maps. DenseNet may look similar to ResNet, which introduces skip connections, however this small modification makes a noticeable difference between the two networks. DenseNet is more efficient than ResNet in parameter usage. Thanks to short connections to all feature maps in the architecture, information from previously computed feature maps can be reused easily.  
Figure 1: Overview of the proposed DSNet for weakly supervised AED: (**a**) the architecture of DSNet, where the dense layer marked with \({}^{*}\) is replaced by a recurrent layer in DSNet-RNN; (**b**) the schema of the dense block. \(C\) denotes a concatenation operation; and (**c**) the schema of the SE block.  
We use four convolutional layers and a single bottleneck layer (a layer that contains few channels compared to the previous layer) in each dense block. To improve computational efficiency, the bottleneck layer compresses all feature maps in the dense block into a reduced number of feature maps using \(1\times 1\) convolution. For all convolutional layers in the model, each side of the inputs is zero-padded by one pixel to keep the feature map size fixed and batch normalization is applied before a rectified linear unit (ReLU) for better training performance. We use more feature maps on the upper dense blocks to compensate for feature map size reduction in each max pooling layer.
# Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks
## 4 Optimization Framework
### Joint Use of MU-MIMO and OFDMA  
Thus far, we have assumed that the nodes cannot operate via MU-MIMO and OFDMA at the same time. In this subsection, we relax this restriction, and extend the model to joint MU-MIMO and OFDMA optimization. To achieve this, several changes must be made to the original problem. First, since OFDMA and MU-MIMO are now being used at the same time, we do not need to use the variables separating each transmission mode, such as \(m_{lf}\) and \(o_{lf}\). We leave only the OFDMA-related variables (i.e., variables starting with \(o\)), and redefine their meanings to include MU-MIMO operations. Variables related to  
Figure 2: Average computation times for varying problem sizes.  
MU-MIMO (i.e., variables starting with \(m\)) are removed from the constraints accordingly. In addition, several constraints such as (26), (27) and (40) are excluded, since they are no longer needed under these conditions. The following constraints are slightly modified compared to the original ones:  
\[o_{ltf}\leq s_{ltf}\leq S_{l}^{\max}\cdot o_{ltf}, \forall l,t,f \tag{42}\] \[s_{itk}^{\rm x}\leq S_{i}\cdot o_{itk}^{\rm x}, \forall i,t,k\] (43) \[s_{itk}^{\rm y}\leq S_{i}\cdot o_{itk}^{\rm y}, \forall i,t,k\] (44) \[\sum_{j\in N_{l}^{\rm int}}s_{jtk}^{\rm x}\leq S_{i}\cdot o_{itk}^ {\rm y}+U\cdot(1-o_{itk}^{\rm y}), \forall i,t,k\] (45) \[-S_{l}^{\max}\cdot(1-z_{ltfs})\leq s_{ltfs}-s\cdot z_{ltfk}\leq S _{l}^{\max}\cdot(1-z_{ltfs}), \forall l,t,f,s,k. \tag{46}\]  
Based on the fact that OFDMA transmissions can now have multiple spatial streams, constraints (28)-(30) and (33) are replaced with constraints (42)-(45). Constraint (46) is newly added to represent \(s_{itk}\), rather than constraints (31) and (32). Since different numbers of spatial streams in different subchannels are now available, we introduce variable \(s_{ltfk}\) to denote the number of spatial streams for flow \(f\) on link \(l\) over subchannel \(k\) at time slot \(t\).