esa-sceva/satcom-chunk-collection huggingface dataset:
# Production and Processing of a Spherical Polybutylene Terephthalate Powder for Laser Sintering
## 1 Introduction
Attempts have been made to describe the droplet size as a function of material and process parameters, as has been reviewed by multiple authors [26; 27]. These models however typically have an empirical basis and hold only for the particular investigated system. As a first step in the current investigation, the extruder rotational speed and nozzle were varied, and the resulting particle size was evaluated. The influence of blend ratio PBT/PEG on the particle size was investigated as well. An ideal and aimed for size range for typical laser sintering powderslies between 10-100 \(\upmu\)m [19]. After optimization of extruder settings and blend ratio, the powder was extracted from the extrudate through a series of washing, drying, and classification steps. These steps are summarized visually in Figure 2. The particle size and shape distribution, thermal properties, and flowability properties of the resulting powder were analyzed. The material was then tested on a commercial laser sintering machine with specifically designed inserts to allow for processing with small amounts of powder. Finally, the mechanical properties of the built parts were evaluated and compared to those of compression and injection molded samples made from the same PBT grade.
# Energy-Aware Next-Generation Mobile Routing Chains with Fog Computing for Emerging Applications
## 3 Proposed Load-Balanced and Energy-Efficient Mobile Routing Protocol
### System Model  
Let us assume that sensors are denoted by \(S_{i}=(S_{1},\ S_{2},\ldots,\ S_{n})\) and are deployed randomly. The nodes that are not in the same radius can only be communicated using multi-hop mode. Nodes are mobile and rotated, and periodically announce their positions. Fog nodes are placed between sensors and the sink node. Initially, nodes cooperatively share their attributes to initiate the formulation of routing tables. Later, routing tables are refined using cooperative decisions of the devices. The sink node is static and has sufficient resources to control the network environment. It keeps track of the entire monitoring field and announces alert messages if any faulty action is identified. Each cluster has only one cluster head at a time, and with the analysis of QoS parameters, the role is updated. Before discussing the proposed algorithm, we highlight the following network assumptions.  
1. Nodes have heterogeneous resources for energy, transmission power, and memory constraints.
2. Each path is assigned a unique identity.
3. After deployment, no batteries can be replaced for sensor nodes.
4. Aggregated data are transmitted to the sink node with the support of fog nodes over the unpredictable communication links
5. The transmission power of the nodes can be adjusted using Received Signal Strength Indicator (RSSI).
# Recent Trends on UV filters
## 3 Results
### SPF Values in Adults' and Children's Sunscreens  
The SPF values of the analyzed sunscreens varied between 15 (minimum) and 50+ (maximum) (Figure 5). The maximum value (SPF50+) was the most frequently found both for adults' (59.4%) and children's sunscreens (81.5%). It is important to highlight the absence of sunscreens with an SPF below 30 in paediatric photoprotectors, as well as residual use in adult sunscreens. The percentage of products containing SPF 30 in adults' sunscreens (24.5%) is higher than the next protection available, SPF 50 (11.1%), but much lower than that of SPF50+ products (59.4%).  
Figure 4: Usage frequency of different galenic forms in adults’ and children’ sunscreens.  
Figure 5: Frequency of SPF values in adults’ and children’s sunscreens.  
According to European recommendations regarding sunscreens, the minimum labelled SPF should be 6 and the maximum 50+. The number of SPF levels should be restricted to eight (SPF 6, 10, 15, 20, 25, 30, 50, 50+) to facilitate comparison between different products without reducing consumer choice [30]. Their effectiveness must be indicated on the label with reference to the following categories: \"low\" (SPF 6 and 10), \"medium\" (SPF 15, 20, and 25), \"high\" (SPF 30 and 50), and \"very high\" (SPF 50+) [2]. With growing consumer awareness of the damaging effects of UVR, there is an increased demand for high SPF photoprotectors (30, 50 and 50+) [2]. In response to this recent and growing concern, manufacturers are marketing sunscreen products with very high SPF values, thus justifying the absence of sunscreens categorized with low SPF protection in this study. Currently, the maximum numerical indication, expressed as 50+, has been agreed upon, as it is understood that SPFs above this value could mislead consumers.
## 3 System Design
### Mathematical Modeling of the Power System
#### 3.2.1 PV Array
where \(I\) is the output current (A); \(I_{L}\) is the photogenerated current (A); \(I_{0}\) is the diode saturation current (A); \(q\) is the unit charge (\(1.6022\times 10^{-19}\) C); \(R_{sh}\) is the series resistance (\(\Omega\)); \(V\) is the output voltage (V); \(\lambda\) is the diode ideality factor; \(K\) is [PERSON]'s constant (\(1.3806\times 10^{-23}\) J/K); \(T\) is the cell temperature (K). In this study, we use the following equations to describe the relationship between output of solar power and radiation intensity [4]:  
\[\left\{ \begin{array}{l}P_{solar}=P_{\max}[1-0.004(T-T_{stc})]\beta_{i}\\ \beta_{i}=i\beta_{1}\beta_{2}\beta_{3}\end{array} \right. \tag{5}\]  
where \(P_{solar}\) is the output of solar power (Wh/day); \(P_{max}\) is the maximum power at standard test conditions (300 W); \(T\) is the ambient temperature (\({}^{\circ}\)C); \(T_{stc}\) is the ambient temperature at standard test conditions (25 \({}^{\circ}\)C); \(\beta_{i}\) is the adjustment parameter, which \(i\) is the average radiation intensity (KW h/m\({}^{2}\)/day), \(\beta_{1}\) is the soiling losses factor 0.97, \(\beta_{2}\) is the non-MPPT point coefficient 0.96, \(\beta_{3}\) is the anti reverse diode coefficient 0.98.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 3 The MASU Framework
### Data Retrieval Mechanism  
The interactions between the different roles of the Sensing Tier enables the MASU units, participating in the collaborative sensing activity, to collect and share data so that all the units can have the information required by the activity. These interactions are mediated by the Collaborative Sensing Module (CoSM), which is in charge of the data retrieval and allows the differential activation of each one of the roles of the Sensing Tier (i.e., consumer, producer, storage and relay).  
This component offers four basic services for service discovery [28] and information distribution: Publish, Find, Subscribe, and Data Dissemination. Figure 5 shows the structure of CoSM, which is composed by three main modules: the Sensing module, the Data Source Manager and the Data Dissemination Manager. They are responsible of the services that enable the differential activation of the roles in the Sensing Tier. The Sensing module interacts with the other two to determine the type of data that a particular unit has to sense and the method that it will use to obtain such data. Next we explain more in detail these components.
# Lp-Norm Inversion of Gravity Data Using Adaptive Differential Evolution
## 3 Lp-Norm Gravity Inversion Based on Adaptive Differential Evolution  
### Forward Modeling  
Assuming that the observed gravity data is a vector written as \(d\), whose length is N. The underground model is represented by \(m\), whose length is M. Then the forward modeling problem can be expressed as:  
\[d=G(m) \tag{19}\]  
where \(G\) is the forward operator. In the traditional gravity inversion method, \(G\) is the linear forward matrix, but here \(G\) is no longer a linear matrix, while the finite volume method is adopted in the forward modeling [42].
## 4 Simulation Results
Therefore, the proposed FRCA generates the optimum number of cluster heads when \(\mu\) \(\geq\) 0.8. Too many cluster heads in clustering results difficulties in the management of clusters. In this paper, we assumed that a cluster head manages optimally about 100 nodes according to our experience. The simulation generated 4 clusters. The processing rate may be improved by adjusting the number of nodes in a cluster. Figure 8: Number of Clusterheads with 0.5 \(\leq\)\(\mu\)\(\leq\) 0.9. Figure 7: Overhead Rate with 0.5 \(\leq\)\(\mu\)\(\leq\) 0.9. In Scenario 4, we showed the performance of the proposed FRCA by varying network sizes. To achieve this, we vary the network size by 350, 400, 450, 500, 550, 600, 650, and 700. The simulation result is shown in Figure 9. As shown in Figure 9, the proposed FRCA achieves better CHER than SCAM that is known for its good performance. Better CHER of the proposed FRCA is due to the classification of nodes as the CH node, CH candidate node, or CH member nodes. Thus, the performance of the proposed FRCA does not degrade with the increase of network size. CHER is influenced by nodes with \(\mu<0.8\) that means low signal intensity and low battery power. Therefore, the simulation is performed with \(\mu\geq 0.9\). In Scenario 5  
Figure 10 shows the average number of clusters is relatively high when the transmission range is small. On the other hand, when the transmission range increases, the number of clusters created decreases. A smaller backbone reduces the routing overhead. Therefore, the transmission power of a node in a heterogeneous environment depends on the quality of dominating nodes. Figure 11 shows the simulation result with the number of nodes N = 400 and \(\mu\) = 0.9. The proposed FRCA creates fewer clusters compared with those of CBRP, WACA, and SCAM. This is because the proposed FRCA applies FRD(\(\mu\)) and results in form fewer clusters. But if FRD(\(\mu\)) decreases more and more, then the cluster number and size decrease in proportion to FRD(\(\mu\)), which affects the performance. Therefore, FRD(\(\mu\)) is important to select the cluster head. Thus, the proposed FRCA selects the cluster head stably by filtering out nodes with low signal intensity and low battery power using the proper FRD(\(\mu\)).
# Multi-Scale Feature Learning for Language Identification of Overlapped Speech
## 1 Introduction  
Language identification (LID) is the process of identifying the language type of a given speech segment [1], and it is a classification task [2]. The overall architecture includes a feature extraction task and a classification task. So, the system can be feature-intensive, as [3] represented by training large-scale data, or it can be model-intensive, as obtained by better classifier models. Both the feature extraction and classification tasks are equally important, but a balance between them is optimal [4], as described in this paper.  
Language identification technology has developed considerably in the last decade. New deep learning frameworks provide new opportunities for the development of language identification research [5]. Recently, people have become increasingly concerned about language identification in real and complex scenes. As the core front-end processing module for multilingual intelligent speech processing tasks, language identification can be used in multiple fields, such as automatic speech recognition, speech translation, and speech generation. In noisy multilingual overlapping speech, even the human ear may not be able to accurately identify contents, the clean single speech-based model is not efficient for overlapped voices. Therefore, it is necessary to separate overlapped speeches before identifying or understanding the contents.  
This study proposes language identification feature extraction technology based on a squeeze-excitation [6] and multi-scale residual [7] network (SE-Res2 Net), which improves the feature extraction method of the baseline model and greatly improves the recognition performance of the original language identification algorithm. Experiments were conducted on the AP17-OLR [8] dataset and a multilingual cocktail party dataset. The accuracy, recall, and F1 values were improved over the baseline model, and the robustness of the model was also improved compared to other models.
# Prevalence and Predictors of Osteoporosis Among the Chinese Population in Klang Valley, Malaysia
## 4 Discussion
This was in accordance with a previous study that reported a significant association between household income and bone health in elderly women [44]. A lower household income may imply limited access to healthcare [25; 45]. Subjects with a lower income might not have the opportunities to engage with medical professionals to ensure their well-being. Besides, they might not have access to healthy food and supplements. Thus, previous studies have indicated that individuals with a lower income also had a greater risk of hip fracture [46]. Lower education level was one of the determinants of osteoporosis among women. Similar findings were reported by [PERSON] et al. (2010), whereby a lower education level was associated with a lower BMD and a higher prevalence of osteoporosis in Moroccan women (n = 356, mean age: 58.9 \(\pm\) 7.7 years) [47]. It is postulated that subjects with lower educational background might have lower knowledge and awareness regarding osteoporosis. Hence, they are less likely to engage in osteoprotective practices. This was confirmed by another study among postmenopausal women (n = 560; mean age: 60.43 \(\pm\) 7.19 years), which demonstrated that subjects with a higher education level had a better bone health status and positive practices towards bone health [48]. There are several limitations that should be considered when interpreting the data of this study. The present study was limited by its cross-sectional design. A prospective study would provide stronger evidence of causality between osteoporosis and its associated risk factors. We also did not determine the dietary and supplementation pattern of the subjects in detail; hence, the relationship between macro- and micro-nutrients and bone health was not investigated. An additional limitation includes the relatively smaller sample size involved in the study. However, to the best of our knowledge, this is the first study to report on the prevalence and predictors of osteoporosis defined by DXA among middle-aged and elderly Malaysian Chinese in Klang Valley. Other similar studies have been conducted in Malaysia, but they used quantitative ultrasonography to determine bone health of the subjects [13; 49; 50].
## 2 Impact of Process Variations (PV) on Different Operation Modes
### Impact of PV on Physical Unclonable Function (PUF) Quality
#### 2.1.2 Loop-Gain at Trip Point  
The \"Loop-gain\" at the metastable trip point of a cross-coupled inverter pair is an important factor that affects the reliability of SPUF [12]. The node voltages of SRAM cells stabilize faster if the loop-gain is higher. According to [13], the loop-gain is formulated as  
\[LG(V_{BIT})=\frac{\partial\text{VTC}_{2}}{\partial V_{BIT}}\cdot\frac{\partial \text{VTC}_{1}}{\partial V_{BITB}}, \tag{2}\]  
where \(V_{BIT}=\text{VTC}_{1}(V_{BITB})\) or \(V_{BITB}=\text{VTC}_{2}(V_{BIT})\). VTC refers to the voltage transfer characteristic function and can be formulated as follows [9]  
VTC for INV-2:  
\[\begin{split}& V_{BITB}=V_{DD}+V_{T}\text{ln}\left\{\frac{1}{2} \left[1-G+\sqrt{(G-1)^{2}+4\text{exp}\left(-\frac{V_{DD}}{V_{T}}\right)G} \right]\right\},\\ & G=\text{exp}\left(\frac{2V_{BIT}}{nV_{T}}-\text{ln}\frac{I_{ S5}}{I_{54}}-\frac{V_{DD}}{nV_{T}}-\frac{V_{th,A}-V_{th,5}}{nV_{T}}\right), \end{split} \tag{3}\]  
where \(I_{Si}\) and \(V_{th,i}\) refer to the saturation current and threshold voltages of the transistor M\(i\) in Figure 1. VTC for INV-1 can be derived similarly.  
As pointed out in [12], large loop-gain degrades the reliability of SPUF as the amplified noise in the power-up process overwhelms the transistor mismatches due to process variation. From Equations (2) and (3), the loop-gain is mainly governed by \(V_{DD}\), \(V_{th}\) and \(V_{T}\).
## 2 System Model with Collision Design and Analysis of Our Proposed System
### Energy Model of Our Proposed System  
Energy models of UWCNs differ considerably from TWSNs because of the special features of the submerged acoustical channel. The following is how the sensor node energy model is stated [50]. The energy efficiency of the communicating data packet transmission is described as follows:  
\[E_{t}(md)=n\times E_{tr}+n\times E_{e}. \tag{11}\]  
Figure 6: Flow chart for our proposed system communication performance.  
where the energy efficiency of the transmission is denoted by \(E_{t}\), and \(E_{tr}\) is the rate of energy efficiency of the transmission, which receives the 1-bit communicating data packet and is measured in joules per bit. The number of bits is denoted by \(n\), while \(E_{e}\) is defined as  
\[E_{e}=E_{0}\times d^{k}\times q^{d}. \tag{12}\]  
The UWCN's energy-efficiency factor \(E_{e}\) equation considers several factors, including the distance \(d\) between the Tx anchor node and Rx node; the spreading factor \(k\), which has a value of 1.7 for cylindrical spreading and 3 for spherical spreading; the power threshold \(E_{0}\), necessary for the Rx node to receive communicating data packets; and a frequency-related term \(\varphi\) derived from the absorption coefficient:  
\[\varphi=10\frac{A(f)}{10}. \tag{13}\]  
where \(A(f)\) is the frequency in kHz and dB/km. The absorption coefficient, a frequency-dependent variable, is represented by \(f\). The aforementioned frequency, expressed by \(A(f)\), is just over one hundred Hz, and can be determined using [PERSON]'s expression from [50] as follows:  
\[A(f)=\frac{44f^{2}}{4100+f^{2}}+\frac{0.11f^{2}}{1+f^{2}}+\frac{0.275f^{2}}{10 ^{3}}+\frac{3}{1000}. \tag{14}\]  
The number of joules of energy required for an Rx node to receive z number of bits of a communicating data packet is  
\[E_{r}(n_{0})=n\times E_{tr}. \tag{15}\]  
An Rx node uses the following quantity of energy when it is only idle listening:  
\[E_{I}(nd)=n\times E_{tr}\times\mu_{0}. \tag{16}\]  
where the ratio of active to passive listening energy is denoted by \(\mu_{0}\), The energy efficiency of submerged acoustical Rx nodes during communication was investigated in [51]. It has been suggested that the submerged acoustical signal's transmission and reception make up around 61.2% of the communicating node's overall consumption of energy. In order to minimize collisions during communication, increase efficiency, and decrease energy inefficiency, energy-efficient MAC protocols will focus on these three challenges in UWCNs.  
Additionally, as communicating nodes can automatically wake up and the submerged habitat does not change significantly over a short period of time, sharing time monitoring is feasible. On the other hand, when comparing the idle mode with the sleep mode, the sleep mode is more energy efficient in UWCNs. Thus, by putting UWCNs in sleep mode when not in use and waking them up when necessary, their energy efficiency may considerably increase, and their lifespan may increase.
# Thermogravimetry Applicability in Compost and Composing Research: A Review
## 3 Results and Discussion
### Compost Maturity Evaluation Research by Using a Variety of Techniques
#### 3.2.2 Composting Maturity Research by Thermogravimetry and Thermal Analysis  
The compost maturity could be characterized by thermogravimetric and thermal techniques or differential scanning calorimetry (DSC) and thermogravimetric analyses (TGA) [5; 12; 13; 37; 38; 42; 48]. In this sense, during the maturation process, the solid content and the microorganism populations are reduced. The trends followed by the degradation profiles obtained from DTG-DTA during this maturation are consistent with this reduction [42]. Moreover, different authors pointed out that the evaluation of the maturity degree of compost through thermogravimetric data (TGA+DSC) was useful and coherent with the quantitative information coming from the chemical analysis of the humidified fraction of organic matter in compost [11; 58; 59]. This information, through the parameter R1, allows us to quantitatively evaluate the stabilization of organic matter in the composting process and could be considered as a reliable parameter to check the maturity level of organic matter [12; 48]. In this form, different studies have been published in which compost with R1 \(\geq\) 0.8 can be considered mature [33; 59]. On the contrary, [PERSON]\({}^{\prime}\)[PERSON] et al. [5] stated that the amount of easily biodegradable organic matter in compost is not directly measured by R1; therefore, it does not correctly evaluate the biological stability. In this sense, [PERSON] et al. [51] reported that although different composts gave the same final value of R1, they showed different values in other maturity parameters evaluated. [PERSON] et al. [61] stated that no significant correlation had been observed among dynamic respiration index (DRI), humification indices and R1.  
In another paper, [PERSON] and [PERSON] [35] showed that DTG peaks corresponding to the degradation of more refractory compounds such as lignin and other resistant aromatic structures increases with the maturity of the compost. In addition, they indicated the displacement of the DTG peaks as the compost matures. These effects, with both increased aromaticity of the final products and increased oxygen content in the functional groups of the more easily degradable compounds, were both associated.