esa-sceva/satcom-chunk-collection huggingface dataset:
# Microstructure and Mechanical Properties of Cement Mortar Containing Phase Change Materials
## 1 Introduction  
The temperature control of mass concrete is an important concern for large-scale construction. High thermal stress that was developed by the rapid escalation of hydration heat in mass concrete can cause unpredictable cracks in the concrete structure. Several methods can be employed to control hydration heat, including the use of moderate heat cement, fly ash, blast furnace slag cement, and admixtures [1; 2; 3] as well as precooling, pipe cooling, and block placement associated with construction methods [4; 5; 6]. However, these methods involve some challenges in terms of constructability and economic efficiency [7].  
The use of phase change material (PCM) is an effective way to control the hydration heat of mass concrete. PCM absorbs and emits heat during the transition phases from solid to liquid and from liquid to solid at the transition temperature boundary [8]. The various kinds of PCM can be divided into organic PCM, inorganic PCM, and eutectic PCM, depending on the melting point and latent heat from melting [9; 10]. In construction, PCM can serve to control the rapid temperature rise of heat-radiating material through latent heat storage [11]. Therefore, researchers have attempted to control the temperature changes of concrete by using PCM to improve both the heat insulation performance and the freeze-thaw resistance of concrete elements [12; 13].  
In order to predict the cracking in mass concrete that is due to a rise in temperature, the strength and hydration characteristics of the concrete need to be evaluated at the initial stages of the concrete placement, which occurs rapidly with a rise in temperature. However, as yet, researchers have not evaluated the hydration characteristics of cement mortar according to changes in initial strength of concrete that are due to the inclusion of PCM as an admixture. Two types of PCM--barium-based (PCM-Ba) and strontium-based (PCM-Sr)--were employed to investigate the microstructure and mechanical properties of cement mortar mixed with these two types of PCM.
# Extraction, Characterization, and Applications of Pectins from Plant By-Products
## 5 Techno-Functional Properties and Application of Pectin
### Water/Oil-Holding Capacity  
OHC or WHC of pectins is defined as the quantity of oil/water fixed by pectin after homogenization, incubation in the oil/water phase, and centrifugation. These quality parameters can be influenced by the hydrophobic/hydrophilic character of constituents and the total charge density [128]. WHC and OHC are two functional properties that are related to texture by the interaction between food product components [129]. OHC is an important feature of pectin in the food system because pectin with a high OHC can be used as a stabilizer or emulsifier in high-fat foods, such as some meat products. WHC depends on the hydration ability of pectin, which could be produced by the OH group in the structure. The high absorption of water by pectin makes it suitable for reducing the syneresis rate in some food products (e.g., yogurts, dairy desserts, etc.) [15]. The WHC and OHC of pectin extracted from watermelon rind are significantly higher than those of commercial gluten [129]. The WHC and OHC of different pectins are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pectin Sources** & **Measurement Conditions** & **WHC** & **OHC** & **References** \\ \hline \multirow{3}{*}{Sunflower stalk} & 0.1 g powder/6.0 g deionized water & & & \\  & mixing for 1 min & 40.2 g water/g & & \\ \cline{1-1}  & incubating at room temperature for 30 min & powder & & \\ \cline{1-1}  & centrifuging at 9000\(\times\)\(g\) for 30 min & & & \\ \hline \multirow{3}{*}{Tomato pomace} & 1 g powder/20 mL deionized water & & & \\ \cline{1-1}  & mixing for 1 min & 3.57 g water/g & & \\ \cline{1-1}  & incubating at room temperature for 60 min & powder & & \\ \cline{1-1}  & centrifuging at 4000\(\times\)\(g\) for 30 min & & & \\ \hline \multirow{3}{*}{Eggplant} & 0.5 g powder/50 mL deionized water & & & \\ \cline{1-1}  & incubating at room temperature for 60 min & 6.02 g water/g & & \\ \cline{1-1}  & centrifuging at 5000\(\times\)\(g\) for 20 min & powder & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Water-holding capacity (WHC) and oil-holding capacity (OHC) of pectin extracted from various sources.
# Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS)
## 2 Method  
The development of the ANN model involved three main steps. The first step was data preparation. In this study, a real-world industry dataset was curated to represent the majority of box dimensions commonly used in packaging applications for training the ANN model. The second step focused on constructing the ANN architecture by determining key modeling parameters--including the number of hidden layers, the configuration of hidden neurons, the number of training epochs, and the number of modeling cycles --using various algorithms. To optimize the hidden layer configuration, five optimization methods were evaluated, which are detailed later in the study. An exhaustive search [14] was used as a validation technique to assess the effectiveness of these methods and to help identify the global minimum of model error during hidden neuron optimization. To ensure stable and conservative results, the process began with a high initial number of epochs and modeling cycles, followed by iterative refinement until the model error converged. The third step involved verifying the model's accuracy through physical testing. Figure 1 presents an overview of the analysis process and methodology.  
This study involved running programming tasks on an HP Laptop 15t-dy100 featuring an Intel(R) Core(TM) i5-1035G1 CPU, operating at a processing speed of 1.00 GHz. The coding process to train the ANN model was conducted using Jupiter Notebook software 6.5.4, an integrated development environment (IDE).
###### Abstract  
Direct-to-satellite Internet of Things (IoT) solutions have attracted a lot of attention from industry and academia recently, as promising alternatives for large scale coverage of a massive number of IoT devices. In this work, we considered that a cluster of IoT devices was under the coverage of a constellation of low-Earth orbit (LEO) satellites, while slotted Aloha was used as a medium access control technique. Then, we analyzed the throughput and packet loss rate while considering potentially different erasure probabilities at each of the visible satellites within the constellation. We show that different combinations of erasure probabilities at the LEO satellites and the IoT traffic load can lead to considerable differences in the system's performance. Next, we introduce an intelligent traffic load distribution (ITLD) strategy, which, by choosing between a non-uniform allocation and the uniform traffic load distribution, guarantees a high overall system throughput, by allocating more appropriate amounts of traffic load at different positions (i.e., different sets of erasure probabilities) of the LEO constellation with respect to the IoT cluster. Finally, the results show that ITLD, a mechanism with low implementation complexity, allows the system to be much more scalable, intelligently exploiting the potential of the different positions of the satellite constellation.  
machine-type communications; IoT; satellite communications +
Footnote †: journal: Sensors
## 2 Results and Discussion
### Volatile Characterization  
#### 2.1.1 HS-SPME-GC-MS Analysis  
Table 1 shows the composition of the volatile profile of the dried onion samples when they were analyzed by HS-SPME-GC-MS either in the presence or in the absence of ascorbic acid. Overall, a total of 53 volatile organic compounds (VOCs) were tentatively identified, according to their RI and matching factor greater than 80%--30 of them are sulfur-containing compounds (3 monosulfides, 16 disulfides, 5 trisulfides, 1 mercaptane, 2 iophenes, 2 trithiolanes and the carbon disulfide), while the remaining 23 VOCs were 15 aldehydes, 1 ketone, 3 carboxylic acids, 1 alcohol, 2 esters and the 2-pentylfuran. The reported data in Table 1 are based on the use of the internal standard, namely 4-methyl-2-pentanol and they have to be intended as a relative quantitation. The total content of VOCs detected in dried onion flakes is much higher than in dried onion rings, with values of 127.26 mg g\({}^{-1}\) in onion flakes, which is the triple of the 42.79 mg g\({}^{-1}\) in onion rings. Sulfides--Disulfides are the most abundant class of VOCs in the volatile fraction of the samples followed by trisulfides. These two classes, together, account for the 92.6% of the total VOCs content in onion flakes and 89.2% in onion rings. In particular, the class of disulfides accounts for the 60.3% in onion flakes and for 76.9% in onion rings, with dipropyl disulfide as the definitely most abundant molecule of this class, followed by propyl _trans_-1-propenyl disulfide, methyl propyl disulfide, methyl cis-1-propenyl disulfide and methyl _trans_-1-propenyl disulfide; the dipropyl disulfide, identified in our samples as the most abundant VOC, was previously reported as linked to green notes of onion. At the same time, the class of trisulfides accounts for 32.3% in onion flakes and for 12.4% in onion rings, with dipropyl trisulfide as the most abundant molecule of this class, followed by dimethyl trisulfide, methyl propyl trisulfide and propyl _trans_-1-propenyl trisulfide. The presence of disulfides and trisulfides in so high percentages in the volatile profile of these dried onion samples is in agreement with previous literature [17; 1], in which the authors stated that frozen onion maintains a profile of VOCs similar to fresh onion, while other transformed onion samples, as dried onion, show the presence of high amounts of di- and trisulfides. Monosulfides were detected in very low amounts (0.32 mg g\({}^{-1}\) in onion flakes and 0.09 mg g\({}^{-1}\) in onion rings), with allyl propyl sulfide as most abundant one in both the samples. To the authors' knowledge, some of the sulfides detected in low amount in this research have never been reported before in the volatile fraction of dried onion samples--allyl propyl sulfide, 1-propenyl propyl sulfide, allyl isopropyl disulfide, allyl cis-1-propenyl disulfide, allyl _trans_-1-propenyl disulfide, 1-(1-(methylthio)propyl)-2-propyl disulfide, methyl 1-(propylthio)propyl disulfide, 1-(cis-1-propenylthio)propylpropyl disulfide, 1-(1-_trans_-propenylthio)propylpropyl disulfide.
# Sensitivity Analysis for Microscopic Crowd Simulation
## 3 Results and Discussion
### Sobol' First and Total Order Indices  
We approximate Sobol' total and first order indices with [PERSON]'s method [53]. The variance-based approach of this metric is suited for most models, since it requires only model evaluations. Nevertheless, a high number of model evaluations is typically required.  
Figure 6 depicts the first order indices for the bottleneck scenario. The first order index \(S_{i}\) describes the variance in the output stemming solely from parameter \(i\). The negative indices are due to numerical errors, a commonly observed issue especially with a small sample size [52]. We can observe fewer negative entries with larger \(M\), which is larger sample sizes.  
We observe a high influence by the bottleneck width. In addition, we observe the lowest variation between the runs for this parameter. The indices reflect that the control parameter has no impact on the quantity of interest. In addition, the mean free-flow speed as well the number of agents do not have a measurable individual impact on the density in front of the bottleneck. However, they might be influential in interaction terms. Therefore, we investigate the total order indices next.  
In addition, Figure 7 plots Sobol' total indices. The sum of the first order indices is almost identical to the sum of the total order indices. That means there are no strong interactions among the factors, compare (5). The number of samples for the matrices \(A\) and \(B\) for [PERSON]'s method are defined by \(N=\lfloor M/(m+1)\rfloor\). For each sampling factor \(M\), three runs are performed in order to  
Figure 6: Sobol’ first order indices for our bottleneck scenario. Calculated with Monte Carlo approach using [PERSON]’s method [52]. For each sampling factor \(M\), 3 runs were performed. The parameter index is mapped as follows: 1—control parameter, 2—free-flow speed mean, 3—free-flow speed standard deviation, 4 —number of agents, 5—bottleneck width, and 6—personal space strength.  
study the variation in the results. The average (solid lines) is shown together with the extremes (area). As expected, the variation decreases with an increasing number of samples. From the total indices, we can conclude that the first (control parameter), second (free-flow speed mean), and fourth (number of agents) parameter are non-influential. The personal space strength (parameter index 6) is the most influential parameter.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Master-Range Extension to End Data Packet Duplication  
In the transmission of the data packet, in rare cases, some of the master nodes cause data packet duplication. This happens when some masters are situated beyond the boundary of the master responsible for forwarding the data packet. Consequently, they cannot receive a control packet from it and, in response, after the expiry of their holding time, consider themselves eligible for forwarding. To deal with this issue, we change the range of the control packets adaptively with the position of masters which are out of the privileged master's range as shown in Figure 6a. When master A sends a control packet to suppress the slave nodes of the next-hop and master nodes of the previous hop, master M\({}_{\text{O}}\) will not receive the control packet. Hence, after the holding time elapses, it will generate a duplicate data packet. To address this problem, node A checks the presence of all master nodes in its range before sending the control packet. If it finds any of them outside the range, it finds the distance between that node and itself using Equation (9), and re-adjusts its control packet energy for that new range as shown in Figure 6b. If it broadcasts the control packet, it will suppress node M\({}_{\text{O}}\) from generating a duplicate packet. This happens quite rarely in our topology because the selection process considers that the masters should be selected only when they are close to each other. Also, such deviation from MSF is possible in a sparse network.  
The master-range extension is based on the distance between the master nodes. This distance is calculated using the following Equation (4):  
\[Dist_{j}^{i}=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}+ \left(z_{i}-z_{j}\right)^{2}}\]  
in case the node is falling outside the range of the forwarding master node, we use this distance to calculate the extended region of the master node for suppressing the duplication of the data packet. Such calculation can be expressed using the following equation:  
\[R_{ext}=Dist_{M_{\mathrm{O}}}^{M_{\mathrm{1}}}-R \tag{8}\]  
where \(R_{ext}\) represents the extra range (master-range extension), \(Dist_{M_{\mathrm{O}}}^{M_{\mathrm{1}}}\) is the distance between first master (\(M_{\mathrm{1}}\)) and the other master which is the out-of-range (\(M_{\mathrm{O}}\)) node, while R is the transmission range.
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 5 Clinical Applications of Ultrasound Elastography
### Breast
#### 5.3.2 Technical Limitations in Breast Elastography  
The main problem with breast elastography is that it can be hard to tell the difference between different types of tissues and if they are cancerous or not. This is because cancerous lesions can sometimes look soft on SWI, and cystic (benign) tissues can look stiff if they are described as breast fibrosis or fat necrosis. This is due to the fact that breast elastography has a difficult time determining whether heterogenous tissues are malignant or benign. Elastogram color coding in shear wave elastography is exhibited as blue for a soft, lean tissue and red for a solid, higher stiffness tissue; however, elastography color coding and scoring suffer from a lack of consistency, and this could cause a problem for the operator [33; 99]. If the mass was located toward the posterior aspect of the breast, then it would be more difficult to scan and assess. Deep tissues may not always be displaced by  
Figure 4: Visual representation illustrating the Tsukuba score. The lesion is depicted in an oval shape, with varying colors denoting differences in lesion stiffness in relation to the surrounding tissue. Specifically, the color blue represents increasing stiffness, while the color red represents decreased stiffness. As the Tsukuba score (ranging from 1 to 5) increases, there is an elevated likelihood of malignancy in lesions. The presence of blue, green, and red (BGR) bands exhibiting a tri-laminar pattern, as shown in the far right image, can serve as a diagnostic indicator for the presence of a cyst when visualized by various ultrasound equipment manufacturers, such as Hitachi and Toshiba [14].  
applying pressure to the breast surface, which increases the requirement for mammography. Another challenge depends on the position of the lesion. It is important to note that there are not many comparative studies to determine which of the USE techniques is preferred over the other, although a compound usage of strain imaging and shear wave imaging USE would increase the accuracy and ameliorate the detection of breast lesions, as well as their staging [33].
## 2 Materials and Methods  
### MFC Design and Operation  
Two identical single-chamber air-cathode MFC reactors, with a total volume of 155 mL, were fabricated from clear/extruded acrylic tubes (5 cm in internal diameter and 7.5 cm in length), covered with 0.25 cm thick plexiglass acrylic endplates at both ends. The anode was made from plain carbon cloth, while the air-cathode was made from 0.5 mg cm\({}^{-2}\) Pt-containing (10% in carbon black) carbon  
Figure 1: A schematic of organic substrate and energy flow in a microbial fuel cell.  
cloth treated with Nafion, and with polytetrafluoroethylene (PTFE) as a gas diffusion backing layer [10]. A 2.2 k\(\Omega\) resistor was connected in the circuit as an external load.  
Anaerobic sludge was obtained for MFC inoculation from a swine manure lagoon at the University of Minnesota Southern Research and Outreach Center in Waseca, MN. The sludge was sieved through a 1 mm mesh sieve to remove sand, gravels, and plant residues prior to use. Sodium acetate was dissolved in 50 mM phosphate buffer solution (pH 7) to prepare a medium with 40 mM acetate. Other nutrients also added to enhance and maintain the development of bacteria included (g L\({}^{-1}\)): 0.31 NH\({}_{4}\)Cl, 0.13 KCl, and mineral and vitamin solutions.  
Data Acquisition  
The voltage (\(U_{ext}\)) across the external resistor was measured by voltage probes and recorded by a CR 1000 data logger (Campbell Scientific, Inc., Logan, UT, USA). The anodic and cathodic potentials were monitored using standard Ag/AgCl electrodes (MF-2072, BASi, Inc., West Lafayette, IN, USA). The data logger supporting software, LoggerNet Version 3.4 (Campbell Scientific, Inc., Logan, UT, USA), was used to monitor and collect the voltage data of the MFC reactors.
# On the Capacity of Optical Backbone Networks
## 2 Basics on Optical Networks and Physical Impairments
### Optical Network Architecture  
This section briefly discusses the architecture of a transparent optical network, with a focus on the backbone tier. Transparent optical networks refer to networks where optical signals are transmitted from the source to the destination without conversion to electrical signals, maintaining their optical nature throughout the network. Therefore, in these  
Figure 1: Spectrum of a WDM signal.  
networks, all node functionalities (such as multiplexing, switching, routing, etc.) take place in the optical domain, and the node structure is based on ROADMs. The ROADM is responsible not only for locally adding and dropping optical channels, but also for performing optical bypassing through the switching of optical channels from the incoming to the outgoing optical fibers [20]. In addition, they can be remotely configured to establish optical channels and to change their paths and wavelengths, making it a premium function in networks automation. Typically, ROADMs perform demultiplexing and multiplexing of the DWDM signals transmitted in optical fibers and wavelength switching using wavelength selective switches (WSSs). In practice, WSSs are responsible for the dynamism of the ROADM, since they can switch optical channels through the action of the control/management plane.  
Figure 2 depicts a simplified architecture of a simple transparent optical network, showing four ROADMs interconnected with optical fibers, as well as bandwidth variable transponders (BVTs) for connecting these network elements to the client equipment. The BVT plays a crucial role in optical networks. First, it is responsible for mapping the signals received on the client side into appropriate containers and adding forward error correction (FEC) codes for error correction purposes. Second, it is responsible for generating optical signals by modulating optical carriers with specific wavelengths on the network side. In this context, BVT can be considered as the source (in the transmitter direction) and the termination (in the receiver direction) of optical channels. BVTs can also be configured remotely to adjust the channel capacity by modifying two key parameters: the number of bits transmitted per symbol, (i.e., the modulation format) and the number of symbols transmitted per second (symbol rate).  
A wide range of modulation formats are available, allowing for the trading of spectral efficiency against optical reach. For instance, the utilization of more efficient modulation schemes, such as 128 QAM or 64 QAM, is limited to optical channels that travel over short distances, while longer distances require the use of less efficient modulation formats, such as quadrature phase-shift keying (QPSK), or even binary phase-shift keying (BPSK). On the other hand, the use of higher symbol rates implies using broader bandwidths, reducing the number of optical channels available in the C-band.