esa-sceva/satcom-chunk-collection huggingface dataset:
# Carbon Footprint Assessment on the Viability of Utilizing Brewer's Spent Grain to Produce Biochar
## 2 Materials and Methods
### System Boundary  
This study conducted an evaluation of biochar production at the laboratory-bench scale, which imposed limitations on production volumes due to equipment size. It is important to note that scale-up of this system could enhance the accuracy of the environ  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Phase** & **Item** & **kg CO\({}_{2}\)e** \\ \hline \multirow{4}{*}{CO\({}_{2,\text{sequest}}\)} & Input & Electricity (sum) & 9.81 \\  & & Nitrogen Gas & \(1\times 10^{-4}\) \\  & Output & Wastewater & \(6\times 10^{-7}\) \\  & & Vented Fumes & 0.074 \\  & & Biochar–Soil Application & \(-8.93\) \\  & & Bio-Oil and Syngas Credit & \(-0.027\) \\  & Total & BSG-to-biochar GWP & 1.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: GWP impact assessment.  
mental impact and potential benefits of this system. Scaling up would also represent more applicable conditions to help determine the feasibility and suitability on a larger scale.  
The boundaries of this system are depicted in Figure 2. Any item shown outside of the system boundary is considered background data, while items listed inside are considered foreground data. The system boundary excludes beer production and the production of brewery cleaning materials, as these processes will occur regardless of this valorization process. The transportation of BSG and caustic waste is excluded from the boundary. Although these steps will differ from the traditional brewing process, the distance will vary, depending on the location of the processing facility in relation to the breweries. The boundary also excludes the industrial production of equipment and materials used in this process, as they will vary depending on the size of the installed pyrolysis system.  
The production process of BSG biochar involves the following six major steps: (1) soaking of BSG in caustic waste in a ventilated area; (2) drying of BSG in an oven; (3) grinding of BSG; (4) weighing of BSG; and (5) pyrolysis of BSG. The primary inputs in steps 2, 3, and 4 are electricity use. Step 5 involves electricity and nitrogen gas.  
The primary output of step 1 is wastewater, with consideration given to vented fumes. Step 2 mainly produces evaporated water. Step 5 results in the production of biochar as well as syngas and bio-oil, which are valuable commodities and can be considered co-products. It is assumed in this system that the source of electricity utilizes natural gas.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 2 Problem Formulation  
### Multi-Role, Multi-Stage Group Cooperative Mission Scenarios  
The research object of this article is a multi-role, multi-stage group cooperative mission, abbreviated as GCM. Consistent with the general project, a GCM involves multiple executors working together, and each executor needs to take on multiple sub-missions, which have cooperative relationships with each other. As shown in Figure 1, a typical multi-role, multi-stage cooperative mission can be split into \"mission-role-stage\" three levels. However, the biggest difference between a mission and a project is that a project generally seeks to shorten the total work time, whereas a mission seeks to keep the time of some critical sub-missions in alignment with the established plan.  
A typical GCM is the unmanned aerial vehicle (UAV) swarm cooperative strike missions (CSM) in military domain. The problem scenario of CSM is shown in Figure 2, where multiple UAVs are divided into different roles such as detection, positioning, suppression, jamming, and strike. These UAVs work in coordination with each other to ensure that the strike UAV is available to strike targets at the specified planned moment. Each UAV carries out its operations according to its own plan, which consists of multiple sub-missions. For the whole CSM, the strike moment (SM) is the most important moment. All assets must work together to ensure that the SM is not delayed nor advanced. This is because if the SM is advanced, then the remaining battlefield support resources such as satellites, communication facilities, etc., may be unavailable due to being on other missions; and if SM is delayed, then the optimal window of time to strike may be missed.
# Improved PPP Ambiguity Resolution with the Assistance of Multiple LEO Constellations and Signals
## 3 Results  
### UPD Results  
The UPD product is an important premise of PPP ambiguity resolution. In this section, we present the wide-lane and narrow-lane UPDs of GPS, GLONASS, Galileo, BDS, and LEO satellites and analyze the temporal characteristic and residual distribution of UPD products. Note that the LEO UPDs presented in this section were estimated based on observations of the 288-LEO constellation.  
Figure 3 shows the narrow-lane UPD series of GPS, GLONASS, Galileo, BDS, and LEO satellites on DOY 001, 2017. We selected seven exemplary satellites of each system and their UPD series are presented in the figure with different colors. It can be seen that the narrow-lane UPD series are quite stable, with the variation of fewer than 0.3 cycles for each system. We further calculated the average STDs of all satellites. The average STDs of GPS, GLONASS, Galileo, BDS, and LEO are 0.035, 0.068, 0.016, 0.026, and 0.014 cycles, respectively. The average STDs of UPD products are all less than 0.1 cycles, which indicates the high temporal stability of estimated narrow-lane UPD products.  
The posterior residual, which can be regarded as the fractional parts of ambiguities after the removal of UPDs, is an important indicator to investigate the quality of the UPD products [40]. Figure 4 presents the wide-lane residual distribution of GPS, GLONASS, Galileo, BDS, and LEO. The average value of wide-lane residuals of each system is close to zero, which indicates that there are no additional systematic biases after the removal of UPDs. With the fixing criteria of 0.15 cycles, the fixing percentage is greater than 90% for all systems. The narrow-lane residual distribution is shown in Figure 5. The fixing percentages within 0.15 cycles are 85.2%, 79.6%, 97.3%, 90.1%, and 84.8% for GPS, GLONASS, Galileo, BDS, and LEO satellites, respectively. Additionally, the STDs are also given in Figure 5, which are no more than 0.35 cycles for all systems. It can be seen that the UPDs of LEO present a comparable performance to GNSS UPDs, which is expected to achieve ambiguity resolution effectively.  
Figure 2: Distribution of MGEX stations used for UPD estimation and PPP ambiguity resolution. The blue dots indicate the reference stations, while the red dots denote the user stations.  
Figure 4: Residual distribution of wide-lane UPDs for GPS, GLONASS, Galileo, BDS, and LEO.  
Figure 3: Narrow-lane UPD series of GPS, GLONASS, Galileo, BDS, and LEO on DOY 001, 2017. The abbreviations GLO and Gal indicate the GLONASS and Galileo, respectively.
# Network Coding for Efficient Video Multicast in Device-to-Device Communications
## 4 System Evaluation
### Network Coding Efficiency  
When network coding is conducted during the transmission process, it generates some computation overhead. Because mobile devices have lower computing power than computers and servers, it is particularly important to evaluate the network coding efficiency of mobile devices. If the coding delay is too long, the performance gain will decline, which will result in poor user experience.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Mobile device & M14 LTE \\ Android version & 6.0 \\ Memory size & 2 GB \\ Video size & 6 MB \\ Package size & 1500 Bytes \\ Number of frames & 960 \\ Number of packets & 8502 \\ Video length & 32s \\ Video Format & H.264 \\ Transmission method & Multicast \\ Transmission distance & 0–5 m \\ Transmission protocol & IEEE 802.11g \\ Number of devices & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment parameters.  
Figure 2: Experiment scenarios.  
In this section, we evaluate the data coding capacity of different mobile devices. In the experiment, the size of the testing file is 10 MB, and generation size \(K\) is in the value range from 2 to 10. Most previous tests in the Android operating system were conducted by using the Java language, the encoding and decoding took more than 2 seconds on average, and this is unacceptable to the users. To reduce delay, we designed and implemented a network coding module with Java native interface, which is implemented with native C language. In accordance with Figure 3, we observe that our scheme requires much less time for encoding and decoding than merely using the Java program. With the increase of \(K\), the device needs more time for network coding, which is mainly due to the increased coding complexity caused by a higher coding dimension.  
With the introduction of the JNI library, the consumption of encoding time is significantly reduced, which is beneficial for the destination nodes to carry out re-encoding operation after receiving data. The experimental results show that it is feasible to research applications based on network coding at current commercial mobile devices.  
Because the main computational work of the scheme proposed in this paper is matrix multiplication, the time complexity of our scheme is O (\(n^{3}\)), and the space complexity is O (\(n^{2}\)).
# Nutritional Values of Onion Bulbs with Some Essential Structural Parameters for Packaging Process
## 3 Results and Discussion
### Vitamin C and Carotenoid Values  
In this research, the extraction of vitamin C using HPLC with fluorometric detectors was carried out. Though the use of UV detection coupled with HPLC for water-soluble vitamins is a sophisticated and expensive procedure, at the same time, it has been established to be a reliable, fast, and simple method [14]. Figure 2a shows the vitamin C contents for different onion bulbs, whichisconsidered as a water-soluble antioxidant. The daily vitamin Cintake should be 45-50 mg according to the World Health Organization [26]. The results reported that the largest vitamin C value was detected in the Red variety (45.07 mg/100 g\({}^{-1}\) FW) followed by the Baby variety (38.12 mg/100 g\({}^{-1}\) FW), while Green and Leek varieties showedthe lowest (10.10 and 12.03 mg/100 g\({}^{-1}\) FW, respectively). These values were nonetheless higher than those reported for other onion bulbs [22].  
Carotenoid content can be influenced by several factors, such as variety, climate, genetic variability, and fertilizers [27]. In this study, Figure 2b reports that the highest carotenoid content was found in the Yellow variety (1.44 mg/mL FW), compared to the Baby variety (0.57 mg/mL FW). Green and Leek varieties reported similar values (0.80-0.85 mg/mL FW). A similar finding was reported for carotenoids that explained that the increased values could be related to sufficient nitrogen uptake which can causeonion bulbs to absorb more nitrogen and to build more chlorophyll structures [28].
## 4 Results and Discussions
### Characterization of Polypropolee Nanocomposite
#### 4.1.2 Morphology Analysis  
Morphological characterization is essential for evaluating the distribution and dispersion of graphene nanoplatelets within the polypropylene matrix. The SEM images in Figure 5a-c depict cracked PP polymer and PP nanocomposites filled with 1 wt% GNP. Figure 5a reveals the foam structure of PP, while Figure 5b shows the flake structure of GNP. Figure 5c illustrates the structural composition of the PP/GNP composite at 1 wt%, demonstrating a uniform distribution of PP on the surface of GNP flakes. Overall, FESEM analysis of PP-GNP composites complements other characterization techniques like XRD and helps in understanding microstructural changes, interface interactions, and the overall morphology that influences the performance of these advanced materials.  
Figure 5d,e present the morphology of PP reinforced with 1 wt% of MWCNT, and it can observed that the PP's foam structure and MWCNT tube structure have an average diameter of 30-50 nm. Moreover, the homogeneous distribution of MWCNT and PP can be observed in Figure 5e.
## 4 Numerical Results and Sensitivity Analysis
### Sensitivity Analyses According to the Customer Preference \(\rho\)
However, for each scaling parameter \(B\), the total savings related to the remanufacturing strategies were significantly higher than the total collection costs, proving the necessity and the profitability of the remanufacturing activities. In addition, the increase in the unit manufacturing cost of new products evoked a rise in the mean of the optimal unit wholesale prices \(W_{p}\)\({}^{*}(t)\) and \(W_{o}\)\({}^{*}(t)\) proposed by the manufacturer by 1.7% and 1.4%, respectively, leading to an increase in the mean of the optimal unit selling prices \(P_{p}\)\({}^{*}(t)\) and \(P_{o}\)\({}^{*}(t)\) proposed by the pharmacy and the online platform of 1% each. Moreover, the rise in the pricing strategies of both distribution channels caused a decrease in the optimal total demand of the pharmacy and the online channel during the planning horizon \(H\) of 238 \(P\) and 324 \(P\), respectively. Since the online platform was more self-price-sensitive than the pharmacy, the lost demand in the online channel was larger than the lost demand in the pharmacy's channel. Therefore, the optimal total profit generated by the manufacturer \(\pi_{M}\) dropped by 2.3% due to the increase in the manufacturing costs. Also, both optimal total profits \(\pi_{P}\)\({}^{*}\) and \(\pi_{O}\)\({}^{*}\) generated, respectively, by the pharmacy and the online platform experienced a decrease of 3.2% and 5.3%, respectively, a direct outcome of the increase in the unit wholesale prices and the lost demands. In summary, efficiently designing and operating the remanufacturing activities impact the manufacturing costs, the pricing strategies, the demands and eventually the total profit of the supply chain.
## 4 Results and Discussion
### SDL Model Implementation on Type-1 Datasets: Forward Wave Propagation Simulation
#### 4.1.1 Evaluation Metric  
To assess the efficiency of the SDL model prediction on wave propagation simulation, we have employed the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), to compute using the equations below:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|Y_{i}-\bar{Y}_{i}| \tag{7}\]  
\[RMSE=\sqrt{(\frac{1}{n})\sum_{i=1}^{n}(Y_{i}-\bar{Y}_{i})^{2}} \tag{8}\]  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{Y_{i}-\bar{Y}_{i}}{Y_{i}}\right| \tag{9}\]  
here, \(Y_{i}\) is the ground truth from FE wave propagation simulation, \(\bar{Y}_{i}\) is the predicted from SDL model generated wave propagation simulation, and \(n\) = number of instances.
# A Prototype of a Portable Gas Analyzer for Exhaled Acetone Detection
## 3 Results and Discussion
### Gas Concentration Calculation  
Having measured the raw sensor array response on the gas sample and corrected it for humidity and temperature, the gas concentration can be established. In order to determine the analytical relationship between the resistance ratio \(R_{\mathrm{S}}/R_{0}\) and the concentration of a given gas in the tested air volume, the sensitivity characteristics presented by the manufacturer in the datasheet expressed in log-log scale was analyzed. Therefore, the following variable is is introduced to simplify calculations:  
\[r=log_{10}\left(\frac{R_{\mathrm{S}}}{R_{0}}\cdot\mathrm{corr}(t,h)\right) \tag{4}\]  
Unfortunately, the required data is presented only in a graphical form, which hinders analysis and is a source of systematic error. Moreover, those relations are provided under the assumption that the sensor is exposed only to one particular gas. Square polynomial regression was used to derive the relation out of the collected data points. A quadratic function describes accurately enough the relation while being fairly computationally inexpensive. The general formula is:  
\[gas(r)=p_{0}+p_{1}r+p_{2}\;r^{2}[ppm] \tag{5}\]  
where \(p_{x}\) are constant coefficients estimated based on the data provided in the datasheet. The polynomial relation coefficients are established based on data for a give a measurable range of gas concentrations, therefore, outside that range, it may not hold. However, since it is a continuous function and the relation appears to be monotonic, Equation (5) can be used to estimate the concentration outside the above-specified range.
## 2 A Mathematical Framework and Its Corresponding Solution for a Symmetric ABH Beam
### Numerical FEM Validation  
It is crucial to ascertain whether the GEM faithfully captures the deformation of a beam that incorporates a truncated ABH. This validation will be accomplished through comparative studies with FEM results within this subsection. The numerical simulation parameters for the ABH beam are specified in Table 1.  
The eigenfrequencies and eigenvectors of the ABH beam are determined utilizing two distinct approaches: the first employs the GEM in conjunction with Equation (17), while the second approach relies on a 2D planar finite element model that encompasses 19,204 elements, all within the COMSOL software environment. Figure 2 illustrates a comparative analysis of the first 25 eigenvalues obtained from both the GEM and FEM, with the scaling factor (as defined in Equation (20)) taken as \(a=7,8,9\), respectively. It is evident that the GEM results quickly align with the FEM results as the value of \(a\) increases. As depicted in Figure 2b, selecting \(a=9\) results in a residual discrepancy of approximately 3% for the first 25 resonant sequences. Figure 3 displays the modal shapes corresponding to various resonant frequencies, as determined by the FEM and GEM outlined in this study. The results indicate that the analytical model using the GEM is capable of accurately capturing the substantial deformations at the tip of the ABH beam.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Geometrical Parameters** & **Material Parameters** \\ \hline \(x_{b1}=0.1\) m & Beam \\ \(x_{b2}=0.2\) m & \(\rho_{b}=7800\) kg/m\({}^{3}\) \\ \(x_{b3}=0.3\) m & \(E_{b}=210\) GPa \\ \(x_{b4}=0.4\) m & \(\eta_{b}=0.005\) \\ \(x_{f}=0.35\) m & Damping layer \\ \(\epsilon=0.5\) m\({}^{-1}\), m = 2 & \(\rho_{d}=950\) kg/m\({}^{3}\) \\ \(h_{0}=0.0002\) m & \(E_{d}=5\) GPa \\ \(h_{b}=0.0052\) m & \(\eta_{d}=0.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the symmetric ABH structure.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Protocol Operation
#### 3.7.2 Query-Based  
Query-based routing protocols are mainly energy savers and can be adapted for networks in which geographic routing information is not available. In these protocols, the BS initiates the communication based on query packets used to retrieve data from the sensor. A path is generated while the query propagates through the network to be used as a data return path. The data-centric protocol ACQUIRE is counted in query-based routing protocols.
# Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
## 4 Track Fault Detection Using Machine Learning  
### Track Irregularities  
Track irregularity inspection includes vertical rail profile, lateral alignment, gauge, cross-level, twist and dipped joint, and corrugation as shown in Figure 3. In the developed monitoring system, the detection of track faults for vertical, lateral alignment, and cross-level is possible by using an on-board sensing device.  
Figure 2: On-board sensing device.  
Track irregularities cause vehicle vibrations that degrade rider comfort and also increase the risk of derailments. Thus, they are among the most important items that are monitored. Vehicle vibrations are strongly correlated with track irregularities, and thus the magnitude of vehicle vibrations is an effective means for assessing general track condition trends. Thus, sections of track over which high levels of vehicle vibrational acceleration are detected can be considered as indicative of degraded track conditions.
## 3 Framework
### Relation-Aware Document-Context Filter
#### 3.3.3 Mathematical Expressions and Parameters  
Entity-entity similarity calculation:  
\[\text{Sim}(s_{i},s_{j})=\text{cos}\big{(}\text{SBERT-WK}(s_{i}),\text{SBERT-WK} (s_{j})\big{)} \tag{10}\]  
Sentence-relation similarity calculation:  
\[\text{RelSim}(s_{i},R)=\max_{r\in R}\text{cos}(\text{SBERT-WK}(s_{i}),\text{SBERT- WK}(r)) \tag{11}\]where \(R=r_{1},r_{2},...,r_{n}\) is a set of relation descriptions. These descriptions can be generated through rule-based templates or large language model generation (e.g., using GPT-3.5 to generate multiple paraphrases of relation descriptions). SBERT-WK(.) is a modified BERT model designed for sentence-level similarity calculation [25].  
Combined similarity score:  
\[\text{CombSim}(cur,s_{i},R)=\alpha\cdot\text{Sim}(cur,s_{i})+\beta\cdot\text{ RelSim}(s_{i},R) \tag{12}\]  
where \(\alpha\) and \(\beta\) are weighting hyperparameters (default: \(\alpha=0.7\), \(\beta=0.3\)).  
Input sequence constraint:  
\[\text{Length}(S^{*})\leq 512\quad\text{(BERT input limit)} \tag{13}\]  
Our relation-aware semantic reorganization approach implements the following functions:  
* **Noise filtering:** Unrelated sentences (such as those containing only a single entity or no co-occurrence) are removed through bridge entity screening and relation relevance assessment.
* **Relation-specific context preservation:** By incorporating relation descriptions into the similarity calculation, we ensure that sentences semantically related to potential relations are prioritized, even if they do not contain explicit entity co-occurrences.
* **Logical coherence:** Semantic reorganization retains cross-document inference chains (such as the path \(e^{h}\to e^{b}\to e^{l}\)) while ensuring relation-relevant information is preserved.
* **Efficiency optimization:** Documents with an average of 4900 words are compressed to within 512 words, reducing computational complexity while preserving the most informative, relation-relevant content.