esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Model Training
#### 3.3.2 Transfer Learning  
Some popular pre-trained CNN models were explored, including VGG16, Inception, ResNet and MobileNet. VGG16 [33] is a CNN architecture known for its simplicity and effectiveness. It consists of 16 layers: 13 convolutional layers and 3 fully connected layers. Inception, also known as GoogLeNet [34], is characterized by its use of \"inception modules\" that efficiently capture information at multiple scales. Inception models employ various kernel sizes in parallel convolutional layers to capture both local and global features. This architecture has been successful in achieving high accuracy on image classification tasks while maintaining a relatively lightweight design. ResNet [35] is an architecture that introduced the concept of residual connections to address the problem of vanishing gradients in deep neural networks. By allowing information to bypass certain layers, ResNet enables the training of extremely deep networks. MobileNet [36] is a lightweight CNN aiming to provide efficient and accurate models that can be deployed on devices with limited computational resources and power constraints. MobileNet achieves this by utilizing depthwise separable convolutions, which separate the standard convolution into depthwise and pointwise convolutions. This reduces the number of parameters and computations required, making it more efficient while still maintaining good performance.  
The pre-trained models were then fine-tuned on the collected dataset by adjusting the internal parameters and retraining the top layers to adapt them to the specific trunk posture classification task.  
Figure 1: Sample images displaying different trunk posture classes—(**a**) Neutral Posture, (**b**) Half Bend Posture, and (**c**) Full Bend Posture.
###### Abstract  
This study aims to investigate the potential of white and bronze whole-grain sorghum flour to develop gluten-free bread (GFB) with acceptable sensory properties. Two \(2^{2}\) factorial designs, with two central point repetitions each, were used to study the effects of white (WS) or bronze sorghum (BS) flours and water (W) levels on the physical properties, acceptability scores, and proximate composition of GFB. The WS or BS levels ranged from 50 to 100% when blended with potato starch, and the W levels ranged from 100 to 140% (flour weight basis). Independent of the amount applied, GFB formulations containing BS were well accepted (acceptability scores for appearance, color, odor, texture, flavor and overall liking \(\geq\) 6.29 on a 10 cm hybrid hedonic scale). No significant differences were observed between the acceptability scores of single and composite formulations for all of the evaluated sensory attributes. Moreover, the W levels had no effect on the acceptability of GFB made with BS. Composite formulations prepared with 50 and 75% WS were also well accepted (acceptability scores for appearance, color, odor, texture, flavor, and overall liking \(\geq\) 7.43 on a 10 cm hybrid hedonic scale). However, increased W levels are required in single formulations to increase the scores for texture and overall acceptability. GFB made with 50% and 75% WS/BS can be classified as a source of fiber since the fiber content is higher than 3 g per 100 g, while GFB made with 100% WS/BS can be classified as high in fiber, as the content of this component is higher than 6 g per 100 g. This research highlights the great potential of whole-grain sorghum flours for producing nutrient-dense and acceptable GFB, which is important for consumers who choose or must adhere to a GF diet.  
blott-free bread; _Sorghum bicolor_; whole grains; sensory acceptance; multiple factor analysis +
Footnote †: journal: Applied Sciences
# Pruning Optimization over Threshold-Based Historical Continuous Query
## 5 Experiment
### Performance of Different Distance Thresholds  
This part of the experiments evaluated the query performance of different distance thresholds, as shown in Figure 10. As the distance threshold increases, the query spatial range of query increases, and  
Figure 9: Performance of different time ranges: (**a**) Number of the accessed nodes, (**b**) selectivity, (**c**) pruning latency, (**d**) query latency.  
the increased speed shows an increasing trend. It causes more index nodes and trajectory segments to be accessed and checked, and the pruning results contain more candidates. As shown in Figure 10a,b, the growth rate of the number of accessed nodes, and that of selectivity will also increase. Naturally, when the distance threshold is increased from one nautical mile to five nautical miles. It can be seen from Figure 10c,d that the pruning latency and query latency of the three methods also increases. Among them, the performance of BFT is still better than that of the other two. In terms of pruning latency, it takes less than one-fifth of the two methods. In terms of query latency, it takes less than half of that of TDS, and is an order of magnitude smaller than that of CDQLTD.
# Recent Advancement in Assessment and Control of Structures under Multi-Hazard
## 2 Risk: Hazard, Exposure, and Vulnerability
### Exposure, Multi-Hazard Mapping and Planning  
In the context of disaster risk, the UNDRR (United Nations Office for Disaster Risk Reduction) defines exposure as \"the situation of people, infrastructure, housing, production capacities and other tangible human assets located in hazard-prone areas\" [33]. Exposure is a necessary factor for disaster risk. Exposure is one of the risk determinants that can be controlled, to some extent, by proper planning. Such decisions are, however, not feasible in cases of existing risk: for example, large cities already built-in hazardous space. Reducing exposure to multi-hazards is more challenging than if only a single hazard is considered. Urban planning, land-use policy-making, environmental protection decisions, etc., need to rely on, and benefit from, multi-hazard considerations.  
Local and regional scale mapping of different types of hazards is essential in multi-hazard considerations when analyzing exposure. Although significant advancements have been made in mapping individual hazards, mapping multi-hazard is challenging due to the differences in their physical phenomena, measures of frequency/amplitude, impact on structures, etc.  
[PERSON] et al. [34] present a multi-hazard map for different districts of Bangladesh based on local historical disaster database and comparison of scenario hazard scales with those in other countries. Their study includes earthquakes, tornadoes, floods, and cyclones, which are combined through a weighing scheme. [PERSON] et al. [35] present multi-hazard mapping of Fars Province in southern Iran. They consider floods, fires, and landslides. They test two different machine learning algorithms in predicting distribution of these hazards based on historical data, and make use of different conditioning factors such as aspect, elevation, drainage, annual mean rainfall, etc. They highlight the importance of multi-hazard mapping in land-use planning, sustainable development, and watershed management in the study region. A similar study for the western region of Iran is presented in [PERSON] et al. [36].  
For multi-hazard mapping of relatively small areas, the Analytical Hierarchy Process (AHP) has been proposed as a suitable method (see, for example, [3]). It is a class of Multi-Criteria Decision Analysis (MCA) and relies on the connection between influencing factors and hazards rather than statistics from historical databases. In this sense, the method is subjective in assigning intensities and weights of different hazards. Some examples of AHP application in multi-hazard mapping can be found in [PERSON] et al. [3], [PERSON] [37], and [PERSON] et al. [5].
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 3 Problem Formulation
### Initial Static Task Model
#### 3.1.3 Total Optimization Function and Constraints  
For the global system, the total task execution time \(T_{all}\) of the multi-UAV-vehicle cooperative data acquisition system is defined as shown in Equation (10). The total task execution time \(T_{all}\) represents the completion time, or cost, of a global feasible solution \(S\). Therefore, by minimizing the total task execution time \(T_{all}\) as the objective function, the multi-UAV-vehicle cooperative data acquisition problem is modeled as a 0-1 integer programming problem. The expression of the total optimization function is shown in Equation (11).  
\[T_{all}=T_{c}+T_{uav}=\frac{l_{r_{0}}}{v_{c}}+\sum_{i=1}^{K}\frac{l_{a_{i}}^{ u_{max}}}{v_{nav}}=\frac{\sum_{a_{i}\in A}\sum_{a_{j}\in A}p_{r_{0}}(a_{i},a_{j})d _{a_{i},a_{j}}}{v_{c}}+\sum_{i=1}^{K}\frac{\sum_{r_{j}\in R}z(u_{k},r_{j})x(a_ {i},r_{j})l_{r_{j}}}{v_{nav}} \tag{10}\]  
\[T=min\left(\frac{\sum_{a_{i}\in A}\sum_{a_{j}\in A}p_{r_{0}}(a_{i},a_{j})d_{a _{i},a_{j}}}{v_{c}}+\sum_{i=1}^{K}\frac{\sum_{r_{j}\in R}z(u_{k},r_{j})x(a_{i},r_{j})l_{r_{j}}}{v_{nav}}\right) \tag{11}\]  
Equation (12) ensures that the number of UAVs is at least 2, which is required for load balancing among multiple UAVs. Equation (13) states that the single-trip flight distance for any UAV must not exceed its maximum flight distance \(d_{max}\). In Equation (14), the distance from each data collection task point \(p_{j}\) to anchor point \(a_{i}\) must not exceed the communication radius \(R_{c}\) of the UAV, ensuring access synchronization and communication safety between the UAV, the vehicle, and ground control. Equation (15) ensures that the distance from each data collection task point \(p_{j}\) to anchor point \(a_{i}\) does not exceed half of the UAV's maximum flight distance \(d_{max}\). Equation (16) ensures that all data collection task points are visited exactly once, where the symbol \(N_{p}\) denotes the total number of task points. An auxiliary decision variable \(x(p_{i},r_{i})\) is used to indicate whether data collection task point \(p_{i}\) is included in a single-trip route \(r_{i}\) of a UAV. If it is included, then \(x(p_{i},r_{i})=1\); otherwise, \(x(p_{i},r_{i})=0\). Equation (17) ensures that each data collection task point can only be assigned to one docking anchor point. A two-dimensional auxiliary decision variable \(q(p_{i},a_{j})\) is used to indicate whether task point \(p_{i}\) is assigned to anchor point \(a_{j}\). If it is, then \(q(p_{i},a_{j})=1\); otherwise, \(q(p_{i},a_{j})=0\).  
\[M_{aux}\geq 2 \tag{12}\]  
\[\sum_{q_{i}\in Q}\sum_{q_{j}\in Q}y_{r_{i}}(q_{i},q_{j})d_{q_{i},q_{j}}\leq d _{max},\forall r_{i}\in R_{aux} \tag{13}\]  
\[d_{p_{j},a_{i}}\leq R_{c},\forall p_{j},a_{i}\in r_{k} \tag{14}\]  
\[d_{p_{j},a_{i}}\leq\frac{1}{2}d_{max},\forall p_{j},a_{i}\in r_{k} \tag{15}\]  
\[\sum_{p_{i}\in K}\sum_{r_{i}\in K}x(p_{i},r_{i})=N_{p} \tag{16}\]  
\[\sum_{a_{j}\in A_{c}}q(p_{i},a_{j})=1,\forall p_{i}\in R \tag{17}\]
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 4 WUR in Cellular Standards
### Fifth-Generation NR LP-WUS and LP-SS
#### 4.2.1 Lp-Wus  
While the technical specifications have not been finalized yet, 3 GPP RAN1 has already established several working agreements and assumptions. In addition, 5G also utilizes OFDM and shares a similar resource grid structure as LTE (Figure 6). Urban scenarios are prioritized, with a 30 kHz subcarrier spacing in 4 GHz and 2.6 GHz carrier frequency  
Figure 6: LTE\(/\)NB-IoT resource grid, frame structure, and resource block.  
Figure 7: NWUS physical structure.  
bands. The LP-WUS transmission is assumed to occupy 12 RBs, equivalent to a 4.32 MHz bandwidth, in a 20 MHz channel (Figure 8). Additional guard bands can be placed between the LP-WUS and other NR traffic to relax the hardware filtering requirements and improve interference rejection.  
In addition to the wake-up instruction (to wake up the MR or not) and target device ID, the LP-WUS is expected to carry additional information to offload the MR radio resource measurement (RRM) in order to reduce latency. Unlike NWUS, which is a complex Zadoff-Chu sequence and requires a coherent WUR, LP-WUS is designed to emulate on-off keying (OOK) in the time domain to reduce hardware design complexity and relax the power overhead. This is referred to as multi-carrier OOK (MC-OOK) modulation. Figures 9 and 10 illustrate the two proposed mechanisms, namely OOK-1 and OOK-4, used to generate LP-WUS with existing 5G gNB (base station) hardware. Both methods are compatible with existing gNB and would only require a firmware upgrade.  
is also studying the feasibility of packing more information into the OOK-4 LP-WUS by overlaying each ON chip with an OFDM sequence. The overlaid LP-WUS can be decoded by a high-power OFDM receiver more quickly, potentially saving power through a shorter on-air time.
# Enhanced IoT-Based End-To-End Emergency and Disaster Relief System
## 3 The Crow\({}^{2}\) System
### Crow\({}^{2}\) System Enhancement  
As depicted in the layer-based architecture in Figure 2a, CROW\({}^{2}\) consists of two Wireless Body Area Networks (WBANs), or more, connected to a cloud IoT platform through the CC node. Each WBAN node is composed of: (i) a WBAN coordinator, which is a wireless device with advanced energy and communication features, (ii) on-body sensors, which may feature different communications technologies (i.e., Bluetooth IEEE802.15.1, WiFi IEEE802.11a/b/g/n, ZigBee IEEE802.5.4 and WBAN IEEE802.15.6). Sensors are connected among one of the previous technologies to the WBAN coordinator. The BBN routing is assured by the ORACE-Net routing protocol according to the architecture depicted in Figure 1. As the payload at the application layer, we deployed an Message Queuing Telemetry Transport [29] client (on tactical and mobile devices) to push data to the IoT platform.  
The CROW\({}^{2}\) system is improved through this work. Compared to our previous work [3], we have installed on-body sensors provided by Shimmer [30]. Therefore, the current system payload consists of real sensed vital sign data from the human body towards the IoT platform. To improve connectivity and mitigate interference, we reduced the tactical devices (OTDs) to four. Additionally, we reduced the number of active indoor wireless access points, since we assume that during the disaster, they will be damaged.
In order to further improve the convergence rate and avoid the premature convergence problem, new biological behavior characteristics were introduced into PIO. Some predatory-prey pigeon-inspired optimization (PPPIO) algorithms were proposed and applied to UAV path planning in 3D static and dynamic environment [32; 33]. The most recent hybrid pigeon-inspired optimization with quantum theory (QPIO) was proposed to solve the premature convergence problem and continuous optimization problems [34]. However, the existing PIO algorithm and its variants did not perform initial route optimization process. PPPIO, along with QPIO, failed to take account of global search and local search synchronously. Moreover, the path generated by QPIO is not smooth, and the existing problem of population decline too fast in landmark operator. To address the above shortcomings, we introduce some adaptive operators into conventional QPIO to improve the capability of searching appropriate solutions. The chaotic strategy is introduced in our algorithm to generate some initial solutions with wider solution space coverage. Differingfrom other conventional swarm-intelligence algorithms, the map and compass factor \(R\), varying with the number of iterations, is adopted to balance global search ability and local search ability in our algorithm. At the landmark operator, a new population updating equation is proposed to increase the search diversity. Simulation results and comparisons verify the feasibility and effectiveness of our proposed algorithm. The rest of the paper is organized as follows: Section 2 describes the preliminaries, including the problem formulation of UAV path planning and the theory of QPIO. Section 3 shows the implementation procedure of UAV path planning using our proposed adaptive operator quantum-behaved pigeon-inspired optimization (AOQPIO). Then, we compare the quality of the path produced by PIO, PSO, QPIO, and AOQPIO in Section 4. Section 5 is the conclusion.
## 3 Results and Discussion
### Electricity Sales Package Recommendation Analysis
#### 3.2.2 Electricity Sales Package Recommendation and Effects Evaluation  
Based on the above results, the electricity sales packages provided to the target customer and the ranking results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \begin{tabular}{c} **Electricity Sales Package** \\ \end{tabular} & \
\begin{tabular}{c} **Recommendation Ranking of** \\ **This Paper** \\ \end{tabular} &
\begin{tabular}{c} **Actual Ordering of Simulations** \\ **Actual Ordering of Simulations** \\ \end{tabular} \\ \hline \(A_{1}\) & 2 & 3 \\ \(A_{2}\) & 4 & 4 \\ \(A_{3}\) & 1 & 1 \\ \(A_{4}\) & 3 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recommended ranking results and simulated actual ranking results.  
Figure 4: Sample customer clustering results.  
As seen in Table 4, for customer \(R_{4}\), the package ranking results obtained by the method proposed in this paper are mostly consistent with the simulated actual ranking results, with a root mean square error (RMSE) of 0.2371. The difference in the ranking of \(A_{1}\) and \(A_{4}\) arises because, while the costs of \(A_{1}\) and \(A_{4}\) are similar and the types of value-added services and reward policies are the same, the additional premium of package \(A_{1}\) is lower. As a result, the comprehensive score for package \(A_{1}\) is higher, indicating that the ranking results obtained by the method align with the actual situation.  
Taking the residential electricity customer \(R_{4}\) as an example, the customer's overall scores for each electricity sales package are shown in Figure 5.  
As seen in Figure 5, the comprehensive score for electricity sales package \(A_{3}\) is the highest for customer \(R_{4}\), with a score of 6.6467, indicating that the customer's satisfaction with the package falls between \"Good\" and \"Very Good\". In fact, customer \(R_{4}\) is a residential electricity customer who is particularly concerned about electricity costs. Therefore, they are more satisfied with the electricity sales package \(A_{3}\), which offers lower costs, certain electricity bill discounts, and low additional premiums.  
Additionally, compared to high-quality energy services, this type of customer has a greater demand for energy-saving management services. Since \(A_{3}\) fully meets their electricity consumption needs, the analysis and calculation results align with these findings.  
Similarly, the overall scores for other residential electricity customers regarding the electricity sales packages are shown in Figure 6.  
Figure 5: Sample customer \(R_{4}\)’s overall score for each electricity sales package.  
Figure 6: Distribution of residents electricity customers’ overall score of electricity sales packages.
## 3 Results and Discussion
### Scattering Angle Characterization  
Figure 11 shows sample B-scans from the scattering angle characterization experiment. Figure 12A shows the intensity of each pathlength for each angular displacement, and Figure 12B plots the intensities after azimuthal averaging. The sample A-scans (Figure 11) are also indicated in the same color by vertical dashed lines in Figure 12B.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Eye Segment** & \(n_{0}\) & **B** & **C** & \(n\) **(at 1.31 \(\upmu\)m)** & **RMS Error** \\ \hline Cornea & 1.399 & \(-2.177\times 10^{-3}\) & \(1.260\times 10^{-3}\) & 1.398 & \(1.674\times 10^{-4}\) \\ Aqueous & 1.289 & \(3.277\times 10^{-2}\) & \(-1.382\times 10^{-3}\) & 1.313 & \(1.834\times 10^{-4}\) \\ Lens & 1.635 & \(2.831\times 10^{-3}\) & \(4.411\times 10^{-3}\) & 1.639 & \(2.567\times 10^{-4}\) \\ Vitreous & 1.319 & \(8.008\times 10^{-3}\) & \(2.551\times 10^{-4}\) & 1.326 & \(1.228\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Conrady parameters for the murine ocular media.  
Figure 9: (**A**) A standard interferogram resulting from the A-scan in the sample (**C**). (**B**) The FFT of the interferogram; the peaks indicate the changes in refractive index that result in backscattering. (**D**) The way the interferogram changes with the introduction of a PME, and (**E**) the effect on the resultant A-scan. (**F**) The way the L/H\({}_{2}\) is calculated for the A-scan.  
Experimental results determined that the SAR-OCT murine retinal imaging system can detect changes in bulk scattering angle up to eight degrees from a direct backscatter. That is, from a zero-degree backscattering angle to an eight-degree backscattering angle, the combination of light from Pathlengths 1-3 have unique contributions to the total image intensity. Light scattered from a sample at an angle greater than eight degrees is not recorded by the SAR-OCT system.  
Figure 11: Sample B-scans show how the intensity of Pathlengths 1–3 vary for different angles. At A-scan 300 of B-scan 50, Pathlength 3 (H\({}_{3}\)) has the strongest intensity. This indicates that the mirror was at an extreme angle. In contrast, for A-scan 300 of B-scan 800, Pathlength 1 (L) has the strongest signal; this indicates that the mirror was at an angle closer to zero (direct back-scatter). A-scan numbers represent a linear increase in bulk scattering angle in one dimension, and B-scan numbers represent a linear increase in bulk scattering angle in the other dimension. See Figure 12A for the transformation from A-scan/B-scan number to angle.  
Figure 10: Optical model (Zemax) overlaid with mouse eye cross-section. Incident collimated light focuses on the retina.
## 3 Methodology  
This research employed a mixed-method approach comprising both qualitative and quantitative methods. As a qualitative method to identify potential metrics, we employed in-depth interviews followed by three rounds of Delphi surveys. An in-depth interview is a qualitative research technique that involves individual interviews with a small number of respondents to explore their opinions on a particular idea [42]. The Delphi technique is a method for consensus-building using a series of questionnaires with multiple iterations to collect data from a selected panel [43]. The second method employed a quantitative method by testing the new metric in actual coal-hauling operations. The new metric was tested in an actual coal-hauling operation, and data were collected for three months to utilize the metric to measure and analyze the effectiveness of the coal transportation process. We proposed a methodology based on the approach in Figure 1. The main steps in our approach included identifying the potential metrics through a literature review and conducting an in-depth interview (step 1), obtaining the potential metric (step 2), designing the questionnaire (step 3), conducting the Delphi survey (step 4), obtaining the final metric (step 5), constructing the proposed framework (step 6), implementing the metrics in actual coal-hauling operation (step 7), analyzing the data and summarizing the result (step 8).  
This study aimed to answer these research questions (RQ):  
RQ1. What are the suitable metrics to measure overall mining transportation effectiveness?  
Figure 1: Methodology of the study.  
RQ2. How can the selected metrics be applied to measure mining transportation effectiveness?