esa-sceva/satcom-chunk-collection huggingface dataset:
# ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET
## 3 Experimental Results
### Packet Loss Ratio Verses Different UEs  
Figure 6 shows the packet loss rate for various scheduling algorithms. Packet loss rate (PLR) is one of the major parameters in wireless communication that directly affects transmitted video quality. The proposed scheduling algorithm achieves lower PLR than other scheduling algorithms. The proportional fair algorithm has the highest packet loss ratio. When the speed of the UEs is increased, the PLR of QuAS and PSS are also increased. The proposed algorithm maintains the same PLR for all the various speed of UE. The packet loss ratio may impact huge effect for transmitting large amount of data. The scheduler has not been affected while the total amount of users increased. The existing methods did not provide the priority for the packets while performing the scheduling process. On the contrary, the proposed technique has the implementation of packet priority for transmission so that the packet loss ratio is the minimum value for the proposed technique compared to the relevant ones.  
Figure 5: Packet delay versus different UEs.
## 1 Introduction  
Earthquake risk to people and property is primarily conceptualized as a function of buildings' seismic vulnerability [1]. Assessment of the seismic vulnerability of buildings at a city, regional, or national scale has traditionally been expensive and time-consuming, requiring highly complex and multifaceted data on building characteristics as well as sophisticated computational models. For the assessment of buildings' seismic risk at a regional scale, catastrophe risk modeling (CRM) has been widely used. CRM typically consists of four modules--hazard, exposure, vulnerability, and loss--with risk estimated as the impacts of a hazard that exploits the vulnerabilities of a system of interest on its exposure to that hazard [2]. In the case of earthquakes, the hazard module determines the characteristics of the potential seismic events that would affect the system and the exposure module collects data on the geological and built environment. Then, based on the information collected in the exposure module, the seismic vulnerabilities of buildings are determined in the vulnerability module.  
Finally, through the various loss-interpretation models included in the fourth module, building damage can be translated into specific types of losses.  
Gradual improvements have been made in hazard modules' ability to estimate the locations and return periods of seismic events, and exposure module by using advanced geographic information system for data collection [3; 4; 5]. However, the vulnerability module has proved an impediment to the effective use of CRM for seismic-risk assessment. Computational models for simulating a building's seismic vulnerability, such as static push-over or non-linear dynamic models, are costly in both time and economic terms, requiring highly complex and multifaceted data on each building's structural configuration and material properties. Moreover, for these types of computational analysis to be applied at a regional scale, they must be repeated thousands of times. Thus, based on a data-mining (DM) technique (SVM algorithm) that focuses on the prediction of a building's spectral yield and ultimate acceleration-displacement points, the present study developed a methodology for rapid, macroscale assessment of buildings' seismic vulnerability using relatively small sets of basic building characteristics. As well as addressing the inefficiency of current CRM vulnerability modules, it is hoped that the present research will serve as a basis for further macroscale studies of the vulnerability of constructed facilities facing a variety of natural hazards, and thus facilitate more accurate and rapid risk assessment.
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 3 Methodology
### Building Vulnerability Parameters (Inputs to the Proposed Model)
#### 3.3.1 Top Hill-Slope Effect  
On top of hills, topographic amplification may increase ground motion intensity. Buildings on steep slopes steeper than 30 degrees also often have stopped foundations that are incapable of dispersing ground distortion equally to structural members [53]. It is advisable to avoid hillside slopes that are prone to sliding during an earthquake and instead choose only stable slopes for construction placement. Moreover, it would be more desirable to have many blocks on terraces rather than a single huge block with footings at significantly varying heights [24]. Seismic risk assessments should consider these two factors. A street survey can easily reveal both factors [53].
## 3 LiDAR Range Measurement  
### Pulse-Time Range Measurement  
When the laser pulse is emitted from the instrument, it hits the object's surface. The backscattered light reflected from the object's surface is recorded by the instrument (Figure 3). The distance between the instrument and the object point is estimated using the known light traveling time in Equation (1).  
\[r=\frac{1}{2}c\Delta t \tag{1}\]  
\(r\): Measured distance  
\(c\): Light speed  
\(\Delta t\): Round trip time-of-flight  
Since the laser beam travels twice the distance between the instrument and the scanning object point, the distance is calculated by dividing two of the flight time. In Equation (1), \(c\) is the speed of light in space. However, the measuring light interacts with different air environments in terms of temperature, pressure, and particles during travel. These effects decrease the accuracy of the distance measured. The SNR is low at long distances. Because the interaction between an object and air reduces the energy of a laser beam, the energy of the returned light will be lower than the emitted. Thus, measuring long distances requires higher energy than shorter ones. The pulse method is proper for long-distance measurement.  
Figure 3: Pulse-based range measurement.
## 4 Experiment
### Results and Discussions
#### 4.4.2 Comparison 2
The prediction uncertainty of the result can be proved that controlled by the Wiener-LSTM model in Figure 12. The error of the predicted result is in a stable state, which means the uncertainty of the predicted result is in a stable state without large fluctuations. It is important for the maintenance decisions in the industry. The MAE of the Wiener-LSTM model is lower than the LSTM model on bearing 1_4, as shown in Figure 12b, and it is more smooth than the LSTM model. As shown in Table 2, the proposed method is applied to the test bearing1_3 and bearing1_4 data. The MAE error between the remaining useful life predicted by the proposed method and the true remaining useful life is much lower than that of the traditional LSTM method. The larger MAE error in the testing process indicates a lower accuracy of the model, and conversely, a minor MAE error indicates a higher accuracy. It is evident that the uncertainty of the result can be well controlled by the Wiener-LSTM model by the experiments on the test bearing1_3 and bearing1_4. The large fluctuations can be removed from the RUL prediction by using the proposed Wiener-LSTM model. Figure 11: The predicted RUL by the Wiener–LSTM model and LSTM model. (**a**) The predicted results on test bearing1_3. (**b**) The predicted results on test bearing1_4. The blue line is the real RUL from 1000–0 s. The yellow line is the predicted result by the normal LSTM model using the MSE loss function. The orange line is the predicted result by the Wiener–LSTM model using the proposed loss function.
## 3 Basic System Operation  
### Network Architecture  
The surface/platter of a magnetic disk storage device consisting of tracks and sectors provides an interesting approach that may be applied to a large scale WSN. This assumption led to a Disk Based Data Centric Storage (DBDCS) architecture, as shown in Figure 1a, dividing the rectangular field into a matrix of storage cells (referred to as a sector) where row and column represent track (\(T_{i}\)) and sector (\(S_{j}\)), respectively. In DBDCS, the covered network is considered as one of the storage surface and sector is considered as the core cell of storage. However, unlike magnetic storage disk, in DBDCS, the header file for data mapping is not located in one single particular location rather a dynamic mapping algorithm is used using hashing. Hence, each SH could calculate the target sector to read/write corresponding data. The physical deployment is mapped to an \(m\)\(x\)\(n\) matrix, where \(m\) is the number of tracks and \(n\) is the number sectors for each track. Hence, the nodes in the network are divided into \(S\)_(mxn)_ sectors, each comprising a Sector Head (SH) and sector members that communicate _via_ one hop to the SH (see Figure 1c), where \(SH_{i}\)\(\epsilon\) [1...\(S\)]. Each node is configured to be aware of the deployment layout by knowing: (1) All SHs are assigned with the sector number as a virtual address and node id, and (2) All member nodes know their own node id and number of tracks (\(m\)) and sectors (\(n\)) of the network field. As shown in Figure 1b, the intra-sector communication (_i.e._, communication from sector members to \(SH\) or _vice-versa_) is constrained to one hop while inter-sector transmission is multi-hop. For simplification, the sensor nodes inside each sector are not shown explicitly in Figure 1b. Instead, an aggregated link (see Figure 1c) is shown to represent the total traffic from member nodes to head node.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Mechanisms**} & **Schemes** \\ \hline \multirow{3}{*}{Range Query} & \multicolumn{3}{c}{Bit Code Mapping} & MDA [9] \\ \cline{2-6}  & \multicolumn{3}{c}{Multiply Rooted Hierarchical Index} & DIFS [10] \\ \cline{2-6}  & Tree Structure & \multicolumn{3}{c}{Binary Tree} & DIM [12] \\ \hline \multicolumn{6}{c}{Dimension (attribute)} & Range Query _vs._ Point Query & Load Balancing & Data Aggregation \\ \hline \multirow{3}{*}{Similarity Searching} & Single & Both & Yes & No & SSA [6] \\ \cline{2-6}  & Multi & Range & Yes & No & SDS [7] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of relevant DCS schemes.
## 4 Discussion
### Statistical Significance Analysis and Model Comparison
#### 4.5.1 One-Way ANOVA Test  
To determine if the observed differences in model performance were statistically significant, a one-way Analysis of Variance (ANOVA) test was applied. ANOVA is a strong statistical method that is used to compare the means of multiple groups (in this case, the performance metrics of different models) and assess whether at least one model performs differently from the others. The null hypothesis (\(H_{0}\)) assumes no significant difference among the models, while the alternative hypothesis (\(H_{1}\)) suggests that at least one model shows superior performance. The F-statistic and corresponding \(p\)-values were calculated for each performance metric (\(R^{2}\), MSE, and MAE). A \(p\)-value less than 0.05 indicates that the null hypothesis can be confidently rejected and confirms that significant performance differences exist between models (refer to Table 16).
## 2 Literature Review
### DRL for Solving JSP
[48] introduced a new DRL algorithm that utilizes Long Short-Term Memory (LSTM) networks to capture the temporal dependencies within scheduling state sequences. [PERSON] et al. [49] focused on the permutation flow shop problem (PFSP) and developed a dedicated PFSPNet, trained using the actor-critic reinforcement learning approach. In summary, the application of DRL in solving Job Shop Scheduling Problems has demonstrated high efficiency and the ability to quickly generate effective scheduling strategies. Moreover, DRL's generalization capabilities make it suitable for handling systems of varying sizes and complexities. Although existing studies have demonstrated that Deep Reinforcement Learning (DRL) methods perform well in solving scheduling problems, there remain inherent limitations when applied to flexible Job Shop Scheduling Problems (FJSPs): (1) the state space of FJSP is complex, involving large-scale matching relationships among jobs, operations, and machines. Current state encoding methods struggle to comprehensively represent the scheduling environment, thus affecting model generalization; (2) scheduling problems have sparse rewards, typically only provided at the final makespan, which leads to low training efficiency and policies prone to local optima; (3) most studies focus on static FJSP and lack dynamic adaptive scheduling capabilities for practical disturbances such as machine failures and order insertions; and (4) in the collaborative optimization of production and logistics scheduling, existing research mostly assumes sufficient logistics resources or treats logistics scheduling independently, without systematically considering practical issues such as limited AGV resources, AGV charging and charging station constraints, and AGV speed degradation effects, limiting their industrial applicability. Currently, research on production-logistics collaborative scheduling is limited. Some studies employ rule-based methods, heuristic algorithms, or traditional optimization models for joint optimization but lack DRL methods designed for high-dimensional, dynamic, and complex environments. To address these shortcomings, this paper designs a flexible DRL framework that fully integrates production and logistics scheduling processes, jointly considers limited AGV transportation resources, charging constraints, and AGV speed degradation effects, thereby enhancing the practicality and intelligence level of scheduling systems in complex real-world environments.
## 4 Methodology
### Video Transmission and Visualization  
When implementing the live streaming feature of the drone's camera video feed to HoloLens 2, we had to consider specific requirements regarding latency and frame rate. In order for the video stream to be usable for piloting, it had to be low in latency and high in frame rate. Overall latency is the sum of two values: the latency from the drone to the remote controller, which depends on the drone manufacturer and the streaming protocols used, and the latency from the remote controller to the HoloLens, via the UAV interface app, which depends on the presented solution. In this case, DJI uses CcuSync for video transmission, which adds a nominal latency of 120 to 270 ms. The following text focuses on the latency between the remote controller and the HoloLens.  
Ideally, the overall latency should be low enough to allow easy and safe control based solely on the video feed (e.g., in FPV mode) and frame rate should be high enough for the video to be viewable as a continuous stream. Therefore, we set targets for <350 ms overall latency (hence 120 ms for the Android-to-HoloLens latency) and >15 frames per second (FPS).
# Lung Field Segmentation in Chest X-rays:
###### Abstract  
The delineation of bone structures is a crucial step in Chest X-ray image analysis. In the case of lung field segmentation, the main approach after the localization of bone structures is either their individual analysis or their suppression. We prove that a very fast and approximate identification of bone points that are most probably located inside the lung area can help in the segmentation of the lung fields, without the need for bone structure suppression. We introduce a deformation-tolerant region growing procedure. In a two-step approach, a sparse representation of the rib cage is guided to several support points on the lung border. We studied and dealt with the presence of other bone structures that interfere with the lung field. Our method demonstrated very robust behavior even with highly deformed lung appearances, and it achieved state-of-the-art performance in segmentations for the vast majority of evaluated CXR images. Our region growing approach based on the automatically detected rib cage points achieved an average Dice similarity score of 0.92 on the Montgomery County Chest X-ray dataset. We are confident that bone seed points can robustly mark a high-quality lung area while remaining unaffected by different lung shapes and abnormal structures.  
image segmentation; Chest X-ray (CXR); lungs; region growing; rib cage +
Footnote †: journal: Medical Image Analysis
## 3 Results and Discussion
### Optimization of the Microwave Vacuum Drying Technology
#### 3.3.4 Parameter Optimization  
To obtain the combination of optimal microwave vacuum drying parameters, the model was optimized using the Design-Expert software (v.13) under the target values of the soluble solid content, chewiness, and maximum Vc content. On this basis, the combination of optimal technological parameters in the experimental range was a material surface temperature of 50.761 \({}^{\circ}\)C, a vacuum degree of 0.073 MPa, and a microwave power of 370.558 W. Under these conditions, the soluble solid content, chewiness, and Vc content were 43.967%, 9.701, and 0.785 mg\(\cdot\)(100 g)\({}^{-1}\), respectively.  
A verification experiment was carried out with the optimization parameters in the laboratory. To eliminate random errors, experimental parameters were optimized and rounded  
Figure 5: Effects of two factors on the VC content. (**a**) Effect of the material surface temperature and microwave power on the Vc content. (**b**) Effect of the material surface temperature and vacuum degree on the Vc content. (**c**) Effect of the vacuum degree and microwave power the on Vc content. Note: The response surface color in the figure is usually used to represent the numerical size or degree of the response variable, where darker areas represent higher response values and lighter areas represent lower response values.  
off numbers were taken: material surface temperature--51.0 \({}^{\circ}\)C, vacuum degree--0.07 MPa, and microwave power--370 W. Three repeated tests were carried out and the mean was chosen (Table 14). According to the analysis, the average error between the test results and software-optimized parameter values was 5.88%. This proves that the test results were basically consistent with the optimization results. In other words, it is reasonable for choosing the influencing factors of mulberry drying quality.
## 4 Results and Discussion
### Respondent's Demographics  
Among the 146 participants who completed the questionnaire, the majority were male (94 or 64.4%), while 52 (35.6%) were female. This result is representative of the field. The tech ecosystem in Cameroon, similar to that of sub-Saharan Africa, is predominantly male-dominated, with most startups being founded and led by men. As of 2020, 85% of co-founders or C-level executives in Africa were male [25]. This can be attributed to cultural practices in the region which significantly contribute to the underrepresentation of women in technical fields. These practices shape societal expectations, gender roles, and access to education, which together create barriers for women [42; 43]. The age distribution showed that most respondents were between 21 and 30 years old (69 or 47.3%), followed by those aged 31 to 40 years (30 or 20.5%), with the smallest group being those aged 51 and above. The mean age of the respondents was 30.98 years. This result is representative of the field.  
Figure 1 presents the composition of the respondent's affiliation in the Silicon Mountain technology ecosystem obtained from the field survey. The data indicate that 56 respondents (38.4%) are associated with the academic sector within the Silicon Mountain technology ecosystem. Start-up firms comprise 22 respondents (15.1%), developer communities include 15 respondents (10.3%), and incubators account for 18 respondents (12.3%). Furthermore, 4 respondents (2.7%) represent SMEs utilizing IT systems, 16 respondents (11.0%) are technology promoters or enthusiasts, 6 respondents (4.1%) are NGOs involved in technology promotion, 4 respondents (2.7%) are government agencies, and the remaining 5 respondents (3.4%) fall into other categories. This result is representative of the field.  
The majority of respondents, 99 (67.8%), held entry-level positions (students, interns, administrative assistants, etc.) in their respective organizations. Additionally, 18 (12.3%) held managerial positions (head of departments, operations managers, product managers, etc.), 15 (10.3%) were co-founders, and the remaining 14 (9.6%) held junior-level positions (junior software developers and marketers).  
Figure 2 presents the fields of specialization of the respondents. The majority, 49 (33.6%), were from the telecommunications sector, followed by 38 (26.0%) from software development. Additionally, 13 (8.9%) were from health sciences and computer science, 12 (8.2%) were from the field of computer networks, 7 (4.8%) were from computer maintenance, and the smallest group, 4 (2.7%), were from agriculture. This indicates that within the Silicon Mountain technology ecosystem, the telecommunications sector has the highest representation, while the agricultural field has the lowest. This result is representative of the field.
## 3 Results and Discussion
### Effect of the \(O_{2}\) Concentration on Blended Fuels in the \(O_{2}\)/\(Co_{2}\) Atmosphere  
Figure 4 shows the thermo-gravimetric curves of different oxygen concentrations under a 21% O\({}_{2}\)/79% CO\({}_{2}\) atmosphere. The figure shows that as the oxygen concentration increases, the thermo-gravimetric curve gradually shifts to the left. This shows that under a high oxygen concentration, the sample burns faster, the reaction temperature range is gradually reduced, and the maximum reaction rate peak gradually increases. Meanwhile, the peak value of the DTG curve increases and the peak corresponding temperature decreases, indicating that the increase of oxygen concentration makes the sample burn at a lower temperature. The combustion peak is reached at a lower point, which is beneficial for combustion. There are two main reasons why the sample burns faster: first, as the oxygen concentration increases, the material comes into contact with oxygen more fully and the combustion is strengthened; second, the increase in oxygen concentration means that the CO\({}_{2}\) concentration decreases, which makes the combustion process more efficient. Less heat is taken away and the temperature rises in the furnace, which is more conducive to combustion [3].  
Figure 3: Ignition and burnout temperature and CCI at different mixed proportions and combustion conditions.  
Figure 5 shows that as the oxygen concentration increases from 10% to 40%, the ignition temperature \(T_{i}\) of the mixed fuel decreases from 264.13 to 237.30 \({}^{\circ}\)C and the burnout temperature \(T_{b}\) decreases from 737.82 \({}^{\circ}\)C to 635.85 \({}^{\circ}\)C, resulting in comprehensive combustion; the CCI increases as well. However, the decrease in temperature gradually slows as the oxygen concentration increases. This is mainly because when the pulverized coal particles are filled with high-concentration oxygen, the diffusion control zone of the combustion reaction moves to the reaction power control zone and the oxygen concentration is no longer a key factor affecting the combustion characteristics of the blended fuel [3; 16]. Compared with Reference [2], with an increase in O\({}_{2}\) concentration from 10% to 40%, the ignition temperature changed over a small range but the burnout temperature declined from 745.8 to 641.4 \({}^{\circ}\)C, whereas the activation energy and CCI improved. The brewery wastewater sludge has higher combustion temperature characteristics under the O\({}_{2}\)/CO\({}_{2}\) atmosphere.