esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Power Required for the Evaporation of the Total Volume of Liquid  
Assuming that the total volume of liquid displaced from the porous medium per unit of time is previously evaporated at the interface of the plate and porous medium, the required thermal power is expressed as:  
\[\dot{W}_{ev}(t)=\rho_{l}\frac{d}{dt}(V_{dt})\big{\{}\Delta h_{l,v}+c_{p}\Delta T \big{\}} \tag{7}\]  
At all times, the values of \(t,\ T,\ \Delta T\) and \(V_{dt}\), and thus \(\rho_{l},\ c_{p}\) and \(\Delta h_{lv}\) are known. The value of \(d(V_{dt})/dt\) is calculated from the function \(V_{dt}=V_{dt}(t)\). Having neglected the term \(c_{p}\Delta T\) because it is much smaller than \(\Delta h_{lv}\), the use of Equation (7) in relation to the porous media B+DS and S+DS gives the results shown in Figure 10.  
In Figure 10, the curves \(\dot{W}_{tf}=\dot{W}_{tf}(t)\) of Figure 6 have also been drawn to identify the intervals of \(t\) in which the thermal power transferred to the porous medium by the heating plate, \(\dot{W}_{tf}\), is lower than that required for the evaporation of the total volume of liquid displaced from the porous medium per unit of time.  
The thermal power required to evaporate the total volume of liquid displaced per unit of time is much higher than the power available during practically all the heating time of the porous medium. This means that the displacement of liquid is almost entirely due to a piston effect created by capillary action.  
The vapor generated by the thermal power that the plate transfers at each instant to the porous medium at the interface produces an increase in the pressure in the two-phase region. This pressure promotes the displacement of liquid from the saturated region until, by expansion, it reaches a new state of equilibrium. This displacement occurs as soon as the following condition is satisfied:  
\[p_{c}\geq g\bigg{(}\int_{0}^{c_{cs}}\rho dz+\int_{0}^{h}\rho dh\bigg{)}+ \Delta p_{R} \tag{8}\]  
where \(p_{c}\) is the capillary pressure, \(e_{cs}\) is the thickness of the saturated layer, \(h\) is the height of the liquid layer in the trap, and \(\Delta p_{R}\) is the resistance to flow in the capillary defined by Rumpf-Gupte's law.  
Figure 10: Power required for the evaporation of the total volume of liquid.
## 4 Model Training and Discussion of the Results Obtained
### Performance Results of Module 1 (of Figure 3) for Document-Image Deblurring
Image (**c**) shows average-quality samples. Image (**d**) shows good-quality samples. Image (**e**) shows very-good-quality images. Figure 10: Samples of contrast adjustments of natural document images with contrast distortion. The main reason for these harsh conditions is to provide strongly distorted images (i.e., contaminated by a mixture of distortions) in order to better stress test the robustness of the models involved in a comprehensive benchmarking (our novel model and a selection of the top best models from the relevant literature). Figure 12 shows, for illustration, the performance of our global model in the presence of sample document images captured by a mobile phone camera. As one can see, the image quality is significantly enhanced so that the OCR system that could previously (on the non-enhanced input images) not detect even a single character can now (after enhancement) catch all the text information without any problem. Image (a) in Figure 12 is the document that has a mixture of distortions, namely motion blur, contrast, and noise problems. Image (b) in Figure 12 is an enhanced image through our novel global model. Image (c) in Figure 12 is the output of the OCR by using image (a), whereby none of the text in the image (a) is detected. The image (d) in Figure 12 is the output of the OCR by using the image (b) in Figure 12, whereby all the texts in this image are well caught/detected by the OCR without any significant error (Figure 12d). Table 5 shows the performance of our novel global model compared to the other related best models. For stress testing different models in this benchmarking, we used our dataset of real-world document images (obtained under harsh conditions) that mobile phone cameras have captured. One can see (see Table 4) that our model is about 13 percent better than the state-of-the-art model for deblurring combined with a state-of-the-art model  
Figure 12: Effect of blind enhancement on OCR text detection (using our global model consisting of Module 1 + Module 2). Image (**a**) shows the original image. Image (**b**) shows the blind enhanced image of Image (**a**). Image (**c**) shows the detected texts through Tesseract OCR software [73] (no text is marked). Image (**d**) shows the detected texts marked with yellow. The recognized texts are shown by red text. for denoising. This test/benchmarking demonstrates and underscores the superiority of our novel model compared to a combination of the state-of-the-art best models. On the other hand, taken alone, the models developed solely for deblurring are not good enough for real-world hard cases; one can see that there are too many recognition errors in Figure 12c.
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
## 4 Numerical Experiments
### Impact of the Marginal Distribution of the Arrival Process
#### 4.1.1 Frame Synchronous Coded-Slotted ALOHA
Again, note that the differences in average delay for \(k=\{1,2,3\}\) can be large, even for a finite decoding window (the frame length in these experiments is \(n=200\) slots).
# Structured Data REST Protocol for End to End Data Mashup
## Structured Data Rest Protocol
### SDR Validation, Transformation and Population Algorithm  
Data Mashup Definition (DMD) is defined by DSC at its end whereas SDR is generated at MSP's/DSP's end and provided to DSC through mashup data service identifier. Numbers of attributes in DMD and that of mashup data service may differ or may be equal. The SDR, which is supposed to be populated, may be ignored, if it is not matched with the specification of SDX defined for the SDMB. SDR would be valid if all of its attributes are matched with DMDAttributes of targeted SDMB. Valid SDR is transformed into mashup data record and populated into relevant SDMB. The Algorithm 4 depicts how each SDR is validated and transformed into Mashup Data Record (MDR) before populating into proper SDMB.  
The transformation of structured data record into mashup data record requires fetching DMDAttribute of the corresponding SDRAttribute from SDXMapping and value of SDRAttribute is assigned to DMDAttribute. Created MDRValues (name-value pair) is added to MDRs of SDMB as mentioned in line SDMB.MDRValues.add(h=z.value).  
```
**Algorithm 4** Validation, Transformation and Population of SDR

## 6 DSC's Data Mashup

DSC may be either a user or an MSP. The user does mashup for viewing the required mashed up data in a single screen whereas MSP fetches data from DSPs or from other MSPs and forward them to its user after performing the data mashup. The following section explains how data mashup is performed by MSP and EU.

### MSP's Data Mashup

MSPs generally do not require creating views of mashed up data but need to integrate mashed up data and forward the resultant structure data module to its users. Algorithm 5 processes JSON based structure data module to integrate and forward the resultant mashed up JSON-SDM to its users.
```
1:\(t=\text{getSDM}(s)\)
2:\(v=\text{getSDR}(t)\)
3:for each \(\  
```
\(p=\text{getSDMB()}\) \(q=\text{getMDSIs(}p\)) \(h=\text{new JSON\_SDM}0\) For each MDSI \(s\in q\)  begin1 \(r=\text{getDSP(}p,s\)) \(t=\text{getJSON\_SDM(s)}\) \(u=\text{getJSONObjects(}t\)) For each JSONObject \(v\in u\)  begin2 \(w=\text{getJSONAtributes(}v\)) For each JSONAttribute \(x\in w\)  begin3 [_fetch Sdx#ld from Table 7_] \(Sdx\#ld=\text{getSDXId(}r,s,p\)) [_fetch SDX Mapping from Table 6_] \(y=\text{getSDXMapping(}Sdx\#ld)\) \(z=\text{getDMDAttribute(}x,y\)) \(v\).replaceJSONAtribute(\(x,z\)) end3 \(h\).add(\(v\)) end2 end1
```  
**Algorithm 5** MSP's Data Mashup Algorithm
# Application of Split Coordinate Channel Attention Embedding U2 Net in Salient Object Detection
## 2 Related Work  
### Salient Object Detection Research  
In recent years, salient object detection (SOD) methods utilizing encoder-decoder and feature aggregation architecture have achieved high performance. Below, we will briefly review models related to this work.  
PAGE-Net [15] exploits an essential pyramid attention structure for emphasizing salient object detection while focusing on the importance of salient edges and attributes the unclear boundary to the smoothness of convolution kernel and downsampling. F\({}^{3}\)Net [16] proposes a cross feature module (CFM) to refine both high-level features and low-level features and consists of a cascaded feedback decoder (CFD) to correct the output features before the final output. BASNet [17] designs a refinement module (RM) and a hybrid loss function which consists of BCE loss, SSIM loss, and IoU loss. Introducing the calculation of similarity and intersection, BASNet better localizes the boundary of salient object detection. GateNet [18] contains multilevel gate units to address the issue of interference control between the encoder and decoder during information exchange. To deal with the separate extraction of RGB and thermal features, CFIDNet [19] designs a feature-enhanced module to extract informative depth cues and leverage multiscale complementary information, avoiding the noise-degraded saliency map result from simple element-wise addition or multiplication. CCFENet [20] proposes an essential cross-collaboration enhancement strategy (CCE), which facilitates the interactions when encoding. HFANet [21] jointly models boundary learning to salient object detection, addressing the issue of cluttered backgrounds, scale invariance, complicated edges, and irregular topology. It extracts abundant context in the deep semantic features using Gated Fold-ASPP, integrating adjacent features with an unparameterized alignment strategy by AFAM. ERPNet [22] is delivered into two parallel decoders for edge extraction and feature fusion, respectively. ERPNet addresses the challenges posed by diverse object types, various object scales, numerous object orientations, and cluttered backgrounds in optical remote sensing images. EDN [23] employs an extreme downsampling technique to enhance high-level features and recovers object details with an elegant decoder. CASNet [24] trains a baseline encoder-decoder model using the Lovasz softmax loss function, which outperforms 19 state-of-the-art image-and-video-based algorithms.  
The research above mainly focuses on extracting semantic information and feature fusion between early and deeper levels. It explores the boundaries of salient objects, studies more realistic loss functions during training, and designs a more efficient network. These studies have improved the detection performance of the model to varying degrees. However, current models still encounter issues such as poor baseline model performance, inaccurate localization of salient objects, and failure in edge detection, among others.
# Groundwater Storage Changes in China from Satellite Gravity: An Overview
## 3 Results
### Song-Liao Basin
Note that different scales were used for \(y\)-axes in Figure 7a,b. Based on the Groundwater Bulletins of China Northern Plains (GBCNP) and the Groundwater Bulletins of Song-Liao River Basin (GBSLRB) released by the MWR, annual GWS anomalies in the plain area of LRB are in good agreement with results from WGHM at interannual time scales (Figure 7b). It should be noted that the groundwater bulletins only provide annual GWS changes in the plain of LRB. However, the largest GWS depletion occurs in the mountain regions, i.e., in Chifeng, as shown in Figure 8. Thus, the GWS depletion signal from bulletins is less than that from monitoring wells and GRACE. Compared with GRACE and well observations, both WGHM and PCR-GLOWB underestimate the amplitude of temporal variability of GWS changes signal in the LRB (Figure 7a,b). Nevertheless, in the spatial domain, GWS declines in Chifeng and Tongliao can still be captured by WGHM and PCR-GLOWB (Figures 6 and 8). In addition, interannual variability of GWS from GRACE, well observations, WGHM, and groundwater bulletins agrees well with annual precipitation anomalies and PDSI over the LRB. As shown in Figure 7, during the drought conditions from 2006-2009, the GWS declined significantly. Due to more precipitation in 2010 and 2013, the groundwater recovered correspondingly as shown in results from GRACE and WGHM. PCR-GLOWB, however, fails to simulate interannual GWS variations in the LRB well.
# Modeling the Relationship between Business Process Reengineering and Organizational Culture
## 4 Research Methodology
### Prioritize BPR Constructs  
Analytical hierarchical process (AHP) is a preferred tool that researchers choose to solve complicated organizational problems in research, especially those with many aspects/dimensions [40]. It is a tool used to break the complex problem and structure it into a hierarchy. This helps the researcher to prioritize the solutions effectively [41]. AHP is a multi-criteria decision-making approach that sets a weighting process, with many criteria represented based on their relative importance to each other [42]. The FAHP approach has been adopted and is justified because the BPR critical success factors are subjective and AHP emerges as the prominent choice for computation of weights in the case of subjective factors. Various researchers [43; 44] have also adopted this approach for qualitative computing weights.  
Although many techniques are available in the MCDM domain, researchers in different decision-making areas have widely utilized the fuzzy analytical hierarchy process (FAHP) to identify criteria' intensity and weights. The major benefit of using FAHP is its capability to tackle vagueness. This approach is carried out by using pairwise comparison. However, the judgments made by decision-makers are further analyzed for consistency to ensure that these judgments are consistent. For a better understanding of the fundamentals of FAHP, the readers can refer to [45; 46]. The detailed procedure for executing FAHP is presented in Figure 2. The basic steps involved in FAHP are shown below:  
1. Develop a decision panel.
2. Define the fuzzy scale for making pairwise comparisons and follow systematic procedure; see Figure 1.
3. Prepare the fuzzy pairwise comparison matrix (FPCM).
4. Convert the FPCM into Crisp Comparison Matrix (CCM).
5. Conduct the consistency check Table 1 for random index).
6. Determine the weight of each factor included within the matrix.
7. Prioritize each factor according to the global weight.  
Figure 2: Steps to execute FAHP to prioritize the BPR elements.  
Thus, the following constructs of BPR implementations are resulted to be used for this study: (i) organizational strategy; (ii) transfer of experience; (iii) leadership; (iv) communication with stakeholders; (v) IT; (vi) end-user satisfaction; (vii) conclusive decision-making; (viii) business case; (ix) project management; and (x) organizational culture change. Moreover, OCP elements were selected to be studied in this study with the addition of one element: the higher educational level of staff.
# A Channel Compensation Technique Based on Frequency-Hopping Binary Offset Carrier Modulated Signal
## 3 Performance Analysis
### Matched Filters

Table 1: RMSEs and comparative ratio under different Doppler condition. Figure 12: Up-sampling process demonstration for a short time. (**a**) Signals’ Real Components Comparison. (**b**) Signals’ Imagine Components Comparison. which is quite a half alleviation. Although near the level of 20,000 Hz, a large Doppler shift \(RMSE1\) behaves similar to \(RMSE2\) for only 3.42% reduction, and speaking from a positive view, it has \(\eta<1\) among these Doppler situations and a raised cosine filter showing much more effective in a small Doppler shift situation. Generally speaking, up-sampled OFDM signals through a raised cosine filter are more stable whenever there exists a Doppler shift, especially a relatively small one, for demonstrating its superiority on pulse shaping property.
# Software Defined Wireless Mesh Network Flat Distribution Control Plane
## 2 FD-SDWMN Architecture
### Global Functionality  
To collect and maintain a global network view in each controller supporting network efficient QoS routing and reservation functionalities, the following agents are defined and implemented.  
* The connectivity agent: is in charge of maintaining peering links between the controllers. The work of this agent is based on event-driven fashion, it only sends information if a new controller is discovered or existing peering link between controllers is changed. This information is grouped and maintained into the aggregated-DB, and like other information it is accumulated by all neighboring connectivity agents. Eventually, the connectivity information is used to make global routing decisions by the path computation module.
* The monitoring agent: periodically receives information (QoS properties) about available links and their capabilities, in order to support traffic transmission among network domains.
* The Reachability agent: advertises the hosts presence on local domain margins so they become reachable. It offers a roadmap between domain switches and hosts.
* The reservation agent: is concerned with overall network flow setup, e.g., link teardown, and updates requests and application requirements such as QoS. It is like the Resource Reservation Protocol (RSVP). Each controller handles these requests by the service manager, and the reservation agents of these controllers along the path communicate to create and maintain the needed path.  
In order to achieve FD-SDWMN control plane consistency, the agents publish and consume messages managing the required topics. The aggregated information that concerns reachability (reachable hosts listed in the network), connectivity (a list of peering controllers) and monitoring (peering transit paths status, in terms of QoS metrics, etc.). This way, a controller can build a view of the entire network. Thus, it has the capabilities to perform path reservation, routing and manage SLAs.
# Random Segmentation: New Traffic Obfuscation against Packet-Size-Based Side-Channel Attacks
## 2 Related Work
Signal-jamming approaches can serve as a defense against traffic analysis attacks in wireless networks, all without the need for adding dummy packets or intentional delays. Generally, this method employs antennas to disrupt traffic at possible adversary positions, effectively elevating the noise level [19]. However, this tactic generates interference that impairs the performance of nearby networks and, furthermore, it is considered unlawful ([[https://www.fcc.gov/general/jammer-enforcement](https://www.fcc.gov/general/jammer-enforcement)]([https://www.fcc.gov/general/jammer-enforcement](https://www.fcc.gov/general/jammer-enforcement)) (accessed on 15 May 2023)). Packet-size randomization has previously been proposed to address the side-channel leakage in secure shell (SSH) communications [20; 21; 22], widely used for secure remote access and communication. However, their proposed modifications are specific to the SSH protocol. Many IoT devices often rely on lightweight messaging protocols to fulfill the IoT communication requirements [23], such as Hypertext Transfer Protocol Secure (HTTPS),Message Queuing Telemetry Transport (MQTT), Constrained Application Protocol (CoAP), Extensible Messaging and Presence Protocol (XMPP), and Data Distribution Service (DDS). Our work proposes a novel use of random segmentation at the transport layer, responsible for passing the data received from all the application layer communication protocols mentioned above. In this manner, our defense is less demanding for deployment and suitable for the IoT architecture. TCP segmentation was previously proposed to reduce the per-packet overhead on host processors for wired networks [12]. This approach delays segmenting the data into smaller units and sends it as a larger TCP segment to improve efficiency. Unlike this work, our technique makes the segmentation random and, thus, unpredictable to evade patterns on side-channel information that can be used to identify IoT devices.
## 4 Detection Performance and Discussion
### Detection Performance with Different Azimuth Angles  
In this section, the influence of the azimuth angle between the subject and antenna on the detection performance is investigated. In this case, the subject is behind the wall at a distance of 1100 cm from the antenna. The detection performance with a 0\({}^{\circ}\) azimuth angle was discussed in the previous section. The results with an azimuth angle between the antenna and subject of 30\({}^{\circ}\) and 45\({}^{\circ}\) are shown in Figure 11a. After removing the clutter and linear trend, the corresponding normalized spectrums of the resulting signals using the proposed method are shown in Figure 20a. These results indicate that the amplitude of the respiration signal decreases as the azimuth angle increases.  
As stated previously, the periodic respiration signal is the most important and reliable indication of a trapped victim. The detection results obtained indicate that the largest signal amplitude is obtained with a 0\({}^{\circ}\) azimuth angle, i.e., when the subject is facing the antenna. Figure 20b shows the respiration frequency results using the proposed and FFT methods with a 45\({}^{\circ}\) azimuth angle, and the corresponding results with a 30\({}^{\circ}\) azimuth angle are given in Figure 20c. These indicate that the proposed method is much better at determining the respiration frequency than the FFT method. This is because it is better at suppressing the harmonics and interference.  
Figure 19: The estimated human respiration frequency using the proposed method at distances between the antenna and human subject of (**a**) 900 cm, and (**b**) 1100 cm.  
Figure 18: (**a**) Respiration signal spectrum and (**b**) area of interest obtained using the MHOC method.
## 6 Evaluation Experiment of the Proposed Approach  
The subcomponents of the crowd selection approach (role identifier and expertise retrieval) have been evaluated using quantitative methods. In addition, the complete proposed approach is evaluated through conducting a controlled crowdsourcing experiment. The aim of the evaluation is to investigate whether the proposed approach is effective in producing quality mobile apps' requirements that are crowdsourced from a crowd which is selected using our approach.  
In our evaluation experiment, we crowdsourced a requirements elicitation task which is one of the critical stages of requirements engineering. Each crowd member contributed independently to the task. The nature of this task is fully dependent on human knowledge in terms of expressing needs and requirements for a mobile app. Due to that, the output of this task would be a reliable indicator of the effectiveness of the proposed approach when crowdsourcing RE.  
The evaluation procedure consists of five major phases and starts with preparing the information of the mobile app where the crowd will be selected according to. Second, thecrowd selection is run using the prepared mobile app information as an input. Third, the resulting crowd shall be invited to participate in crowdsourcing requirements elicitation for the identified mobile app. Fourth, the crowdsourced requirements are qualitatively analyzed by experts considering five crucial quality factors. Last, the outcomes from the analysis are presented and investigated to ascertain the effectiveness of our proposed approach. The evaluation procedure is depicted in Figure 9, and each phase shall be discussed in detail in the following sections.