esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Striations Interference Phenomenon and Its Formation Mechanism
### The Phenomenon of Striations Discontinuity and Its Generation Mechanism  
In the simulation experiment of the bistatic mode shown in Figure 4, there were discontinued in the interference pattern and the amplitude changed more drastically with the increase in frequency. We did not see these changes in the monostatic mode given in Figure 3. The discontinuity of the striations was related to the magnitude of the scattering function shown in Figure 2.  
Figure 9 is the interference striation diagram obtained in the monostatic and bistatic configuration simulation experiments and the variation relationship diagram of the scattering function amplitude during the movement of the target. The variation of the sound field intensity showed agreement with the change of the scattering function amplitude. In Figure 9b, the amplitude of the scattering function was almost zero around 4000-5000 Hz, so the striations appeared discontinuous near the corresponding frequency.  
This can explain the reason for the difference in the discontinuity of the striations between the monostatic and bistatic configurations. For the monostatic configuration in Figure 9a, the source and the receiver were located on the same platform, the range between them was extremely small, and the scattering angle was basically kept around 180 degrees during the movement of the target, so the amplitude of the scattering function changed almost smoothly with the change of frequency. For the bistatic configuration shown in Figure 9b,c, the source and the receiver were located in two parts. When the target moved, the scattering angle changed, so the amplitude of the scattering function also changed. The sound field interference striations appeared discontinuous. Comparing Figure 9b,c, the degrees of discontinuities striations with different frequencies were also different. For the bistatic configuration, the scattering angle of the object constantly changed during the target movement, the scattering function amplitude changed drastically at high frequencies. In addition, the degree of discontinuity striations became more obvious. Therefore, the striations discontinuity phenomenon under the condition of 40-80 kHz was more obvious than that at 4-8 kHz. The experimental striations shown in Figures 7b and 8b were the results of the monostatic configuration test, but they showed the phenomenon of striations discontinuity. On the one hand, the signal-to-noise ratio of the signal was large at high frequencies, so the structure of the striations was clearer than at the low frequency. On the other hand, during the lake test, the target sphere did not move along the specified straight line due to the actual conditions. The scattering angle changed while the target sphere moved, so the experimental striations showed the phenomenon of striations discontinuity.
## 4 Conclusions  
A new mass and calcification detection algorithm is proposed and termed as \"Gabor cancer detection\" (GCD) algorithm. A circular Gaussian filter (CGF) is used in segmentation (generating alarm segments); while the EHD (edge histogram descriptor) features extracted from the Gabor-filtered images are used in classification (reducing false alarms). The histogram analysis makes the alarm threshold adapted to the CGF-filtered images and thus makes the alarm generation process vigorous and adaptive to variant intensities from case to case.  
The GCD algorithm is tested on a relatively large sample of mammograms (a subset of DDSM database). The overall performance (TP and FPI) of the GCD algorithm is very good and the accuracies in locating both masses and calcifications (measured with overlapping ratio hereinabove) are very high. Furthermore, the GCD algorithm can successfully detect early-stage masses and calcifications (of low values of Assessment & Subtlety in DDSM).
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 5 E-Health Sustainability Model
### Experts Evaluation  
To verify the validity and the comprehensiveness of the e-health sustainability model, interviews were conducted with three experts in IT from three different hospitals. One hospital is in the eastern province of Saudi Arabia and the other two are in the western province of Saudi Arabia. The experts are healthcare providers and staff, who have been working for an average of 10 years in the healthcare industry. They have direct experience in managing the adoption of e-health solutions within their hospitals. The sampling technique used is judgmental sampling [48]. The duration of the interview discussions was from 45 to 60 min.  
The objective of meeting experts was to collect their feedback on (1) the validity and correctness of the dimensions and their elements and aspects, (2) the comprehensiveness of the model in terms of representing the e-health sustainability dimensions, (3) and the applicability of each dimension and their aspects to assess the sustainability of e-health solutions. In terms of the validity and comprehensiveness of the proposed model, the three experts agree that the model is comprehensive, and the construct is correct. In addition, two experts suggested (1) adding software integration as an element in the system quality aspect in the technology dimension, and (2) considering the commitment and support of the e-health vendor as an element that impacts the sustainability. Accordingly, these two aspects were added to the model as the experts had suggested. In terms of the applicability, the experts found the sustainability model applicable in real-world cases of evaluating sustainability. However, one expert suggested that there has to be a list of tools to assess each dimension and its aspects in order use the model in practice.
# Improved Autocollimator for Roll Angle Measurement with an Enlarged Measuring Range
## 3 Experimental Results
### Tests of Stability and Repeatability  
Stability is the capability of a measuring instrument to keep its metrological characteristics constant over time and is also the prerequisite for the instrument to measure accurately. It is largely affected by electrical noise and the peripheral environment and should be examined immediately after the instrument is constructed. The stability of the improved autocollimator was tested on an optical stage in a laboratory. The indoor temperature was kept at about 25 \({}^{\circ}\)C and there was no lighting. The WP was installed on a three-dimensional rotary table. We adjusted the rotary table such that light spot 1 was located at the origin of the CMOS, and the straight line determined by light spots 1 and 2 was at an angle of about \(-92.7^{\circ}\) to the \(x\)-axis. After preheating for 30 min, the improved autocollimator took photos at a frame rate of 0.5 fps during a 10 min period. Figure 4 maps the stability curve of the WP's roll angle. The peak-to-valley value of the curve was 3.62 arcsec and the standard deviation was 0.73 arcsec, indicating that the improved autocollimator tended to run stably.  
Figure 3: Photograph of improved autocollimator for large-range roll angle measurement.  
To investigate the measurement repeatability of the improved autocollimator, several independent measurements were performed. While the autocollimator was running stably, a piece of cardboard was placed ahead of it and withdrawn a few seconds later. Then, a measured value was logged. This process was executed ten times, and the measured values (after subtracting the initial measured value) are shown in Table 1. The standard deviation of the measured values calculated using the Bessel formula was 1.72 arcsec, which was considered the repeatability error.
# Development and Testing of an Active Noise Control System for Urban Road Traffic Noise
This development involved optimizing the noise reduction algorithm parameters, understanding the ANC principles, and selecting the appropriate software and hardware. We also conducted extensive tests on single-frequency, dual-frequency, and real traffic noise. The results of these tests provide valuable technical insights for implementing ANC in roadside noise control.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### Comparison of IoT Communication Approaches
#### 3.8.8 Data Discovery  
IoT infrastructures are large distributed systems, where each application needs to have a way to find the information it needs; ensuring adequate communication; connect sources and destinations. For broker-based approaches such as AMQP and MQTT, the broker provides an abstraction layer and therefore, applications just need to know the identifier of brokers to rely upon. This reduces the need to register every node. Still, nodes need to know broker identifiers. OPC UA supports discovery of servers (via multicast DNS), which assists in reducing costs associated with configuration. While NDN supports direct data searches, via named-based routing.
# Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation
## 2 Elastic Wave Monitoring for Sand-Filling of Immersed Tube Tunnel
### Sand-Filling Project of Immersed Tube Tunnel Foundation  
1. Case study: Jinguangdong immersed tube tunnel  
The Jinguangdong immersed tube tunnel in Guangzhou, China, was selected as the research object. The foundation of the Jinguangdong immersed tube tunnel was constructed using the sand-filling method. The tunnel is located at 23\({}^{\circ}\)2\({}^{\prime}\)10\({}^{\prime\prime}\) N, 113\({}^{\circ}\)24\({}^{\prime}\)30\({}^{\prime\prime}\) E, crossing the Pearl River, with a buried depth of approximately 11 m underwater, as shown in Figure 2. The total length of the tunnel is 460 m, with six pipe sections, and the length of each section is approximately 75-77 m. The size of the cross-section was 8.55 m \(\times\) 22.1 m. The construction sequence was ordered P6, P5, P1, P2, P3, and P4.  
2. Monitoring the cushion filling effect  
The sand-filling effect of the sand deposit corresponding to each sand-filling hole is defined as the cushion filling effect. As shown in Figure 3a, each pipe section was equipped with 17 sand-filling holes, and the distance between neighbouring sand-filling holes was 9 m. The sand-filling process started to fill the side holes (W1, W8, E1, and E9) and ended by filling the middle holes of the pipe section. The designed sand-deposit diffusion radius R\({}_{\text{d}}\) of each sand-filling hole was 7.5 m. Levelling and rock filling of the river bottom foundation were carried out before sand-filling. The water-sand mix ratio of the filling sand was controlled at 7:1 to 9:1.  
Figure 2: Jinguangdong immersed tube tunnel.  
Auxiliary monitoring methods, such as the quantity of sand-filling, settlement and lifting, and pressure of sand-filling, were adopted to ensure the quality of the sand foundation, as shown in Figure 2(b). Sand-filling quantity monitoring determines the sand-filling process by recording the volume of the poured sand. Settlement and uplift monitoring measures the settlement and uplift of the pipe section through a level gauge. Generally, in the process of sand-filling, the filling or blockage of the hole would result in the sharp rise of the pressure gauge. The pressure gauge monitor judges the state of sand-filling based on the recorded pressure. As is shown in Figure 2(c), at 10 th hour, the sand-filling pressure suddenly increases, reflecting the completion of sand-filling. All the above indirect methods provide references for the training of the elastic wave monitoring model. The pressure gauge data, uplift state, and sand-filling volume record during the sand-filling process of the P6 pipe section are listed in Table 1.  
Figure 3: Foundation cushion filling effect monitoring.
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 2 Background
### Conventional Channel Extension Scheme (Conventional AIFS)  
When a 20-MHz channel is composed by two contiguous 10-MHz channels, the most simple 20-MHz channel access scheme reuses the existing wide-bandwidth channel access scheme defined in IEEE 802.11n and IEEE 802.11 ac [14], which is used to access wider channels of 40 MHz, 80 MHz, and 160 MHz channels. In IEEE 802.11n and IEEE 802.11 ac, a primary channel is defined as a common access channel used by all stations including AP in a BSS, and secondary channels are defined as the remaining channels of 40 MHz, 80 MHz, and 160 MHz with the primary channel.  
The wide channel access in IEEE 802.11n and IEEE 802.11 ac follows the EDCA channel access procedure on the primary channel and ED sensing on the secondary channel for a certain period before the end of the channel access procedure in the primary channel. The ED sensing period of the secondary channel depends on the operating band: PIFS in the 5 GHz band and DIFS in the 2.4 GHz band. When a busy channel is indicated in the secondary channels by ED sensing, the station may transmit a frame using only the primary channel, or a back-off procedure is restarted with a new back-off counter, preserving the contention window value and retransmission counter.  
The previously described channel-access rule for 40-MHz channel access can be applied to 20-MHz channel access in IEEE 802.11 bd. In reference [14], the primary channel was defined as the 10-MHz channel where an EDCA channel access procedure is performed, and the secondary channel was defined as the extended 10-MHz channel composing the 20 MHz channel. In order to provide enhanced fairness with stations using the secondary channel, using AIFS instead of PIFS is currently under discussion [14], as depicted in Figure 3. However, despite using AIFS, which has a longer waiting period than PIFS, there still exists a fairness problem with stations on the secondary channel because there is no back-off of the secondary channel. The fairness problem becomes worse when the channel load in the secondary channel is higher than the channel load of the primary channel because stations  
Figure 2: Channel access scheme in IEEE 802.11p.  
on the secondary channel perform the back-off procedure as a result of EDCA procedure. The fairness problem should be carefully considered because fairness is one of the crucial requirements of IEEE 802.11 bd [5,6].
# Advances in Artificial Intelligence for Perception Augmentation and Reasoning
## 4 Application of Deep Learning in Remote Sensing  
This Special Issue also contains two novel learning applications in the field of remote sensing. In recent years, deep learning has been widely used to solve major technical problems in the field of remote sensing. [PERSON] et al. [7] proposed a road extraction learning method D-DenseNet with a new structure. The network combines the extended convolution layer and the previous layer into a building block, so the extended convolution can obtain more global context information through the entire network. The authors' low-level road extraction experiments showed that this method can effectively reduce the model size and improve the segmentation accuracy IoU, in addition to having good extraction performance in complex scenes.  
The application of machine learning in the field of river flood disasters is relatively mature. For the monitoring of urban waterlogging disasters, [PERSON] et al. [8] proposed a waterlogging prediction model based on a long short-term memory (LSTM) neural network. The authors selected Jianye District (Nanjing City, in the southeast of China) as the study area. They established a multistep multivariable waterlogging prediction model based on the precipitation and waterlogging monitoring data in the study area and trained three models based on three loss functions. The model prediction results and the measured waterlogging data showed that LSTM made full use of the nonlinear relationship between the rainfall data of each rainfall station and the waterlogging data of a single waterlogging station, as well as having a good multistep prediction effect on the future waterlogging process.
# Moodle LMS Integration with Amazon Alexa: A Practical Experience
## 3 Method
### Alexa Skill Implementation
#### 3.2.1 Account Linking Web Service
* **modelToken**, the token retrieved from Moodle after user's authentication through the login form. With the implementation of the JWT token we were able to identify the user when needed, as well as being capable to interact with Moodle Web Services on user's behalf. However, we felt that passing the JWT token as plain text might be insecure due to the fact that the Moodle token of the user would be readable to any third party that might gain access to the JWT token. For this reason, we decided to encrypt the JWT token before passing it through to Alexa service. The main objective of cryptography is to keep data safe from unauthorized access. Figure 7: Flow diagram for Alexa access token exchange (Source: own authors’ elaboration). ```
Listing 2: JWT structure example. ``` Header: Algorithms and Token Types { \"alg\": \"HS256\", \"typ\": \"JWT\" } Payload: Data { \"iss\": \"XXX\", // Public Claim \"iat\": XXX, // Public Claim \"userId\": \"XXX\", // Private Claim \"username\": \"XXX\", // Private Claim \"moodleToken\": \"XXX\", // Private Claim } Signature: results from Hash { HMACSHA256( base64 UrlEncode(header) + \".\" + base64 UrlEncode(payload), secret) } ```
Through cryptography we can achieve security by encoding messages in unreadable format so that the original message would only be read by the intended user [42]. While there are many encryption mechanisms currently available, we decided to use \"Threefish\" to securely encrypt the JWT token. \"Threefish\" is a large, tweakable block cipher, developed by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] in 2008 [43]. It is defined for three different block sizes: 256 bits, 512 bits, and 1024 bits. We used 512 bits as the block size to encrypt the JWT token. The key is the same size as the block, and the tweak value is 128 bits for all block sizes [44]. Finally, we encoded the encrypted token using Base64 algorithm. The Base64 algorithm allows for encoding and decoding an object into ASCII format, and it is used in multiple cases, including URL encoding purposes [45]. By encoding the encrypted token we made sure the data integrity would remain during the sending and receiving of the token. The final encoded token is then ready to be sent back to Alexa service and complete the Account Linking process successfully. The encoded token would be then sent as part of any future Alexa request. Receiving the encoded token within the requests would allow us to identify the user who sent the request, and successfully communicate with Moodle Web Services on user's behalf, as the Moodle account token would be contained within the JWT as claim, once it is decoded and decrypted.
## 3 Advanced Techniques in Fault Injection and Error Rate Estimation
### Automatic Configuration Memory Fault Injection Tool  
#### 3.2.1 Architecture  
The Automatic Configuration Memory Fault Injection Tool (ACMFI) is a tool that automates statistical fault injection tests for FPGA configuration memory using SEM IP. The FPGA Fault Injection Tool determines the location of the CLB (configuration logic block) where the designed circuit is synthesized and generates a list of statistical fault injection test scenarios that can randomly inject faults (single-bit flips) within the specified memory range. This list is subsequently applied sequentially to the target board, and the results of each test scenario are recorded. Finally, the results of the statistical fault injection scenario list are compiled, and the overall failure rate is provided.  
The ACMFI architecture comprises a host PC and a target board (Zybo Z7-20 Board (Xilinx, Taiwan)), as illustrated in the Figure 7. The host PC receives statistical fault injection test information from the target board (including the test scope and scenarios), conducts the tests automatically, and presents the performance results to the user in a list format. The target board executes the fault injection test scenarios configured on the host PC for the implemented circuit and communicates the output results (normal or failed) back to the host PC. The final system SER is computed based on the received test results.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 4 Proposed Fast CU Size Selection Algorithm
### Overall Encoding Algorithm  
The overall fast encoding algorithm proposed in this manuscript is illustrated in Figure 5. As illustrated, the proposed method first builds the SVMs using RDO and operates in two levels--before encoding the current depth; and after encoding the current depth.  
The SVM models generated at L-1 predict the CU's \"split\" (\(\eta=+1\)) and \"non-split\" (\(\eta=-1\)) decisions for a given CU depth level \(i\in\{0,1,2\}\). As illustrated in Figure 5, the CU size decisions that cannot be predicted by the L-1 SVMs are subjected to the L-2 SVMs. For example, any CU that is classified into the middle region identified in the Figure 4, is evaluated for all its PU modes and passed on to L-2 model to determine whether to further split the CU or select the current CU size as it is.  
Complexity Control Parameter (\(\delta\))  
The proposed method introduces a complexity control parameter to allow \(\delta\)% number of CUs that reach L-2 models to go through traditional RD-optimisation, to determine their CU split decisions.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Feature Category** & **Feature** & **Depth(s)** \\ \hline Texture Information & \(\theta_{1}\) & 0, 1, 2, 3 \\ \hline Context Information & \(\pi_{1}\) & 0, 1 \\  & \(\pi_{2}\) & 2, 3 \\ \hline Coding Information of Current CU & \(\tau_{1}\) & 0, 1, 2, 3 \\  & \(\tau_{2}\) & 0, 1, 2, 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features used for different CU depth levels in Stage 2.  
Controlling \(\delta\) in the proposed algorithm facilitates the flexibility to trade-off coding complexity to the coding efficiency.
# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 3 Experimental Results
### Evaluation Metrics  
Usually, the segmentation results are evaluated for CSF, GM, and WM tissues using the following three evaluation metrics: overlap measure, boundary measure, and volume measure. In this paper, we evaluate our proposed method using all three measures.  
Dice similarity Coefficient (DC): Dice similarity coefficient [35] is used to estimate the spatial overlap between the ground truth and the segmentation results, using the following equation.  
\[\text{DC}=\frac{2*|Seg_{\_}Im\cap GT\_}{|Seg_{\_}Im|+|GT\_}_{\_}{|GT\_}{|GT\_} \tag{27}\]  
where \(Seg\_}\_Im\) is the segmentation result of the proposed method, and \(GT\_}\_Im\) is the ground truth. Higher DC represents more accurate segmentation.  
Hausdorff Distance (HD): The Hausdorff distance [36] is used as the boundary measure, and it is calculated between the ground truth points \(\varphi\) and the segmented points \(\hat{\varphi}\) using the following equation:  
\[\text{HD}=\max_{\hat{\varphi}\in Seg\_}\_}\min_{\varphi\in GT\_}\_}\_}\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\