esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 5 Implementation Setup and Results Discussion
### Scenario 4: Fluctuation in Delay Offset and Idle Time  
The special change made in this scenario is that the end-to-end delay offset does not remain constant during the whole period of execution. Sender shuffles the offset between 100, 200 and 300 at runtime. Figure 14, represents idle time against varying values of an end to end delay offset. Before having a look at the graph, it is important to know that why there is a need to change the end to end delay offset at runtime? The encircled area of the graph indicates the negative value of idle time. Negative idle time can only be possible if the packet is being received later than its playout time. In such a case if the receiver(s) are suffering from lots of delayed packets due to some reason, the sender can increase the value of playout offset to compensate the delayed packets. It can be seen from the graph that packets are delayed at receivers when the delay offset is 100. As soon as the value of delay offset is increased to 200, there is no more negative idle time value. Very large values of idle time may also not be accepted as they can cause the whole stream to start late and also the receiver can go out of buffer space soon. Larger values of idle time can be observed when the end to end delay offset is 300 in the graph. To best utilize the resources (buffer space) end to end delay offset value might need to be reduced from 300 at runtime. It is important for the sender to know about the buffer status of the receivers to provide an appropriate value of playout time.  
Figure 13: Idle time vs. one-way delay (OWD).
## 4 Simulation Results
### Path Planning for 3-D Formation Rendezvous  
In the case of 3-D, obstacles are modeled as cylinders and no-fly zones are represented by rectangles with infinite heights. The initial poses of UAVs and the formation poses are given in Table 5. The desired formation configuration is the same as above. The maximum curvature and torsion of UAVs are \(\kappa_{\text{max}}=\tau_{\text{max}}=2\text{km}^{-1}\). The terrain is modeled using Equation (13). Other conditions and parameters are the same as above.  
The paths of UAVs generated by the proposed DCPSO with cooperation in 3-D environments are shown in Figure 6. The curvature and torsion variations with respect to path length of each UAV are given in Figure 7. The length of three paths and their minimum separation distances are given in Table 6. Simulation results show that all three paths avoid the obstacles, no-fly zones, and the terrain. Both the curvature and torsion of the three paths change continuously while meeting the maximum-curvature and maximum-torsion constraints of UAVs. Meanwhile, the maximum difference of lengths is only 0.0081 km, which means simultaneous arrival of UAVs can be achieved and no collision happens between UAVs. Therefore, the algorithm proposed in this paper is also suitable in cooperative path-planning for UAV formation rendezvous in 3-D environments.
## 3 Experimental Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Steganography Scheme** & \multicolumn{3}{c}{**HILL**} & \multicolumn{3}{c}{**Mi-POD**} & \multicolumn{3}{c}{**S-UNIWARD**} & \multicolumn{3}{c}{**WOW**} \\ \hline Payload (bpp) & 0.2 & 0.3 & 0.4 & 0.2 & 0.3 & 0.4 & 0.2 & 0.3 & 0.4 & 0.2 & 0.3 & 0.4 \\ \hline SRNet & 0.4180 & 0.3468 & 0.3170 & 0.3967 & 0.3212 & 0.2638 & 0.3392 & 0.2495 & 0.2087 & 0.2505 & 0.1832 & 0.1401 \\ \hline Ye-Net & 0.4601 & 0.3994 & 0.3527 & 0.4052 & 0.3509 & 0.3262 & 0.3864 & 0.3065 & 0.2511 & 0.3009 & 0.2402 & 0.1947 \\ \hline Yedroudj-Net & 0.4426 & 0.3819 & 0.3257 & 0.4036 & 0.3570 & 0.2775 & 0.3835 & 0.2968 & 0.2543 & 0.2831 & 0.2130 & 0.1677 \\ \hline Zhu-Net & 0.3718 & 0.3169 & 0.2414 & 0.3209 & 0.2658 & 0.2421 & 0.2875 & 0.2102 & 0.1695 & 0.2315 & 0.1702 & 0.1057 \\ \hline Proposed Scheme & 0.3608 & 0.2867 & 0.2476 & 0.3109 & 0.2495 & 0.2203 & 0.2691 & 0.1922 & 0.1546 & 0.2066 & 0.1561 & 0.1011 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Detection errors of proposed and other schemes using BOWS2 and BOSSBase image sets.
# First Observations of Cirrus Clouds Using the UZ Mie Lidar over uMhalthuze City, South Africa
## 3 Methods and Data
### Calipso  
The CALIPSO satellite was launched on 28 April 2006 and is an integral part of NASA's A-Train satellite constellation [20]. Its aim is to study the role that clouds and aerosols play in regulating the earth's weather, climate, and air quality. The main objectives of the CALIPSO are to (1) provide statistics on the vertical structure of clouds and aerosols around the globe, (2) detect sub-visible clouds in the upper troposphere and Polar Stratospheric Clouds (PSC), and (3) provide statistics on the geographic and vertical distribution of aerosols and clouds around the globe. The primary instrument aboard CALIPSO is the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a two-wavelength laser (532 nm and 1064 nm) operating at a pulse repetition rate of 20.16 Hz [21]. Table 2 shows some specifications of CALIOP. More information on the technical specifications of CALIPSO can be found in [PERSON] et al. [22] and [PERSON] et al. [23]. Furthermore, [PERSON] et al. [24] provide a detailed discussion on the algorithm that was developed to identify aerosol and cloud layers and to retrieve a variety of optical and microphysical properties.
# Tracking and Monitoring System Based on LoRa Technology for Lightweight Boats
## 4 IoT-Based Communication Technologies in Coastal Scenarios
### Limitations on Existing Solutions  
As mentioned above, so far, two different families of IoT-based communication technologies for sensing and monitoring applications in coastal scenarios have been employed, namely, short and medium range technologies. However, these communication technologies present notable impediments for deploying sustainable and cost-effective solutions: (i) transmission range, which notably determines the network organization and its scalability, (ii) edge-nodes' dimensioning and power efficiency, and (iii) network management costs [7]. Please note that we do not consider satellite communications as an enabling technology in these scenarios due to its high cost and energy consumption, which clearly differ from the basic requirements of IoT systems.  
Regarding short range technologies, WSN solutions such as ZigBee or 6 LoWPAN have been adopted in many scenarios [14; 15]. The main drawback of these alternatives is their restricted transmission range, which severely limits the operation area and increments the complexity of network management tasks. The use of this kind of solutions implies the configuration of a multi-hop network in order to send the information to a collection point or using other alternatives even more complex and expensive. In turn, medium range communication technologies such as cellular systems, e.g., GSM or 3G, have also been employed for providing ships or monitoring platforms with connectivity [22]. However, although this approach permits longer radio-links, it presents important deficiencies in order to be directly used by constrained end-devices. They are motivated by the initial design of cellular technologies, which were focused on human-type communications, i.e., they provide broadband access to a limited number of users. Nevertheless, unlike this scheme, typical monitoring scenarios will host a notable number of devices generating short packets sporadicly. In addition, current cellular technologies are not battery friendly either, which is a critical requirement of unattended maritime deployments.  
Figure 3: LoRaWAN network architecture.
## 4 Improvement Proposals
### Vsm  
In Gemba, by observing and analyzing the productive process, a Flowchart of it was developed. Then, to analyze where waste and improvement opportunities existed, the production process of the Flowchart (the most critical process of the most representative product repaired in this work section--a seat) was subdivided into various steps. Afterwards, each step was detailed in terms of the activities that comprise it, movements made, and existing wait, and each activity was then classified as VA, NVA, and/or NNVA according to the criteria presented in Section 2.2.2. The cycle time of the different phases as well as the waits were measured by direct observation; timing; and, where timing was not possible, by estimation indicated by the workers of the light motor vehicle section. With this information, the VSM of the Current State was developed, from which the values presented in Table 6 were obtained.  
The Lead Time corresponds to approximately 8 h and 17 min. This is the total time that it currently takes to repair and renovate a seat upholstery in the SE allocated to the study. Although, in this section, more products than the seats are renovated, maintained, and/or repaired, to visualize the current situation, it will be considered that the company, in the light motor vehicle work section, only works on the repair and maintenance of the vehicle's seat upholstery. Knowing that a month has 22 working days and that the company only works on one 8 h shift, in a month, the SE works 633.600 s. Thus, theoretically, the SE would currently be repairing about 21 seats per month according to Equation (1).  
\[\frac{633.600}{29.801}\ \approx\ 21\ \text{seats} \tag{1}\]  
Given the Current State, proposals for improvements to reduce and/or eliminate NNVAs and eliminate NVAs were investigated and explored. These proposals will be detailed in the next subsection according to the Lean tool that allows their reduction and/or elimination. Given these proposals, each cycle time will take new values. With those values, the VSM of the Future State was developed, from which the values presented in Table 7 were obtained.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Activities** & **Total (s)** \\ \hline VA & 27.554 \\ \hline NVA & 1.503 \\ \hline NNVA & 24.423 \\ \hline Lead Time * & 29.801 \\ \hline \hline \end{tabular}  
* The Lead Time was calculated according to some details, namely, the fact that some of the process’s phases happen in parallel with each other. This particularity was accounted for.  
\end{table}
Table 6: VSM of the Current State values.  
The Lead Time of a seat upholstery in the Future State is approximately, 6 h and 7 min according to Table 7. Similarly to what was done for the Current State, it will be considered that the SE, in the light motor vehicle section, only operates in the repair of the vehicle's seat upholstery. Attending to this consideration, in the Future State, the SE would repair about 28 seats per month according to Equation (2).  
\[\frac{633.600}{22.042}\ \approx\ 28\ \text{seats} \tag{2}\]
# Creating a Diagnostic Assistance System for Diseases in Kampo Medicine
## 1 Introduction
In the latest study of diagnosis from the tongue, [PERSON] et al. [7] collected tongue images from about 1000 people using a device that can perform gloss removal imaging to diagnose diabetes. [PERSON] et al. [8] conducted a study to estimate the Frequency scale for the symptoms of gastroesophageal reflux disease from images of the tongue. However, these recent studies have the problem of gloss removal: in these studies, the gloss of the tongue was completely removed, which is different from the Kampo doctor's observation. To solve this problem, [PERSON] et al. [9] used a combination of the integrating sphere method and directional illumination to capture images of glossy and non-glossy tongues in order to estimate the moisture content of the tongue. However, because the integrating sphere is large and expensive, it is not appropriate for personal use. In the present study, we photographed the tongue using a polarizing camera that simultaneously captures glossy and non-glossy images with more compact and cheaper equipment than an integrating sphere which is conventionally used. In addition, to collect tongue images more efficiently, we proposed a physiologically compensated and effective data augmentation method. We then used these images to assess each patient's diseases. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Qi** & **Blood** & **Fluid** \\ \hline Deficiency & Tongue & Face & Face \\  & & Tongue & Tongue \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Body parts (tongue and face) used to diagnose each of the six diseases in Kampo medicine. A system of quantitative evaluation was constructed by machine learning of the values and features obtained from the tongue images. The accuracy of the evaluation system was verified via comparison with the distribution and variability of the assessments by four Kampo medicine specialists. This research provided a quantitative diagnostic method for Kampo diseases from the tongue using image processing and machine learning techniques. The contributions and novelty of this paper are therefore as follows:  
* We proposed a tongue imaging method using a polarized light camera that is simpler and more effective than the conventional method. In particular, the use of polarized light cameras provided accurate gloss information and showed their effectiveness in diagnosing yin deficiency and fluid stagnation, conditions closely related to fluid content, as shown in Section 2
* We proposed a physiologically compensated and effective data augmentation method. We show that the proposed data augmentation method improved the distribution of correct values in Section 4. In addition, the estimation accuracy was improved by the data augmentation. These results indicate the effectiveness of our method.
## 2 Background and Related Work
### Related Work
#### 2.2.3 Efficiency and Effectiveness  
Efficiency and effectiveness are essential sub-dimensions of usability that together contribute to the overall assessment of a system's capacity to assist users in achieving their objectives, according to the ISO 9241-11 framework [8]. While efficiency shows the promptness of these responses, effectiveness, as determined by mistake rates and task success, concentrates on the system's capacity to deliver accurate and satisfying solutions. When taken as a whole, these factors offer a thorough understanding of usability and how it affects the user experience.  
Recent systematic reviews advocate for a mixed-methods approach to evaluating usability, emphasizing the need to assess effectiveness and efficiency as distinct dimensions to overcome traditional evaluation limitations [63].  
Efficiency, measured by response time, reflects a chatbot's ability to process and deliver information promptly. Optimal response times align with established usability standards [64], significantly influencing user satisfaction, engagement, and overall UX [65]. Similarly, effectiveness, measured by error rates, reflects the chatbot's ability to provide accurate and satisfactory responses [64]. By minimizing errors, especially in critical applications like healthcare, developers can enhance user satisfaction and interaction quality [66].  
In educational chatbots, such as those designed for Arabic-based computer skills classes, incorporating efficiency and effectiveness has demonstrated the potential to improve student engagement, communication, and learning outcomes [67]. These usability sub-dimensions enable chatbots to create flexible, interactive learning experiences, aligning with this study's objective of enhancing the holistic user experience.  
Furthermore, the effectiveness of chatbots is influenced by various elements, such as user-friendliness, perceived quality, and enjoyment, all of which play a crucial role in enhancing user satisfaction [68]. Increased satisfaction levels are linked to users' willingness to persist in using and endorsing chatbots, highlighting the significance of design that prioritizes usability. Chatbots improve efficiency and effectiveness through the integration of interactive, student-centered learning approaches, facilitating tailored educational experiences that foster active engagement and enhance academic success. This method is backed by well-established theories, including constructivism, self-directed learning, and cognitive multimedia learning, which highlight the importance of personal relevance, customized support, and active participation.
# Energy Efficiency in Short and Wide-Area IoT Technologies--A Survey
## 2 IoT Technologies
### Low-Power Wide-Area Networks (LPWANs)
#### 2.2.2 Cellular IoT
In order to exchange data with the network, a NB-IoT device first performs the random access procedure, also usually denoted as RACH, which includes the RRC connection setup. When a device requires an uplink/downlink service, first it listens to cell information, and then it sends a random access request to the base station. The base station responds sending a random access response (RAR), indicating the resources that were reserved for that device. As a result of the random access, the device enters the RRC Connected mode, where it receives on the narrowband physical downlink control channel (NPDCCH),both information of the resource allocated for data reception and data packets. EC-GSM-IoT  
EC-GSM-IoT is another cellular LPWAN technology. It is built upon GSM networks and existing cellular base stations with a simple software update [56]. This allows EC-GSM-IoT networks to coexist with 2G, 3G, and 4G mobile networks. EC-GSM-IoT also benefits from all the security and privacy features from tradition cellular networks, such as support for user identity confidentiality, entity authentication, confidentiality, data integrity, and mobile equipment identification. GSM uses a combination of FDMA and TDMA as modulation schemes [56]. The channels occupy 200 kHz and have an absolute placement, called channel raster, which is also defined in steps of 200 kHz. Following GSM, EC-GSM-IoT has TDMA frames that are divided in eight time-slots, with such frames grouped in hierarchical structures denoted as multiframe, superframe and hyperframe. EC-GSM-IoT inherits from GSM the burst as the basic transmission unit. The following burst types are defined:  
* _Frequency Correction Burst (FCB)_: It is a time slot of information in a GSM system, containing a 142 bit pattern of all 0 values, where the reception and decoding of these bursts allows the mobile device to better adjust its timing in order to better receive and demodulate the communication channel;
* _Synchronization Burst (SB)_: provides synchronization for the UEs on the network;
* _Access Burst (AB)_: Dedicated to the uplink. This burst is used by the UE to access the system. It is shorter than the other burst types. The system after receiving this burst, sends back an acknowledgment and a time alignment command for the UE to transmit;
* _Dummy Burst (DB)_: Dedicated to the downlink, and it is a burst of information that contains no user data information. They are used to fill a time slot or frame, in order to ensure a continuous flow of data;
* _Normal Burst (NB)_: It carries both signaling and user information. EC-GSM-IoT has the same symbol rate as GSM of roughly 270.83 keywords/s, and uses 8 PSK modulation, among other possible solutions. EC-GSM uses a blind transmission technique, where the transmitter transmits a predefined number of transmissions without needing any feedback from the receiving devices [63].
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 6 Performance Evaluation
### Emulation-Based Analysis for IoT Networks
#### 6.4.2 Calculation Time  
First, the calculation time required for each cryptographic calculation was evaluated. The average calculation time required for routing is shown in Figure 5. The performances  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Implementation Device** & **Raspberry Pi 4 (Model B)** \\ \hline OS & Ubuntu 18.04 LTS \\ \hline CPU & 1.5-GHz Quad-Core Cortex-A72 \\ \hline Main memory & LPDDR4-2400 SDRAM 4 GB \\ \hline Language & C with GCC 7.5.0 \\ \hline External libraries & OpenSSL 1.1.1d [33] and PBC Library 0.5.14 [31] \\ \hline Transmission rate \(R\) & 250 [kbps] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Emulation specifications.  
of the compared methods are flat regardless of the \(x\)-axis because these methods need to communicate via end-to-end routing packets. We repeated the cryptographic operations required for routing in each method 1000 times on a Raspberry Pi and averaged the results. Regarding the communication time (Figure 3), the proposed method can reduce the computation time, as the nodes close to 5 generate RREPs (i.e., \(N\) is smaller). However, as the number of hops increases, the computation time increases compared to the other methods, as the proposed method verifies the signatures in past packets, thereby increasing the number of calculations required of each node.  
Next, the calculation time performance for various numbers of intermediate nodes was evaluated. To clarify whether the proposed method outperforms the others, we evaluated a threshold of \(N\) where the proposed method is superior to each comparison method in terms of the calculation time. Figure 6 shows the threshold of \(N\) against the total number of intermediate nodes \(n\). This figure reveals that the proposed method can be superior in terms of computation time even for small networks such as \(n=1,2\). As \(n\) increases the computation time can be further improved, even if a node far from 5 generates RREPs. In contrast, compared with ISDSR+, the threshold value of \(N\) is zero even at the scale of \(n=4\), indicating that the conditions for reducing the computation time are limited. This is because ISDSR+ uses IBSAS, which aggregates multiple signatures into a single signature, making it faster than ordinary ID-based signatures.  
Figure 5: Calculation time performances when \(n=5\).  
Figure 6: Threshold of \(N\) for \(n\) at which the proposed method is superior to the compared methods in terms of calculation time.
## 5 Base Shear and Bending Moment Coefficients  
To explore the influence of the rib extensional depth on the overall base wind effects, Figure 11 presents the base shear force coefficient \(C_{\text{Fx}}\) and base bending moment coefficient \(C_{\text{My}}\) under different wind incidence angles for all tested cases. To accurately compute the base shear force and base bending moment, the measured layer force coefficients are interpolated with the cubic spline algorithm to derive the corresponding values for unmeasured layers. The wind incidence angle ranges from 0\({}^{\circ}\) to 90\({}^{\circ}\), where the force coefficients in the angles of 45\({}^{\circ}\)-90\({}^{\circ}\) are attained symmetrically from those in the \(y\)-direction under incidence angles of 0\({}^{\circ}\)-45\({}^{\circ}\). Figure 11 shows that the base shear and base bending moment reach their maximum values at the wind incidence angle of 0\({}^{\circ}\). When the wind incidence angle of the minimum value shifts from 75\({}^{\circ}\) to 85\({}^{\circ}\), \(b\)/B increases from 0% to 4%. The base shear and base bending moment for cases of \(b\)/B between 0% and 2% share almost the same variation trend with the wind incidence angles while the models with \(b\)/B = 3% and 4% bear larger maximum negative pressures at the wind incidence angle of 80\({}^{\circ}\) and 85\({}^{\circ}\), respectively, but smaller maximum positive pressure at the wind incidence angle of 0\({}^{\circ}\). The maximum absolute values of both the base share and base bending moment appear at the incidence angle of 0\({}^{\circ}\), making it the control case for wind-resistant design. In this case, the addition of vertical ribs with a large extensional depth (e.g., 0.04B) will improve the wind-resistant performance of a high-rise building by reducing the base shear and bending moment by 8.8% and 7.4%, respectively.