esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Reviewing Background and Related Work
### MQTT IoT Communication
Although it uses the same TCP/IP structure as HTTP for data transfer, the main change is that device endpoints must first specify a proxy to communicate with, which acts as a bridge between publishers and subscribers. The most important feature of the message exchange is that it defines three QoS (Quality of Service) conditions [9; 10]. Among them, QoS 0 is sent at most once, which does not guarantee that the message can be sent successfully; QoS 1 is sent at least once, which guarantees the message can be sent out. QoS 2 is transmitted once, and the message is divided into two parts during transmission to ensure that the message can reach the receiving end, but it may also cause a higher delay. Due to its characteristics of occupying less bandwidth, better power-saving performance, and more extensive data transmission capacity, this technology is currently widely used on the Internet of Things communication environment. The MQTT architecture uses the Publish/Subscribe mode. So, we can allow multiple publishers to publish messages for different topics. By subscribing to a theme, an attendee can receive relevant notifications according to different themes. The intermediary conversion tool, the broker, does not manage the encryption operations of each subscriber. It only forwards the message to all subscribers who have subscribed to the topic, as shown in Figure 1 above. A subscriber can receive different notifications by subscribing to other topics and stop receiving messages related to a topic by unsubscribing. Figure 1: The message in MQTT broker structure. Therefore, from the above architecture, we expect to encounter the first technical bottleneck: if different subscribers subscribe to the same topic, it means that the topic's message information can be stolen and monitored. If the subscribers themselves publish messages on the same topic, this will cause the information that initially needed to be sent to be duplicated and replaced. For this reason, even though MQTT provides TLS-encrypted events, the MQTT client will use the TLS protocol to encode the message when multiple subscribers/publishers subscribe to notifications on the same broker [11]. The architecture, where the MQTT server will use TLS to decrypt the message and publish it to all clients that have subscribed to the corresponding topic, is still insufficient to filter out subscribers using the same topic to avoid legitimate monitoring of message sending and delivery.
# Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves
## 2 Materials and Methods
### Color Information Collection Methods  
Color information (RGB values, as well as HSL and HSV values calculated based on RGB values) was obtained from the images captured from the leaf patches. HSL values are values in the HSL color system, where H, S, and L represent hue, saturation, and lightness, respectively. The values of H and S in the HSL color system are reported to be indices that are theoretically unaffected by the intensity of illuminance, because illuminance information is concentrated only in L [13]. Therefore, it is possible to reduce the influence of the light source on the photographic environment.  
Figure 1: Schematic diagram of the experimental device to capture images of radish leaves.  
The HSL color system can be converted from the RGB color system in a cubic space through the icosahedral hue, saturation, and intensity (HSI) color system, which is similar to the HSL color system and the HSV color system. The difference between the HSL and HSV color systems lies in the color when S (saturation) is reduced. The HSL color system ranges from 0 to 360 for H, 0 to 100 for S, and 0 to 100 for L. The HSV color system ranges from 0 to 360 for H, 0 to 100 for S, and 0 to 100 for V. In this study, HSL and HSV values were calculated from each RGB value using a colormap in a Python library and normalized to the range of 0 to 1 in each value range.
# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 4 AIS to Secure the IoT: Literature Review and Analysis  
AIS applications are artificial intelligence (AI) techniques inspired by the intelligence of the human body's immunology. Given its ability to detect unseen attacks and its low complexity, various AIS-based methods are proposed in the literature for IoT security. An immune-based architecture was presented in [44] to secure the IoT using edge technologies based on IoT system requirements. As rightly highlighted by the authors, the architecture meets IoT security requirements, such as adaptability and lightweight, and can secure IoT nodes from various security threats and attacks. However, the proposed method is to secure the IoT using edge technologies, which means it is limited to a certain IoT system architecture. Moreover, the availability of this method has not been considered during the evaluation process. In addition, to secure Internet protocol version 6 (Ipv6) in the IoT, a bio-inspired method was presented in [45]. An AIS-based method is implemented in the routing protocol for low-power and lossy networks to enhance the security level and performance with the given limited resources in the IoT. The main limitation of this approach is that it is time and energy consuming, which makes it difficult to secure IoT devices with limited resources. In the following section, we review AIS methods for IoT malware detection, including negative and positive selection algorithms and immune and artificial immune-based methods.
# Comparison of Single Control Loop Performance Monitoring Methods
## 2 Materials and Methods
### Implemented CPM Indices
In general, \(OCE_{total}\) is a product of three separate factors, as indicated in Equation (7):  
\[OCE_{total}=OCE_{a}\times OCE_{p}\times OCE_{q}. \tag{7}\]  
In Equation (7), \(OCE_{a}\) is the availability, or the portion of time the controller autonomously produces good-quality data. \(OCE_{p}\) is the performance, or the accuracy to follow the setpoint value without oscillation, and \(OCE_{q}\) is the quality, or the ability to continue as part of the production process in the future. In this application, availability is calculated based on the proportion of automatic and manual control of the studied control loop, while performance is related to the setpoint tracking error. Quality is related to several indices describing the control loop performance in long-term trends. The details of the quality factors are omitted due to company confidentiality reasons. Overall, the calculation of the \(OCE_{total}\) value relies on a statistical algorithm. The OCE method includes parameters to finetune the process, and in this paper, the two parameters that describe the number of days in the buffer and evaluation are considered. In both cases, a value of 10 was used. Since \(OCE_{total}\) represents a product of the three aforementioned factors, it is sensitive to variability in any of the indices. An extreme example is that if any of the factors are zero, the whole \(OCE_{total}\) value becomes zero. Moreover, in the case of asymmetric indices, OCE may become less appropriate.
# Instability and Drift Phenomena in Switching RF-MEMS Microsystems
## 4 Instabilities Due to Temperature Variations
A real example of this behavior is reported in Figure 5, where a clamped-clamped capacitive switch with a gold suspended beam is measured with an optical profiler at different temperatures. From the figure, we see that the shape does not change until 65 \({}^{\circ}\)C and that the critical temperature is between 65 and 80 \({}^{\circ}\)C. The buckling is clearly evident at 80 \({}^{\circ}\)C and even more so at 100 \({}^{\circ}\)C. In fact, the non-perfect planarity of the membrane at 20 \({}^{\circ}\)C was due to thermal-stress-related processes in the release phase [31] at the end of the switch fabrication process. The membrane shape above the critical temperature depends on its geometry and dimensions [28] and may vary several times while the temperature keeps increasing. In general, membrane anchors are the most important design detail in order to avoid unwanted deformations [32]. Anchoring systems that allow, at least partially, the lateral expansion of the membrane have a higher critical temperature and are less deformed by the temperature. In the example reported above, the actuation voltage was around 40 V at room temperature and around 20 V at the critical temperature (80 \({}^{\circ}\)C) [30].
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 5 Conclusions  
This study presents a comprehensive method for extracting accurate and practical indoor navigation network models from 3D semantic point clouds, with a focus on addressing key challenges in complex environments, such as staircases and multi-floor spaces. To ensure consistency in the data input, we developed a series of preprocessing rules. Additionally, we refined the staircase node extraction algorithm and provided a more comprehensive solution for hallway sub-node extraction. Furthermore, we designed a lightweight JSON data structure to store the extracted indoor navigation network data, enabling the successful construction of indoor navigation networks using semantic point clouds.  
To evaluate the effectiveness of our approach, we first validated the method using the S3 DIS dataset. Subsequently, we conducted further validation on a self-collected dataset, which consisted of 3D data from a multi-floor building captured using HoloLens 2 and pre-processed to generate the required semantic point clouds. This two-step evaluation demonstrated the robustness of our method across various spatial environments. Finally, we evaluated the path planning capabilities of the navigation network using the Dijkstra algorithm. The results showed that our network effectively supports spatial connectivity analysis and performs well in different indoor environments. Additionally, wheelchair accessibility was considered, and the paths were correctly determined.  
While our method successfully extracted indoor navigation networks from semantic point clouds, and applied them to shortest path planning, our research mainly focuses on Manhattan-world models [38] and static point cloud data. In the future, we aim to further improve our method by adapting it to more complex indoor environments. Additionally, we plan to integrate scene recognition and self-adaptive algorithms to achieve more efficient and flexible construction of indoor navigation networks.  
Conceptualization, J.H., P.H. and D.I.; methodology, J.H.; software, J.H. and P.H.; hardware, D.I. and P.H.; validation, J.H.; formal analysis, J.H. and P.H.; investigation, J.H. and P.H.; resources, D.I.; data acquisition, J.H.; writing--original draft preparation, J.H.; writing--review and editing, P.H., D.I. and J.H.; visualization, J.H. and P.H.; supervision, D.I. and P.H. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the China Scholarship Council, Grant/Award Number: 202108130064.  
Not applicable.  
The data supporting the reported results can be obtained by contacting the first author or corresponding author via email.  
We would like to acknowledge the Open Access Publishing Fund of the Technical University of Darmstadt for its support.  
The authors declare no conflicts of interest.
# A Hybrid SWIM Data Naming Scheme Based on TLC Structure
## 3 Naming Scheme of SWIM Data
### Routing Based on Swim Content Name  
Each piece of data in SWIM has a name to ensure the liquidity of the content by not revealing its address. Finally, addresses no longer need to be allocated and managed in the local network, which is especially helpful for sensor networks. The SWIM service node consists of three parts: a forwarding information table (FIB). FIB is used to determine the interface of a data packet, where a data packet comes from, and where it is going. Waiting for the interest table (PIT), PIT stores all requests that do not meet the FIB table to future content sources. Content storage table (CS), CS is a buffer memory, each node stores the searched name to respond to the request. The data forwarding process based on name forwarding in SWIM is shown in Figure 7.  
When a node intends to retrieve content, it sends an interest packet indicating the name of the required content. During each hop of forwarding, the result of the forwarding decision-making lookup operation depends on the FIB, PIT, and CS tables of the requested name. If the searched content does not match successfully in CS, PIT will update the tracking interest to the next interface. Then, by looking up the FIB table to find the most suitable forwarding port, the interest packet will be forwarded to the next hop. The data packet will be returned to the requesting node to activate theThe content storage table of the routing node is mainly used to cache data packets passing through the node so that subsequent interest packets can respond quickly when requesting the same data packet. When the cache space of the content storage table of the node is full, a corresponding replacement strategy is needed to support the new data packet cache and discard the old data packets. The cache of this new routing node is different from the node cache in the traditional IP network. The data packet cached in the traditional IP routing node is only for a specific connection. The data packet cache is mainly to facilitate retransmission after packet loss. It is not used to be requested by other nodes. The data packets cached in the router can be reused to better realize the sharing and distribution of data; while the data packets cached in the IP router cannot be reused, which leads to a large number of redundant transmissions in the IP network.  
FIB stores a collection of prefixes with different names mapped to different interfaces. It is mainly used to forward interest packets to potential data packet nodes. FIB is similar in structure to PIT, mainly composed of name prefix and interface list. In the structure of FIB, since one prefix can map multiple interfaces, it can easily realize the multipath forwarding of interest packets. In the creation or update of FIB, it is very similar to the traditional routing table, which can be configured manually or automatically generated through related protocols.
## 4 Analysis of Test Results
### Total Pressure Recovery Coefficient versus Mass Flow Rate  
Figure 10 shows the variation of the total pressure recovery coefficient versus mass flow rate at different Mach numbers. In terms of the overall variation trend, when the variable geometry auxiliary inlet is at a large opening (\(K>25\%\)), the total pressure recovery coefficient increases first and then decreases with the increase in the mass flow rate, but the overall fluctuation is relatively small. When the variable geometry auxiliary inlet is at a small opening (\(K\leq 25\%\)), the total pressure recovery coefficient decreases sharply with the increase in the mass flow rate.  
In addition, when the mass flow rate is constant, the total pressure recovery coefficient gradually increases with the increase in the opening, reaching the highest value at a \(50\%\) opening. After that, the total pressure recovery coefficient begins to decline with the increase in the opening. The higher the Mach number, the more obvious the downward trend. This is due to the fact that in the small opening interval, the inner profile of the inlet is  
Figure 10: Total pressure recovery coefficient versus mass flow rate: (**a**) \(Ma=0.4\); (**b**) \(Ma=0.65\); (**c**) \(Ma=0.75\); and (**d**) \(Ma=0.85\).  
Figure 9: Comparison of simulation results and test results.  
relatively moderate and the flow field quality is better, and the resistance loss along the inlet is dominant. With the increase in the opening, the captured flow area of the inlet gradually increases, the resistance loss decreases, and the total pressure recovery coefficient increases gradually. As the opening continues to increase from 50%, it is easy to induce large-scale boundary layer separation when the incoming flow passes through the entrance ramp, which will significantly increase the flow loss and reduce the flow capacity. The higher the Mach number is, the thicker the boundary layer will be, the more easily the separation will occur, and the more obvious the decrease of the total pressure recovery coefficient will be. In order to ensure the minimum flow loss, the opening should be reasonably matched according to the demand of ram air. Under the condition of a high Mach number (\(Ma>0.4\)), the inlet opening of this test configuration should not exceed 50%, otherwise the total pressure recovery coefficient will decrease significantly.  
Another point that can be clearly seen from Figure 11 is that the total pressure recovery coefficient is inversely proportional to the Mach number. The main reason is that as the Mach number increases, the flow velocity inside the inlet increases, resulting in increased resistance loss along the inlet.
## 4 Experiments
### Results  
The ANOVA results revealed the main effects of Q4 (\(F(2,86.5)=7.31,p<0.01\), \(\eta^{2}~{}=~{}0.10\)), i.e., \"There was a change in the wording of the dialog system (e.g., formal versus informal style)\", Q5 (\(F(2,86.5)=7.62,p<0.01,\eta^{2}~{}=~{}0.10\)), i.e., \"I felt that the utterances of the dialog system were a mixture of formal and informal styles\", and Q6 (\(F(2,86.5)=2.44,p<0.1,\eta^{2}~{}=~{}0.03\)), i.e., \"I decided on my own wording while keeping the wording of the dialog system in mind\", which were used to examine whether the perception of the participants regarding the speech level changes of the dialog system was correct. The results of Q4, Q5, and Q6 are depicted in Figure 3. Multiple comparisons using the Tukey method for Q4, Q5, and Q6 demonstrated that the shifting speech level condition was rated higher than the informal style condition for Q4 (\(p<0.01,d~{}=~{}0.11\)) and Q5 (\(p<0.01,d~{}=~{}0.12\)).  
[PERSON]'s alpha coefficients were calculated to assess the reliability of the questionnaire. The alpha coefficients were calculated for each of the four evaluation categories shown in Figure 3. The values shown below are the mean (standard deviation) of the alpha coefficients calculated for the three experimental conditions for the four categories.  
* Human-like: 0.72 (0.10);
* Recognition of speech level: 0.77 (0.07) (without Q6), 0.40 (0.07) (include Q6);
* Dialog breakdown: 0.67 (0.11);
* Overall impression: 0.81 (0.09).  
The alpha coefficients for \"Human-like\", \"Dialog breakdown\", and \"Overall impression\" are reasonably high. The alpha coefficients for \"Recognition of speech level\" are low for all items, but the alpha coefficient rises to a reasonably high value when Q6 is excluded. In other words, the internal consistency of Q4 and Q5, which showed significant differences in Figure 3, is reasonably high.  
The formal speech style rate of the experimental participants was examined and found to be 62.0% under the shifting speech level condition, 80.4% under the formal style condition, and 22.8% under the informal style condition. These results suggest that the participants tended to use the same speech level as the dialog system. Moreover, four participants had a formal style rate of 0% and six had a formal style rate of 100%, indicating that some participants did not apply any shift in their speech level. Next, we analyzed the extent of speech level shifting performed by the participants. In 19 (41.3%) of the dialog logs, the user either did not change their speech level or changed it only for greetings or assistance. In 19 (41.3%) of the logs, the user changed their speech level within two turns, according to the speech level shift of the dialog system. In the remaining 8 (17.3%) dialog logs, the user changed their speech level regardless of the speech level of the dialog system.
# Natural Creation of Large Rock Cavern: Can We Construct Them? Jenolan Caves as a Case Study
## 4 Discussion of the Findings Based on Two Parts of Stability Simulations  
Stable natural caves with a span of over 55 m can be found around the world, such as the Grand Arch of the Jenolan Caves. Theoretically, empirical rock and structural mechanics principles could be used to interpret the physical phenomena of stability with large-span rock-opening structures. In this study of the Jenolan Caves as a learning object, we used the assumed rock mass parameters and available geological information for stability simulation. The results demonstrate that a stable large-span rock cave can be formed naturally under certain ground and rock mass conditions. The findings can also be applied to the man-made opening with or without stabilization measures, e.g., using the rock-reinforcement technique.  
With the numerical modeling results, several key parameters play primary functions to control the stability of these natural openings, as well as man-made rock openings. These parameters include the following:  
* Rock mass quality, rock structure, and joints density;
* Rock strength and shear strength of rock joints;
* In situ stress field;
* Shape of opening.
# Ultrasonic VMCF Tests on Very Large Specimens with Risk-Volume Up to 5000 mm+
## 3 Results
### P-S-N Curves  
The marginal P-S-N curves were finally estimated, according to [15]. In particular, the fatigue life was modeled by considering a monotonically decreasing behavior in the finite life region, with an asymptotic behavior at the end (fatigue limit). According to Equation (1), the cumulative distribution function (cdf) of the fatigue life, \(F_{Y}(y;x)\) (being \(x\) and \(y\) the logarithm of the applied stress and of the number of cycles to failure) can be expressed by multiplying the conditional cdf of the fatigue life \(F_{Y|\sqrt{a_{d0}}}(y;x,\sqrt{a_{d0}})\) (conditioned to the defect size \(\sqrt{a_{d0}}\)) by the probability density function (pdf) of the defect size, \(f_{\sqrt{a_{d0}}}(\sqrt{a_{d0}})\):  
\[F_{Y}(y;x)=\int_{0}^{\infty}F_{Y|\sqrt{a_{d0}}}(y;x,\sqrt{a_{d0}})f_{\sqrt{a_ {d0}}}(\sqrt{a_{d0}})d\sqrt{a_{d0}}, \tag{1}\]  
According to [15; 16], the finite fatigue life was assumed to be Normally distributed, with mean linearly dependent on the applied stress amplitude and on the defect size \(\sqrt{a_{d0}}\) and constant standard deviation. The fatigue limit was assumed to be Normally distributed with mean value depending on \(\sqrt{a_{d0}}\), whereas the defect size was assumed to follow the Largest Extreme Value Distribution (LEVD), according to [3]. The median, the 0.95-th and the 0.05-th quantiles of the P-S-N curves are shown in Figure 7, together with the experimental data. In particular, Figure 7a,b depict the P-S-N curves for the hourglass and the Gaussian specimens, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen** & **Hourglass (\(\mu\)m)** & **Gaussian (\(\mu\)m)** \\ \hline \(\sqrt{a_{d,0}}_{min}\) & 11 & 40 \\ \hline \(\sqrt{a_{d,0}}_{max}\) & 36 & 68 \\ \hline \(\sqrt{a_{d,0}}_{median}\) & 19 & 52 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Size of the critical defect, \(\sqrt{a_{d,0}}\), in hourglass specimens and in Gaussian specimens.  
According to Figure 7, the estimated marginal P-S-N curves are in good agreement with the experimental data. Indeed, for the hourglass and for the Gaussian specimens, the median P-S-N curve is above about the 50% of the experimental failures, and all the data are within the 0.05-th and the 0.95-th quantiles of the P-S-N curves.