esa-sceva/satcom-chunk-collection huggingface dataset:
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 4 Experimental Method
### LoRa Physical Layer Settings  
The configuration of the LoRa radio parameters for the transmitter and receiver nodes are summarized in Table 2, where a bandwidth of 125 kHz is used as the narrowest _BW_ value that can be configured by the LoRa RN2903 Module. The aim of using this value is to ensure the highest sensitivity rate and long communication range. The used values of _SF_ ranges from 7 to 12. Those values can be chosen by the default settings recommended by Semtech, the LoRa developer.
## 4 Applications
### Smart Cities  
With the support of SAGINs, drones can function as mobile cache nodes, executing caching and distribution tasks for static resources to alleviate network load and enhance resource access efficiency. For instance, during large-scale sporting events, concerts, or other outdoor gatherings, the surge in network demand caused by crowd concentration may exceed the capacity of ground-based communication infrastructure, resulting in degraded service quality. UAVs can pre-cache popular content or essential data resources [155], providing temporary rapid distribution services during these events. This approach helps to reduce the burden on terrestrial base stations and improves user experience. While in flight, drones can exchange data with ground-based stations or satellites to dynamically update cached content, adapting to real-time changes in user demand and ensuring the timely delivery of popular content. In the context of smart city development, this caching and distribution method, supported by integrated space-air-ground networks, can effectively improve the quality and responsiveness of public services. It provides a flexible solution for managing network peak loads and enhances urban infrastructure's adaptability in response to surges in network traffic. In modern urban logistics distribution, traditional ground transportation methods often face uncertainties such as traffic congestion and accidents, especially during peak commuting hours or when delivering goods to specific areas, significantly impacting logistical efficiency. By leveraging an integrated space-air-ground network, drones can operate within low-altitude airspace as critical tools for delivering small cargo, providing rapid transport services. The literature [156] has discussed the use of UAVs to load drugs, avoid ground traffic congestion through air routes, and quickly deliver drugs to target locations. In this scenario, SAGINs enable seamless communication, ensuring that drones maintain data connectivity with command centers throughout their flight. This facilitates the comprehensive monitoring and dynamic adjustment of the delivery process, enhancing the flexibility and emergency response capability of the logistics network, thus offering essential support for the development of smart cities in the future.
# Control Strategies for Gait Tele-Rehabilitation System Based on Parallel Robotics
## 5 Conclusions  
As robotic systems for lower limb rehabilitation evolve and new features become available, such as potential use in tele-rehabilitation, new control strategies may be used to further increase the functional outcome of therapy. In this work, we have proposed two novel approaches for controlling a gait simulator based on two Stewart-Gough platforms. One technique enables direct control of the robotic platform by the therapist, which is particularly interesting considering the broad operational space of such a robotic system. Another method aims at improving patient engagement, providing the user limited control over the platform. Here, we enabled control of gait speed using the trunk inclination measured using a wireless sensor. Feasibility experiments were conducted with healthy subjects, and the obtained results indicate the system presents sufficient performance to justify broader quantitative studies.  
Conceptualization, A.P.L.B., M.H. and D.E.; methodology, A.P.L.B., M.H., L.C. and G.C.-P.; software, G.C.-P., L.C., A.P.L.B. and M.H.; data curation, A.P.L.B. and M.H.; writing--original draft preparation, A.P.L.B. and L.C.; writing--review and editing, A.P.L.B., L.C., G.C.-P., M.H. and D.E.; funding acquisition, A.P.L.B., M.H. and D.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in the context of the STIC-AmSud Program, with funds from INRIA (France), CAPES (Brazil), and CONCYTEC (Peru).  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Pontificia Universidad Catolica del Peru's Research Ethics Committee.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Backflow Velocity Distribution of Tip Clearance
In the surface further away from the blade tip, such as SPN = 0.85 to 0.95, the backflow region is significantly diminished and is mainly present near the leading edge of the blade. Notably, no backflow is observed near the outlet of the rear impeller's flow passage. From Figures 12 and 13, under different flow rates, there are significant differences in the backflow regions within the impeller flow passages, and the velocity distributions in these backflow regions also vary. To further quantify the variation of the backflow velocity along the spanwise direction, the average backflow velocity was calculated. Figure 14 shows the spanwise distribution of the average backflow velocity in the impeller. From Figure 14a, when the flow rate is greater than 1.0 \(Q_{\rm BEP}\), the backflow velocity in the front impeller decreases with the decrease in SPN, and eventually approaches 0 m/s (no backflow appears in the flow passage), and the influence range of the backflow region is within SPN = 0.98 to 1.0. As the flow rate further decreases, the influence range of the backflow region significantly increases. The backflow velocity decreases sharply with the decrease in SPN and then gradually stabilizes, eventually approaching 0 m/s. From this, it can be seen that under low flow rates, the influence range of backflow in the spanwise direction will significantly increase, and the backflow velocity is generally higher than that under large flow rates. Figure 14b shows that the trend of the backflow velocity of the rear impeller with SPN is consistent with that of the front impeller, but there are significant differences in the distribution of backflow velocity. When the flow rate is less than 1.2 \(Q_{\rm BEP}\), under the same flow rates, the backflow velocity of the rear impeller is generally greater than that of the front impeller. Under low flow rates, the difference between the two will further increase, especially in the blade tip clearance region. It should be noted that under low flow rates, the backflow velocity of the rear impeller in the spanwise direction is higher than that of the front impeller, which will seriously hinder the mainstream in the flow passage.
# Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks
## 3 RAN Methodology to Simulate Recall Operations
### Regulation Mechanism
#### 3.3.1 Impact Factor (\(\sigma\)) Construction and Interpretation  
The impact factor is a function that interprets the CSRL weight values (in the range [0, 1]) as excitatory, inhibitory, or neutral weights. The purpose of CSRL weights is to determine how concurrently two nodes (e.g., S\({}_{1}\) and S\({}_{2}\)) are active. If the CSRL weight is intermediate, i.e., 0.5, it signifies that the two nodes are 50% concurrently active (depicting a state of confusion). Therefore, these nodes do not have an impact on each other in the same layer. If the CSRL weight of the two nodes were \"0\", then the two nodes were never active simultaneously. This also indicates that the two nodes are inhibitors of each other. Finally, if the CSRL weights of the two nodes were \"1\", then the nodes were always active conjointly, exciting the activation of each other.  
\[\sigma_{m\to n}=[2*(W_{m\to n}-0.5)]^{3} \tag{5}\]  
The aforementioned comprehension of CSRL weights \(W\) is exhibited by a mathematical Equation (5), where \(\sigma_{m\to n}\) is the impact of node \(m\) over node \(n\). Figure 6 shows a graphical view of the impact factor \(\sigma\) (Equation (5)), depicting the excitatory, inhibitory, or neutral interpretations of CSRL weights. Figure 5 shows the CSRL and their respective \(\sigma\) weights for both layers. At layer 0, the nodes S\({}_{1}\) and S\({}_{2}\) have a very minimal excitatory impact on each other (in Figure 3, every node at layer 1 can be related to clusters C\({}_{1}\),..., C\({}_{5}\) serially). However, at layer 1, node C\({}_{1}\) has no impact on node C\({}_{2}\) (and vice versa). There are many negative weights in the \(\sigma\) matrix of layer 1, indicating that these nodes inhibit each other. In Figure 3, we see that the clusters C\({}_{2}\) and C\({}_{5}\) are very close, and the activations observed at both of the nodes must be very similar. Hence, high CSRL weight is learned between nodes C\({}_{2}\) and C\({}_{5}\). Notably, both exhibit good excitatory behavior towards each other.
## 3 Meso-Scale Simulation of Moisture Transfer Characteristics
### Mesoscopic Moisture Migration Characteristics in Wetting and Drying  
#### 3.2.1 RH Distribution and \"Flow Around\" Phenomenon in Wetting and Drying  
(1) Wetting process  
The calculation results at 72 h of wetting are shown in Figure 5. The internal RH of concrete is between 0.75 and 1. Due to the presence of random aggregate, the moisture migration path inside the concrete has a detailed \"bending\" phenomenon, and the humidity distribution is uneven. The RH at \(x=5\) cm in concrete with a coarse aggregate volume fraction of 0.30, 0.45, and 0.60 is 0.855, 0.843, and 0.836, respectively. The larger the volume fraction of the coarse aggregate, the lower the moisture migration rate. At the same time, the local diffusion path around the coarse aggregate shows that the humidity is more inclined to migrate through the ITZ, resulting in an obvious \"flow-around\" phenomenon near the coarse aggregate and ITZ, and with the increase in the aggregate volume fraction, the \"flow-around\" phenomenon is more obvious, resulting in a non-uniform distribution of humidity. According to the moisture diffusion coefficient model, it can be seen that the moisture diffusion coefficients of each phase are significantly different when RH > 0.8, so the \"flow around\" phenomenon is more likely to occur in the higher part of RH.  
(2) Drying process  
The calculation results at 168 h of drying are shown in Figure 6. The relative humidity inside the concrete is between 0.55 and 1, which is the same as the wetting process. The relative humidity distribution inside the concrete during the drying process also shows non-uniformity and an obvious \"flow around\" phenomenon. The relative humidity at  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Working Condition** & **Boundary Position** & **Boundary RH** & **Internal Initial RH** \\ \hline Wetting process & \(x=0\) cm & 1 & 0.5 \\ Drying process & \(x=0\) cm & 0.5 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundary conditions of wetting and drying.  
Figure 4: Finite element simulation models: (**a**) aggregate volume fraction of 0.3; (**b**) aggregate volume fraction of 0.45; and (**c**) aggregate volume fraction of 0.6.  
\(x=2\) cm inside the concrete with a coarse aggregate volume fraction of 0.30, 0.45, and 0.60 is 0.877, 0.881, and 0.887, respectively. The larger the aggregate volume fraction, the lower the moisture migration rate in the drying process. At the same time, it can be seen from the above humidity distribution law that the volume fraction of coarse aggregate has little effect on the humidity distribution, but the larger the volume fraction of coarse aggregate, the slower the water migration, whether it is wet or dry. The moisture diffusion characteristics under different aggregate volume fractions also further prove that, when the aggregate volume fraction increases, the effect of the additional ITZ on the surface on the moisture diffusion coefficient of concrete is less than that of the aggregate volume fraction.
## 2 Materials and Methods
### Deep Learning Segmentation Approach
#### 2.3.2 Simple Linear Iterative Clustering (SLIC) Superpixel  
SLIC Superpixel is a k-mean adaptation technique for superpixel generation. The SLIC method calculated the distance from each cluster center to pixels within the 2S \(\times\) 2S region, whereas the conventional k-mean clustering technique calculated the distance from each cluster center to all the pixels in the images. The SLIC algorithm helps to increase the segmentation performance in the CNN algorithm [17; 18]. Therefore, it is used in this work to execute the segmentation of helminth ova and artifacts in images as a presegmentation method. The SLIC method used in this segmentation process depends on the pixel intensity values, which are the pixel values of RGB and the spatial distance of the pixel, which is the x and y coordinate information to the superpixel center [19]. The process formed by SLIC segmentation into each pixel in the image is shown in Equation (9).  
\[d_{rgb}=\sqrt{\left(r_{j}-r_{i}\right)^{2}+\left(g_{j}-g_{i}\right)^{2}+\left( b_{j}-b_{i}\right)^{2}} \tag{9}\]  
where \(j\) represents the center pixel, \(i\) represents the value to be clustered, \(d_{rgb}\) represents the distance of the corresponding pixel to the center, and \(r\), \(g\), \(b\) represent the brightness values of the pixel [20]. Equation (10) shows the distance of the coordinate of each pixel to the cluster center.  
\[d_{xy}=\sqrt{\left(x_{j}-x_{i}\right)^{2}+\left(y_{j}-y_{i}\right)^{2}} \tag{10}\]  
where \(x_{j}\) and \(y_{j}\) are the horizontal and vertical coordinate information of each center pixel, and \(x_{i}\) and \(y_{i}\) are the coordinate information of each pixel to be clustered. Equation (11) represents the simplified distance of the coordinate of each pixel to the cluster center.  
\[d_{s}=d_{rgb}+\frac{m\times d_{xy}}{N} \tag{11}\]  
where \(d_{s}\) is the sum of the \((x,y)\) plane distance normalized by the grid interval \(N\) and the lab distance. The parameter \(m\) is defined as the compactness of superpixels and it is allowed to weigh the relative importance between color similarity and spatial proximity [21]. The higher the value of \(m\), the more compact the shape of the superpixel. In contrast, the lower the value of \(m\), the shape of the superpixel will appear in less regular shape and size.  
In this paper, the number of the superpixels, \(k\), of the image was adjusted to 1000 while the compactness of the superpixel, \(m\), was adjusted to 5. The chosen number of superpixels, \(k\), is significant as it can determine the number of vectors. A small \(k\) value does not provide a sufficient signature, while a large \(k\) value will cause over-fitting and increase complexity [21].
# Lamb Wave-Based FDM-PPM Method Data Transmission Scheme in Plate Structures
## 4 Discussion  
### Parameter Analysis of Fdm-Ppm  
According to Equation (3), the maximum number of bits an FDM-PPM signal can represent is closely related to the length of the time slots and the number of subcarriers. When the number of subcarriers is held constant, increasing the number of time slots in the signal raises the maximum number of bits the signal can represent. Conversely, if the number of time slots remains unchanged, increasing the number of subcarriers can also enhance the maximum number of bits the signal can represent. The following discussion will delve into the specific impact of FDM-PPM signal parameters on improving the communication rate based on the modulation principle of the proposed method. Figure 12a illustrates the trend of how changes in FDM-PPM signal parameters affect the number of bits.  
Figure 12a demonstrates a close relationship between the number of bits in the signal and the number of time slots and subcarriers. Using a specific example, the following discussion explores the trend of another parameter's influence on the number of bits when the numbers of subcarriers and time slots are fixed. Figure 12b shows the trend of how the number of time slots affects the number of representable bits when the number of subcarriers is fixed at 2. It is evident from the figure that the increase in the number of bits is more pronounced when the number of time slots is low. However, as the number of time slots increases, the growth rate of the number of bits gradually slows down. This is because the interval between signal pulses narrows with an increase in time slots, which  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of** & **Number of** & **Number of Correctly** & **Demodulation** \\
**Symbols** & **Transmitted Bits** & **Demodulated Bits** & **Accuracy** \\ \hline
2 & 60 & 60 & 100\% \\
3 & 90 & 70 & 77\% \\
4 & 140 & 140 & 100\% \\
5 & 160 & 140 & 87.5\% \\
6 & 240 & 220 & 91.6\% \\
7 & 280 & 250 & 89.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The demodulation result of the Lamb wave FDM signals.  
may adversely affect the accurate demodulation of the transmitted information. Figure 12c illustrates the trend of how the number of subcarriers affects the number of representable bits when the number of time slots is fixed at 4. The figure shows that the number of bits increases approximately linearly with the number of subcarriers.
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 3 The Proposed Scheme
### User Registration Phase  
The user performs the following steps to be a legal user through a secure channel.  
Step 1: The user \(U_{i}\) freely selects the identity \(id_{i}\) and password \(pw_{i}\), and sends the registration request message \(\{id_{i},\ h(id_{i}\parallel pw_{i})\}\) to the \(GWN_{k}\) through a secure channel.  
Step 2: After receiving the registration request message, the \(GWN_{k}\) computes \(M_{1}=h(id_{i}\parallel S_{K})\bigoplus h(id_{i}\parallel pw_{i})\), where \(x\in(-\infty,+\infty)\). Then, the \(GWN_{k}\) stores \(\{h(.)\), \(M_{0},\ M_{1}\}\) in a smart card (SC) and safely issues the SC to the user \(U_{i}\).  
Step 3: The user \(U_{i}\) computes \(M_{3}=h(id_{i}\parallel M_{0}\parallel pw_{i})modn_{0}\), where \(n_{0}\in\big{(}2^{4},2^{8}\big{)}\), and stores \(\{h(.)\), \(M_{0},\ M_{1},M_{3},n_{0}\}\)  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Description** \\ \hline \(U_{i}\) & The \(i^{th}\) user \\ \(GWN_{k}\) & The \(k^{th}\) gateway node \\ \(Sn_{j}\) & The \(j^{th}\) sensor node \\ \(SC\) & Smart card of user \(U_{i}\) \\ \(id_{i}\), \(pw_{i}\) & Identity and password of \(U_{i}\), respectively \\ \(Gid_{k}\) & Identity of the \(k^{th}\) gateway node \\ \(S_{K}\) & Secret key of the \(GWN_{k}\) \\ \(Sid_{j}\) & Identity of the \(j^{th}\) sensor node \\ \(S_{j}\) & Secret key of the \(Sn_{j}\) \\ \(rug_{j}\), \(ru\) & Two random numbers selected by \(U_{i}\) \\ \(r\) & The random number selected by \(GWN_{k}\) \\ \(T_{i}\) & Timestamp \\ \(\Delta T\) & The allowable maximum transmission time interval \\ \(M_{1}\parallel M_{2}\) & Data \(M_{1}\) concatenates with data \(M_{2}\) \\ \(M_{1}\bigoplus M_{2}\) & XOR operation of \(M_{1}\) and \(M_{2}\) \\ \(h(.)\) & A secure one-way hash function \\ \(E_{k}(.)/D_{k}(.)\) & Symmetric encryption/decryption using the key \(k\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.
# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
###### Abstract  
Research in mobile robotics requires fully operative autonomous systems to test and compare algorithms in real-world conditions. However, the implementation of such systems remains to be a highly time-consuming process. In this work, we present an robot operating system (ROS)-based navigation framework that allows the generation of new autonomous navigation applications in a fast and simple way. Our framework provides a powerful basic structure based on abstraction levels that ease the implementation of minimal solutions with all the functionalities required to implement a whole autonomous system. This approach helps to keep the focus in any sub-problem of interest (i.g. localization or control) while permitting to carry out experimental tests in the context of a complete application. To show the validity of the proposed framework we implement an autonomous navigation system for a ground robot using a localization module that fuses global navigation satellite system (GNSS) positioning and Monte Carlo localization by means of a Kalman filter. Experimental tests are performed in two different outdoor environments, over more than twenty kilometers. All the developed software is available in a GitHub repository.  
Keywords:autonomous navigation; mobile robots; Monte Carlo localization; SLAM; GNSS; planning; control; Kalman filter +
Footnote †: journal: Applied Sciences
## 4 Performance Evaluation
### Metrics for Evaluation  
In order to guarantee a thorough analysis, we developed a set of comparative metrics that enable us to evaluate the resource management algorithms' performance under defined parameters. These parameters include the following, based on [28]:  
1. Power consumption.
2. Memory utilization.
3. CPU utilization.
4. Disk utilization.
5. Latency.  
Metrics such as CPU load, RAM load, and disk utilization offer valuable information on how algorithms efficiently manage and employ available resources, guaranteeing peak performance with these resource-limited scenarios. In edge environments, where power supplies may be scarce or costly, energy conservation is essential. Evaluating power consumption allows us to identify algorithms that minimize energy usage while maintaining performance, promoting sustainability and cost-effectiveness.  
In real-time or low-latency applications in particular, edge services' responsiveness is essential. Insights into the latency at which services can react to requests are provided by latency metrics, guaranteeing an excellent user experience. Network circumstances and workload variations are common in edge situations.  
We can determine which algorithms perform well in edge cloud environments by assessing resource usage, energy efficiency, service quality, and scalability. This allows us to determine which algorithms are performing best to solve the various needs and challenges associated with edge computing and systematically and consistently perform the task for which they were created.  
We seek to offer insights into the efficacy, scalability, and efficiency of each strategy for managing computational resources in edge cloud environments by comparing these metrics in the various methods.