esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Two-Hop mmWave Relay Nodes over the 5G NR Uplink Signal
## 4 Simulation Results and Discussion  
The obtained results from the simulator are explained in this section. Therefore, to evaluate the validity of the two-hop uplink mmWave MIMO RN co-operative network through the proposed link-level co-operative simulator described in this paper, Monte-Carlo simulations are performed for each signal-to-noise ratio (SNR) transmission and \(10^{3}\) frames, where the relaying protocols process the uplink received signal and forward it to the destination. Link-level simulations are generally used to simulate end-to-end physical layer technologies, where there is one base station, and consider an accurate scenario model to emulate channels and propagation losses. Furthermore, in each iteration, independent sets of channel realizations and noise are used. For the simulations, only one gNodeB, one new radio relay node, and one NR-UE are considered. In the section, the key performance indicators (KPIs), bit error rate (BER) and throughput versus SNR of the proposed two-hop uplink relay co-operative system, have been assessed. We consider a network encompassing one NR-UE equipped with \(\rm{N_{T}^{S}}=\{1,2\}\) transmitter antennas, and transmitting 64-QAM and 256-QAM modulation schemes. The RN (A&F or D&F) is equipped with \(\rm{N_{R}^{R}}=\rm{N_{T}^{R}}=\{1,2,4\}\) receiver and transmitter antennas. Additionally, the gNodeB is carried out using \(\rm{N_{R}^{D}}=\{1,2,4\}\) receiver antennas. Note that, when performing MIMO simulations, you will have as many transmitter and receiver chains as the number of antennas used in the simulation. This process is then repeated iteratively, as described above, to obtain more accurate results. In Table 3, the simulation parameters are summarized. Two types of NR co-operative relay nodes are considered: A&F and D&F. In the case of the NR D&F relay node, two situations are developed: (a) an exact knowledge of the channel (PCE) is available and (b) an estimation (LS) of it is made. Furthermore, we have denoted \(\rm{N_{T}^{S}}\times\rm{N_{R}^{R}}=\rm{N_{T}^{R}}\times\rm{N_{R}^{D}}\), like the considered MIMO scheme in our uplink mmWave co-operative network.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 6 Hybrid Trust Value Based on Weighted Vehicle Behavior
### Average Total Trust Value at the Cluster Head Layer  
In dynamic vehicle clusters, vehicles periodically send a list of total trust values \({T_{tot}}(v_{i})\) of their neighbor vehicles to the cluster head vehicle. The cluster head vehicle calculates the average total trust value of its cluster member vehicles, incorporating the association relationship between the vehicle \(v_{j}\) and the cluster as a weight in the trust assessment. Vehicles more closely associated with the vehicle cluster are considered more reliable in terms of the total trust values list they provide. Assuming the vehicle cluster is \(C_{i}\) with the cluster head \(CH_{i}\), and the cluster member vehicles are \(v_{i},v_{j}\), where \(j=[1,n]\). Based on mining the relationship between vehicle nodes and vehicle clusters, the association weight \(\omega_{C_{i}j}\) between \(v_{j}\) and \(C_{1}\) is calculated as follows:  
\[\omega_{C_{i}j}=Rv_{j}(C_{1})=\frac{\left|{e_{j-n}\cap C_{1}}\right|}{\left| C_{1}\right|} \tag{16}\]  
The expression for the average total trust value of vehicle \(v_{i}\) in vehicle cluster \(C_{1}\) is as follows:  
\[{T_{totm}}(v_{i})=\frac{\sum_{j=1}^{n}\omega_{C_{i}j}T_{tot}^{j}(v_{i})}{n} \tag{17}\]  
where \({T_{totm}}(v_{i})\) is the average total trust value of \(v_{i}\), \(n\) is the number of times the total trust value of vehicle \(v_{i}\) appears in the cluster head's database, and \(T_{tot}^{j}(v_{i})\) is the total trust value of vehicle \(v_{i}\) calculated by vehicle \(v_{j}\).
## 3 Computational Examples and Analysis
### Case Study III: Optimization with Four Objectives
#### 3.3.1 Data Description  
The rubber and basalt fibre modified concrete was also optimized by using RSM. Based on the Box Behnken design, the experiment with three independent factors at three experimental levels was designed. The preparation parameters are regarded as the water-binder ratio (X\({}_{1}\)) and basalt fibre content (X\({}_{2}\)) as well as rubber content (X\({}_{3}\)); and slump (Y\({}_{1}\)), flexural strength (Y\({}_{2}\)) and compressive strength (Y\({}_{3}\)) represent response variables. The experimental design and outputs with 17 groups of rubber and basalt fibre-modified concretes are listed in detail in the study [54].
## 2 Methodology of Sensitivity Analysis  
To determine the relative significance of each uncertain input variable to the uncertainty of output values which are the so-called engineering design parameters (EDPs), two methods can be used: the First-Order Second-Moment (FOSM) method [7] and tornado diagram [4; 6; 8]. For the FOSM method, a FE model is used as the method to develop the performance function, and the response sensitivities can be computed using the finite difference approach by perturbing every variable and calculating the corresponding change in response through multiple deterministic analyses [9; 10]. The tornado diagram method is commonly used to indicate the sensitivity of uncertain output values in terms of \"swings\" which are displayed in the descending order of swing size on the horizontal bar charts [4; 7], as illustrated in Figure 2.  
The sources of uncertainties come from both aleatory (physical randomness) and epistemic (state of knowledge) uncertainties, which include applied load (e.g., recorded or artificial time-history ground motions), material properties, mass, and damping. The issue that relates to the partially restrained connections of the steel structure is not considered in this study because the information of the uncertainty of the joints (i.e., welded or bolted) is not fully studied and not reliable to be applied for the container crane. For this study, the output values (or EDPs) are assumed to involve a known deterministic function of a variety of input variables, in this case a finite element model that gives the structural responses from a set of input variables. Each input variable is defined by a probability distribution with median (best estimate) and coefficient of variation (COV). First, to determine the best estimate of the output variable (or to establish the baseline output of the tornado diagram), all the input variables are set to their medians. Then, one input is set twice, each time using one of the extreme values which can be the upper or lower bounds of its probability distribution, and the output is measured to yield the upper and lower bounds of the output results of each input variable [6]. The absolute difference between these two bounding values is referred to as the swing. The same process is repeated for the next input variables. Figure 2 shows the procedure of developing a tornado diagram. A larger swing reflects the significance of the input uncertainty, while a small swing of the EDP means that the uncertainty of the input variable is not notable; hence, it can be considered as a fixed variable (i.e., median value) to reduce the cost of numerical analysis in practice.
# Effects of the October 2024 Storm over the Global Ionosphere
## 1 Introduction
(2025) [18] described a reversed C-shaped depletion band stretching from the magnetic equator to auroral latitudes. This feature was driven by intensified vertical drift at the equator and zonal drift at higher latitudes, causing the polar irregularity region to extend into midlatitudes and merge with elongated equatorial plasma bubble (EPB) structures around \(\sim\)45\({}^{\circ}\) MLAT. They observed longitudinal asymmetry in EPB development: along South America's western coast (\(\sim\)0\({}^{\circ}\) MLON), EPB activity appeared at midlatitudes, while along the eastern coast (\(\sim\)20\({}^{\circ}\) MLON), it remained equator-bound. These patterns were linked to differences in electric field penetration efficiency due to seasonal changes in ionospheric conductivity. Additionally, DDEFs sustained elevated F-region altitudes and triggered rare post-sunrise EPBs. By incorporating a wide range of satellite and ground-based observations, the present analysis investigates the global ionospheric response to the 10-11 October 2024 geomagnetic storm. Using Constellation Observing System for Meteorology, Ionosphere, and Climate--2 (COSMIC--2) RO profiles, in situ Ne measurements from Swarm satellites, ion drift data from Digsondes, and global TEC maps, the analysis offers a comprehensive view of storm-time ionospheric dynamics, particularly over equatorial and midlatitude regions and at higher altitudes where significant plasma redistribution occurs. Additional validation from far-ultraviolet emissions from the GUVI instrument aboard TIMED supported the findings. The present article is organized into six sections. Section 1 provides an overview of existing literature on both May and October 2024 geomagnetic storms, offering essential context and comparative insight into the characteristics, impact, and underlying dynamics of these two significant space weather events. Section 2 describes the data sources and the methodology. Section 3 examines the solar and geomagnetic activity associated with the event. Section 4 presents the results, followed by Section 5, which discusses the findings in the context of physical processes driving ionospheric variability. Finally, Section 6 summarizes the key observations and insights.
## 2 Proposed MOT Method  
The multi-object tracking algorithm designed in this paper, as shown in Figure 1, consists of three parts: (1) prediction, loading detections, and extracting features, (2) similarity measurement, and (3) data association. Given the detections in frame t, we first extract the appearance feature of each object using the designed CNN, and use a Kalman filter [16] to predict its motion information. Then, similar to the above, we load the appearance features of the existing trajectories in the buffer, and use a Kalman filter to predict their motion information. After that, the similarity analysis between detections in frame t and the trajectories in frame t-1 is carried out from the aspect of appearance and motion. According to the measurement results, we employ our proposed matching strategy to associate the trajectories and detections so that the new trajectories in frame t can be obtained. In the following sections, we will discuss the details of each part of the algorithm.
## 4 Proposed Architecture
### Perception Layer  
The Perception Layer comprises wearable medical devices equipped with physiological sensors capable of monitoring vital signs such as heart rate, body temperature, and blood oxygen saturation. The IoT node handles both sensing and computation tasks, performing lightweight encryption on collected data using ciphers suitable for constrained environments, such as ChaCha20 or Salsa20. Once encrypted, the data is transmitted wirelessly using MQTT over TLS, with the microcontroller acting as the MQTT publisher. This direct publishing model eliminates the need for a separate edge gateway, reducing system complexity and improving responsiveness.  
To enhance system robustness in case of local broker failure, edge device publishers implement automatic reconnection logic. Upon detecting a disconnection, the MQTT client on the publisher side iteratively attempts to reconnect to a predefined list of alternative  
Figure 2: End-to-end secure IoT architecture for e-health applications using layered encryption and multi-broker MQTT.  
local brokers (e.g., covering different floors or subnets within the hospital). This fallback list is configured during the device provisioning phase.
## 3 Proposed Scheme
### CRC Interleaving Algorithm  
The CRC sequence in the CRC encoding stage is interleaved to improve the reliability of information transmission, that is, important bits are transmitted through a highly reliable channel. The sequence interleaving scheme performed is introduced below.
## 3 The Methodology
### Simulation Study
#### 4.1.3 Impact of Delay  
Most energy-saving solutions fail to reduce delay, as has been explored in the literature. Delays in the proposed task are kept to a minimum with the use of optimal CH selection, security provision, and routing mechanisms. Figure 9 shows a comparison of network latency from end to end. According to the findings, the suggested work outperforms prior studies in the field. This study demonstrates that minimizing end-to-end latency cannot be achieved by only cutting energy use. The inefficient information transmission of the EIR-CIoT approach causes a significant delay of up to 5 s. The proposed work has latency of 1 second in an environment of fifty packages per second, and it is much less latency than the EIR-CIoT approach. In addition, the EIR-CIoT approach is narrowly focused on RSS-based routing at the expense of other crucial factors. But with the help of authentication, appropriate CH selection, routing, and packet validation at intermediate nodes, the suggested approach improves the network's overall performance. As a result, the proposed project achieves a shorter duration of execution than that of similar studies.
## 3 Multi-Sensor Fusion
### Tight Coupling of Dual Lidar and IMU
#### 3.4.1 External Parameter Calibration of Horizontal Lidar and IMU and Time Synchronization  
Similar to Section 3.3, the external parameters of the lidar and IMU are also obtained based on the CAD model. To accurately fuse data from the horizontal lidar, the vertical lidar, and the IMU, the IMU coordinate system \(\mathbf{I}\) is also registered in this frame coordinate system \(\mathbf{L}_{1}\) through the rotation matrix \(\mathbb{R}_{\mathbf{I}}^{\mathbf{L}_{1}}\) and the translation matrix \(\mathbf{H}_{\mathbf{I}}^{\mathbf{L}_{1}}\). It can be seen from the structure of the scanning arm in Figure 1 that the IMU is located directly under the horizontal lidar. Therefore, the IMU coordinate system can be registered in the horizontal lidar coordinate system only by translation, where  
\[\mathbb{R}_{\mathbf{I}}^{\mathbf{L}_{1}}=\left[\begin{array}{ccc}1&0&0\\ 0&1&0\\ 0&0&1\end{array}\right] \tag{7}\]  
where the translation matrix \(\mathbf{H}_{\mathbf{I}}^{\mathbf{L}_{1}}=[-0.62.66,0,-0.125]\).  
In this paper, YISS10A IMU is used, which supports trigger correction of its internal clock by PPS signal. Lidar-IMU time synchronization is achieved through hardware time synchronization, with GNSS used as the master clock. GNSS can output PPS signals to unify the clock source of lidar-IMU and achieve time synchronization.
## 4 Joint Optimization Problem Formulation
### The Backhaul Overhead  
\[R_{BH}=I(\hat{\mathbf{y}}_{B};\mathbf{y}_{B})=\sum_{k=1}^{K}I(\hat{y}_{B,k};y_{B,k}) \leqslant C_{BH} \tag{8}\]  
Let \(\mathcal{R}=\{\mathbf{r}|\mathbf{r}\in\mathbf{R}_{+}^{\mathbf{K}},\mathbf{1}^{\mathbf{T}}\mathbf{r} \leqslant C_{BH}\}\) denotes the feasible set of all possible compression rates vector, where the \(k\)-th element \(r_{k}\) denoted as the compression rate used on the \(k\)-th subcarriers:  
\[r_{k}=I(\hat{y}_{B,k};y_{B,k}) \tag{9}\]  
Obviously, this rate vector can also be viewed as a backhaul resource allocation vector. We will use these two terms without distinction in the rest of the paper.
_Appl. Sci._**2013**, \(3\), 614-623; doi:10.3390/app3030614  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Communication_  
**Aqueous Microwave-Assisted Solid-Phase Synthesis Using Boc-Amino Acid Nanoparticles**  
**[PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON]**  
Faculty of Pharmaceutical Sciences & Cooperative Research Center of Life Sciences, Kobe Gakuin University, Chuo-ku, Kobe 650-8586, Japan; E-Mails: pjcdy075(@s.kobegakuin.ac.jp (N.S.);  
[EMAIL_ADDRESS] (Y.F.); [EMAIL_ADDRESS] (H.I.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +81-78-974-40055[PERSON]  
_Received: 13 May 2013; in revised form: 26 June 2013 / Accepted: 5 July 2013 / Published: 24 July 2013_  
**Abstract:** We have previously developed water-based microwave (MW)-assisted peptide synthesis using Fmoc-amino acid nanoparticles[PERSON] It is an organic solvent-free, environmentally friendly method for peptide synthesis. Here we describe water-based MW-assisted solid-phase synthesis using Boc-amino acid nanoparticles. The microwave irradiation allowed rapid solid-phase reaction of nanoparticle reactants on the resin in water. We also demonstrated the syntheses of Leu-enkephalin, Tyr-Gly-Gly-Phe-Leu-OH, and difficult sequence model peptide, Val-Ala-Val-Ala-Gly-OH, using our water-based MW-assisted protocol with Boc-amino acid nanoparticles.  
**Keywords:** aqueous synthesis; solid-phase peptide synthesis; microwave assisted synthesis; nanoparticles; Boc chemistry
## 3 Results and Discussion
### Co\({}_{2}\): WRF-Chem vs. Observations
The prevailing winds at this site may play a large role in this issue, as noted in [PERSON] et al. [40]; we explore this possibility in further detail in Section 3.4.2. While the total simulated CO\({}_{2}\) line without the urban heat island generally follows the corresponding line in the model that included the urban heat island effect, the match is not perfect. Table 6 shows a series of comparisons at each site for each analysis period to determine how well their performance compares to each other relative to the measured enhancements. This was achieved by first taking the mean value of the magnitudes (i.e., the absolute values) of the differences between either simulation and the measured enhancement value at a particular hour. The value in any given cell in Table 6 is the mean difference value from the simulation with the urban heat island effect minus the mean difference value from the simulation without the urban heat island effect. Because there were no viable data from the ERSA background station during Springtime 2017, this comparison cannot be made for that period. The results in Table 6 indicate that the simulation with the urban heat island turned off was generally better able to match the measured enhancements, i.e., there are more positive values. In both summer and winter, the simulation without UHI performed significantly better at the coastal SME station (by almost half of a ppm of CO\({}_{2}\) in the Summertime 2016 period); this may be due to the coastal position of that station and to the complexities of atmospheric dynamics, particularly in the evolution of the boundary layer caused by the unique topography of Marseille and its interaction with the different sea breeze conditions. This was detailed by [PERSON] et al. (2018), who found that the daytime evolution of the boundary layer over Marseille can become dominated by influences from the sea breezes rather than urban effects, such that a UHI module might be overemphasizing these dynamics and causing a mismatch with measurements. Additionally, it is worth noting that the site/period combinations with the most disagreement and the most agreement with respect to each other (SME in Summertime 2016 and CAV in Winter 2017, respectively) correspond with the worst and best site/period combinations for PBL agreement in Table 3.
# Personalized Daily Hand Movement Training Methods and Effects: A Case Study
## 2 Methods
### Experimental Design  
This experiment aims to improve the patient's hand function, happiness, confidence, and independent living abilities through Happiness Memory Therapy and Positive Creation Mirror Theory. The experiment includes the following three main activities: Leaf Collecting  
Figure 3: Happiness memory experience journey map.  
Game, Cooking Potato Chicken Nuggets, and Family Assistance Activity Game. The objectives, detailed process, and expected future impacts of the experiment were explained to the patient, and the experiment commenced after obtaining the patient's consent and cooperation.  
**Experiment 1: Leaf Collecting Game**  
Objective: Train the hand's pinching ability and improve fine motor skills.  
Activities:  
1. Finding Leaves: The patient searches for leaves from different trees outdoors, especially leaves with insect bites.
2. Picking Leaves: Use fingers to pinch and pick the leaves, placing them into a basket.
3. Making Bookmarks: After air-drying the collected leaves, make them into bookmarks.
4. Criteria for Success: Successfully picking a complete leaf (the affected thumb pad and the pad of the same-side index finger are able to perform a pinching action).  
**Experiment 2: Cooking Potato Chicken Nuggets**  
Objective: Train hand pressing and bilateral coordination abilities and boost the patient's confidence.  
Activities:  
1. Pressing Potatoes: The patient uses the affected hand to press potatoes.
2. Cutting: Use the healthy hand to cut the potatoes into pieces.
3. Cooking: Follow the patient's favorite recipe to make potato chicken nuggets.
4. Criteria for Success: Successfully preparing a complete dish.  
**Experiment 3: Family Assistance Activity Game**  
Objective: Train fine motor skills through daily life activities, enhance confidence, and promote independence.  
Activities:  
1. Household Activities: Actively identify and perform household chores, such as mopping, sweeping, drawing curtains, pouring tea, delivering napkins, distributing utensils, charging mobile phones, cleaning tables, etc.
2. Scoring System: Successfully helping earns 5 points, and being helped deducts 5 points, with each person starting at 100 points.
3. Game Rewards: At the end of the game, the person with the highest score can make a small, realizable request from the other participants.
4. Criteria for Success: Achieving a final score of over 50 points by the tester.  
Over a 30-day period, Experiments 1 and 3 were performed daily, with Experiment 3 continuing all day at the patient's request. Experiment 2 was conducted once a week, totaling four times.
## 4 System Model and Design Goals
### Design Goals  
There are five goals that need to be achieved in our model:  
* Maintain function privacy: The function ciphertext and public verification data cannot reveal any information about the function, so the encryption schemes that we use should be semantically secure so that no adversary can infer any information from the obtained ciphertext.
* Maintain input privacy. Similarly, the input ciphertext must not reveal information about the input. The encryption scheme for the input, thus, needs to be semantically secure.
* Maintain result privacy. No one should be able to obtain information about the result plaintext except for the request user. The result ciphertext cannot be distinguished from the random elements.
* Achieve public verification. Anyone can complete the verification with the publicly available information, and there is no malicious CSP that can persuade the public to accept an incorrect result with a non-negligible probability. We assume that the public has limited computational resources, and so verification must be efficient.
* Ensure the function owner's control over the function. CSPs cannot complete the computation if the function owner does not allow the user to use their function model.  
Figure 2: The publicly verifiable outsourcing computation model with privacy.