esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Single Link System: Hybrid ARQ  
This paper focuses on the optimization of HARQ protocols, abstracting some of the other practical complexities that are present in real world communication networks. For example, the paper assumes perfect synchronization between all the nodes and error-free, albeit limited-capacity, feedback links. Feedback links are assumed to offer no more than one bit of feedback per packet, allowing for 256 possible responses to a bundle of eight packets, for instance. However, most of the proposed HARQ strategies do not require that many feedback messages, so the required number of feedback bits can be lower.  
It is also assumed that the receiver can request as many rounds of incremental redundancy as needed until the whole bundle is successfully decoded. Each round is penalized with an adjustable overhead cost of \(c_{R}\) per link plus a decoding cost of \(c_{D}\) for each codeword for which decoding is attempted.
## 3 Results
### Adsorption Performance of MIPs and NIPs
#### 3.3.3 Adsorption Isotherm Analysis  
To further investigate the binding performance of MIPs and NIPs, static adsorption data were collected at initial concentrations of \(L\). _monocytogenes_ ranging from \(7.5\times 10^{1}\) to \(7.5\times 10^{5}\) CFU mL\({}^{-1}\). Figure 5b shows the adsorption isotherm curve: the binding capacity increased with increasing initial bacteria concentrations and the MIPs exhibited a significantly higher adsorption capacity than that of the NIPs across the tested concentration range (\(p<0.05\)). These results were mainly due to the specific adsorption of imprinted sites, leading to superior adsorption of \(L\). _monocytogenes_. A trace of bacteria was absorbed on the NIPs simply based on non-specific adsorption. At an initial concentration of \(3.8\times 10^{4}\) CFU mL\({}^{-1}\), the adsorption capacities of the MIPs (355.6 CFU mg\({}^{-1}\)) were almost 4.57-fold that of the NIPs (77.8 CFU mg\({}^{-1}\)), while the imprinting factor (IF) value reduced to 2.04 when the initial concentration increased to \(7.6\times 10^{5}\) CFU mL\({}^{-1}\). At high concentrations, the surface-bound bacteria were able to attract more bacteria cells to self-assemble and aggregated as a membrane-bound cluster on the surface polymer beads, leading to an increase in non-specific adsorption.  
Figure 5: **(a)** Kinetic data of MIPs and NIPs, **(b)** static isotherm curve of MIPs and NIPs, and **(c)** the selectivity adsorption of MIPs and NIPs on four bacteria mixing solutions.
# The Influence of Processing on the Bioactive Compounds of Small Berries
## 5 Health Implications and Allergic Reactions to Small Berries
### Diabetes Management  
The polyphenic compounds from berries were reported to be promising in type 2 diabetes mellitus management [138]. In vivo studies involving animals and an in vitro investigation on model cell culture suggested that the polyphenols (especially C3G) found in berries may help in maintaining glucose homeostasis by protecting injured pancreatic \(\beta\)-cells, which are responsible for insulin release, against oxidative stress [138]. In this respect, various studies reported positive results provided by maqui berry (_Aristotelia chilensis_), black chokeberry (_Aronia melanocarpa_ L.), and goji (_Lycium barbarum_) extracts and other berries [139; 140; 141].
# Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO
## 3 Design of the Speed Controller Based on Improved SMC
### Controller Stability Proof Analysis of New Approach Law Characteristics  
The common second-order system is considered as an example in MATLAB to conduct simulation verification and analysis on the traditional and new improved convergence laws. The controlled object is set as [37]:  
\[\dot{\theta}=-f(\theta,t)+bu(t) \tag{27}\]  
where \(f(\theta,t)=25\dot{\theta}\); \(b=133\); and \(u(t)\) is the controller. The sliding form surface is as follows:  
\[s=15e+\dot{e} \tag{28}\]  
After taking the two convergence laws \(u\) and \(u_{2}^{\prime}\) separately, we obtain:  
\[u_{3}(t)=[\xi\text{sgn}(s)+qs+15\dot{e}+\dot{\theta}^{*}+f(\theta,t)]/b \tag{29}\]  
Figure 4: Characteristic curve under the u\({}_{3}\)(t) control law. (**a**) Angle tracking. (**b**) Angular velocity tracking. (**c**) Control input. (**d**) Phase trajectory.  
Figure 5: Cont.  
The results show that there is minimal difference between the controllers of the convergence law design in terms of the tracking angle and angular velocity by comparing and analyzing Figures 4a,b and 5a,b. The controller control inputs in Figures 4c and 5c clearly show that the controller with the improved convergence law design is much better. Simultaneously, according to the phase trajectory comparisons in Figures 4d and 5d, we determine that the improved exponential convergence law greatly reduces the convergence time, can converge to the slip surface quickly, and converge to the equilibrium point. Therefore, according to the simulation verification of the improved convergence law of the SMC compared with the traditional SMC output chattering reduction, chattering is significantly reduced.
###### Abstract  
In the present study, the performance of the moment method, in terms of accuracy and computational efficiency, was evaluated at both the macro- and microscopic levels. Three different types of non-equilibrium gas flows, including the force-driven Poiseuille flow, lid-driven and thermally induced cavity flows, were simulated in the slip and transition regimes. Choosing the flow fields obtained from the Boltzmann model equation as the benchmark, the accuracy and validation of Navier-Stokes-Fourier (NSF), regularized 13 (R13) and regularized 26 (R26) equations were explored at the macroscopic level. Meanwhile, we reconstructed the velocity distribution functions (VDFs) using the Hermite polynomials with different-order of molecular velocity moments, and compared them with the Boltzmann solutions at the microscopic level. Moreover, we developed a kinetic criterion to indirectly assess the errors of the reconstructed VDFs. The results have shown that the R13 and R26 moment methods can be faithfully used for non-equilibrium rarefied gas flows in the slip and transition regimes. However, as indicated from the thermally induced case, all of the reconstructed VDFs are still very close to the equilibrium state, and none of them can reproduce the accurate VDF profile when the Knudsen number is above 0.5.  
Keywords:R13 moment method; R26 moment method; slip regime; transition regime; discrete velocity method +
Footnote †: journal: Applied
# Determination of Axial Force in Tie Rods of Historical Buildings Using the Model-Updating Technique
## 4 Case Study Using the Proposed Methodology
### Numerical Simulation
#### 4.3.2 Updated Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & **2B-C** & \multicolumn{2}{c}{**3B-C**} & \multicolumn{2}{c}{**4B-C**} & \multicolumn{2}{c}{**SB-C**} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Mode** \\ **Num.** \\ \end{tabular} } & \multirow{2}{*}{\(\kappa\)} & \(\mathbf{P_{n}}\) & \(\mathbf{\sigma_{n}}\) & \(\mathbf{P_{n}}\) & \(\mathbf{\sigma_{n}}\) & \(\mathbf{P_{n}}\) & \(\mathbf{\sigma_{n}}\) & \(\mathbf{P_{n}}\) & \(\mathbf{\sigma_{n}}\) \\  & & **(dN)** & **(MPa)** & **(RN)** & **(MPa)** & **(RN)** & **(MPa)** & **(RN)** & **(MPa)** \\ \hline \(1\) & 3.534 & 122.8 & 33.0 & 170.8 & 54.5 & 166.3 & 53.0 & 215.2 & 59.8 \\ \(2\) & 6.777 & 137.2 & 36.9 & 188.7 & 60.2 & 208.1 & 66.4 & 219.6 & 61.0 \\ \hline \multicolumn{8}{c}{**Mean values**} & \multicolumn{1}{c}{130.0} & 34.9 & 179.8 & 57.3 & 187.2 & 59.7 & 217.4 & 60.4 \\ \hline \end{tabular}
\end{table} 
Table 6: The value of forces \(\mathrm{P_{n}}\) (RN) and stress levels \(\mathrm{\sigma_{n}}\) (MPa) for the observed mode shapes in the tie rods observed in the Cathedral of St. James in Sibenik at level R4. Figure 13: Change of force values depending on the ratio of numerical and experimental frequency values for two mode shapes (\(\mathrm{P_{1}}\), \(\mathrm{P_{2}}\)). \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Mode Number**} & \(\mathbf{f_{n}^{sum}}\) & \(\mathbf{P}\) & \(\mathbf{\sigma}\) & \(\mathbf{\kappa}\) \\  & **(Hz)** & **(RN)** & **(MPa)** & **(MPa)** \\ \hline \(1\) & 6.94 & 122.8 & 33.0 & 3.534 \\ \(2\) & 17.5 & 137.2 & 36.9 & 6.777 \\ \hline \end{tabular}
\end{table} 
Table 5: The force values read for the first two mode shapes and the value of coefficient \(\kappa\) determined by the calculation of the updated model. The shown stress levels were determined as the arithmetic values of the stress determined for the first two mode shapes (Table 6). Based on Figure 14, we concluded that the stress levels in the observed tie rods at level R4 in the Cathedral of St. James are far below the yield strength. Similar values were presented in study [28].
# Towards Strengthening the Resilience of IoV Networks--A Trust Management Perspective
## 3 Trust Management in the Internet of Vehicles
### Trust Models
#### 3.2.1 Classification of Trust Models
In [34] CTM inherits the advantages and disadvantages of ETM and DTM. It not only needs to build a complete trust relationship between vehicles but also needs to be able to evaluate the credibility of each data point. Compared with the former two trust models, the trust evaluation process is more complicated, and the system overhead will be higher. At the same time, this model also has some limitations, such as sparse data. The main function of the trust model is to transmit reliable, true, and effective information between V2X. There have been many articles about different trust models, as demonstrated in Table 3. A new context-based trust assessment and management framework was proposed in [19]. The authors tested five parameters of three trust models, EOTM, DOTM, and HTM, in four different contexts, and the four contexts were divided into mobility and attack types. This paper mainly considers the QoS and security of the system, tests some practical evaluation criteria, and uses the actual scenario of OpenStreetMapto set up different scenarios in urban and rural areas. In [35], the authors put forward a task-based experience reputation (TER) framework, in which the fog node is used as the memory to store the updated trust value, and the authors also proposed two reputation-updating methods, which were compared by simulation experiments. Finally, this model was evaluated by using the overhead and workload of information transmission. In [36], the authors developed a quantifiable trust evaluation model based on machine learning. This paper focused on how to use the combination of trust parameters to find the optimal trust boundary so as to distinguish between trusted nodes and untrusted nodes. In [37], the authors put forward a risk-based trust evaluation advanced model (RTEAM). The model integrates risk into the trust model by calculating the possibility of performing incorrect behavior and the impact of such a possibility. This model was evaluated by UND and TPR, and other trust models were compared. In [38], the authors proposed a distributed trust model based on recommendation, which detected non-stable malicious behaviors. The model evaluation used two parameters, PDR and network throughput, which were compared with some existing trust models in the testing process, especially with regard to improving PDR. In [39], the authors put forward a new trust model, REK, which was used to simulate the process of human perception in the SIoT environment.
# Internal Interface Diversification as a Security Measure in Sensor Networks +
## 2 Interface Diversification
### Attack Scenarios Mitigated by Diversification
#### 2.2.4 Compromising Node's Integrity  
Attackers may also aim to attack a sensor in order to change the data that has been collected or to compromise the integrity of the sensor's software. Internal interface diversification can be used to supplement the security measures related to node integrity. For example, remote attestation [9] is a mechanism usually used to measure the internal state of a sensor node. The resulting information is reported to a remote verifier so that the node's integrity can be checked. However, the drawback of the method is that it concentrates on executable files during the loading phase but usually does not prevent runtime attacks. Therefore, extra protection for runtime attacks, such as those that exploit buffer overflow vulnerabilities, is needed. In a buffer overflow, a program accidentally overruns the buffer's boundaries and overwrites the data in memory. This vulnerability can be exploited by writing malicious code into areas where executable code is held.  
In a diversified system, however, the harmful code would use the wrong (undiversified) functions or system calls and as a result could not run successfully. Verification of the integrity of the static boot time system would not be able to catch attacks of the kind that exploit vulnerabilities in trusted pre-approved binaries. In contrast, diversification can protect against attacks that work by introducing new code into a vulnerable process. The attacker would still be able to inject code, but the code would likely not function within the context of the diversified system. In this way, we view diversification as an important protection mechanism to be used in addition to other forms of defense. Internal interface diversification can also act as a replacement for other runtime protection mechanisms, such as data execution prevention (DEP), that have not necessarily been implemented in many IoT operating systems.
## 3 Results  
### Descriptive Statistics  
The descriptive statistics for the U14 experimental group in the (It) and (Ft) (Table 1) show that in the T agility test, initially, the mean time was 12.35 s with a standard deviation of 1.22 s, showing a moderate variability in performance. The final mean improved to 10.98 s, with a reduced standard deviation of 0.49 s, indicating more consistent performances post-training. The coefficient of variation (CV), which represents the ratio of the standard deviation to the mean, decreased from 9.88% to 4.46%, further evidencing improved consistency. In the T agility test with ball, the initial mean time was 17.13 s and the final mean was 13.61 s. The standard deviation decreased from 2.12 to 0.90, and the CV reduced from 12.38% to 6.61%, indicating enhanced performance and consistency in the final test. The trend of improved mean scores and reduced variability is also evident in other tests for the U14 experimental group, such as the T reactive agility test (initial mean: 14.28 s; final mean: 12.23 s); T reactive agility test with ball (initial mean: 19.61 s; final mean: 15.79 s); the Illinois agility test (initial mean: 17.83 s; final mean: 16.02 s); and the Illinois agility test with ball (initial mean: 20.41 s; final mean: 17.08 s). For the U14 control group, similar agility tests show less pronounced improvements. For the T agility  
Figure 3: Gant chart about experimental training program. test, the initial mean was 13.32 s, and the final mean was 13.09 s, with a slight increase in the CV from 7.36% to 8.10%. The Illinois agility test shows a similar pattern, with a modest decrease in mean time from 19.00 to 18.68 s, and a slight reduction in CV. Overall, the U14 experimental group shows more significant improvements in mean times and consistency (lower standard deviation and CV) across all agility tests compared to the U14 control group. This suggests that the training program of the experimental group was effective in enhancing agility and reactive agility (Table 1). In Table 2, descriptive statistics for the U16 experimental and control groups in both initial (It) and final (Ft) phases of different agility tests are detailed. For the U16 experimental group, improvements are seen in agility test performances from the initial to the final phase. In the T agility test, the initial mean time was 12.41 s with a standard deviation of 1.35, and this improved to a final mean of 11.08 s with a standard deviation of 0.76. The coefficient of variation (CV) also decreased from 10.88% to 6.86%, indicating increased consistency in performances. In the T agility test with a ball, the initial mean was 14.02 s, which improved to 12.25 s in the final test, with a decrease in standard deviation from 2.23 to 1.23 and a reduction in CV from 15.91% to 10.04%. Similar patterns of improvement are observed in the other tests like the T reactive agility test (initial mean: 13.77 s; final mean: 12.03 s), T reactive agility test with ball (initial mean: 15.99 s; final mean: 13.01 s), Illinois agility test (initial mean: 19.66 s; final mean: 18.45 s), and the Illinois agility test with ball
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 3 Sensor Localization  
Assigning a defined position to an autonomous moving object, known as a target node, in relation to a known reference--that is, a collection of spatial coordinates, such as Cartesian coordinates--that clearly indicates the latter's location in the system reference is the aim of a localization (or positioning) system. It is implied that a precision value related to the position is also stated, and that this precision is connected to the measurement error's propagation. The precision of a localization system is thus characterized by the size at which the system is offered. The ordered historical sequence of successive positions of a target is then configured as tracking of the object and benefits from many other results borrowed from other disciplines. In general, tracking requires the observation of a subset of physical quantities.  
This chapter is dedicated to positioning principles, which are typically applied when what is being observed and measured is a radio frequency waveform. Although there are other quantities that can be monitored to determine the position, those based on the same equipment used for radio communication have the undoubted advantage of not having to duplicate the sensors and are therefore particularly suited to the modern paradigm of the Internet of Things and home automation.  
There are two possible configurations for the nodes in a WSN: (i) methodologically: the nodes' coordinates can be encoded into the devices, defining a reference system that is known a priori and (ii) randomly: the nodes must be able to determine their own position automatically.  
The fact that the exact location of sensors is not always known beforehand should serve as a helpful reminder. If the position is unknown, the data obtained will be useless since, even if the target's position were predicted, the position would be determined using an imprecise reference system.
# On Ontology-Based Tourist Knowledge Representation and Recommendation
## 4 Prototype Implementation and System Evaluation
### Implementation Environment  
The technology used for implementing the prototype include an Intel CoreTM i5-760- 2.8 GHz PC, Microsoft Windows 7 Professional, Internet Information Services (IIS), Microsoft SQL Server 2012, and Microsoft Visual Studio 2010. Figure 18 shows the tourist knowledge structure-based tourist attraction recommendation mechanism framework, which includes three layers of user interface, mechanism operation, and tourist knowledge repository, as explained below.  
1. User interface layer: The users of the program are travelers. The system provides two major functions: tourist attraction reasoning and tour planning. With regard to the former, after the travelers input their location, the program uses the direct and indirect relations to recommend nearby tourist attraction. For tour planning, the travelers first decide the starting and final tourist attractions. The program then uses the tourist knowledge ontology to find attractions between these two points.
2. Mechanism operation layer: There are five modules in this mechanism, as follows: application module, tourist attraction evaluation module, tourist knowledge ontology construction module, and tourist attraction recommendation module.  
* Application module: This includes the tourist reasoning application component, tour planning application component, and tourist knowledge structure loading component.
* Tourist attraction evaluation module: The analysis result will be the source of tourist attraction evaluation.
* Tourist knowledge ontology construction module: This includes a concept set generation component, hierarchy relationship generation component, and distance calculation component.
* Tourist attraction recommendation module: This includes the tourist attraction matching component, tourist attraction reasoning component, tour planning component, and tourist attraction sorting component.
3. Tourist knowledge repository layer: This includes the following five repositories: OTR content repository, tourist attraction name repository, positive appraisal word repository, negative appraisal word repository, and tourist knowledge ontology (OWL) repository. The OTR content repository stores OTR content. The tourist attraction name repository stores tourist attraction names. The positive appraisal word repository stores positive appraisal words related to tourist attractions, while the negative appraisal word repository stores negative words. The tourist knowledge ontology (OWL) repository stores the relationships and attributes among nodes in the tourist knowledge ontology.
## 2 Islanding Detection Methods
### Remote Methods
#### 2.4.3 Supervisory Control and Data Acquisition Method (SCADA)  
In this method, the auxiliary contacts of the main grid circuit breakers are monitored to check the condition of the islanding operation. In the case of islanding, tripping signals will be sent by the SCADA system to the corresponding circuit breakers to be disconnected [33]. The effectiveness of this method for islanding detection is high, but it is expensive and requires many sensors, instruments, devices, and communication links in multiple inverters systems, and so is not considered proper for small-scale systems. Moreover, when the system has some disturbances, the detection speed of this method can be slow.
# Design of MIMO Antenna with Double L-Shaped Structure for 5G NR
## 2 Design of Proposed MIMO Antenna
### Current Distribution  
In MIMO antenna designs, antenna elements are usually placed farther away from each other, or decoupling structures are introduced, to avoid the inter-element coupling,  
Figure 1: Geometry of the proposed MIMO antenna system. (**a**) Perspective view. (**b**) Detailed structure of the slot antenna element (taking Ant1 as an example).  
Figure 2: Design evolution of the antenna element structure. (**a**) The structure of the reference and proposed antenna. (**b**) The comparison of the reflection coefficient between the reference and proposed antenna elements.  
Alternatively, a low coupling effect is achieved in the proposed MIMO design by simply yet rationally adjusting the layout of antenna elements to mitigate the need for additional decoupling structure. Figure 3 shows the current distribution of Ant1 when excited at 4.4 GHz, and the current distribution on the ground of the antenna visually illustrates the coupling effect between the antennas. The half side of the T-shaped slot, which has a length of 11 mm and is nearly one-third of the 4.4 GHz wavelength, is where the strong current is concentrated when the slot antenna runs at 4.4 GHz. This shows that the antenna functions in 0.33 resonant mode at 4.4 GHz (corresponding to 4.4 GHz wavelength). When one antenna element is activated, the current does not propagate to the surrounding antenna elements, according to the illustration. Essentially, the coupling between the antenna elements is reduced. In this design, by reducing the size of individual antenna elements, the distance between the antennas is increased, yielding high isolation.