esa-sceva/satcom-chunk-collection huggingface dataset:
# Eight Element Wideband Antenna with Improved Isolation for 5G Mid Band Applications
## 5 Results and Discussion
### Channel Capacity  
The proposed eight-element MIMO system's channel capacity (CC) is calculated using the Shannon formula given in Equation (6) [4; 32].  
\[\mathrm{C}=\mathrm{klog}_{2}\bigg{[}\mathrm{det}\bigg{(}[\mathrm{I}]+\eta\frac{ \mathrm{SNR}}{\mathrm{k}}[\mathrm{H}]\Big{[}\mathrm{H}^{\star}\Big{]}\bigg{)} \bigg{]} \tag{6}\]  
Here, SNR represents the mean signal-to-noise ratio, \(I\) denotes the identity matrix, \([H]\) is the channel matrix, \(\big{[}H^{\star}\big{]}\) is the Hermitian transpose matrix, and \(\mathrm{k}\) is the rank of the \([H]\big{[}H^{\star}\big{]}\) matrix.  
MIMO channel models utilizing ray tracing and their associated channel models are normally used for calculating channel capacity [4]. The correlation matrix and the simulated efficiency of the modeled MIMO system were therefore utilized to calculate the channel capacity. Figure 25 shows that the estimated capacity of the eight-element MIMO antenna lies between 41.8 and 42.6 bps/Hz throughout the frequency of operation, which was estimated by norming over 10,000 distinct Rayleigh fading realizations concerning 20 dB SNR in an identically distributed propagation scenario [1; 4]. The optimal channel capacity of 8 \(\times\) 8 MIMO and single-input single-output (SISO) systems is provided for easier comparison. The estimated CC of 41.8-42.6 bps/Hz of the eight-element antenna is very close to the optimal capacity of 46 bps/Hz of 8 \(\times\) 8 MIMO systems, indicating that the performance is sufficient for satisfactory performance in real-time scenarios.
## 4 Effects of Cannabinoids on Gastrointestinal Motility
### Endocannabinoids
#### 4.1.3 Summary of Endocannabinoid Influence on Gastrointestinal Motility  
In summary, endocannabinoids and endocannabinoid-like compounds seem to exert mainly inhibitory effects on GIT contractility. In contrast, several studies have observed the opposite to be true. The majority of studies report activity at the CB1 and TRPV1 receptors for these compounds; however, some papers have found endocannabinoids to influence GIT motility via pathways that were not able to be fully classified, some pointing to possible involvement of lipoxygenase metabolites. A few research papers have also linked GIT endocannabinoid dysregulation to pathological states of the intestine, although the relationship is not yet clear. A summary of endocannabinoid influence on gastrointestinal motility is given in Table 2 and schematically shown in Figure 5.  
Figure 5: Distribution of receptors in the enteric nervous system (ENS) involved in regulating gastrointestinal motility by cannabinoids. AEA and phytocannabinoids influence cannabinoid receptors 1 (CB1) located on enteric neurons, inhibiting the release of acetylcholine (ACh) and muscle contractility [8; 71]. The role of CB2 receptors in the regulation of motility is significantly smaller and seems to be important in pathophysiological states such as inflammation [6]. AEA can influence gut contractility via a non-cannabinoid receptor pathway, through the transient receptor potential cation channel subfamily V member 1 (TRPV1) [50], located on the enteric neurons, and G protein-coupled receptor 55 (GPR55) [51], as a putative endocannabinoid receptor. CM—circular muscles, LM—longitudinal muscles, MP—myenteric plexus, SMP—submucosal plexus, VIP—vasoactive intestinal peptide, SP—substance P. The figure was created with BioRender.com, accessed on 29 April 2024.
# Reaction Forces and Apparent Thrust in Dual Oscillating Control Moment Gyroscopes
## 2 Materials and Methods
### Presentation of the Mechanical Set-Up  
We consider a cart supported by four wheels: two positioned at the front (as illustrated in Figure 2) and two at the rear (not shown). Two contra-rotating gyroscopes are mounted on the cart at symmetrically placed points, denoted as \(O_{1}\) and \(O_{2}\). The axle of each gyroscope is actively driven to oscillate (i.e., undergo forced precession) about its respective support point \(O_{1}\) and \(O_{2}\).  
Figure 2: Location of gyroscopes for polar angle: (**a**) \(\theta=0\) and (**b**) \(\theta>0\).  
This mechanical set-up follows the operating principle of a control moment gyroscope (CMG), a device widely used for attitude control in spacecraft. One possible miniature implementation is available at [[https://www.gyroscope.com](https://www.gyroscope.com)]([https://www.gyroscope.com](https://www.gyroscope.com)) (accessed on 17 July 2025). In simple terms, the change in the orientation of the axles--through forced precession--leads to a change in the system's angular momentum, thereby generating an internal torque (see the details in Section 2.4). In the case of a spaceborne application, where no external torques are present, this internal torque results in a reorientation of the entire spacecraft to conserve angular momentum. However, since the current device operates on the ground, it raises the important question of how this internal torque manifests itself in terms of ground reaction forces.  
In terms of construction, the spin of each gyroscope is maintained at a nearly constant magnitude by an electric motor (driver) mounted directly on the gimbal, as shown in Figure 3. The forced precession of each gyroscope is controlled by a dedicated servo motor, which enables synchronized and timed oscillations while the rotors spin under their respective motor drivers. Mechanically, the servo motors allow for a motion range of approximately \(\pm 90^{\circ}\), though this is constrained by the controller to a working range of about \(\pm 45^{\circ}\). In practice, the normal operational range is typically limited to about \(\pm 20^{\circ}\), as discussed in detail in [25].
# A Low-Profile HF Meandered Dipole Antenna with a Ferrite-Loaded Artificial Magnetic Conductor
###### Abstract  
In this paper, a low-profile HF (high-frequency) meandered dipole antenna with a ferrite-loaded artificial magnetic conductor (AMC) is proposed. To operate in the HF band while retaining a compact size, ferrite with high permeability is applied to the unit cell of the AMC. The operating frequency bandwidth of the designed unit cell of the AMC is 1.89:1 (19-36 MHz). Thereafter, a meandered dipole antenna is designed by implementing a binary genetic algorithm and is combined with the AMC. The overall size of the designed antenna is \(0.06\times 0.06\times 0.002\)\(\lambda^{3}\) at the lowest operating frequency. The proposed dipole antenna with a ferrite-loaded AMC is fabricated and measured. The measured VSWR bandwidth (\(<\)3) covers 20-30 MHz on the HF band. To confirm the performance of the antenna, a reference monopole antenna which operates on the HF band was selected, and the measured receiving power is compared with the result of the proposed antenna with the AMC.  
artificial magnetic conductor (AMC); ferrite; HF (high-frequency); low-profile; meandered dipole antenna +
Footnote †: journal: Applied Sciences  
12 February 2021  
1122370228 February 2021  
Subject: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# The LHAASO PeVatron Bright Sky: What We Learned
## 3 Overview of the 12 LHAASO Sources
### LHAASO J1843-0338  
LHAASO J1843-0338, detected with an \(E_{M}\approx 260\) TeV, is in a very complex region (see Figure 9, left), and is associated problem includes the two HESS VHE detections, HESS J1844-030 and HESS J1843-033, which are candidates as TeV counterparts.  
The first source, HESS J1844-030, is a faint point-like VHE source, spatially coincident with a number of distinct objects. Initially, it was reported as a component of HESS J1843-033 [158] and then was disentangled in the HESS Galactic Plane Survey [99]. In particular, this emission turned out to be spatially coincident with the radio source G29.37+0.1, which has a very complex morphology. Later, a deep study developed in [159] concluded that this radio source is the superposition of a radio galaxy, a composite SNR with a shell, a pulsar powered component, and maybe also a neutron star. At the same time, the authors could not explain if the nature of TeV \(\gamma\)-ray was produced in the lobe of the radio galaxy or in the SNR/PWN environment. A further analysis explained all the multiwavelength emissions from G29.37+0.1 (radio, X-ray and \(\gamma\)-ray) as due to PWN-SNR interaction, excluding an extra-galactic counterpart [160].  
The centroid of this LHAASO source is more coincident with the second potential TeV counterpart, eHWC J1842-035/HESS J1843-033. This is an unidentified source discovered by HESS [99], with a spectrum extended up to 30 TeV. Its complex morphology could be a hint of the presence of overlapping sources, merged in the Galactic Plane Survey. In TevCat, this is associated with ARGO J1841-0332 [161] and 2 HWC J1844-32 [21], and the Tibet As\(\gamma\) array recently detected a TeV emission above 25 TeV near the position of HESS J1843-033, TASG J1844-038 [162]. The Tibet-AS\(\gamma\) emission has a measured spectrum above 100 TeV, in perfect agreement with the LHAASO results [162] (see Figure 9, right). The authors discussed the origin of this emission, concluding that both the associations with the SNR G28.6-0.1, filled with non-thermal X-ray [163], and PSR J1844-0346, can be considered valid.
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 3 Classification of Attacks in VANETs
### Classification Based on VANET Components  
We can also categorize the VANET attacks based on the components attacked. Three categories exist here. They are as follows.  
1. _Vehicles_: Vehicles are the mobile units which contain OBU and AU used in the communication. These mobile components may be easily targeted because these are the least secure units in the VANETs. Some of the attacks which may be launched against these units are social engineering attack, sensor impersonation attack, malware integration to vehicle attack, etc.
2. _Information_: Information which flows in all directions of the network is also targeted by the attacker by launching different novel attacks like eavesdropping, jamming spoofing, and false position attack, etc. These attacks may hamper both safety and non-safety applications of the network.
3. _Infrastructure_: It includes RSUs, central registration agency, charging spots of EVs, trusted authority, video cameras, and other components place alongside the road or at any other place like a parking place. Attacks that may be launched include network attack, DoS/DDoS, sybil attack, man in the middle attack, etc. The computer network software based attacks are also viable for VANET environment [81]. Component-based attacks are listed in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Attack** & **Attack Layer** \\ \hline False position attacks & Application \\ \hline Sybil attack & Application, transport, network, MAC \\ \hline Tunnel attacks & Transport \\ \hline Man in the middle attack & Transport \\ \hline Session hijacking attack & Transport \\ \hline Location disclosure & Network \\ \hline Packet dropping & Network \\ \hline Flooding attack & Network \\ \hline Wormhole & Network \\ \hline Blackhole attack & Network \\ \hline Routing attack & Network \\ \hline Signal jamming attack & MAC and LLC \\ \hline Collision attack & MAC and LLC \\ \hline GPS spoofing attack & Physical \\ \hline Jamming attack & Physical \\ \hline Message altering attack & Physical \\ \hline Passive eavesdropping & Physical \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
# A Comprehensive Review of Milk Components: Recent Developments on Extraction and Analysis Methods
## 7 Micro-Constituents' Analysis
### Spectrophotometric Methods  
Spectrophotometric methods are used for the quantification of compounds like vitamins (e.g., vitamin A, D, E, K) and certain bioactive compounds like polyphenols. Several colorimetric assays are available for vitamins determinations.  
For example, C vitamin (or ascorbic acid, AA) is determined by a spectrophotometric method which involves cupric ions reduction. Specifically, when present, AA reduces the cupric ions Cu\({}^{++}\) to cuprous ion Cu\({}^{+}\) that, in presence of cuproine, forms a complex that adsorbs at 545 nm. This protocol is sensible and provides LOD in order of the mg/L [147].  
Also, the total polyphenols content (TPC) can be measured by spectrophotometric assay. This is a fast and sensible method and can be applied to more types of matrixes [148; 149]. Spectrophotometric methods for polyphenols content cover a wide range of years and matrix [150; 151]. As a limitation, these assays can measure only the total content of phenols, losing the determination of a single phenolic compound.
and biological sensors [15], leading to embedded, flexible and wearable circuits [16] and point-of-care applications [17].  
Among these applications, in the last decade organic electronics is digging its way in the field of bioelectronics and researchers started to develop neural interfaces based on organic semiconductors and polymers. The interest in such technologies arises from the intrinsic properties of organic materials, such as their flexibility [18] and the suitability in realizing all organic printed systems [19]. Moreover, there are many other interesting properties that make organic platform so appealing [20], including (i) transparency of the thin-film materials, which allows optical investigation of the tissue in direct contact with the sensor; (ii) polymers and molecules can be tuned to meet the desired specifications in different kind of sensors [21; 22]; (iii) organic semiconductors are soft material that can be self-assembled and self-organized mimicking the biological structures [23; 24]; (iv) organic compounds can be functionalized by means of bio-molecular groups thus promoting cell viability [25]; and (v) organic electronics devices and biosensors can be fabricated using natural, cheap, bio-degradable and bio-resorbable materials [26; 27].  
Despite these remarkable properties, organic neural interfaces have not yet reached the market, because organic transistors and sensors still show poor electrochemical stability when exposed to light, air, moisture and water environments [28]. In addition, organic transistor-based sensors typically feature much smaller mobilities and, consequently, lower performances with respect to their inorganic counterpart [29].  
However, these limits are mainly ascribable to the relatively novelty of the technology. In fact, organic field-effect transistors featuring high air [30], thermal [31] and bending [32] stability, as well as high carrier mobility have been recently reported for both p-type [33; 34] and n-type [35; 36] semiconductors. Therefore, organic electronics holds great promise for future reliable and durable neural interfaces for both in vitro and in vivo applications.  
In this review, we explore the world of flexible and organic biosensors for sensing neural activity. We will first discuss specifications and requirements that need to be considered in designing a new neural interface. Then, flexible neural interfaces and organic thin-film transistors to sense and stimulate cells electrical activity will be reviewed. Later on, we will discuss how neural interfaces can be modelled to study and analyse how a living cell and a neural interface interact each to other. Finally, conclusions will be drawn and some future perspectives will be given.
## 3 System Architecture of Resource Platform  
The entire system is divided into five software-level layers, which are, from the bottom to the top, the basic data layer, the back-end, the interaction layer, the front-end, and the access layer. The data layer serves as the persistence support for the whole system, providing relevant data to external services through connection pools. Specifically, it contains relevant stored procedures and predefined transactions. The back-end mainly uses the Spring Security framework to classify access to requests and deploy the Spring application on the server to manipulate various types of data. The interaction layer mainly uses the Nginx server to handle the upper application requests. It accesses the back-end server through interface-routing hops according to the configuration file format to achieve load balancing [37]. The front-end is mainly the display part of the page, developed by Hyper Text Markup Language (HTML), Cascading Style Sheets (CSS) for page rendering, and finally integrated by the Vue front-end tool. The access layer is how users access the resource platform using tools such as browsers. The system uses a front-end and back-end deployment approach, with the front-end using the Vue framework and the back-end using the Spring Boot framework for development [38; 39] through the Maven packaging tool for deployment. The data storage uses the MySQL database to store project details and teacher account information for the subsequent release of shared resources. The Spring Security framework is used for the security verification of the website and user authentication. The front- and back-end separation architecture of the system is shown in Figure 1.
## 4 Routing Protocol Design
### SOV Finding Process  
Initially, an SUV should find one suitable SOV that owns the target content and has the will to share it. The SUV will broadcast a _content_request_ message to all vehicles within a prescribed distance defined as follows. To reduce the transmission overhead and possible delay, a Time-To-Live (TTL) value is defined to prevent long-distance flooding. For example, if TTL = 5, it means that at most, the five-hop neighbors will be queried about the target content. At each hop, the TTL value carried by the forwarded message will be decremented by one, if it is greater than zero. The position and speed information of the SUV are included in the _content_request_ message.  
A vehicle node \(S\) that receives the request message conducts the following verification process:  
* If \(S\) has already received an identical _content_request_ message before, the currently-received message will be dropped, to avoid duplicate requests.  
Figure 3: The flowchart for the designed routing scheme.  
Figure 2: A sketch map for illustrating the designed routing protocol.  
* If instead the _content_request_ message is new to \(S\), then two alternative actions can possibly be carried out by \(S\):
* Reply: If the \(S\) owns the target content, it sends out an \(SOV\_offer\) message, briefly denoted as an ACK, back to the SUV.
* Forward: In case \(S\) does not have the requested content, if the _content_request_ message \(TTL>0\), the \(TTL\) is decremented by one, and the _content_request_ message is sent forward; otherwise, if \(TTL=0\), the received _content_request_ message will be dropped, since the maximum transmission distance has been reached.
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 2 Scenario Definition  
The evaluation of the localization improvements based on the combination of GNSS and cellular technologies requires a generic and representative framework, which does not specifically favors one technology. This approach is typically followed by standardization bodies that define reproducible scenarios in order to evaluate candidate specifications. For instance, scenario conditions for GNSS-based localization are defined in ETSI TS 103 246-3 [13] and 3 GPP TS 36.171 [14], and for LTE-based localization in 3 GPP TR 37.857 [11]. However, current standards do not define scenarios to evaluate the positioning performance of hybrid GNSS and cellular solutions. Thus, this section introduces a novel scenario definition to assess the stand-alone positioning capabilities of both technologies, and the enhancements of the complementary use of cellular networks to GNSS. The main scenarios are defined for outdoor urban, suburban and rural environments, while indoor scenarios are not considered in this work due to the lack of GNSS satellite visibility.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 3 Method
### Kurtosis Coefficient  
The kurtosis coefficient was calculated as in Equation (8):  
\[g_{2}=\sqrt{\frac{n}{24}}\left[\frac{1}{n}\sum_{i=1}^{n}\left(\frac{y^{\prime}{}_{i }-\overline{y^{\prime}}}{ss}\right)^{4}-3\right] \tag{8}\]  
\[ss=\sqrt{\frac{1}{n}\frac{n}{i=1}\left(y^{\prime}{}_{i}-\overline{y^{\prime}} \right)^{2}} \tag{9}\]  
where \(y^{\prime}{}_{i}\) is the value of the first derivative of the input data; \(\overline{y}\) is the mean value of \(y^{\prime}{}_{i}\); and \(ss\) is the standard deviation (see Equation (6)). \(g_{2}\) is dimensionless.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**W/B**} & \multirow{2}{*}{**T (\({}^{\circ}\)C)**} & \multicolumn{6}{c}{**\#days =**} \\ \cline{3-8}  & & **3** & **7** & **28** & **56** & **90** & **120** \\ \hline \multirow{8}{*}{0.4} & \multirow{2}{*}{25} & 26.3, 26.6, 26.6 & 31.8, 31.6, 31.7 & 41.7, 41.5, 41.9 & 44, 44.1, 44.2 & 47.4, 46.9, 46.7 & 48.1, 47.7, 47.6 \\ \cline{2-8}  & & 0.17 & 0.10 & 0.20 & 0.10 & 0.36 & 0.26 \\ \cline{2-8}  & \multirow{2}{*}{50} & 27.8, 27.6, 27.4 & 34.6, 34.2, 35 & 43.2, 43, 43.4 & 47.1, 47.3, 47.5 & 51.3, 51.4, 50.9 & 53.8, 54.2, 53.7 \\ \cline{2-8}  & & 0.20 & 0.40 & 0.20 & 0.20 & 0.26 & 0.26 \\ \cline{2-8}  & \multirow{2}{*}{75} & 29.5, 29.1, 29 & 37.8, 37.5, 37.5 & 46, 46.3, 46 & 49.2, 48.9, 49.8 & 52.9, 52.9, 52.6 & 54.3, 54.8, 54.4 \\ \cline{2-8}  & & 0.26 & 0.17 & 0.17 & 0.46 & 0.17 & 0.26 \\ \hline \multirow{8}{*}{0.5} & \multirow{2}{*}{25} & 23.3, 23.5, 23.4 & 27.2, 27.4, 27.6 & 34.6, 34.9, 34.6 & 38.4, 38.7, 38.1 & 43.6, 43.1, 43.5 & 45.6, 45.2, 45.4 \\ \cline{2-8}  & & 0.10 & 0.20 & 0.17 & 0.30 & 0.26 & 0.20 \\ \cline{2-8}  & \multirow{2}{*}{50} & 26, 25.8, 25.9 & 30.3, 30.4, 30.5 & 38.2, 38.3, 38.1 & 44.7, 44.7, 44.1 & 46.6, 46.4, 46.5 & 47.5, 46.9, 47.2 \\ \cline{2-8}  & & 0.10 & 0.10 & 0.10 & 0.35 & 0.10 & 0.30 \\ \cline{2-8}  & \multirow{2}{*}{75} & 27.5, 27.6, 28 & 31.4, 31.9, 32.4 & 39.3, 39.3, 39.9 & 47, 47.3, 47.3 & 49.6, 49.3, 49 & 51.6, 51.7, 51.2 \\ \cline{2-8}  & & 0.26 & 0.50 & 0.35 & 0.17 & 0.30 & 0.26 \\ \hline \multirow{8}{*}{0.6} & \multirow{2}{*}{25} & 18.1, 17.8, 17.8 & 21.1, 21.2, 20.7 & 24.6, 24.4, 24.2 & 27.7, 28.1, 27.6 & 33.6, 33.8, 33.7 & 35.3, 35.3 \\ \cline{2-8}  & & 0.17 & 0.26 & 0.20 & 0.26 & 0.10 & 0.17 \\ \cline{2-8}  & \multirow{2}{*}{50} & 19, 18.8, 18.3 & 21.8, 21.9, 22 & 33.6, 33.4, 33.5 & 38.3, 38.4, 37.9 & 42, 41.8, 41.6 & 43.8, 43.6, 43.7 \\ \cline{2-8}  & & 0.36 & 0.10 & 0.10 & 0.26 & 0.20 & 0.10 \\ \cline{2-8}  & & 20.5, 20.2, 19.9 & 22.5, 22.6, 22.1 & 34.9, 35.3, 35.1 & 40.6, 40.3, 40.6 & 42.8, 42.7, 43.2 & 45.1, 45.1, 44.8 \\ \cline{2-8}  & & 0.30 & 0.26 & 0.20 & 0.17 & 0.26 & 0.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Raw data and standard deviation of compressive strength with 20% BJPA substitution (unit: MPa).