esa-sceva/satcom-chunk-collection huggingface dataset:
## Exploiting the In-Network Capabilities of Multicast to Discover Proximate IPTV Channels  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Telecommunications Software and Systems Group, Waterford Institute of Technology, Cork Road, Waterford, Ireland; E-Mails: [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (D.B.); [EMAIL_ADDRESS] (W.D.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +353-51-302971.  
_Received: 11 August 2010; in revised form: 24 September 2010 / Accepted: 25 September 2010 / Published: 29 September 2010_  
**Abstract:** IPTV has become the next generation of television due, in part, to its ability to support features that have been lacking in conventional broadcasting--for example, end-user interactivity, personalisation and localisation. Providers are also searching for the most efficient delivery methods to provide the greatest amount of contents at the lowest cost. At present IPTV uses IP multicast to deliver live TV channels in an over-provisioned walled-garden network due to issues of deploying multicast and QoS challenges in the public Internet. However, IPTV is likely to shift into some parts of the public Internet in the future as a managed service. Multicast routing is performed on a per-session destination-address basis so each router maintains a table of all of the multicast addresses to which the content is being forwarded. We exploit this information to discover and join the in-progress channels of geographically proximate users and to create a new incentivised premium service in future IPTV networks called _ProxyTV_. This approach is expected to minimise network bandwidth requirements as it enables ISPs to optimise bandwidth on their edge networks. This becomes increasingly significant as TV content consumes more and more bandwidth, especially with the onset of HD and 3D capabilities. In this paper, we have presented in detail the concept with the results of a survey and an analysis of network traffic to justify the proposed approach.  
**Keywords:** IP multicast; IPTV; channel proximity; user-provided networking; social TV; traffic localisation
# An Intrusion Detection System for RPL-Based IoT Networks
## 4 IDS Design
### Detection Methods  
The following types of intrusion detection methods can be distinguished: signature-based, specification-based, anomaly-based, and hybrid. Signature-based detection aims at matching existing signatures with incoming packets to check for intrusions. Some of the limitations of this approach are the large database needed and the impossibility of detecting 0-day attacks. Specification-based and anomaly-based approaches share some similarities. Specification-based detection modules are typically configured manually, whereas anomaly-based are often supported by machine learning algorithms or by algorithms that allow the IDS to adapt to network changes [53]. Both approaches are based on specific statistics in a network by gathering relevant data from the devices. If the IDS detects an anomalous behaviour (i.e., different from the expected functioning of the network), it will raise an alert. Therefore, for some types of attacks, especially 0-day attacks, the IDS has a higher chance of detection. However, such approaches may produce a high rate of false positives since a small change in the network may be interpreted as an attack. Finally, the hybrid-based detection strategy consists in a combination of the previously mentioned detection methods to maximise the advantages and reduce the encountered issues.  
In our design, with the aim of detecting multiple attacks, a hybrid detection strategy is composed of a single module that handles possible attacks by using different types of detection methods. However, due to the placement characteristics of the IDS nodes (see Figure 1) and the nature of the attacks (some of them cannot be detected if not in range), central IDS and IDS detector nodes use a different hybrid detection method. These are detailed in the following subsections. The meanings of the symbols used in Algorithms 1-5 are given in Table 1.
# IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing
## 3 The Proposed Method
### The Transformed Atmospheric Scattering Model  
We can rewrite Equation (1) for the clean image as the output:  
\[J(x)=\frac{I(x)-\alpha(1-t(x))}{t(x)}. \tag{3}\]  
Existing works such as [19; 31; 41] usually utilize empirical rules to estimate \(\alpha\) and deep learning models to estimate \(t(x)\). Estimating \(\alpha\) and \(t(x)\) separately will lead to certain errors. The output clean image obtained by combining \(\alpha\) and \(t(x)\) may have a greater cumulative error.  
The transformed atmospheric scattering model is proposed [21] to reduce the cumulative error caused by separate estimating. The two parameters, \(\alpha\) and \(t(x)\), are unified into one formula to avoid the potential cumulative error caused by estimating the two parameters separately. Model (3) can be rewritten as follows:  
\[J(x)=D(x)I(x)-D(x)+1, \tag{4}\]  
where  
\[D(x)=\frac{\frac{(I(x)-\alpha)}{t(x)}+(\alpha-1)}{I(x)-1}. \tag{5}\]  
It is worth noting that in Equation (5), \(\alpha\) and \(t(x)\) together form a new variable \(D(x)\). The clean image can be obtained by estimating \(D(x)\). The unified variable \(D(x)\) can effectively reduce the cumulative error caused by estimating \(\alpha\) and \(t(x)\) separately.
# An Overview of CubeSat Missions and Applications
## 9 Review Conclusions and Discussion
### CubeSat Platform Limitations and Intrinsic Challenges
3 GPP 3 rd Generation Partnership Project  
I2C  Inter-Integrated Circuit  
ABS  Arcylonitrile Butadiene Styrene  
ADS-B  Automatic Dependent Surveillance-Broadcast  
AI  Artificial Intelligence  
AIS  Automatic Identification System  
AOCS  Attitude and Orbit Control System  
ASI  Agenzia Spaziale Italiana  
ATF  Along-Track Formation  
CAN Bus  Controller Area Network Bus  
CDS  CubeSat Design SpecificationCLSI &CubeSat Launch Initiative  
CMOS & Complementary Metal Oxide Semiconductor  
COMM & Communication System  
COTS & Commercial of the Shelf  
CPU & Central Processing Unit  
CT & Computed Tomography  
CZT & Cadmium Zinc Telluride  
EPS & Electrical Power System  
ESA & European Space Agency  
ESPA & Evolved Expendable Launch Vehicle Secondary Payload Adapter  
FCC & Federal Communications Commission  
FPGA & Field-Programmable Gate Arrays  
FUV & Far-Ultraviolet  
GFP & Green Fluorescent Protein  
GIRO & Global Ionospheric Radio Observatory  
GNSS & Global Navigation Satellite System  
GNSS-R & Global Navigation Satellite System Reflectometry  
GNSS-RO & Global Navigation Satellite System Radio Occultation  
GNC & Guidance, Navigation and Control  
GPS & Global Positioning System  
GSD & Ground Sampling Distance  
GSFC & Goddard Space Flight Center  
GPU & Graphic Processing Unit  
GRBs & Gamma-Ray Bursts  
I2C & Inter-Integrated Circuit  
IADC & Inter-Agency Space Debris Coordination Committee  
IDM & Ion Drift Meter  
IEEE & Institute of Electrical and Electronics Engineers  
INMS & Ion and Neutral Mass Spectrometers  
INTA & Spanish Institute of Aerospace Technology  
IOV & In-Orbit Validation  
IOD & In-Orbit Demonstration  
IR & Infrared  
ISL & Inter-Satellite Link  
ISR & Incoherent Scatter Radar  
ISS & International Space Station  
ITU & International Telecommunication Union  
JAXA & Japan Aerospace Exploration Agency  
JEM & Japanese Experiment Module  
JPL & Jet Propulsion Laboratory  
J-SSOD & JEM Small Satellite Orbital Deployer  
LANL & Los Alamos National Laboratory  
LEO & Low Earth Orbit  
LET & Linear Energy Transfer  
LIDAR & Light Detection And Ranging  
LVF & Linear Variable Filter  
LWIR & Long-Wave Infrared  
MEO & Medium Earth Orbit  
MIMO & Multiple-Input-Multiple-Output  
MIT & Massachusetts Institute of Technology  
MSFC & Marshall Space Flight Center  
MWIR & Medium-Wave Infrared  
NEA & Near-Earth Asteroid  
NIR & Near-Infrared
## 1 Introduction  
Malonic acid (MA) plays an important role as a competitive inhibitor [1] and as a crosslinker with minimal toxicity compared to glutaraldehyde or glyoxal [2]. MA produces numerous valuable and biodegradable compounds [3] that are commonly used in foam packaging, bags, and other related products. The mechanism of using MA as a crosslinker was described by [PERSON] and [PERSON] [4]. When individualized hydrophobic halloysite nanotubes (HNTs) [5] are mixed with polyvinyl alcohol (PVA), HNTs-PVA is characterized as a fully biodegradable nanocomposite [4] with enhanced mechanical properties. Nanotubes such as HNTs [6; 7; 8; 9] or carbon nanotubes (CNTs) [10] are very effective with even small quantities of loading with polymers such as thermoplastic nanoparticles, which could result in significant changes in the physical and chemical properties. The thermoplastic polymers, such as PVA, are also biocompatible polymers that are widely used in industry for films, paper, and adhesive coating due to their attractive properties [11; 12]. Crosslinking has been utilized to improve the thermo-physical properties including the solubility of many polymers [13; 14].  
Throughout the process of sample preparation, sodium dodecyl sulfate (SDS) is used as a good dispersing agent for HNTs in aqueous solution [15; 16]. The chemical structure of HNTs shows that the external surface contains siloxane (Si-O-Si) groups, aluminol (Al-OH), and silanol (Si-OH) groups [17; 18]. The effect of the biopolymer charge on nanocomposite materials was thoroughly discussed in [19]. HNTs possess a much better dispersion property and potential ability for the formation of hydrogen bonding [20] than other natural silicates such as montmorillonite and kaolinite. HNTs were acid-treated by [PERSON] et al. [21; 22] and [PERSON] et al. [23] who reported improvement in the specific surface area to 250 m\({}^{2}\)/g and 267 m\({}^{2}\)/g from about 60 m\({}^{2}\)/g, respectively. HNTs' wear resistance was discussed by [24] while creating a mechanically robust spray of epoxy-coated halloysite composites was discussed in [25]. Recently, two publications, [PERSON] et al. [26] and [PERSON] et al. [27], both in press, have shown that the specific surface area was improved to new frontiers, as far as 608 m\({}^{2}\)/g and 306 m\({}^{2}\)/g, by treating HNTs with alkali solution, followed by acid etching, respectively. Briefly, esterification is obtained via PVA-MA crosslinking [28; 29]. Researchers have been exploring different levels for the effectiveness of MA crosslinking while their expectations varied within a reasonable level of differences, especially in thermal properties. The highest level of HNTs-PVA nanocomposite was achieved by producing water-insoluble PVA [30]. The qualities of the composite by individualization of HNTs with the assistance of MA crosslinking with PVA were also studied [31]. In this work, the specific surface area was improved to 511 m\({}^{2}\)/g from about 60 m\({}^{2}\)/g for neat HNTs, which is considered the second highest increase to the best of our knowledge.
# Miniaturized Antenna Array-Based Novel Metamaterial Technology for Reconfigurable MIMO Systems
###### Abstract  
In this work, a highly miniaturized microstrip antenna array based on two elements is proposed for multiple inputs multiple outputs (MIMO) application systems at sub-6 GHz frequency bands. The antenna is structured from a meander line in conjugate with an interdigital capacitor when excited through the monopole basic antenna. The proposed antenna elements are separated with a Minkowski factor-shaped metamaterial (MTM) column to achieve a separation distance (D) of 0.08\(\lambda\) at 3 GHz when printed on an FR-4 substrate. Later on, the antenna performance in terms of bandwidth and gain is controlled using a photonic process based on optical active switches based on light-dependent resistances (LDR). Therefore, the reconfiguration complexity with such a technique can be eliminated significantly without the need for a biasing circuit. The antenna design was conducted through several parametric studies to arrive at the optimal design that realizes the frequency bandwidth between 3 and 5.5 GHz with a maximum gain of about 4.5 dBi when all LDR terminals are off. For a wireless channel performance study-based massive MIMO environment, the proposed antenna is suitable to be configured in arrays of 64 \(\times\) 64 elements. From this study, it was found the maximum bit error rate (BER) does not exceed 0.15 with a channel capacity (CC) of 2 Gbps. For validation, the antenna was fabricated based on two elements and tested experimentally. Finally, it was revealed that the measured results agree very well with simulations after comparing the theoretical calculations with the measured data.  
5G; MIMO; sub-6; MTM; reconfigurable; BER +
Footnote †: journal: Sensors  
**Citation:** [PERSON]; [PERSON]; [PERSON] Miniaturized Antenna Array-Based Novel Metamaterial Technology for Reconfigurable MIMO Systems. Sensors **2023**, 23, 8671. [[https://doi.org/10.3390/s23135871](https://doi.org/10.3390/s23135871)]([https://doi.org/10.3390/s23135871](https://doi.org/10.3390/s23135871))
## 3 Example Analysis
### Error Indicator  
The missing data can be discussed in the following two situations: For a single missing data, according to the characteristics of the smooth variation in the power load curve, the average load of the two data points before and after the data point is taken to fill in the missing data. For continuous multiple missing data, according to the characteristic that the load curves of the power system have roughly the same trend, we use similar load curves in adjacent dates to replace them.  
We use five predictors to demonstrate the validity of the method proposed in this paper. The formulae for the forecast error assessment indicators are shown below.  
Mean Absolute Percentage Error (_MAPE_):  
\[MAPE=\frac{100\%}{N}\sum_{i=1}^{N}\left|\frac{P_{ture}-P_{fore}}{p_{ture}}\right| \tag{25}\]  
Mean Absolute Error (_MAE_):  
\[MAE=\frac{1}{N}\sum_{i=1}^{n}\left|P_{ture}-P_{fore}\right| \tag{26}\]  
Mean Square Error (_MSE_):  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}\left(P_{ture}-P_{fore}\right)^{2} \tag{27}\]  
Symmetric Mean Absolute Percentage Error (_SMAPE_):  
\[SMAPE=\frac{100\%}{N}\sum_{i=1}^{N}\frac{\left|P_{ture}-P_{fore}\right|}{\left( \left|P_{ture}\right|+\left|P_{fore}\right|\right)} \tag{28}\]  
Root Mean Square Error (_RMSE_):  
\[RMSE=\sqrt{\frac{1}{N}\sum_{t=1}^{N}\left(P_{true}-P_{fore}\right)^{2}} \tag{29}\]  
In the above formulae, \(N\) is the predicted point; \(P_{ture}\) is the actual value of the resident load; and \(P_{fore}\) is the predicted resident load.
# Delaying the Occurrence of Bar Buckling in RC Columns Confined with SRG Jacketing
## 2 Experimental Program  
### Design of Specimens and Parameters of Investigation  
A total of 24 short RC columns of 200 mm square cross section and of 320 mm in height were tested in uniaxial compression. The specimen geometry was dictated by former studies of the authors [22] along with the restrictions imposed by the loading frame machine. Thus, a 1:2 scale of an actual column was chosen. The edges of all specimens were rounded off by a corner radius r = 25 mm aiming to facilitate the best practice of SRG jacketing (a precaution step before casting, Figure 1). The longitudinal reinforcement comprised four bars of diameter D\({}_{\text{b}}\) = 12 mm [\(\rho_{\text{l}}\) = 4 \(\times\)\(\pi\)\(\times\) D\({}_{\text{b}}\)\({}^{2}\)/(4 \(\times\) 200\({}^{2}\)) = 1.13%] placed at the corners of the specimens as shown in Figure 1. The clear cover of the steel gage was 20 mm. Three transverse reinforcement arrangements were used corresponding to 6 mm diameter stirrups: two with their ends bent at 90\({}^{\circ}\), spaced at s = 150 mm (s/D\({}_{\text{b}}\) = 12.5) and at s = 75 mm (s/D\({}_{\text{b}}\) = 6.25) referring to old practice and one with tie ends bent at 135\({}^{\circ}\) and spaced at s = 50 mm (s/D\({}_{\text{b}}\) = 4.2) referring to concurrent code requirements. An additional 8 mm stirrup (tie ends bent at 135\({}^{\circ}\)) was placed at the specimen ends, near the loading surfaces, aiming to prevent local crushing of concrete and to drive failure into the region under study.  
Based on the stirrups' arrangement, the specimens were divided into three groups: Group A with s/D\({}_{\text{b}}\) = 12.5, Group B with s/D\({}_{\text{b}}\) = 6.25 and Group C with s/D\({}_{\text{b}}\) = 4.17 (Table 1). Six columns (two from each group) served as the control specimens whereas the rest (eighteen in total, six from each group) were retrofitted by three different SRG jacketing configurations depending on the number of layers and steel textile density (cords/in) as  
Figure 1: Geometry and reinforcement detailing of the columns (dimensions in mm), steel cages of the three groups of specimens and mould with rounded corners.  
follows: (i) one layer of 4 cords/in, (ii) two layers of 4 cords/in and (iii) two layers of 12 cords/in density steel textile. The notation of the specimens is as follows: X denotes the group of the unconfined specimens (A, B or C); for the rest SRG jacketed columns, the notation DJX applies, where D indicates the density of the fabric with L (light) and H (high) for the 4 and 12 cords/in textiles, respectively; j refers to the number of layers (1 or 2); and X corresponds to the group name of the specimens (A, B or C). Two identical specimens were tested for each configuration. The specimen details appear in Table 1.
## 2 Methods  
The systematic review was performed according to PRISMA and PICO protocols. Databases PubMed, Web of Science, and Scopus were searched from January to June 2024, using the query (\"placenta\" OR \"placental protein\") AND (hair OR alopecia OR effluvium OR bald* OR pilo* OR pili). The \"all fields\" option was active in the query, no additional filters like language or publication date were applied. We included only original articles describing results of clinical trials on placenta, placental proteins and hydrolyzed placental proteins on hair loss or hair growth in humans (Table 3).  
We excluded review articles, as well as studies published only as abstracts, posters, or meeting reports. The GRADE classification was used to assess the quality of evidence for the studies included in this review as either very low, low, moderate, or high [79]. Altogether, 2922 articles were identified, of which 199 duplicates were excluded. Further 2687 articles were excluded after screening of abstracts or full texts. A detailed full-text analysis was performed on 36 articles. Finally, only 3 articles met the inclusion criteria and were included in this systematic review (Figure 1).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Component CAS No/EC No.** & **Characteristics** \\ \hline Magnesium & Important for many biochemical processes and is, therefore, quite common in humans. The majority of magnesium is stored in the bones (>50\%), while the remainder is stored in muscle, soft tissue, red blood cells and serum [74]. \\ \hline Zinc & Microelement involved in various aspects of cellular metabolism. It was estimated that approximately 10\% of human proteins may bind zinc, in addition to hundreds of proteins that transport and traffic zinc. It is required for the catalytic activity of more than 200 enzymes, and it plays a role in immune function, wound healing, protein synthesis, DNA synthesis, and cell division [75]. \\ \hline \hline \end{tabular} Abbreviations: CAS—Chemical Abstracts Service; EC—European Community Number.  
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**PICO Criterion** & **Description** \\ \hline Patients/Participants & People suffering from baldness, hair loss, effluvium or alopecia, or healthy volunteers \\ Intervention & Placenta derivatives in topical anti-hair-loss preparations \\ Comparator/Control & Placebo or other topical anti-hair-loss preparations, lack of controls \\ Outcomes & Trichogram, trichoscopy, number of hairs shed, investigator assessment (IA), participant assessment (PA) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Inclusion criteria for the human studies covered in this systematic review.
## 4 Service and Communication Technology Adaptation Method  
### Construction of an Adaptability Evaluation System of Service and Communication Technology  
To accurately describe the communication service requirements and communication technology performance of the data center management system, we consider selecting six typical factors that affect the adaptability between service and communication technology, and construct an evaluation system for the adaptability between service and communication technology of the data center management system [15], as shown in Figure 2. This system is based on the Analytic Hierarchy Process [16] and is divided into target layer, index layer, and scheme layer. The target layer evaluates the adaptability between a certain load management service and communication technology. The index layer reflects the influencing factors of service requirements and communication technology performance. The solution layer is an alternative local and remote communication technology.
# Cooperative Power-Domain NOMA Systems: An Overview
## 2 Non-Orthogonal Multiple Access
### Basic Principle of NOMA  
It is commonly accepted, e.g., in [47; 48; 49; 50] that to separate user information, NOMA uses the successive interference cancellation (SIC) method at the receiver's end. During the SIC process, however, information from users with lower channel gains is extracted by users with higher channel gains.
###### Abstract  
This study proposes an enhanced derivative control strategy, named _PI-D\({}^{\mathbf{E}}\)_, designed to overcome key limitations of the derivative (\(D\)) term, such as noise amplification, derivative kick (\(D\)-\(k\)), and tuning difficulties. These issues often arise in high-frequency or rapidly changing systems, in which traditional PID controllers struggle. The proposed solution introduces a novel adaptive exponent algorithm (\(\mathbf{\AE}\)) that dynamically modulates the \(D\) term based on the evolving relationship between system output and setpoint. This yields the _PI-D\({}^{\mathbf{E}}\)_ controller, which adapts in real time to changing conditions. The results show significant performance improvements. Simulation results on two systems demonstrate that _PI-D\({}^{\mathbf{E}}\)_ achieves a 90% faster response time, a 35% reduction in peak time, and a 100% improvement in settling time compared with conventional PID controllers, all while maintaining a near-zero steady-state error even under external disturbances. Unlike more-complex alternatives such as fuzzy logic, neural networks, or sliding mode control, _PI-D\({}^{\mathbf{E}}\)_ retains the simplicity and robustness of PID, avoiding high computational costs or intricate setups. This adaptive exponent strategy offers a practical and scalable enhancement to classical PID, improving performance and robustness without added complexity, and thus provides a promising control solution for real-world applications in which simplicity, adaptability, and reliability are essential.  
adaptive PID control; nonlinear control; robust control; PID tuning algorithms 20252018391  
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