esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Analysis of Text Positioning Results  
The text localization dataset was fed into the YOLOv8 network for training, and the training results of the text localization network were obtained through 50 iterations of network training, as shown in Table 3.  
Table 3 showed that YOLOv8 performs well in text localization, with an average precision of 99.76%. This indicated accurate localization of almost all manhole cover texts. At the same time, the network's recall rate reached 96.31%, indicating that most of the manhole cover texts were successfully localized. From Figure 16, it could be seen that the YOLOv8 network could accurately locate the text in the incomplete manhole covers a and f, and it could also be well positioned for the manhole covers covered by stains. In addition, for the square manhole covers a and b, YOLOv8 could also be well positioned. The manhole covers d and c under different weather and light conditions could also be accurately positioned by the network. Therefore, text positioning through the YOLOv8 network can accurately obtain the position of the text.  
Figure 15: Display of the results of manhole cover localization. (**a**) sunny day shot of manhole covers (**b**,**e**) cloudy day shot with different types of manhole covers (**c**,**d**) mutilated manhole covers (**f**) stained cover manhole covers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluating Indicator** & **YOLOv8** \\ \hline Precision & 98.54\% \\ Recall & 96.31\% \\ mAP & 99.76\% \\ F1\_Score & 97.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: YOLOv8 text positioning results.
## 3 Physical Layer
### Summary of Adaptive PHY  
The context for this discussion is set by the recognition that radio frequency is an essentially scarce natural resource historically regulated in a static and inflexible manner. As a result, whilst some parts of the spectrum are sparsely utilized, others such as the industrial, scientific and medical (ISM) bands are under increasing pressure from numerous devices and associated protocols operating in the relatively narrow space. This situation will only become more challenging with the popularity of wireless communication and upcoming new technologies.  
The knowledge of spectrum condition provides vital information for any potential measures aiming to mitigate the problem described above. A number of spectrum-sensing techniques have accordingly been proposed. The matched filter and cyclostationary feature detector can be used to capture the presence of target signals whose parameters such as waveform and cyclic patterns are known in advance. Although both provide accurate detection results, the prior availability of information may not be satisfied in every case, hence they are better suited under certain limited circumstances. In contrast, energy-based and eigenvalue-based spectrum sensing do not rely on such assumptions and are classified as \"blind\" detection [29]. Energy detection uses purely the received signal strength to determine the channel condition. Despite the simplicity, its detection accuracy is prone to noise uncertainty. Eigenvalue-based detection, on the other hand, examines the eigenvalue of received signal covariance and does not share the drawback of energy detection [31]. Although energy detection is, in many cases, outperformed by the eigenvalue-based approach [30], its remarkably low computational complexity and flexibility makes it one of the most popular measures, especially when spectrum sensing is purely used for probing the interference level rather than the detection of particular signal sources.  
By exploiting the newly available information obtained via one of more of the above techniques, processes further up the protocol stack come up with educated decisions on how adaptations should be made to maximize the performance and minimize the adverse effects. Possible adaptive measures include the switching of operating frequencies, adjustments of transmission powers, suspension of communications, and so forth. In summation, PHY techniques discussed in this section are crucial for communication schemes in maintaining the capability to communicate amid growing competition for resources [35].
# Analysis and Optimization of Output Low-Pass Filter
## 4 Design Trade-Offs
### Summary of Effects of Variations  
Figure 21 provides an overview of how the losses in the damping resistor vary as a function of THD under different configurations involving \(C_{n}\), \(f_{c}\), and \(Q\) at the rated output power \(P_{0}\). When the cutoff frequency is \(f_{c}=1\) kHz, the system experiences the highest levels of THD. Conversely, the most significant power loss is observed when the cutoff frequency is \(f_{c}=250\) Hz. Among all the examined configurations, a quality factor of \(Q=8\) emerges as the most favorable design, effectively minimizing both THD and power dissipation \(P_{d}\). Despite operating with a lower quality factor, the baseline filter also shows close-to-optimal performance. It performs similarly to the configuration with capacitance \(C_{n}=0.15\) pu at the rated output power, suggesting that careful tuning of the quality factor and cutoff frequency can substantially improve harmonic distortion and power losses. This shows the importance of optimizing these parameters to achieve efficient filter performance.  
Figure 19: The CSI output power is analyzed in relation to (**a**) output current THD, (**b**) PF, and (**c**) \(P_{d}\), with variations in cutoff frequency as a function of irradiance.  
Figure 20: The CSI output power is analyzed in relation to (**a**) output current THD, (**b**) power factor, and (**c**) \(P_{d}\), by varying the quality factor across different irradiance levels. \(C_{n}=0.1\) pu and \(f_{c}=500\) Hz.
## 3 Combustion of Non-Conventional Liquid, High-Moisture, and Slurry Fuels
### Summarizing the Results on Combustion of Low-Grade Liquid, Wet, and Slurry Fuels  
A review of the known research results in the field of combustion of low-grade liquid, slurry, and high-moisture fuels has shown that they can be quite promising for the energy industry. Potential benefits lie in the area of expanding the fuel base, reducing the cost of fuel preparation and improving several environmental performance indicators of a boiler. The main practical conclusions and projections for future research are given below:  
(i) Compared to the incineration of dehydrated waste, low-grade slurry and liquid fuels can be burned even more efficiently, since they can be sprayed in the furnace as droplets, which provides better conditions for heat transfer and interaction with an oxidizer. In addition, some studies have confirmed that water and steam do not worsen, but, on the contrary, improve the characteristics of ignition, burnout, and contribute to multiple reductions of emissions (mainly sulfur and nitrogen oxides have been studied).  
(ii) When burning low-grade liquid and slurry fuels, the following problems can potentially arise (Figure 3): long ignition delay, incomplete combustion, low combustion temperature, high emissions (including particulate matter, polycyclic aromatic hydrocarbons), low specific calorific value, low melting point of the mineral part (as a result--fast slagging), and high viscosity (as a result--difficult spraying).  
(iii) A successful solution to these problems is possible due to conventional approaches (Figure 3), such as the use of auxiliary fuel during ignition starting up; boiler modification to ensure maximum burnout of droplets and particles; the use of modern equipment for cleaning flue gases. Special methods include oxy-fuel combustion, the use of an O\({}_{2}\)/H\({}_{2}\)O atmosphere, and the preparation of multi-component fuels, including the use of additives. The latter direction is of particular interest since it opens up wide opportunities for stabilizing important process parameters (combustion temperature, calorific value, etc.) and for obtaining synergistic effects, which, however, can be both positive and negative (for example, an increase in emissions).  
(iv) One of the problems in the development of waste-to-energy technologies is the wide variety of properties of components, the chemical composition of their organic and inorganic parts. Therefore, it is quite difficult now to create a unified theory of combustion of mixed liquid, slurry fuels of different compositions, and, based on this theory, general standards for the design of boilers and other technological plants. Thus, it is very important to obtain new results on the combustion of unconventional fuel mixtures and individual components both in laboratory and in large-scale plants. Of particular interest is the detailing of the chemical reactions during the combustion of new fuels, as well as studies aimed at determining the optimal values of important parameters (both the fuel itself and external conditions).
## 2 Related Work
### Advances in Text Summarization  
Automatic text summarization is the process of computationally shortening a set of data to create a subset that represents the most important information within the original content [24]. Summarization is considered one of the most increasingly demanded tasks in natural language processing, as a means to unlock the abundance of wealth hidden underneath the vastness of textual data. At present, there exist two main methods for text summarization: abstractive and extractive [25]. In abstractive summarization, the summary is generated by novel sentences paraphrasing existing words, while in extractive summarization, the content is composed by unmodified sentences from the original text.  
Recent work in abstractive summarization involves the use of sequence-to-sequence frameworks based on attentional recurrent neural network (RNN) encoder-decoder [26; 27; 28] or transformer [29; 30] architectures to generate concise summaries from input documents. With regard to extractive summarization, proposed approaches employ lexical features [31; 32], statistical methods such as TF-IDF [33; 34], or unsupervised learning techniques [35] to extract keywords and phrases from large corpora, with the most recent ones also involving transformer architectures [36]. While implementations following the abstractive approach are more closely emulating human summarization, even those based on ANNs (and considered state-of-the-art) are relying on large training corpora, have limited generalization on the document level, and usually suffer from semantic and grammatical errors [37]. Extractive summarization, on the other hand, has reached its maturity stage and--although most extractive summaries may lack in readability and cohesion--they generally succeed at capturing the key points of the digested text [38; 39]. At the same time, since they just highlight portions of the original content, there is no danger of generating sentences with irrelevant/wrong interpretations, which is especially important in sensitive domains like biology or medicine.
# Protein Kinases in Mediating Phage-Bacteria Interactions
## 3 Bacteria-Encoded STKs in Phage-Bacteria Interactions
### Enterococcus faecalis IreK: Sensing Plage Infection Signals [33]  
IreK, a STK in _Enterococcus faecalis_, plays a central role in mediating stress-responsive activation of the type VIIb secretion system (T7 SS), which drives collateral damage to non-target bacteria in multi-species communities. During lytic phage infection, membrane damage triggers IreK-dependent signaling, leading to transcriptional upregulation of T7 SS genes in coordination with OG1 RF_11099, a GntR-family transcription factor. This activation induces contact-dependent antagonism of bystander Gram-positive bacteria through toxin (LXG)-mediated activity, while immunity proteins protect kin cells.  
Pseudomonas aeruginosa KKP: Dual-Function Module Balancing Prophage Activation and Antiphage Defense [34]  
The KKP (kinase-kinase-phosphatase) module comprising two Ser/Thr kinases (PfkA, PfkB) and a phosphatase (PfpC) is a dual-function regulatory system in _Pseudomonas aeruginosa_ Pf filamentous prophases that coordinates prophage activation and antiphage defense. The KKP system controls virion production of co-resident Pf prophases by modulating phosphorylation level of MvaU, a host nucleoid-binding protein and prophage silencer. The kinase activity increases MvaU phosphorylation to repress prophage gene expression thereby sustaining lysogeny, while the opposing phosphatase activity reduces MvaU phosphorylation to derepress prophage gene expression and trigger virion release. Additionally, KKP functions as a tripartite toxin-antitoxin system, providing defense against diverse lytic phages: when lytic phages infect, a conserved phage replication protein (e.g., T4 phage Gp59) inhibits PfpC phosphatase activity, unleashing toxic kinase activity that blocks lytic phage replication. The conservation of KKP-like modules across >1000 temperate prophases suggest widespread roles in balancing lysogeny and lytic defense, with implications for biofilm formation, virulence, and phage competition in bacterial populations.  
These examples underscore _divergent_ STK roles: while _Staphylococcus_, _Pseudomonas_ and _Streptomyces_ STKs mediate anti-phage defenses, _M. smegmatis_ StpK7 is subverted by phages to disable host immunity.
## 4 Proposed Routing Protocols
### Proposed Two-Level Phantom with a Backbone Route Protocol (PhaT)
#### 4.2.2 PHASE 2: Selection of \(npN\) and Packet Routing from \(epN\) to \(npN\)  
When the \(epN\) receives the packet from the source node, it activates the \(npN\) selection process to select one \(npN\) from the list of \(npN\). A random selection factor (\(SF\)) is generated by the \(epN\). The \(SF\) is distributed between [0, 1]. If \(SF>T\), \(npN\) is randomly selected from the candidate \(npN\)s in \(northR\). Otherwise, \(npN\) is selected from the candidate \(npN\)s in \(southR\). After \(npN\) is selected, phase 2 routing is done. The \(epN\) forwards the packet to the \(npN\) using the directed random-walk routing strategy.
###### Abstract  
Software-Defined Networking (SDN) and Internet of Things (IoT) are the trends of network evolution. SDN mainly focuses on the upper level control and management of networks, while IoT aims to bring devices together to enable sharing and monitoring of real-time behaviours through network connectivity. On the one hand, IoT enables us to gather status of devices and networks and to control them remotely. On the other hand, the rapidly growing number of devices challenges the management at the access and backbone layer and raises security concerns of network attacks, such as Distributed Denial of Service (DDoS). The combination of SDN and IoT leads to a promising approach that could alleviate the management issue. Indeed, the flexibility and programmability of SDN could help in simplifying the network setup. However, there is a need to make a security enhancement in the SDN-based IoT network for mitigating attacks involving IoT devices. In this article, we discuss and analyse state-of-the-art DDoS attacks under SDN-based IoT scenarios. Furthermore, we verify our SDN sEcure CControl and Data plane (SECOD) algorithm to resist DDoS attacks on the real SDN-based IoT testbed. Our results demonstrate that DDoS attacks in the SDN-based IoT network are easier to detect than in the traditional network due to IoT traffic predictability. We observed that random traffic (UDP or TCP) is more affected during DDoS attacks. Our results also show that the probability of a controller becoming halt is 10%, while the probability of a switch getting unresponsive is 40%.
# An Ultra-Thin Composite Metasurface with Hybrid-Damping Modes for Broadband Sound Absorption
## 3 Compound HR Structure with Hybrid-Damping Modes
### Broadband Absorption of Hybrid-Damping Unit  
For broadband continuous absorption, a hybrid-damping HR structure, composed of 16 detuned cells, is designed with a thickness of only 21 mm. As shown in Figure 4a, the cells 1-8 are the hybrid-damping compound cells, of which the peak frequency can be adjusted by the cavity depth \(l_{1}\) and \(l_{2}\). The cells 9-16 are the single-cavity ones used to fill the valley of the spectrum of the compound cells. The hole diameters of the compound cells are \(d_{1}=4\) mm and \(d_{2}=1.7\) mm, while that of single-cavity cells is \(d_{0}=2.2\) mm. Most cells have a cavity cross-section of \(a\times b=10\) mm \(\times\) 10 mm, and to ensure optimal utilization of space, minor adjustments are made to the cross section of certain cells. The length of the cells has a large span from \(l_{1}=54\) mm to \(l_{16}=10\) mm.  
The absorption coefficient of the HR structure is presented in Figure 4a, from which it can be observed that it obtains excellent performance in the range of 750-2900 Hz. Owing to the over-damping impedance, the first part of the band (green area), composed of the first peaks of cells 1-8, is extended to the low-frequency regime with the maximum and average absorption of 90% and 80%, respectively. The second part (blue area) can achieve 86% average absorption due to the weaker impedance, which includes the peaks of cells 9-16 and the second peaks of cells 1-8. As depicted in Figure 4b, it is evident that the first eight zeros are located below the real axis due to the over-damping impedance, while the remaining zeros are distributed near the real frequency axis, indicating higher absorption abilities. It should be noted that the absorption band exceeds nearly 3 octaves with a thickness of only 21 mm, 1/22 of the wavelength at \(f\) = 750 Hz, demonstrating the advantages of this ultra-thin structure in low-frequency broadband absorption and the significant engineering application potentials.  
Figure 4: (**a**) Sound absorption coefficients and (**b**) the complex frequency plane representation of the broadband HR structure with hybrid-damping modes.  
Figure 3: The schematic diagram of the finite element model for the metasurface unit.
## 3 Control Strategy Description
### Design of the Disturbance Observer and Compound Controller  
In the analysis of the previous section, although the introduction of the integration module in the design of the sliding mode control system can reduce the sensitivity of the system to mismatched channel disturbances, the prerequisite for this control performance is that the disturbance is a slowly varying variable. Therefore, it is difficult to achieve high-performance speed tracking control by relying only on the I-SMC when the system is subject to continuous abrupt disturbances. In this section, a nonlinear disturbance observer is designed to compensate the disturbance, and the specific design process is as follows:  
Rewriting Equation (6) as an observation Equation, as shown in Equation (17):  
\[\begin{array}{l}\dot{z}=f(z)+\lambda_{1}u+\lambda_{2}d(t)+\omega,\\ \lambda_{1}=\left[\begin{array}{cc}0&g\end{array}\right]^{T}\!\!,\lambda_{2 }=\left[\begin{array}{cc}1&0\end{array}\right]^{T}\!\!.\end{array} \tag{17}\]  
where \(z\) is the observed state quantity. When the system is in a steady state, there is \(\dot{z}=0\), and then Equation (17) can be simplified to Equation (18).  
\[-\lambda_{2}d(t)=f(z)+\lambda_{1}u+\omega \tag{18}\]  
Let \(\hat{d}(t)=p+lz\)  
The nonlinear disturbance observer is designed as follows:  
\[\left\{\begin{array}{l}\dot{p}=-l\lambda_{2}p-l[\lambda_{2}lz+f(z)+\lambda_ {1}u+\omega]\\ \hat{d}(t)=p+lz\end{array}\right. \tag{19}\]  
where \(p\) is the observer state parameter, and \(l\) is the observer gain parameter, whose value is a constant greater than zero. The matrix form of the observer is shown in Equation (20).  
\[\begin{array}{l}\begin{bmatrix}\dot{p}_{1}\\ \dot{p}_{2}\end{bmatrix}=\begin{bmatrix}-l_{1}p_{1}\\ 0\end{bmatrix}-\begin{bmatrix}l_{1}^{2}e\\ 0\end{bmatrix}-\begin{bmatrix}l_{1}&0\\ 0&l_{2}\end{bmatrix}\left\{\begin{bmatrix}a&b\\ c&d\end{bmatrix}\begin{bmatrix}e\\ x_{2}\end{bmatrix}+\begin{bmatrix}0\\ g\end{bmatrix}u+\begin{bmatrix}a\\ c\end{bmatrix}x_{1}d\right\}\end{array} \tag{20}\]  
The disturbance of the mismatched channel is input into the controller in the form of feedback, and then the NOD-SMC controller is constructed. The control output signal is shown in Equation (21):  
\[u=-g^{-1}\Big{[}(ac_{1}+c)e+(bc_{1}+d)x_{2}+(ac_{1}+c)x_{1d}+\hat{d}(t)+K \text{sgn}(s)\Big{]} \tag{21}\]
## 2 Method
### Range Offset  
In order to verify the round trip time measured by the SAR instrument (\(\tau_{SAR}\)), the predicted propagation time (\(\tau_{geometric}\)) is derived from the geometry. For this purpose, the spacecraft position (\(\vec{\mathbb{R}}_{SC}\)) is estimated for both timestamps, first by the pulse transmission (\(t=tx\)) and second by its reception (\(t=rx\)) as depicted in Equation (2). With the known target position on ground (\(\vec{\mathbb{R}}_{target}\)), the signal propagation path length is estimated as the sum of the transmission and the reception path and converted into a propagation time by dividing through the speed of light:  
\[\tau_{geometric}\ =\ \frac{1}{c}\left|\vec{\mathbb{R}}_{SC}\ |_{t=tx}\ -\vec{\mathbb{R}}_{target}\right|\ +\ \frac{1}{c}\left|\vec{\mathbb{R}}_{SC}\ |_{t=rx}\ -\vec{\mathbb{R}}_{target}\right|. \tag{2}\]  
The range delay offset results from the difference between the signal propagation time (\(\tau_{SAR}\)) measured by the SAR instrument and the predicted one (\(\tau_{geometric}\)). These offsets are analyzed not only for a single range line, but for a set of subsequent pulses with a sufficient signal-to-noise ratio for the target response.  
Additional effects that extend the signal propagation have to be considered, mainly the internal delay of the SAR instrument itself and the atmospheric delay caused by the troposphere and ionosphere. The atmospheric corrections can be derived from the zenith path delay (ZPD) and the total electron content (TEC), both measured by nearby reference stations (see Section 4.2). The remaining range delay offset is converted into a geometric range offset by multiplying by half the speed of light, which considers the two-way pulse propagation.  
Figure 2: Range compressed data of a single range line containing the target response of a corner reflector at 131.84 μs. The origin (\(\tau=0\)) corresponds to the start of the receiving window; artificial data points for a negative fast time result from the match filter procedure.
# An S/C/X-Band 4-Bit Digital Step Attenuator MMIC with 0.25 \(\upmu\)m GaN HEMT Technology
###### Abstract  
In this paper, a 4-bit digital step attenuator using 0.25 \(\upmu\)m GaN HEMT technology for wideband radar systems is presented. A switched-path attenuator topology with resistive T-type attenuators and double-pole double-throw (DPDT) switches was used to achieve both low insertion loss and phase/amplitude error. The measured insertion loss of the reference state is 2.8-8.3 dB at DC-12 GHz. The input and output return loss are less than 12 dB at DC-12 GHz. An attenuation coverage of 30 dB with a least significant bit of 2 dB was achieved at DC-12 GHz. A root mean square (RMS) amplitude error of 1 dB and a phase error of 8.5\({}^{\circ}\) were achieved, respectively. The attenuator chip size is 2.45 mm \(\times\) 1.75 mm including pads. To the best of the authors' knowledge, this is the first demonstration of a GaN-based digital step attenuator.  
digital step attenuator; GaN; phased array antenna; DPDT switch +
Footnote †: journal: Applied  
Academic Editor: [PERSON]  
Received: 12 April 2022  
Accepted: 5 May 2022  
Published: 7 May 2022  
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