esa-sceva/satcom-chunk-collection huggingface dataset:
# Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network
## 2 Materials and Methods
### Global Semantic Relevance Module  
The global semantic correlation module is shown in the lower part of Figure 3. It is used to estimate the levels of dependency among blocks and will be referred to as GSR. For feature map \(X\in R^{C\times H\times W}\), obtained through feature extraction at a given scale \(b\), \(X\) is processed by adaptive pooling to obtain a feature map \(S\in R^{C\times b\times b}\) of size \(C\times b\times b\). Each point in set \(S\) corresponds to the feature block at the corresponding position in the LSS module. The non-local block then estimates the similarity between each position in \(S\) and all other positions, capturing long-range dependencies by estimating the correlation between each block in feature map \(d\). Afterward, the obtained features are input into the pixel attention module. This module adjusts the weight of each pixel dynamically, highlighting important pixel regions and suppressing unimportant ones, thus enhancing the representation ability of pixel-level features. Finally, the feature map \(D\in R^{c\times b\times b}\) is obtained by the sigmoid function, and the operations performed in the process are described by Equations (3)-(5):  
\[D_{m}=\delta(PA(\beta{\sum_{j=1}^{b^{2}}\omega_{mj}\psi(S_{m})+S_{m})}) \tag{3}\]  
\[\omega_{mj}=\frac{1}{Z_{m}}\exp\left(\theta(S_{m})^{\top}\phi\big{(}S_{j} \big{)}\right) \tag{4}\]  
\[Z_{m}={\sum_{j=1}^{b^{2}}\exp\left(\theta(S_{m})^{\top}\phi\big{(}S_{j}\big{)} \right)} \tag{5}\]  
where \(D_{m}\) represents the \(m\)th element of \(D\), PA represents the pixel-level attention module, \(\delta(.)\) represents the sigmoid function, \(\omega_{mj}\) represents the element in the \(m\)th row and \(j\)th column of the coefficient matrix, and \(\psi(.)\) is the \(1\times 1\) convolution operation.  
The feature map \(Q\) obtained after processing the feature map \(X\) with the LSS module represents the semantic associations at the local level, while the feature map \(D\) obtained after the GSR module represents the correlations between feature blocks. To integrate the two types of information, the obtained \(D\) was used as the convolution kernel, and the feature map \(Q\) was convolved. In order to preserve the information conveyed by the original feature map \(X\), the final output \(m\) of the context semantic expression module is obtained by adding and fusing the result of the convolution operation with \(X\). The operation in the process is shown in Equation (6).  
\[M_{i}=Q_{k}^{j}*D_{i}+X_{i} \tag{6}\]
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### AI in Neuro-Ophthalmology
#### 4.10.1 Neuro-Ophthalmology  
The anatomy and physiology of the ocular network extends behind the eye to the posterior areas of the brain. Therefore, individuals with intracranial diseases frequently present with visual disturbances, and an ophthalmologist is consulted.  
Neuro-ophthalmology, as a subspecialty at the confluence of ophthalmology and neurology, deals with conditions that have an impact on the afferent (e.g., vision) and/or efferent (i.e., eye motions, pupillary feedbacks) pathways that connect the eyeballs to the rest of the nervous system.  
Current neuro-ophthalmology is a unifying specialty, connecting not only ophthalmologists with neurologists, but also neurosurgeons, neuro-radiologists, neuro-otologists, geneticists, neuro-immunologists, and neuropathologists. These multi- and interdisciplinary perspectives often require difficult diagnostics and skills for neuro-ophthalmic conditions. These are relatively infrequent and complex tasks in comparison with ophthalmic and neurological diseases taken separately.  
In short, neuro-ophthalmic diseases may be classified into  
1. _diseases impacting the afferent ocular system_ (the pathway of retina-optic nerve, chiasm, retro-chiasmal pathways, and occipital lobes), which are sources of high-order optical abnormalities;
2. _diseases that affect the efferent visual system_, which are sources of central ocular motor disorders (at the cortical level, brainstem), gaze instability, cranial motor ocular neuropathies, pupillary conditions, and several peripheral abnormalities impacting the neuromuscular junction as well as the muscles themselves.  
The set of diseases that can specifically impact these anatomical tissues is substantial: autoimmune, ischemic, inflammatory, infectious, traumatic, compressive, congenital, and degenerative conditions. For example, sometimes a rather benign neuro-ophthalmic dysfunction (e.g., inflammatory optic neuropathy) indicates other more severe neurological diseases (e.g., multiple sclerosis). Likewise, acute strabismus because of the obtained ocular misalignment (or inflammation of the optic nerve head) may be the sole expression of a life-threatening condition (cerebal aneurysms, tumors, systemic metabolic diseases, etc.), which requires quick detection and treatment.
# Linear System Identification and Vibration Control of End-Effector for Industrial Robots
###### Abstract  
This paper presents the discrete state space mathematical model of the end-effector in industrial robots and designs the linear-quadratic-Gaussian controller, called LQG controller for short, to solve the low frequency vibration problem. Though simplifying the end-effector as the cantilever beam, this paper uses the subspace identification method to determine the output dynamic response data and establishes the state space model. Experimentally comparing the influences of different input excitation signals, Chirp sequences from 0 Hz to 100 Hz are used as the final estimation signal and the excitation signal. The LQG controller is designed and simulated to achieve the low frequency vibration suppression of the structure. The results show that the suppression system can effectively suppress the fundamental natural frequency and lower vibration of end-effector. The vibration suppression percentage is 95%, and the vibration amplitude is successfully reduced from \(\pm\)20 \(\upmu\)m to \(\pm\)1 \(\upmu\)m. The present work provides an effective method to suppress the low frequency vibration of the end-effector for industrial robots.  
Keywords:linear system identification; low frequency vibration; end-effector +
Footnote †: journal: Applied sciences
## 2 Experimental System and Scheme
### Experimental Scheme  
The droplet size and particle size distribution were measured by the Malvern particle size analyzer after atomization. The types of outlet diameters were 0.5 mm, 0.7 mm, and 1.1 mm, and the water supply pressure was adjusted to 1.0 MPa, 2.0 MPa, and 3.0 MPa by adjusting the frequency of the pump. The droplet size at 50 cm and 70 cm from the nozzle outlet was measured according to the same procedure, and a total of 27 sets of experimental data were obtained.  
This study analyzed the ultrasonic atomization nozzle's atomization performance using the orthogonal design method. Multiple levels and factors can be analyzed using an orthogonal experimental design by using the fewest number of tests, and equivalent results can be achieved for a large number of comprehensive trials. Before performing the orthogonal experiments, we determined the protocol using orthogonal tables [18].  
The droplet size of the circular-hole rotating core micro-fog nozzle was studied by orthogonal experiments. For the circular-hole rotating core micro-fog nozzle, the axial distance (S), the outlet diameter (D), and the water supply pressure (P) are the factors that affect the atomization parameters. Therefore, the axial distance (S), the outlet diameter (D), and the water supply pressure (P) were taken as the three influencing factors of the orthogonal design. Each factor was set at three levels, and the orthogonal design method of the \"three factors and three levels\" was used for the research; the factor levels are shown in Table 1. The results of the orthogonal experiments are shown in Table 1.
# IPv6 Convergence for IoT Cyber-Physical Systems
## 3 IP-Convergence Model
### 6 LoWPAN
#### 3.1.1 Challenges  
6 LoWPAN also presents a number of challenges. Specifically, IP support over IEEE 802.15.4 networks is not trivial; the worst-case 802.15.4 protocol data unit (PDU) size is 81 octets, whereas IPv6 maximum transmission unit (MTU) requires 1280 octets [35]. Stacking IP and above layers \"as is\" may not fit within one 802.15.4 frame; for example, IPv6 40 octets, TCP 20 octets, UDP 8 octets and other layers (security, routing, etc.) can result in a large overhead.  
Not all ad-hoc routing protocols may be immediately suitable for LoWPAN. Dynamic source routing (DSR) may not fit within a packet. Ad-hoc on-demand distance vector (AODV) requires increased memory support. Current service discovery methods are considered too heavyweight for LoWPAN. In order to support seamless IoT deployments, limited configuration and management overhead are attractive features. Security for multihop networks is a critical requirement also. Regarding performance of large IPv6 packets, fragmentation over low-power wireless mesh networks may lead to poor performance especially in lossy energy-constrained networks where packet losses are frequent. Lost fragments can cause the whole packet to be retransmitted rather than the missing fragments. Low-bandwidth and wireless channel behavior, for example, delay, must be accounted for when preparing data for dispatch, that is, pre-processing or data distillation is an important consideration for maximising networking efficiency.  
The LoWPAN IP header compression (LoWPAN_IPHC) encoding format was developed for effective compression of unique local, global and multicast IPv6 addresses based on shared state within contexts. When routing over multiple IP hops, LOWPAN_IPHC can compress the IPv6 header down to 7 octets. This frame structure, depicted in Figure 6, comprises:  
In our proposed approach, we also explored how fragmentation and worst-case protocol data-unit-size challenges could be addressed. In particular, our key design considerations were as follows:  
1. How could we support at least a PDU size of 81 octets to meet the worst-case 6 LoWPAN PDU size requirement?
2. How can we support multi-hop networks?
3. What new research was needed to address PDU size support for less than 81 octets to enable IPv6 support on highly constrained networking technologies?
## 6 Proposed Scheme
### Authentication Process  
In authentication process, the proposed scheme provides the user's privacy by using pseudo-identity and secret parameters in the SG environments. Before the starting session, \(SD_{i}\) request an authentication request to \(UC_{j}\) in order to ensure secure communication and establish the session key \(SK_{ij}\). Figure 8 describes the authentication process of proposed scheme and the steps of this process are given below.  
Figure 6: Smart grid device registration process of the proposed scheme.  
Figure 7: Utility center registration process of the proposed scheme.  
**Step 1:**: \(SD_{i}\) computes \(a_{i}=C_{i}\oplus h(ID_{i}||B_{i})\), \(RID_{i}=h(ID_{i}||a_{i})\), \(X_{i}=A_{i}\oplus h(RID_{i}||a_{i})\), and \(B_{i}^{*}=h(RID_{i}||X_{i})\). Then, \(SD_{i}\) checks whether \(B_{i}^{*}\stackrel{{?}}{{=}}B_{i}\). If the condition \(B_{i}^{*}\stackrel{{?}}{{=}}B_{i}\) is valid, \(SD_{i}\) generates a random nonce \(R_{SD}\) and computes \(M_{1}=X_{i}\oplus R_{SD}\), \(M_{2}=RID_{i}\oplus h(X_{i}||R_{SD})\), and \(M_{3}=h(RID_{i}||X_{i}||R_{SD})\). After that, \(SD_{i}\) sends authentication request message \(\{M_{1},M_{2},M_{3}\}\) to \(UC_{j}\) over insecure channel.
**Step 2:**: After receiving the message from \(SD_{i}\), \(UC_{j}\) retrieves \(\{V_{i}\}\) in database and calculates \(X_{i}=V_{i}\oplus ID_{j}\), \(R_{SD}^{*}=M_{1}\oplus X_{i}\), and \(RID_{i}^{*}=M_{2}\oplus h(X_{i}||R_{SD})\). Then, \(UC_{j}\) retrieves corresponding \(\{RID_{i}\}\) in database and checks whether \(RID_{i}^{*}\stackrel{{?}}{{=}}RID_{i}\). If the condition \(RID_{i}^{*}\stackrel{{?}}{{=}}RID_{i}\) is valid, \(UC_{j}\) calculates \(M_{3}^{*}=h(RID_{i}^{*}||X_{i}||R_{SD}^{*})\) and checks whether \(M_{3}^{*}\stackrel{{?}}{{=}}M_{3}\). If the condition \(M_{3}^{*}\stackrel{{?}}{{=}}M_{3}\) is correct, \(UC_{j}\) generates a random nonce \(R_{UC}\) and computes \(M_{4}=R_{UC}\oplus h(X_{i}||R_{UC})\), \(M_{5}=RID_{j}\oplus R_{UC}\), \(SK_{ij}=h(R_{SD}||R_{UC})\) and \(M_{6}=h(RID_{i}||X_{i}||R_{SD}||R_{UC})\). Finally, \(UC_{j}\) sends authentication message \(\{M_{4},M_{5},M_{6}\}\) to \(SD_{i}\) over insecure channel.
**Step 3:**: After receiving the message from \(UC_{j}\), \(SD_{i}\) computes \(R_{UC}=M_{4}\oplus h(X_{i}||R_{UC})\), \(RID_{j}=M_{5}\oplus R_{UC}\), \(SK_{ij}=h(R_{SD}||R_{UC})\), and \(M_{6}^{*}=h(RID_{i}||X_{i}||R_{SD}||R_{UC})\). After that, \(UC_{j}\) sends authentication message \(\{  
\(SD_{i}\) checks whether \(M_{6}^{*}\stackrel{{?}}{{=}}M_{6}\). If the condition \(M_{6}^{*}\stackrel{{?}}{{=}}M_{6}\) is correct, the \(SD_{i}\) and \(UC_{j}\) achieve mutual authentication successfully.
# Adaptive Refinement in Advection-Diffusion Problems by Anomaly Detection: A Numerical Study
## 3 Numerical Results
### Example 3: The Pinched Domain  
In this example, we consider a disk with a hole. The boundary of the domain consists of two disconnected components: \(\Gamma_{1}:=\{x=\cos(t);y=\sin(t)\ ;t\in[0,2\pi]\}\) is the outer boundary, while \(\Gamma_{2}:=\{x=0.3+0.3\cos(t);y=0.3\sin(t)\ ;t\in[0,2\pi]\}\) is the boundary of the inner hole. The input data are: \(\varepsilon=10^{-10}\), \(f=0\), \(\alpha=1\), \(\mathbf{b}=[2,1]\). We apply discontinuous Dirichlet boundary conditions: \(u=0\) on \(\Gamma_{1}\) and \(u=1\) on \(\Gamma_{2}\). Therefore, we expect the rise of the two inner layers. The results for the adapted meshes with the three error estimators are shown in Figures 15-17. In Tables 7-9, we report the error estimates in the \(L^{2}\) norm and \(H^{1}\) seminorm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(n\) & **NT** & \(L^{2}\) & \(H^{1}\) \\ \hline
0 & 672 & \(1.1\times 10^{-1}\) & \(6.8\times 10^{-3}\) \\
1 & 1603 & \(1.0\times 10^{-1}\) & \(3.5\times 10^{-3}\) \\
2 & 2042 & \(9.2\times 10^{-2}\) & \(3.0\times 10^{-3}\) \\
3 & 3396 & \(7.6\times 10^{-2}\) & \(2.7\times 10^{-3}\) \\
4 & 6004 & \(4.5\times 10^{-3}\) & \(2.2\times 10^{-3}\) \\
5 & 8006 & \(3.9\times 10^{-3}\) & \(1.5\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Error in \(L^{2}\) norm and \(H^{1}\) seminorm for example 3 obtained with the residual-based error estimator.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{\(c\) = auto} & \multicolumn{5}{c}{\(c\) = 0.3} \\ \hline \(n\) & **NT** & \(L^{2}\) & \(H^{1}\) & **NT** & \(L^{2}\) & \(H^{1}\) \\ \hline
0 & 672 & \(1.1\times 10^{-1}\) & \(6.8\times 10^{-  
Figure 16 on the right--to limit the number of triangles that would be refined. The use of \(c=0.3\) did not produce any significant change when \(\eta_{R}\) was employed, while, when \(\eta_{N}\) was adopted, we could appreciate a drastic reduction in the refined number of elements at every level, which did not affect the reached accuracy.  
In Figures 18-20, we report the comparison with the global refinement strategy. Regarding the use of \(\eta_{R}\) and the contamination parameter \(c=0.3\), the performance becomes worse, as it becomes comparable to global refinement. Concerning the \(\eta_{Z}\) error estimator, although the error reduction is still better than global refinement in the \(L^{2}\) norm, we can observe a rather uniform behavior when it comes to the \(H^{1}\) seminorm. Regarding the \(\eta_{N}\), the achieved results are better than global refinement for the \(L^{2}\) norm, while, in the \(H^{1}\) seminorm, the use of the contamination parameter yields a relevant reduction in the error per used degrees of freedom.
# Low-Light Photodetectors for Fluorescence Microscopy
## 3 Single Point Detectors
### Hybrid Photodetector (HPD)  
The hybrid photodetector (HPD) is a hybrid of an avalanche diode (AD) and a photocathode, both of which are in a vacuum tube (Figure 6) [23; 26; 28; 29; 30; 31]. When light is incident onto the photocathode, photoelectrons are emitted from the photocathode. The photoelectrons are then accelerated by a high negative voltage to directly bombard the AD where electron-hole pairs are generated and the signal is amplified. The amplification is termed \"electron bombardment gain\".  
In the case of an HPD manufactured by Hamamatsu Photonics, the electron bombardment gain is approximately 1500 with the photocathode supply voltage of \(-8\) kV. The signals of electron-hole pairs are further amplified to 80-fold (avalanche gain) by applying a reverse voltage of about 400 V to the avalanche diode. Then, the total gain will therefore be as much as about 120,000.
## 3 Architectural Approaches
Finally, the cloud layer demonstrates the distributed cloud concept of bee hive architecture, which is similar to the fog architecture of computing; each town cell device communicates with the local town cell cloud in the form of slices, then the town cloud communicates with the city gateway cloud (CGC). In [79], the paper discusses the different ongoing research activities on 6G architecture and outlines the potential evolution of the different parts of the network. In this context, the necessity of a large-scale intent manager is highlighted, which can provide, among others, dynamic NF onboarding as well as optimization of network resources. Moreover, a RAN low-layer split is also highlighted in 6G, which in turn would enable technologies such as CF-mMIMO that can potentially increase the user spectral efficiency compared to other cellular RAN architectures. In [80], the PREDICT-6G framework is presented and discussed, which includes, among others, AI-driven network management, multi-domain service composition, and model-driven open interfaces. Finally, in [81], various 6G use cases are presented and discussed, such as high-performance precision agriculture, intelligent transport systems, and intelligent automation systems. In the same context, the evolution and potential of vehicular ad hoc networks (VANETs) in 6G networks are discussed well, as well as the incorporation of air and space networks to ensure seamless vehicle communication across global locations. To this end, the authors highlight the importance of MEC solutions and AI/ML approaches that can leverage big data collection and analytics with minimal response times.
# Citizens' Perception of Smart Cities: A Case Study
## 4 Survey Methodology and Analysis
### Research Questions  
Based on the current literature review on smart cities and the purpose of this study, various research questions have been identified and used for further analysis. These questions lead to conclusions that reveal the Greek and Cypriot citizens' point of view on the smart city objectives, elements, and applications. The same applies to a small sample of Greek citizens who live in other European countries and have also participated in this survey. Conclusions were eventuated through multiple comparisons and statistical analyses. In addition, in this study a questionnaire was designed based on the identified research questions so that data could be gathered and analyzed to provide thorough answers to the research questions. The research questions correspond to two main categories: the first category includes the participants that have either a small or broad knowledge of the smart city concept and its contributions while the second category includes the participants that are not familiar with the term at all. The research questions that correspond to the first category are shown below:  
1. How many of the citizens who participated in the survey have heard about the \"smart city\" term? How is the result formed when age, gender and city factors are taken into account?
2. How many of the participants have a decent (or probably better) knowledge of what a smart city is? How is the result formed when age, gender and city factors are taken into account?
3. How many of the participants believe that actions have been made in their city so that it can be transformed into a smart city in a holistic way? How is the result formed when the city factor is taken into account?
4. According to the participants, Which challenges are considered as the most important for the development of a smart city? How do these results form when the city factor is taken into account?  
The research questions that correspond to the second category of participants (the \"non-familiar with the smart city term\" ones) are listed below:  
1. Does the \"Education level\" factor play a significant role in how the number of non-familiar participants is formed?
2. Are the non-familiar participants interested in acquiring further information about the smart city concept in the future? How is their opinion affected by their age and city?
## 2 Materials and Methods
### Screening for Antibiotic Resistance, Efflux Pumps and Enterotoxin Genes by PCR
#### 2.3.4 Staphylococcal Enterotoxin Genes  
All _S. aureus_ isolates were tested for the enterotoxin genes _sea_, _seb_, _sec_, _sed_, _see_, _seg_, _sle_, _sei_, and _sej_ by mixing 1 \(\mu\)L (20-100 ng) of extracted DNA, 1 \(\mu\)L (400 nM) of each primer (Table 2), 10 \(\mu\)L of master mix (Promega, Sao Paulo, Brazil), and ultrapure water (Promega, Sao Paulo, Brazil) to complete 20 \(\mu\)L. Conditions of the PCR tests were 5 min at 95 \({}^{\circ}\)C, 1 min at 95 \({}^{\circ}\)C, 1 min at 53 \({}^{\circ}\)C to 63 \({}^{\circ}\)C, depending on the toxin, 45 s at 72 \({}^{\circ}\)C, and 5 min at 72 \({}^{\circ}\)C. Amplicons and a 100 bp marker (Promega, Sao Paulo, Brazil) were submitted to 2% agarose gel electrophoresis for 30 min/100 V, stained with SYBR Safe (Invitrogen, Waltham, MA, USA), and visualized using the Gel Doc XR\({}^{+}\) system (BioRad, Hercules, CA, USA). The positive controls were _S. aureus_ ATCC 13565, for gene _sea_, _S. aureus_ ATCC 10995, for genes _seb_ and _S. aureus_ ATCC 23235, for genes _sed_, _seg_, _sei_, and _sej_, _S. aureus_ ATCC 27664 for gene _see_.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Gene** & **Primers** & **Sequences** & **Amplicon (bp)** & **Reference** \\ \hline _mecA_ & MEC-1 & AAAATCGATGTAAAGGTTGGC & & \\  & MEC-2 & AGTTCTGCAGTACCGGATTGTC & & 532 & [24] \\ _aacA-aphD_-1 & TAATCCAAGCAATAAGGGC & & & \\  & _aacA-aphD_-2 & GCCACACTATCATAACCACTA & & 227 & [24] \\ _tetK_ & TETK-1 & GTAGGCACAATAGGTTATATAGT & & \\  & TETK-2 & GTAGTGACAATTAAACCTCTA & & 360 & [24] \\  & SEA-1 & GCAGAAGAAGCTGAGGTTCG & & \\  & SEA-2 & CCAGTCCAATTACTAGCCTG & & \\  & SEB-1 & GCCATAAGTCAGAGTTTATGGA & & \\ _smr_ (_qacC/D_) & SEB-2 & GACTACGGTGTTAAGATAACCT & 195 & [25] \\ \hline \end{tabular}  
* _mecA_ = gene encoding resistance to beta-lactams; _aacA-aphD_ = gene encoding resistance to aminoglycos; _tetK_ = gene encoding resistance to tetracycline; _qacA/B_ and _smr_ (_qacC/D_) = efflux pump genes encoding resistance to quaternary ammonium biocides.  
\end{table}
Table 1: Primers used in the investigation of antibiotic resistance and efflux pump genes.