esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Security and Privacy Challenges and Limitations in AMRSys
### Intruders  
Any device or system or individual adult or minor who aims to steal, tamper with, or destroy information can considered an intruder. It would be hard to identify and distinguish between an intruder and any unintended access attempt from authorised users, in terms of their activity. Even if activity is monitored to detect and prevent intrusion, it can be challenging to identify an attack in time, depending on the nature of the attack. An attack on a system can take multiple forms, and methods include flooding attacks, redirection attacks, replay attacks, malware attacks, etc. As such, the best first line of defence should include malware detection, patching of software flaws, white-listing, and application execution control, as well as the monitoring of activity and incorporating a network defence system if remote access is required. Above all, the designing of efficient continuous authentication and authorisation mechanisms for a successful secure access control technique is required [62].
## 1 Introduction  
Maxillary sinus augmentation (MSA) is a well-documented treatment modality for rehabilitating pneumatized posterior maxilla [1]. Despite the reported high predictability, several complications following MSA have been reported. These include graft infection, acute/chronic sinusitis, implant failure, and implant displacement [2, 3]. However, some complications, such as postoperative maxillary cyst (POMC), appear not to have wide awareness among dentists.  
POMC or surgical ciliated cyst is a benign expansive cystic lesion of the maxilla. POMC was first described in a Japanese article in 1927 [4], possibly due to specific etiologic factors in a specific region. Evidence indicates that Caldwell-Luc operation, orthognathic surgery in the maxilla, and maxillofacial trauma can predispose to POMC [5, 6, 7]. The most commonly accepted mechanism of POMC is entrapped sinus mucosa after surgery/traumatic event and gradual growth as an epithelial lining cavity [8], potentially indicating that MSA cannot be excluded from the causality of POMC.  
In 1991, [PERSON] et al. first demonstrated POMC after MSA [9], followed by a few case studies regarding this issue [10, 11, 12, 13]. POMC after MSA in previous literature presented with the following features: (1) sinus membrane perforation during MSA might be an etiological factor, (2) duration of development or detection largely varied between 3 months and 10 years, (3) the expansion of POMC resorbed the adjacent bone structures and augmented bone, sometimes involving a dental implant(s), and (4) intraoral surgical approach was generally chosen for complete enucleation of POMC.  
This case report presents an invasive case and the relevant treatment course of POMC, which was detected 18 years after MSA and invaded the inferior meatus of the nasal cavity.
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 3 Technical Challenges and Requirements
### Backhauling  
Satellite and 5G technologies are considered as the main candidate technologies to provide wireless backhauling for U-RANs. Satellite backhauling offers unlimited coverage and allows aerial networks to be connected for any practical distance (see Figure 4). However, the latency introduced by satellite links considerably affects delay-sensitive services, such as real-time audio/video conversations. On the other hand, compared to satellite backhauling, terrestrial links have the advantages of lower cost and latency, but they provide reduced backhaul coverage. Other solutions for wireless backhauling are mmWave and FSO communications with ground stations. The choice of the best backhauling technology is a challenging issue possibly affecting the overall performance of U-RANs [41; 59; 60; 61; 62].
# Management Platforms and Protocols for Internet of Things: A Survey
## 4 IoT Network Management Protocols
### Network Configuration Protocol (NETCONF)  
The NETCONF is a protocol for network configuration and monitoring, as defined by the IETF [35] and, therefore, has better features than the SNMP, which had as its weakest point the absence of network configuration resources, i.e., the interface BER (Basic Encoding Rules) and proprietary MIBs. The NETCONF protocol was developed to be the natural successor to SNMP, because SNMP is focused on monitoring and not on network configuration while NETCONF uses mechanisms that allow the installation, manipulation, and removal of network device configuration through a client-server implementation, as shown in Figure 7.  
After establishing the secure transport session between client and server, the NETCONF protocol sends a _HELLO_ message to announce the protocol capabilities and supported data models. NETCONF also supports the subscription and receipt of event notifications asynchronously as well as the partial closing of a current configuration of a network device. This feature allows multiple editing sessions, streamlining the configuration process. NETCONF allows monitoring and management of an autonomous entity (the NETCONF manager) that uses the repository of data, sessions, closings, and statistics available on the NETCONF server.  
The NETCONF protocol transports this information to an application manager, who can infer the required settings for the network devices. YANG is a formal language with clear text of the data model with syntax and semantics that allow the construction of network applications [36]. The YANG model  
Figure 6: Illustration of the SNMP protocol for IoT devices.  
Figure 7: Illustration of the NETCONF Protocol for IoT Architecture.  
can be translated into an XML (Extensible Markup Language) or JSON (JavaScript Object Notation) file, structured in a tree for each module, with properties that correspond to the functionalities of the device and declarations of types, data, constraints, and additions of reusable structures.  
Heterogeneous networks are characteristic of the IoT, and the NETCONF protocol is used to efficiently manage and resolve issues of this network. Most operating systems developed for IoT [37] such as TinyOS and Contiki OS already have the NETCONF protocol built into their operating system.  
_Pros: Robust and security features. Cons: a data model and an architecture of standardized implementation._  
Use Case  
Currently, most vendors, e.g., Cisco, already use the NETCONF protocol on their equipment as a standard model. Another example, the OpenFlow devices controller communicates with connected devices in an SDN architecture, also defining a protocol for such communication. The OpenFlow provides means to control network devices using NETCONF, without the need for manufacturers to expose the code of their legacy products [38].
# Camera Calibration with Phase-Shifting Wedge Grating Array
## 2 Principle
### Calibration Pattern  
Phase-shifting methods are widely used in optical metrology, which can provide very high precision and dense coding [14,20]. This paper encodes the feature points into the carrier phase of three PWG arrays with a phase shift of \(2\pi/3\). The wrapped phase of the patterns can be recovered with phase-shifting algorithm. The intensities of the PWG are, respectively, described as [17]:  
\[\left\{\begin{array}{l}I_{1}(x,y)=A+B\cos(2\pi f\theta-2\pi/3)\\ I_{2}(x,y)=A+B\cos(2\pi f\theta)\\ I_{3}(x,y)=A+B\cos(2\pi f\theta+2\pi/3)\end{array}\right., \tag{5}\]  
\[\theta(x,y)=\arctan\left(\frac{y-y_{0}}{x-x_{0}}\right), \tag{6}\]  
where \((x,y)\) denotes one point on the PWG; \(A\) is the average intensity, and \(B\) is the intensity modulation; \(A\) and \(B\) are constant, usually we set \(A=B=0.5;2\pi f\theta\) is the phase to be determined, and \(f\) is the frequency, \(\theta(x,y)\) is the angle with x-axis that is expressed as Equation (6); the radius \(r=\sqrt{\left(x-x_{0}\right)^{2}+\left(y-y_{0}\right)^{2}}\) is the Euclidean distance between the points \((x,y)\) and the PWG center \((x_{0},y_{0})\), and \(r_{\min}<r<r_{\max}\) is used to restrict the size of the PWG. Figure 1a shows the PWG arrays contains \(3\times 3\) uniform PWGs with \(f=2/\pi\) and a phase shift of \(2\pi/3\). Figure 1b shows a single PWG, also the basic unit of the second PWG array, the corresponding wrapped phase of the PWG is shown in Figure 1c.  
Figure 1: Phase-shifting wedge grating (PWG) pattern. (**a**) Three PWG arrays; (**b**) a single PWG; (**c**) its wrapped phase; and, (**d**) detected \(2\pi\)-phase lines and their intersection.  
Obviously, the \(2\pi\)-phase points are distributed on two straight lines that intersect at the PWG center, as shown in Figure 1d. If we can detect the two \(2\pi\)-phase lines in the image, the PWG center can be located through computing the intersection. Since one PWG only has one center, some identical PWGs are arranged to generate PWG array, and their centers are used as feature points. Figure 2a shows the PWG array consisting of \(3\times 3\) PWGs. Specifically, for the \(M\times N\) PWG array, the PWG at \(m-th\) row and \(n-th\) column is centered at:  
\[\left\{\begin{array}{l}x_{0}^{mn}=x_{0}+(n-1)d\\ y_{0}^{mn}=y_{0}+(m-1)d\end{array}\right., \tag{7}\]  
where \(m=0,1,\cdots,M-1\); \(n=0,1,\cdots,N-1\); \(d\) indicates the distance between adjacent PWG centers between the horizontal or vertical direction, which must be greater than \(2r_{\max}\) to avoid interference between adjacent PWGs. Figure 2b shows its wrapped phase image.
## 3 Microwave Photonics Applications of DFT
### DFT for Microwave Arbitrary Waveform Generation
#### 3.2.1 General Concept  
Among the various photonics-assisted approaches, DFT technique has also been extensively investigated for photonic generation of high-frequency and broadband microwave waveforms with arbitrary shapes. A schematic diagram showing the fundamental principle of the technique is illustrated in Figure 5. The system consist of an ultrashort pulsed source, a spectral filter and a dispersive element. The broadband optical pulse is first sent to an optical spectral filter with a properly designed spectral response. The spectrum-shaped optical pulse is then passing through a dispersive medium to undergo the dispersion-induced frequency-to-time mapping. A microwave waveform with an envelope identical to the shaped optical power spectrum is finally obtained at the output of a high-speed photodetector. A key feature of this technique is that the temporal pulse shaping is performed in the frequency-domain, which is easy to implement using an optical spectral filter.  
The key device in a DFT-based microwave arbitrary waveform generation system is the optical spectral shaper. A number of approaches for optical spectral shaping of an optical pulse have been proposed. For example, spectral shaping can be implemented in the spatial domain based on spatial masking of the spatially dispersed optical spectrum, where a spatial light modulator (SLM) is usually used to manipulate the input optical spectrum [98]. The key advantage of the SLM-based spatial domain optical spectral shaping techniques is that the filter response can be reconfigured in real time with the help of a computer [22, 99]. Recently, a fully-programmable integrated optical spectral shaper fabricated on a silicon photonic integrate circuit has been demonstrated for microwave arbitrary waveform generation [100]. The spectral shaper is based on cascaded multi-channel microring resonators and response reconfigurability is achieved by thermal tuning using micro-heaters. These techniques, however, suffer from the difficulties such as complex alignment with strict tolerances, high cost and high coupling loss between free-space/waveguide optics and fiber optics. One solution is to use all-fiber optical spectral filters, which have the advantages of alignment free, simpler structure, better stability, lower loss and cost, and inherently good compatibility with other fiber-optics devices. In addition, an all-fiber system has high potential for integration. As a unique in-fiber device, FBG has been widely applied in optical spectral shaping as both the magnitude response and the phase response of an FBG can be precisely designed for complex spectral filtering [13].
## 4 Discussion
Then, the distance matrix is sorted twice by row and column following Equation (2). The running time to generate matrix \(A\) is \(O(nklogk)\) and the time to generate \(B\) is \(O(knlogn)\). Finally, the allocating process costs \(O(nk)\) following Equation (5) and the updating of the centroids costs \(O(nd)\). To sum up, the proposed algorithm can be implemented in time \(O((nkd+nklogk+knlogn+nk+nd))=O((nkd+knlogn)i)\), where \(i\) is the number of iterations needed for convergence. The time complexity can be simplified as \(O(n\text{logn})\), if \(k\), \(d\), \(i\) are treated as constants. This value is relatively larger than the running time \(O(n)\) of the conventional K-means algorithm, as an additional ranking process is introduced to balance the uniformity of the object number in the cluster. Figure 14 demonstrates the computational time in MATLAB of the proposed algorithm in terms of the number of objects. It is worth noting that the algorithm is proposed to guide the design of the fixed beam antenna of the multi-beam satellite. It would be implemented only in the early stages of design for task analysis until the allocation of the beam spots is completed; there is no real-time requirement in this application scenario. Figure 13: Characteristics of the beam widths: (**a**) Standard deviation of half-cone angles in terms of \(\alpha\) and \(\beta\); (**b**) Maximum of half-cone angles in terms of \(\alpha\) and \(\beta\).
## 3 Experimental Results  
### Nonmetallic Pipe Penetrations  
Figure 4 presents the fire-resistance test results for firestop systems used in nonmetallic pipe penetrations. In these specimens as described above, thermocouples were installed at the slab, mounting frame, and pipe, to measure the time-temperature response on the unexposed side. Penetrant and opening diameters were grouped into four nominal ranges (50-75 mm, 75-100 mm, 100-125 mm, and 125-150 mm), and the firestop-material volume was reduced proportionally as the opening size increased.  
The slab area exhibited the most stable temperature distribution under all test conditions, with most readings remaining below 100 \({}^{\circ}\)C throughout the test. This stability is attributed to the slab's horizontal orientation, which reduces direct radiant-heat exposure, and its relatively high thermal capacity, which suppresses temperature increases. However, in the 50-75 mm and 75-100 mm specimens, elevated initial temperatures occurred when the nonmetallic pipes softened and formed voids that were not immediately sealed by the firestop material. As the intumescent material expanded, these voids closed, and temperatures stabilized.  
In the mounting frame area, heat concentration intensified with increasing opening diameter. This effect was especially pronounced when the penetrant diameter exceeded 100 mm, with temperatures approaching 120 \({}^{\circ}\)C. This junction between the pipe and slab is subject to complex interactions between the firestop-material density and thermal-stress concentration, making it a vulnerable zone where sealing performance may be weakened by delayed intumescent expansion or uneven material distribution.  
Figure 3: Test-specimen configurations and thermocouple positions: (**a**) nonmetallic-pipe; and (**b**) metallic-pipe.  
The pipe area showed the highest temperature rise among all measurement points, reaching approximately 170 \({}^{\circ}\)C under the 125-150 mm condition. Rapid softening and collapse of the nonmetallic pipe formed internal voids that the firestop material could not seal fully, resulting in heat leakage. Similar findings by [PERSON] [11] indicate that the thermal-deformation properties of nonmetallic pipes can critically affect fire-resistance performance.  
Nonetheless, all specimens remained below the 180 \({}^{\circ}\)C temperature-rise limit, suggesting that firestop material alone can provide sufficient thermal insulation. Despite nonmetallic pipes softening and thereby enlarging the penetration under high heat, effective fire resistance was still achieved with appropriate firestop-material distribution and intumescent expansion. This trend is further supported in subsequent sections, where the firestop-material distribution ratio is confirmed as a key performance variable. Figure 5 presents the post-test condition of the nonmetallic-pipe specimens. All penetrants remained structurally stable, with slight surface charring observed around some firestop interfaces.
# Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
## 3 Experimental Analysis
### Experimental Implementation  
**Software**: Matlab R2020a (Math-Works); Windows 11 operating system; the processor is Intel-Core i9-10 th; the graphics card is Nvidia GTX1080 Ti.  
**Network structure**: After multiple experiments, the number of hidden neurons was selected as 32, with a structure of 5-10-1 and LSTM layers of four. The loss function was the mean square error RMSE. The attention mechanism dimension was 256, and there were 250 iterations. In addition, learning and momentum factors were included, with values of 0.01 and 0.04, respectively. The input data length was 750 and the output length was 220.  
**Comparison standards**: Three comparison standards were selected in this paper, to evaluate the performance of different algorithms, including the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE).  
**Data preprocessing**: The AW-LSTM network involves different physical quantities, and different input nodes may have varying values, due to different environmental factors [13]. Therefore, this paper first normalized the experimental data, to ensure the stability of the neural network training phase, with data normalization as follows:  
\[\mathbf{x}^{\prime}=\frac{\mathbf{x}-\mathbf{x}_{\text{min}}}{\mathbf{x}_{\text{max}}-\mathbf{x}_ {\text{min}}}, \tag{8}\]  
where \(\mathbf{x}\) represents the original data, \(\mathbf{x}^{\prime}\) represents the normalized data, and \(\mathbf{x}_{min}\) and \(\mathbf{x}_{max}\) represent the minimum and maximum values in the data, respectively.  
**Model training**: To verify the effectiveness of the AW-LSTM model, this paper used the bearing signal data of the first two months as input to predict the load data of the following week. The weights of the neurons in the proposed model were initialized using Gaussian distribution with a mean of 0 and a variance of 1. Figure 6 shows the training error convergence curves of the proposed AW-LSTM algorithm on different datasets. The left figure shows the error curve with 12 months of SCADA data as the training set and the right figure shows the error curve with 12 months of OWT data as the training set.  
Figure 5: Empirical mode decomposition (EMD).  
The AW-LSTM network parameters were set as follows: an Adam optimizer was used for optimization, with an initial learning rate of 0.002. It can be observed from the figure that the proposed AW-LSTM reduced the training error gradually, with the increase of iteration times on different datasets, which demonstrates the effectiveness and generalization of the proposed algorithm. Moreover, it can be observed from Figure 5 that AW-LSTM gradually converged at 1000 iterations on the two public datasets, indicating that the proposed method has good convergence speed.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.15. Pheromone-Based Vehicle to Vehicle (PBV2V) Routing  
PBV2V Routing [25] is a bionic V2V routing scheme that introduces the concept of pheromones. All vehicles frequently exchange their position using beacons to vehicles in the same transmission range, as well as their pheromone densities.  
In PBV2V, each vehicle receives and updates one broadcast message from each neighbor in every period, denoted by \(T_{update}\), and the set of \(i\) neighbors is represented by \(N_{i}\). For every potential destination \(d\), vehicle \(i\) finds the set of neighbors that have the highest pheromone densities, i.e., Equation (20), and the highest pheromone value is \(P_{id}^{MAX}=arg\underset{j\in N_{i}}{\max}\tau_{jd}\).  
\[N_{id}^{MAX}=\left\{f^{\prime}|\tau_{jd}=\underset{j\in N_{i}}{\max}\tau_{jd}\right\} \tag{20}\]  
If \(N_{id}^{MAX}\) is not empty and \(P_{id}^{MAX}>\tau_{id}\), some neighbors must have higher pheromone density (i.e., closer to target \(d\) than \(i\)). Therefore, \(i\) sets its pheromone \(\tau_{id}\) as \(P_{id}^{MAX}-1\). Otherwise, to simulate  
Figure 10: Example with various road-vehicle densities [30].  
evaporation, the pheromone density of \(d\) is reduced by 1. So, the selection of the transmission path from source to destination is based on pheromone density. This proposal reduces network overhead and search time.  
2.16 Adaptive Geographical Routing Based on Quality of Transmission for Urban Vehicular Networks (AGQOT)  
In [42], AGQOT was proposed. The authors proposed a metric named quality of transmission (QOT) to measure the performance of each road segment, which combines the connectivity with Packet Delivery Ratio (PDR). They also proposed an improved greedy-forwarding strategy to guarantee fast and reliable packet transmission.  
The novel metric, the proposed QOT represents connectivity and PDR together on the path segment, and it was evaluated as shown in Equation (21).  
\[\begin{array}{c}p_{opt}=a\cdot p_{connectivity}+b\cdot p_{pdf},\\ a>=0;b>=0;a+b=1;\end{array}, \tag{21}\]  
where \(p_{opt}\) represents QOT, \(p_{connectivity}\) indicates connectivity, and \(p_{pdf}\) stands for the corresponding PDR when the path composed of vehicles is connected.  
For convenience, the weight of each road segment was defined as the negative logarithm of the corresponding QOT, by which these weights across several road segments can be added, i.e., \(C_{i}=-logp_{quat}\).  
Finally, when a packet arrives at one intersection, several adjacent intersections appear as candidates. The one with larger QOT in the set obtains the higher priorities.