esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Network Resilience  
Network resilience is defined as the network ability to provide and maintain an acceptable level of service when facing various faults and challenges to its normal operation [7]. The work in [8] defines the resilience as the probability of at least having another path within the time interval \(T\), given that at least one node on the primary path has failed. It is considered that resilience is not only the desirable requirement for WSNs, but also for any other network and control system. For instance, resilience is commonly found in natural systems and is a hallmark of the biological world [9]. Many analysts in engineering do not differentiate resilience from robustness and survivability [10]. Robustness and survivability tend to be used interchangeably with resilience, even though in practice, these properties are quite different [9]. Resilience, robustness and survivability are system-wide properties [11]. Resilience can also be described as the ability to recover to original status. The goal of resilience is to build fault-tolerant networks. By contrast, robustness stresses the ability to resist attacks, but does not imply an ability to restore from failure [9]. If the system is robust, that means that the system is very difficult to degrade under various attacks. As for survivability, this indicates that the system is very hard to impair completely.  
In ecology, resilience can be measured by looking at the time that the system takes to return to its equilibrium state after a perturbation [11]. However, it is difficult to provide a clear quantitative assessment of network resilience to node failure [12]. The conventional evaluation is the number of failures the network can sustain before network disconnection or the probability that the network has to keep itself connected [13]. Briefly, if the network can tolerate at most \(k-1\) node failures and the resulting network is still connected, the network is called a \(k-connected\) network. If the network is \(k-connected\), this also means that there are at least \(k-disjoint\) paths from any given two distinct nodes. Therefore, if one path fails, there are still \(k-1\) alternative paths available. On the other hand, the resilience of WSNs also depends on the link quality. Reliable links can make better connectivity quality.
# An Aspect-Oriented Approach to Time-Constrained Strategies in Smart City IoT Applications
## 2 Existing Approaches
### Aspect-Oriented Approach to the WoV Problem for Data  
Aspect-oriented software design (AOSD) is a programming paradigm that localizes cross-cutting concerns into separate modules, called aspects [34]. An aspect-oriented program, then, contains \"a standard object-oriented model, with the addition of aspects--an organizational construct which co-locates code pertaining to a cross-cutting concern, even when the execution takes place in different classes\" [16].  
A mechanism called an aspect weaver is used to match particular signatures in the object-oriented model, such as calls to a method or database, and inject relevant cross-cutting code from the aspect into the execution flow of the program [35].  
In [16], we proposed an aspect-oriented mechanism for software architecture in IoT systems with WoV constraints on the data, with the goal of simplifying the architecture of IoT systems which rely on time-constrained data. In [16], we proposed a bespoke aspect weaver responsible for maintaining a table which maps data members in the object-oriented software architecture to the real-time status of their WoVs [16].  
At runtime, the aspect weaver intercepts calls to data members in the IoT device's object-oriented code. When a call is made to a time-constrained data member, the aspect weaver checks if the data member's value is up-to-date according to its WoV [16]. If so, the regular execution flow of the object-oriented program is continued, accessing the data member's value in memory as per a regular object-oriented program [16]. However, where a data member's WoV is invalidated, the aspect weaver uses network communications with other agents in the MAS to acquire an up-to-date value, which is then injected into the object-oriented program's memory [16].  
The aspect-oriented approach has the advantage of addressing both components of the WoV: relevance, through the on-demand resolution of invalidated data, and acceptability, which may be implemented through some metric such as AoI. Critically, because the aspect code is out-of-band from the regular execution flow of the program, it can be applied 'after-market' to existing IoT device code without extensive changes to the existing codebase.
# Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data
## 2 Improved Variogram of Kriging Algorithm for Sparse Data
### Principles of Kriging Interpolation Algorithm  
#### 2.1.1 Principle of Interpolation  
The basic idea of the Kriging algorithm is that a certain weight is assigned to each sample value according to the spatial correlation between sample points, especially the spatial position, and the unknowns in the estimation region were estimated using the weighted average method [25].  
As shown in Figure 1, \(Z_{i}(i=1,2,\ldots,n)\) is a set of measured magnetic field sample data with coordinate position \((x_{i},y_{i})\), and the estimated value \(\hat{Z}_{0}\) of prediction point with coordinate position \((x_{0},y_{0})\) can be expressed via a linear combination of sampled magnetic field samples:  
\[\hat{Z}_{0}=\sum_{i=1}^{n}\lambda_{i}Z_{i}, \tag{1}\]  
where \(\lambda_{i}\) is the weight of the measured point \(Z_{i}\), and \(\hat{Z}_{0}\) is called the Kriging estimation of \(Z_{0}\). As long as the weight coefficient \(\lambda_{i}\) is determined, the forecast point estimation \(\hat{Z}_{0}\) can be obtained. Kriging interpolation estimation calculates the weight \(\lambda_{i}\) under the condition that the estimated \(\hat{Z}_{0}\) is unbiased and the estimation variance is minimum.
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 4 Anti-Collision for RFID Based on TD
### RFID Anti-Collision Scheme Based on TD  
In order to suppress the interference signals from surrounding multiple RFID tags, we propose an anti-collision method based on the TD technique that allocates the tag information on the specific time slot. During reader-to-tag communication, the reader transmits a carrier signal with transmit power \(P_{r}\) to a set of arbitrary multiple tags within their interrogation zone. If the power received by the reader is greater than the minimum power received from the tag, which is known as the reader sensitivity, data transmission may be possible from the tag to the reader. All of the tags in the coverage area of the reader then become activated and transmit their identification information to the reader with a power of \(P_{R-receiver}\) whenever the energy received by the reader is higher than an activation threshold. If the measured \(P_{R-receiver}\) value is higher than the activation threshold, the transmitting signal is effective for transmitting its payload. On the contrary, if the measured value is lower than the activation threshold, the transmitting signal does not effectively transmit its payload and the data transmission may fail. In the proposed RFID system, while the particular tag using the specific time slot transmits data to the reader, other tags using other time slots do not transmit any data, but maintain their sequence order. Since tags transmit data according to their turn, in the reader, interference signals from other tags are efficiently suppressed and the communication reliability is enhanced compared to conventional RFID system which has the low performance of reading capability for multiple tags, because they do not have the interference suppression or anti-collision technique.  
Figure 2 shows an example of the transmitted signals at the tags and the received signal at the reader based on binary phase shift keying (BPSK), which uses a constant carrier signal amplitude with an independent data modulation technique and helps to provide stable power transfer in wireless communication [40; 41; 42]. In the figure, to explain the proposed system, we assume that each tag employs a unique time slot. Although in a real system, some tags can be allocated into the same time slots, interference signals from other tags are effectively suppressed compared to conventional RFID systems. We discuss the interference suppression performance for the proposed RFID system based on TD by performing computer simulations.  
Figure 2: Example of transmitted signals at tags and received signal at the reader in the proposed BPSK-based RFID system.
## 2 The Stator Core Loss Analytical Calculation Method  
An outer rotor PMSM has been manufactured. A 1/12 2D model of the machine cross-section is shown in Figure 1, and basic machine parameters are listed in Table 1.  
Figure 1: 1/12 2D model of an outer rotor space vector pulse width modulation (PMSM).  
In the paper [14], in order to consider the influence of high frequency and high flux density on the core loss of the motor, the interval was divided according to frequency and flux density. Different additional flux density terms were used to compensate for the increased value of hysteresis and eddy current loss caused by the non-linearity of the magnetic chain of the silicon steel sheet and the higher harmonic magnetic field. The calculation model can be expressed as follows,  
\[\left\{\begin{array}{l}P_{h}==\sum_{k=1}^{N}k_{r}k_{h}kf[B_{nk}^{\alpha}(n_{1k }B_{nk}^{\beta_{2k}})]\\ P_{h}=\sum_{k=1}^{N}k_{c}k^{2}f^{2}[B_{nk}^{2}(1+n_{2k}B_{nk}^{\beta_{2k}})]\\ P_{ex}=\sum_{k=1}^{N}k_{cxx}k^{1.5}f^{1.5}B_{nk}^{1.5}\end{array}\right. \tag{1}\]  
where, \(k_{r}\) is the rotating magnetization loss coefficient, \(k_{r}=1+\frac{\left|B_{kmin}\right|}{\left|B_{kmax}\right|}\), \(B_{kmax}\) and \(B_{kmin}\) are the magnetic flux densities of the two axes of the elliptical rotating flux density. \(B_{nk}\) is the \(k\)-th flux density harmonic amplitude. \(n_{1k}B_{nk}^{\beta_{2k}}\) is the low-order term of the additional flux density for hysteresis loss. \(n_{2k}B_{nk}^{\beta_{2k}}\) is the high-order term of eddy current loss and flux density. \(n_{1k},\beta_{1k},n_{2k},\beta_{2k}\) are the additional loss coefficients, which can be obtained by fitting the orthogonal magnetization loss data at different frequencies measured, and the loss coefficients all change with changes in magnetic flux density and frequency.  
Based on the stator loss coefficient and the flux density distribution of each internal split unit, the core loss density of each split unit of the stator was obtained. On this basis, the stator core loss obtained by the summation is,  
\[P_{Fe}=L_{a}\int_{s}(P_{h}+P_{e}+P_{ex})\rho ds=L_{a}\rho\sum_{i=1}^{N_{z}}{(P _{h}^{(i)}+P_{e}^{(i)}+P_{ex}^{(i)})\Delta_{s}^{(i)}} \tag{2}\]  
where, \(L_{a}\) is the axial length of the stator. \(\rho\) is the density of the stator silicon steel sheet. \(P_{h}^{(i)}\), \(P_{e}^{(i)}\), \(P_{ex}^{(i)}\) are the hysteresis, eddy current, and additional loss density of the \(i\)-th stator unit, respectively. \(\Delta_{s}^{(i)}\) is the area of the \(i\)-th unit, and \(N\)s is the number of split units.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 2 Background and Related Work
### SCION Multi-Path Routing Architecture  
SCION is the first clean-slate Internet architecture designed to provide route control, failure isolation, and explicit trust information for end-to-end communication. SCION organizes existing ASes into groups of independent routing planes, called isolation domains (ISD), which interconnect to provide global connectivity. Isolation domains provide natural isolation of the routing failures and misconfigurations, give endpoints strong control for both inbound and outbound traffic, provide meaningful and enforceable trust, and enable scalable routing updates with a high path freshness. As a result, the SCION architecture provides strong resilience and security properties as an intrinsic consequence of its design. In addition to high security, SCION also provides a scalable routing infrastructure, and efficient packet forwarding.  
As a path-based architecture, SCION end hosts learn about available network path segments, and combine them into end-to-end paths that are carried in the packet headers. Path construction embedded cryptographic mechanisms are constrained to the route policies of the ISPs and receivers, offering a path choice to all parties, i.e., senders, receivers, and ISPs. This approach enables path-aware communication, an emerging trend in networking. These features also enable multi-path communication, which is an important approach for high availability, rapid failover in the case of network failures, increased end-to-end bandwidth, dynamic traffic optimization, and resilience to DDoS attacks [14]. SCION enables a native network-wide multi-path routing, allowing traffic differentiation based on latency and the bandwidth requirements, i.e., latency-critical traffic can be sent over the satellites, and the remaining traffic can be forwarded on a terrestrial network, thus improving the cost-effectiveness of the system [40].
# An Assessment of Environmental RF Noise Due to IoT Deployment
## 1 Introduction
### Research Aim  
The paper hypothesized that the higher the environmental noise floor level, the more radio emissions increase. According to [PERSON] [6], the availability of the free industrial, scientific, and medical (ISM) parts of the radio spectrum encouraged countless IoT applications. Consequently, any negligence towards policy and regulatory conformity may causeadverse consequences to the IoT industry and deteriorate some wireless operations. It was, therefore, essential to justify with evidence if these concerns remained applicable to IoT development characterized as low RF power applications. The main aim of this research was to statistically evaluate the proportional relationship between the level of ambient radio noise and the emissions rates generated by artificial IoT RF apparatuses. This paper examined three ISM frequencies, 433 MHz, 868 MHz, and 2.4 GHz, in industrial, urban, and suburban areas distributed over five Durban (South Africa) candidate sites. The IoT radio technologies include Wi-Fi, Bluetooth, ZigBee, Sigfox, Lora, and proprietary protocols, commonly operating under 433 MHz, 868 MHz, 915 MHz, and 2.4 GHz frequencies. The experiment had to comply with the local regulations applied to ISM band usage. Except for the 915 MHz, the Independent Communication Authority of South Africa (ICASA) approved the 433 MHz, 868 MHz, and 2.4 GHz ISM bands [3; 7; 8]. This research applied Universal Software Radio Peripheral (USRP) as a tool for software-defined radio (SDR), and the GNU Radio system for data collection. The study employed descriptive statistical analysis and heuristic empirical methods to provide informative insight.
## 1 Introduction
Some salient features of CeO\({}_{2}\) which make it a potential material for various industrial applications include: (i) exceptional mechanical strength and high transmission, (ii) high oxygen storage capacity and high conductivity, (iii) high specific surface area and good redox performances, (iv) thermally stable at high temperatures and have abundant active sites, and (v) have a lot of oxygen vacancies on the surface. All these properties can further be improved by making composite or doping of metals or metal oxides in ceria. Various transition metal oxides such as zinc oxide, manganese oxide, nickel oxide, titanium oxide, and copper oxide have been doped in ceria to enhance its performance [14]. The composites of cerium oxide have potential applications such as electrochemical sensors, photo-cataly as well as in energy storage systems and supercapacitors. [PERSON] et al. [15] used the solution combustion route to synthesize the nanocomposite of NiO/CeO\({}_{2}\) where they used metal nitrates as oxidizers and glycine as fuel. Different characterization techniques including XRD, TEM, and FTIR were used to investigate structural along with morphological and surface characteristics of this nanocomposite. Their results revealed that these compositions were suitable for electrochemical systems and can be used as electrodes due to their reproducibility as well as electrocatalytic activity and stability. The same group of researchers [16] doped ZnO in cerium oxide via a microwave-assisted method and confirmed the crystalline structure of composites with a high density of particles. [PERSON] et al. [17] investigated the physiochemical characteristics of the nanocomposite of CeO\({}_{2}\) by doping Cu in it. By increasing the doping concentration of Cu, band gap energies were increased which is an indication of the production of oxygen vacancies. These compositions can be utilized as electrode material for fuel cells and electrochemical systems as they can be operated at low temperatures up to 162 \({}^{\circ}\)C [18]. In the present work, we synthesized nanocomposites of NiO/CeO\({}_{2}\) via hydrothermal route and investigated their structural, microstructural, morphological, optical, and electrochemical properties in detail which witnessed the importance of nanocomposites as an electrode material for energy storage devices.
# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
## 3 Multi-Feature Fusion Image Dehazing Network
### Loss Function  
In the field of image dehazing, commonly used loss functions primarily include L1, L2, and structural similarity index (SSIM) loss functions. [PERSON] et al. [44] posited that different loss functions might engender distinct issues in varying application contexts. While relying on the L2 loss function can achieve certain dehazing effects, it may lead to rasterization distortion in the restored images, negatively impacting the overall image quality. Employing the L1 loss function may result in relatively higher peak signal-to-noise ratio (PSNR) values for images, but this does not necessarily indicate that it is the optimal approach. Although using the L1 loss function enhances the PSNR values of images, it does not imply that it is the best choice. This is because the L1 loss function does not take into account the variances in human visual perception of images, which may lead to significant visual discrepancies between the restored images and the original authentic images. In contrast, the SSIM loss function, which integrates structural similarity and luminance information, better reflects human visual perception of image quality.  
To achieve the dual objectives of optimizing visual quality and accuracy in image dehazing, this chapter adopts a hybrid loss function--MS-SSIM + L1 loss [44]--as specified in Equation (14). This mixed loss function ingeniously combines multi-scale structural similarity (MS-SSIM) with L1 loss, reducing prediction error while maintaining image structural details. The MS-SSIM component is employed to assess and optimize the structural similarity of images across different scales, while the L1 component focuses on minimizing the absolute error at the pixel level, thereby enhancing the accuracy of the restored images.  
\[L_{MS-SSIM+\ell_{1}}=\rho\cdot\mathcal{L}^{MS-SSIM}+(1-\rho)\cdot G_{\sigma_{G }^{M}}\cdot\mathcal{L}^{\ell_{1}} \tag{14}\]  
where \(\rho\) is a constant employed to balance the proportions of two loss functions. The experimental results of [PERSON] et al. [44] indicate that the optimal dehazing effect can be achieved when \(\rho=0.84\), and \(G\) represents the parameters of the Gaussian distribution. \(\mathcal{L}^{MS-SSIM}\) and \(\mathcal{L}^{\ell_{1}}\) represent the MS-SSIM and L1 loss functions, respectively, with their specific formulas provided in [44]. By training with this loss function, MFID-Net is able to produce more realistic and clearer dehazed images.
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 2 Relative Work
### Knowledge Distillation  
Rather than preserving old samples, the Learning Without Forgetting (LWF) [15] is a pioneering incremental learning effort that uses knowledge distillation to minimize the representation difference between old and new classes. During the incremental learning process of this method, the network parameters related to the old classes are frozen, only the new parameters are trained, and finally, all network parameters are jointly trained until convergence. However, this method relies highly on the correlation between the new task and the old task, which will cause task confusion when the tasks' difference is significant. The training time increases linearly with the number of tasks. The Learning Without Memorizing (LWM) [27] is also a knowledge distillation approach without the necessity to preserve the old samples. This method employs an information preserving penalty that uses the attention distillation loss to obtain changes in the model's attention graph and preserve old knowledge. Instead of sequentially extracting knowledge only from the model of the penultimate task, the Multi-model and Multi-level Knowledge Distillation for incremental learning (M2 KD) [30] directly applies all previous model information. It also proposes an additional distillation term that runs in the middle layer of the network in addition to the fully connected layer.  
Based on the above analysis, this paper adopts the knowledge distillation strategy. Each incremental phase uses a well-trained model on the old classes to initialize the model that will learn the new classes. The advantage of this method is that it does not require any change in the model structure, while preserving the knowledge that the model has learned.
## 3 Data and Method  
A comparison study has been done to validate the benchmarking of energy consumption of accommodation buildings in Japan. The regression model was used to formulate the benchmark equation, and ANNs were used to validate the regression model, since ANNs have more accurate predictions. Both the training method and hidden layers of ANNs were chosen carefully to obtain high accuracy results. Three fit measures, namely, mean square error (MSE), mean absolute percentage error (MAPE) and the coefficient of efficiency (R) were adopted to evaluate the model performance.  
The study depends on three main variables which are electricity, gas and water. These variables were selected because of several considerations, such as that the DECC database of accommodation buildings contains some other variables, but there is no good correlation with energy consumption. DECC does not have information regarding HVAC and the behavior of guests. Therefore, the future step for this study is to collect more details for some samples in Japan.  
Generally, Japanese accommodation buildings depend on electricity and natural gas for lighting, air condition and hot water supply. In recent years, more buildings have come to depend on natural gas turning on heating and cooling equipment.
## 5 Results
### Ult  
As described in Section 4.4, the ULT measurements have been performed with various frequencies in the range of 3 kHz to 130 kHz. However, the lock-in analysis of only a few frequency bands results in an amplitude and phase image that show some features. These frequency bands are 20 kHz to 30 kHz and 110 kHz to 120 kHz (periodicity: 5s) for the area I and area II respectively. The amplitude and phase images for these frequency bands are shown in Figures 9 and 10 and highlight the presence of the resonating and periodical heating defect rivet and screw which have also been visible in Figures 7b and 8a.  
Figure 8: The band pass filtered and normalized RMS heat map for different frequencies (**a–d**) for area I of the fuselage panel. The color bar at the bottom refers only to (**d**) as it is adapted for each filter according to the description above.  
Figure 9: The amplitude (**a**) and phase image (**b**) of area I for a frequency range of 20 kHz to 30 kHz and a periodicity of \(\tau=5\) s. The dark spot in both images corresponds to the damaged rivet.