esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Reputation Management System
### Reputation Manager
#### 4.2.1 Total Reputation Value of Nodes  
The total reputation of a node in the proposed model denoted by \(\mathbf{f_{T}}\) is the combination of the individual final reputation values of a node in terms of the data packets transmitted, \(\mathbf{\hat{\textit{h}}_{data}}\), and the routing control packets transmitted, \(\mathbf{\psi_{control}}\). Mathematically, \(\mathrm{R_{T}}\) is given by the equation below:  
\[\mathbf{f_{T}}= \lambda\mathbf{\hat{\textit{h}}_{data}}+ \rho\mathbf{\psi_{control}} \tag{7}\]  
where \(\lambda\) and \(\rho\) are given as 0.8 and 0.2, respectively. The values of \(\lambda\) and \(\rho\) are based on the relative importance placed on the final reputation value of a node in regards to the type of packets it transmits continuously for other nodes.
## 3 Results  
### Analysis of Dominant Factors for Extreme Changes in WUE  
#### 3.1.1 Dominant Factors Driving Changes in WUE in China from 2001 to 2020  
The spatial patterns of the dominant factors driving extreme changes in WUE across China from 2001 to 2020 demonstrate a consistent trend (Figure 2a,c). GPP and ET collectively impacted extreme changes in WUE across semi-humid and humid regions (43.72%), with concentrated distributions in the northeastern part of NE, the dominant vegetation types are WSN and DBF. ET had a dominant impact on extreme WUE changes in arid and semi-arid regions (20.17%), particularly in the western part of SW, the eastern part of NW, and the western part of NE, where the dominant vegetation type is GL. This suggests that water availability constrained vegetation transpiration and controlled WUE variations. Conversely, GPP predominantly influenced extreme changes in WUE across humid regions (36.00%), indicating that, under adequate water supply, vegetation carbon fixation, rather than transpiration, becomes the primary limiting factor for WUE changes. Overall, extreme changes in vegetation WUE across China are primarily driven by the combined effects of GPP and ET. However, extreme WUE changes dominated by a single factor (GPP or ET) are closely tied to environmental water conditions. Statistical analysis of seven subregions reveals an opposing trend in the proportions of areas dominated by GPP and ET along a gradient from NW to SC. Specifically, the area dominated by GPP increases gradually, while the area dominated by ET decreases correspondingly, illustrating a clear spatial gradient pattern. Notably, when WUE reaches its minimum, its variation is entirely unaffected by ET in SC (Figure 2b,d).
## 3 Experimental Implementation  
### Single Faults Dataset  
Bearing fault data from Case Western Reserve University (CWRU) were collected for single bearing faults and normal bearings of drive-end defects [18]. The accelerometer and acquisition system used to measure vibration signals were set at a sampling rate of 12 kHz. Defects having multiple crack sizes (0.007, 0.014, and 0.021 inches in diameter) occurred on the ball, outer raceway, and inner raceway. Moreover, the data were also collected at various shaft speeds (i.e., approximately 1722, 1748, 1772, and 1796 revolutions per minute (RPM)) caused by varying loads (i.e., 0, 1, 2, and 3 horsepower (hp)), from the testbed shown in Figure 4.  
From CWRU data, we formulated four different datasets for the experiments, as depicted in Table 1. In each of those datasets, signal samples at a certain shaft speed were used for training CNN models. The number of samples for each bearing fault type at a certain crack size (\(N_{Samples}\)) was 80, the number of different crack sizes (\(N_{Crack\_Size}\)) was 3, and the number of bearing fault types (\(N_{Class}\)) was 4 (inner race bearing fault, outer race bearing fault, roller fault, and normal condition). Therefore, the total number of samples for training (\(N_{Classes}\times N_{Crack\_size}\times N_{Samples}\)) was 960.  
On the other hand, signal samples at the rest of different shaft speeds were used for testing. For testing, the number of samples for each bearing fault type at a certain crack size and at a certain speed (\(N_{Samples}\)) was 40, the number of different crack sizes (\(N_{Crack\_Size}\)) was 3, the number of bearing  
Figure 4: Case Western Reserve University seed fault bearing testbed [18].  
Figure 3: Applied VGG16 architecture for classifying spectrogram images.  
fault type (\(N_{Classes}\)) was 4, and the number of different shaft speeds used for testing (\(N_{Spead}\)) was 3. Therefore, the total number of samples used for testing (\(N_{Classes}\times N_{Check\_size}\times N_{Samples}\times N_{Spead}\)) was 1440.
# Energy Transition on Sifnos: An Approach to Economic and Social Transition and Development
## 1 Introduction  
### The Sifnos Island  
Sifnos is a small, so far non-interconnected island, either with the mainland system or with a neighboring insular grid, located in the western part of the Cyclades complex in the Aegean Sea, Greece (Figure 1), with a population of 2625 and a total area of 73.9 km\({}^{2}\)[1]. The economy on Sifnos is based on tourism. As shown in Figure 2, more than 75% of the human resources on the island are occupied in jobs related to tourism [2,3] (apart from human health, education and administration). The energy consumption on the island is found in:  
* The coverage of the energy needs in the building sector (residential, commercial and administrative facilities), which traditionally refer to heating and cooling, lighting and mechanical power in motors. These energy needs are almost exclusively covered by electricity, except a small contribution of diesel oil and solar collectors for heat production [4,5]. * The municipal public lighting and the water pumping, desalination and distribution facilities, covered exclusively by electricity. * The transportation on the island, covered exclusively by liquid fossil fuels (diesel oil and gasoline). * The overseas transportation from and to the island, covered by liquid fossil fuels (heavy fuel and diesel oil). The final energy consumption levels are analyzed in Table 1[6]. These figures were derived from the Clean Energy Transition Agenda (CETA) of Sifnos. The CO\({}_{2}\) emissions were calculated following the factors given for each different energy source in the Greek Directive for Buildings Energy Performance Upgrade [7]. Figure 1: The map of Sifnos and its geographical location in Greece. Figure 2: Percentage graph of the occupied employees on Sifnos per occupation category. In brief, the following conclusions may be drawn by observing these facts:  
* Electricity and diesel oil are the main energy sources on the island. * Apart from a low heat production from solar collectors for hot water domestic use, there is not any other use of renewable energy sources (RES) for heat production. * Diesel oil and gasoline are the exclusive energy sources used in the onshore transportation sector. The tertiary sector, which on Sifnos actually means tourist facilities, exhibits the highest percentage share (60%) regarding electricity consumption. It is also clarified that the last part of Table 1 presents the energy sources used for heat production apart from electricity, which are obviously consumed for both indoor space heating and hot water production, accounted for in the electricity consumption presented in the first part of Table 1.  
The data presented in Table 1 are also graphically summarized in Figure 3. Specifically, the annual percentage contribution of the final energy uses is depicted in Figure 3a [6], with the transportation sector from and to the island constituting the dominant energy use. In Figure 3b, the percentage contribution of different sectors to the annual electricity consumption is presented.
## 1 Introduction
* This paper formulates the problem of efficient task offloading in the integrated LEO satellite edge computing network. It defines a cost function based on caching and energy consumption and models the dynamic task offloading and transmission scheduling problem as a constrained long-term cost function minimization problem. * The original optimization problem is divided into two key components: data task transmission scheduling and computation task offloading. The data task transmission scheduling problem focuses on determining the most efficient routes for data transmission from observation satellites to ground stations, utilizing [PERSON]'s algorithm to identify the shortest paths while accounting for energy consumption. In contrast, the computation task offloading and computation scheduling problem is modeled as a Markov Decision Process (MDP), where dynamic factors such as the arrival of IoT device tasks and the status of relay satellite buffers are considered. This approach enables the application of reinforcement learning techniques, specifically the Deep Q-Network (DQN) algorithm, to develop optimal strategies for task offloading and computation scheduling, which minimize long-term system costs under varying conditions. * Extensive simulations are performed to evaluate the proposed scheme compared with the existing peers. The rest of the paper is organized as follows. Section 2 presents an overview of related work. Section 3 describes the system model, and Section 4 formulates the joint transmission, task offloading, and computation scheduling problem. The proposed schemes are introduced in Section 5. Simulation results are presented in Section 6. Finally, we conclude this paper in Section 7.
## 3 Methodology and Model
### Channel Attention Block  
The utilization of channel attention induces the activation of more pixels by involving global information to compute channel attention weights. Furthermore, research has demonstrated that convolution aids transformers in achieving better visual representation and optimization ease. Consequently, we introduced a convolution block integrated with channel attention to augment the network's representational capability. The Channel Attention Block (CAB) comprises two standard convolution layers employing Gelu activation alongside channel attention (CA) modules. Within RepViT, there exist two principal processing layers: one dedicated to spatial feature processing, encompassing operations like convolution and pooling, and the other responsible for channel feature processing, managing interactions among feature channels. The CAB is strategically placed within the channel mixer, positioned at the final step of the processing layer sequence. It is responsible for incorporating the channel attention mechanism. Within the CAB, a constant \(\beta\) is used to compress the channel count of the two convolution layers. For an input feature with C channels, the output feature's channel count after the first convolution layer is compressed to \(\beta\)/C channels. Subsequently, the feature is expanded back to C channels through the second layer. Following this, the channel-wise feature characteristics are adaptively rescaled using the standard CA module. Finally, the weighted feature graph, after being combined with the input feature graph and multiplied by a scaling factor, is considered a component of RepViT's output.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ecosystem** & **Time Scale** & **CUE** & **Scale** & **Type of Data** & **Data Source** \\ \hline \multirow{4}{*}{WET} & Annual & 0.607 \(\pm\) 0.133 & Global & Site data & Tang [3] \\  & Annual & 0.550-0.60 & Sudan and Ethiopia & Remote sensing data & Khalifa [32] \\  & Annual & 0.542 & SNP & Remote sensing data & Li [19] \\  & Growing season & 0.488 & SNP & Remote sensing data & Our article \\ \hline \multirow{4}{*}{GRA} & Annual & 0.457 \(\pm\) 0.109 & Global & Site data & Tang \\  & Annual & 0.220–0.560 & Sudan and Ethiopia & Remote sensing data & Khalifa \\  & Annual & 0.567 & SNP & Remote sensing data & Li \\  & Growing season & 0.482 & SNP & Remote sensing data & Our article \\ \hline \multirow{4}{*}{MF} & Annual & 0.464 \(\pm\) 0.127 & Global & Site data & Tang \\  & Annual & 0.350–0.480 & Sudan and Ethiopia & Remote sensing data & Khalifa \\  & Annual & 0.480 & SNP & Remote sensing data & Li \\  & Growing season & 0.530 & SNP & Remote sensing data & Our article \\ \hline \multirow{4}{*}{DBF} & Annual & 0.464 \(\pm\) 0.127 & Global & Site data & Tang \\  & Annual & 0.340–0.420 & Sudan and Ethiopia & Remote sensing data & Khalifa \\  & Annual & 0.479 & SNP & Remote sensing data & Li \\  & Growing season & 0.477 & SNP & Remote sensing data & Our article \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of estimated CUE at different time scales in different researches. increasing trend from May to July and from August to October, respectively, possibly because the hydrothermal condition was more suitable during those two time periods. Increased precipitation may lead to a higher NPP/GPP ratio [6]. The variations of temperature affect both the photosynthesis and Ra rates, resulting in the changes of vegetation CUE [42]. The ratio of NPP to GPP might increase as the annual temperature increased between \(-10\) and \(20\)\({}^{\circ}\)C [1], which was partially explained by the findings of this study. In addition, in recent decades, to improve the local ecological environment and enhance the ecological protection barrier function, the Chinese government and local citizens have taken multiple measures and implemented actions for ecological and environmental protection [2]. Ecological and environmental restoration projects such as the \"Three-North Shelterbelt Project\" and the \"Grain for Green Project\" have achieved some positive effects [43; 44]. We used the same method to calculate the CUE of farmland. By comparison, we found that the average CUE of the natural ecosystem in SNP showed a similar variation as that of the internal farmland from 2001 to 2015 (Figure 15). The CUE of farmland and natural ecosystem increased simultaneously. The respiration consumption of vegetation decreased. This also showed that the ecological protection function of natural ecosystem may have been strengthened during the past 15 years.
# Optimizing HAWK Signature Scheme Performance on ARMv8
###### Abstract  
This study proposes an optimized implementation of the HAWK Signature algorithm, one of the candidates in the first evaluation round for additional digital signature schemes in the NIST Post-Quantum Cryptography competition. The core motivation of this research is to improve the performance of HAWK algorithm. By conducting profiling analysis to identify, we identified the most resource-intensive functions. And then we optimized the functions. The optimization techniques through profiling analysis are not limited to HAWK but can be applied to other algorithms as well. Additionally, the study demonstrates how efficient optimization can be achieved using fewer instructions by leveraging lesser-known ARMv8 instructions. By targeting the functions with the highest overhead and utilizing fewer instructions, a performance improvement of approximately 2.5% for Hawk512 and 4% for Hawk1024 was achieved, respectively. These results confirm that combining profiling analysis with efficient instruction usage can lead to significant performance improvements.  
HAWK; post-quantum cryptography; digital signature; optimized implementation; ARMv8; profiling +
Footnote †: journal: Medical  
Ref. [9] proposed an optimized implementation of the encapsulation and decapsulation processes on ARMv8. In the encapsulation process, the performance was improved by optimizing the computations during syndrome generation, utilizing the fact that most identity matrices are zero to omit unnecessary calculations. In the decapsulation process, the study optimized the multiplication and inversion operations, which consume a significant amount of time in the extended binary finite field \(F_{2^{m}}\) (where \(m=12\) or \(13\)), by efficiently implementing these operations using ARM instructions.  
Through studies like these, it has been confirmed that optimizations on ARMv8 can enable more efficient execution of complex algorithms such as cryptographic computations and mathematical operations. However, for high-performance processors like ARMv8, improper optimization methods can lead to performance degradation. To address these challenges, it is essential to leverage the characteristics of the ARMv8 architecture and improve computational efficiency by utilizing ARMv8-specific instructions. This study aims to optimize the key-generation process of the HAWK scheme on ARMv8 to enhance performance. We propose an optimization technique that uses efficient instructions based on detailed profiling and algorithmic analysis of the computation process. The proposed optimization will contribute to the broader use of ARMv8 in security-sensitive applications. Our main contributions are as follows.
# Impulsive Noise Removal with an Adaptive Weighted Arithmetic Mean Operator for Any Noise Density
## 2 Preliminaries
### Aggregation Operators and Distances  
Aggregation operators are a very helpful tool in order to obtain a result from some given data. Some of their applications are in the fields of decision making, rule-based systems or consensus (see [24; 25]).  
**Definition 5** ([25]).: _An \(n\)-ary aggregation function \(F\) in \([0,1]\) is a function \(F:[0,1]^{n}\rightarrow[0,1]\) that is increasing in each variable and fulfills the boundary conditions \(F(0,\ldots,0)=0\) and \(F(1,\ldots,1)=1\)._  
**Definition 6** ([25]).: _An aggregation function \(F\) has averaging behaviour (or is averaging) if for every \(\mathbf{x}\in[0,1]^{n}\), it is bounded by \(\min(\mathbf{x})\leq F(\mathbf{x})\leq\max(\mathbf{x})\)._  
Some examples of averaging functions are the minimum and the maximum operators, the arithmetic mean and the weighted arithmetic mean. This last one is defined as follows.  
**Definition 7** ([25]).: _Let \(w=(w_{1},\ldots,w_{n})\in[0,1]^{n}\) be a weight vector such that \(\sum_{i=1}^{n}w_{i}=1\). The weighted arithmetic mean function WAM associated to \(w\) is given by_  
\[WAM(x_{1},\ldots,x_{n})=\sum_{i=1}^{n}w_{i}x_{i},\]  
_for all \((x_{1},\ldots,x_{n})\in[0,1]^{n}\)._  
The weights of the WAM function used in the proposed algorithm are defined from distances.  
**Definition 8**.: _Let \(X\) be a non-empty set. A function \(\rho:X\times X\rightarrow\mathbb{R}\) is called a metric or a distance on \(X\) if it satisfies the following conditions:_  
_(a) \(\rho(x,y)\geq 0\) for all \(x,y\in X\)._  
_(b) \(\rho(x,y)=0\) if and only if \(x=y\).__(c)_ \(\rho(x,y)=\rho(y,x)\) _for all_ \(x,y\in X\)_._ _(d)_ \(\rho(x,y)\leq\rho(x,z)+\rho(z,y)\) _for all_ \(x,y,z\in X\)_._  
The following distances, defined on \(X=\mathbb{Z}^{2}\), will be used in the following sections:  
* Manhattan distance: \(\rho_{1}(x,y)=|x_{1}-y_{1}|+|x_{2}-y_{2}|\),
* Euclidean distance: \[\rho_{2}(x,y)=\sqrt{(x_{1}-y_{1})^{2}+(x_{2}-y_{2})^{2}},\]
* Infinity distance: \(\rho_{\infty}(x,y)=\max\{|x_{1}-y_{1}|,|x_{2}-y_{2}|\}\) where \(x=(x_{1},x_{2})\) and \(y=(y_{1},y_{2})\).
# High-Efficiency Quantum Dot Lasers as Comb Sources for DWDM Applications
## 1 Introduction
### Nonlinear Optical Frequency Combs  
As an alternative to DFB arrays, NOFC offer the ability to generate a spectrum of discrete optical frequencies separated by a fixed channel spacing. NOFCs typically employ micro-disk or micro-ring resonators, but a straight waveguide can be used in combination with an MLL [8]. They can offer extremely large bandwidth [12], the channel spacing can be adjusted over an extremely large range [13,14], and individual comb lines can offer superior optical linewidths, making them suitable for coherent communication [15]. Anomalous group velocity dispersion (GVD) Kerr solitons suffer from an inherently low efficiency [16]. Recently, normal GVD dark soliton combs have reached 41% on-chip pump-to-comb efficiency [17]. Their biggest drawback is that the power of individual lines is typically low [18], with an average power per line of about \(-\)10 dBm. The comb generated usually shows large power variation across the comb (10-20 dB); however, recent improvements have increased the power per line [17]. They also suffer from an inherently low efficiency when considering net wall plug efficiency (WPE) of the entire source. Often neglected from the calculation, the wall plug efficiency of the pump laser itself must be considered. Additionally, optical coupling losses between the laser and the NOFC further decrease the efficiency; however, integration will alleviate this [19]. Excluding pump generation and coupling, conversion efficiencies can be as high as 40-60% [17,20].
## 5 Conclusions  
Within the scope of this article, we have provided a detailed survey on WSN standards dedicated to industrial automation networks. The standardization efforts are ongoing and targeting different application areas such as factory automation or process automation. We have focused on the IEEE 802.15.1 and IEEE 802.15.4 standard families, which have been adapted to industrial applications in need of short-range communication with high data rate, and energy-aware applications requiring larger coverage, respectively. We have selected the WirelessHART, ISA100.11a, ZigBee and 802.15.4e Factory Automation MAC standards among the IEEE 802.15.4 standard families and the WISA specification among the IEEE 802.15.1-based standards. Except for the WISA and 802.15.4e Factory Automation MAC, all the selected standards target mainly process automation applications. An overview of each standard has been provided with particular focus on the network elements and the features of the protocol stack.  
In the next step, we have identified several QoS requirements posed by industrial applications, such as support of real-time communications as well as highly reliable communications. The selected standards have been evaluated to determine how real-time and reliability requirements are supported. The results have revealed that no officially released and open standard is currently able to fulfill the strong real-time requirements of the factory automation domain. Moreover, the questions of QoS provisioning over heterogeneous networks as well as over multi-hop routes in homogeneous networks have been raised.  
We have then focused on security issues of the surveyed standards by identifying potential attacks that could threaten the industrial WSNs and affect their operation. The standards have also been evaluated to determine if the proposed security mechanisms are sufficient to protect the WSNs against the derived threats. The evaluation has shown that the standards are resistant against most of the investigated threats, except for continuous jamming at all frequencies, collision attacks and flooding of connection requests. Moreover, we have pointed out that the design and the implementation of the security managers are left to the users or implementers of the standard. Here, the detailed operation of such security and network managers and the corresponding protocol mechanisms are an interesting area for further research.  
We conclude that the selected standards fulfill almost completely the identified QoS and security requirements as long as they operate in single-hop mode. However, some aspects that are of high interest for the domain of industrial automation, including multi-hop operation and support of QoS and security over heterogeneous network segments, need further research.