esa-sceva/satcom-chunk-collection huggingface dataset:
# Mathematical Foundations and Implementation of CONIKS Key Transparency
## 2 Related Work
### Security Assumptions and Models  
In the CONIKS system, several cryptographic assumptions underpin its security framework. Firstly, the security of CONIKS relies on the properties of the hash function \(H\), which is assumed to be collision-resistant, pre-image-resistant, and second pre-image-resistant. These properties ensure that it is computationally infeasible to find two different inputs that produce the same hash value, thereby preserving data integrity [19]. Additionally, the system's security is built upon the robustness of public key cryptosystems, such as RSA or Elliptic Curve Cryptography, which depend on the difficulty of solving mathematical problems like integer factorization or discrete logarithms.  
The security model of CONIKS assumes an adversary with certain capabilities and constraints. The adversary might manipulate or compromise keys, transparency logs, or other components within the system, but they are not able to break the cryptographic assumptions underlying the hash functions and digital signatures [19]. The privacy model ensures that, while transparency logs are publicly verifiable, users' key bindings remain confidential and protected from unauthorized disclosure.  
The mathematical proofs of security in CONIKS include integrity, consistency, and privacy proofs. Integrity proofs are provided through the use of Merkle trees, where any alteration in a leaf node impacts the root hash, thereby proving that changes are detectable throughout the tree [20]. Consistency proofs are achieved via signed updates, where each update to the transparency tree is accompanied by a digital signature and results in a new root hash. This mechanism ensures that updates accurately reflect changes in key bindings and maintain a reconciliation with previous versions of the tree. Privacy is safeguarded through zero-knowledge proofs, which allow users to confirm key inclusion and consistency without disclosing the actual keys, thereby ensuring that the transparency logs, while public, do not compromise the confidentiality of the keys themselves [20]. Through these mathematical foundations, CONIKS effectively supports key transparency, maintaining both the integrity and privacy of cryptographic key management systems.
## 3 Results and Discussion
### Parametric Meshing  
In addition to constructing cylinder meshes with diameter and centerline information, we also developed an automatic parametric meshing algorithm using cubic Bezier models to generate structured hexahedral meshes as shown in Figure 6. The parametric meshing algorithm takes in the diameter and length information form the vessel segmentation and groups the segments into bifurcation, bifurcation to bifurcation, and bifurcation to terminal. For each group, separation points and planes are computed to define the connection between the different groups. The parametric meshing allows us to control the meshes quality and define regions for vessel wall and vessel lumens for hemodynamic simulations. The surface meshes can be improved be adapting parameterized surfaces such as Bezier surface, B-spline surface, and NURBS [53; 54; 55; 56].
# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 3 Methodology
### Aspect Sentiment Classification Structure  
Given a text \(S=\{w_{i}|i\in[1,n]\}\) and aspect terms in the text \(A=\{a_{i}|i\in[1,m]\}\), we need to determine the sentiment (positive, neutral, or negative) of the aspect terms in the text.  
The left structure of Figure 2 shows the architecture of aspect sentiment classification, which consists of the BERT embedding layer, MHSA, and interactive learning layer. It deter mines the local context of aspect terms based on the dependent syntactic tree to eliminate the influence of irrelevant words and determine their sentiment polarity.
## 2 State of the Art  
### Data Acquisition Platforms in IoT Solutions  
After the first attempt to build a DAP applied to the manufacturing tasks by [PERSON] [4], the first solutions based on open architecture CNCs were developed. [PERSON] et al. [8] proposed the connection of an open architecture CNC to a lathe machine with an intelligent system, to overcome the tool-wear effect in workpiece quality. To do so, they installed a linear variable differential transformer and acoustic emission sensors on the lathe machine, demonstrating that on-line workpiece quality monitoring and control can be performed with Ethernet and Internet connections. Along the same lines, [PERSON] et al. [9] presented a stability control system for in-process modification, to avoid chatter vibrations and to achieve the highest process performance in turning operations on an open CNC transversal lathe. By doing so, they were able to maintain performance and to avoid chatter vibrations for a fixed turning process.  
As presented in Section 1, commercial CNCs have only recently included communication capabilities that have been studied in very recent works. [PERSON] et al. [10], proposed an approach for machine-tool energy consumption estimation, based on real-time monitoring measurements using wireless sensor networks. This solution overcomes the lack of data acquisition and transmission in machine-tools previously installed in real workshops. Within the same research area, [PERSON] et al. [11] developed a Radio Frequency Identification framework to overcome communication limitations in mass-production workshops, thereby assuring component traceability. Finally, [PERSON] et al. [1]reviewed the most recent publications in IoT solutions for workshops, concluding that most of the existing solutions have limited capabilities and real-time monitoring, due to the low sensorized rate of existing machine-tools.  
Now that reliable solutions for data communication have been developed, the research emphasis has shifted towards defining the best key performance indicators extracted from IoT platforms for optimal manufacturing [12]. [PERSON] et al. [13] proposed a combination of machine state, energy consumption, and cutting tool identifier. Their solution reduced energy consumption and energy cost by effectively managing the defined machining process, though it is very dependent on reliable identification and supervision of cutting processes. In a more advanced work, [PERSON] et al. [14] tested this general strategy in a real workshops with 25 machine tools, 24 LED lamps, 10 ventilation fans, 5 air conditioners, 2 transport devices, and 2 air compressors. They demonstrated that energy indicators can be improved by considering efficiency indicators, although once again, these indicators require an extensive knowledge of the cutting processes under execution.
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 3 Results and Discussion
### Results on Noisy Datasets  
To verify the noise robustness of the test algorithms, set the SNR of the datasets to be 15 dB, 20 dB, 25 dB, 30 dB, 35 dB and 40 dB, then all algorithms are used to extract endmembers on the datasets. The average SAD between estimated endmembers and ground-truth endmembers are recorded in Table 3.  
Figure 14: From left to right are (**a**) the ground-truth abundance maps; (**b**) the estimated abundance maps; (**c**) absolute error maps of soil, tree and water in Samson. Figure 15: From left to right are (**a**) the ground-truth abundance maps; (**b**) the estimated abundance maps; (**c**) absolute error maps of tree, water, soil and road in Jasper Ridge. It can be seen from the results that with the increase in SNR, the average SADs commonly decline and achieve the best results at 40 dB. It shows that noise will distort the spectra of endmembers and reduce the extraction accuracy. There are special cases in all test algorithms. Since SGA cannot extract all endmembers as previous analyzed, the results of it have a slight change with the increasing of SNR. KMEE has anti-noise ability, but its average SADs vary greatly due to the unstable endmember extraction results. FSPM exhibits strong noise robustness, especially on the Cuprite dataset. As MVSA replaces positive hard constraint on abundance with a soft constraint of hinge loss type, it also shows good robustness to noise. In addition, when SNR is below 30 dB, the results change obviously; when SNR is above 30 dB, the results have little change. It can be inferred that when SNR is set to 30 dB and above, the datasets may reach their best quality, thus limiting the extraction accuracy. Compared to Samson and Jasper Ridge, most algorithms seem to show better anti-noise
# Determining the Quality of a Dataset in Clustering Terms
## 2 Materials and Methods
### Methodology
#### 2.2.2 \(k\)-Means Clustering  
The \(k\)-means algorithm is one of the most widely used clustering algorithms [16]. It clusters the data by attempting to separate the observations into \(k\) groups to minimize the sum of squared distances between each point and its nearest cluster centre. Cluster centres are called centroids.  
The \(k\)-means algorithm can be described in four steps:  
1. Choose \(k\) initial centroids: \(\mathcal{C}=c_{1},c_{2},\ldots,c_{k}\).
2. For each \(i\in\{1,\ldots,k\}\), choose a cluster \(D_{i}\) as a subset of all points that are closest to a given centroid \(c_{i}\).
3. For each \(i\in\{1,\ldots,k\}\), set a new centroid \(c_{i}\) for all points in cluster \(D_{i}\) as \[c_{i}=\frac{1}{|D_{i}|}\sum_{x\in D_{i}}x.\]Repeat steps 2 and 3 until the set of \(\mathcal{C}\) does not change.  
The method depends on the initiation of centroids. The most commonly used distance measures are:  
* The Euclidean distance \[d(x,y)=\sqrt{\sum_{i=1}^{k}(x_{i}-y_{i})^{2}};\]
* The Manhattan distance \[d(x,y)=\sum_{i=1}^{k}|x_{i}-y_{i}|;\]
* The Chebyshev distance \[d(x,y)=\max_{i=1,\ldots,k}|x_{i}-y_{i}|;\]
* The Minkowski distance \[d(x,y)=\sqrt[]{\sum_{i}^{k}(x_{i}-y_{i})^{p}}.\]  
Although the \(k\)-means algorithm is widely used, one must remember that it actually always partitions the data, regardless of its properties. One can use the \(k\)-means algorithm to partition uniform noise into \(k\) clusters in the sense of minimizing the Euclidean or other distances, without having a requirement for the clusters to be meaningful.
# An Interoperable Component-Based Architecture for Data-Driven IoT System
###### Abstract  
The advancement of the Internet of Things (IoT) as a solution in diverse application domains has nurtured the expansion in the number of devices and data volume. Multiple platforms and protocols have been introduced and resulted in high device ubiquity and heterogeneity. However, currently available IoT architectures face challenges to accommodate the diversity in IoT devices or services operating under different operating systems and protocols. In this paper, we propose a new IoT architecture that utilizes the component-based design approach to create and define the loosely-coupled, standalone but interoperable service components for IoT systems. Furthermore, a data-driven feedback function is included as a key feature of the proposed architecture to enable a greater degree of system automation and to reduce the dependency on mankind for data analysis and decision-making. The proposed architecture aims to tackle device interoperability, system reusability and the lack of data-driven functionality issues. Using a real-world use case on a proof-of-concept prototype, we examined the viability and usability of the proposed architecture.  
Internet of Things (IoT); edge computing; system interoperability; component-based design methodology; data-driven mechanism +
Footnote †: journal: Sensors
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 4 Calculation of the Contour Integral  
If \(f(z)\) is analytic in any path between \(z_{1}\) and \(z_{2}\), then the fundamental theorem of calculus along curves applies:  
\[\int\limits_{\Gamma_{z_{1}z_{2}}}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!f(z) \,\mathrm{d}z=F\big{(}z_{2}\big{)}-F\big{(}z_{1}\big{)}. \tag{9}\]  
Expression (9) states a very well-known and useful technique, where \(F(z)\) is the anti-derivative of \(f(z)\); however, it can be directly applied only if \(F(z)\) is continuous along the integration path and \(f(z)\) is defined over the integration path; this later condition may not be satisfied when the point being tested is over the contour. Nonetheless, and based on the definition of the Cauchy Principal Value (CPV), the contour integral can still be calculated even if the function being integrated has a pole on the contour. That can be performed by using expression (10) assuming that the pole is enclosed by a circle of radius \(\delta\), and the fraction of the path outside that circle is named \(\varGamma(\overline{\delta})\), where the function \(f(z)\) is always integrable independently of how small \(\delta\) becomes [27]:  
\[\int\limits_{\varGamma}f(z)\,\mathrm{d}z=\lim\limits_{\delta\to 0}\int\limits_{ \varGamma(\overline{\delta})}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!f(z) \,\mathrm{d}z\,. \tag{10}\]  
Having solved the possible problem of the non-definition of \(f(z)\) in a finite number of points, it only remains the issue of the continuity of \(F(z)\), which is going to be managed with a methodology derived further.
## 3 Results and Discussion  
### Zn Content Characteristics in the Soil-Rice Ecosystem  
Table 2 summarizes the Zn content and bioaccumulation factor (BAF\({}_{\text{Zn}}\)) characteristics of the rice grains and root soil in the study area, including both the Heyuan and Pearl River Delta regions. In the Heyuan region, the Zn content in the root soil ranged from 32.00 to 156.70 mg/kg, with an average content of 72.30 mg/kg. According to the GB15618-2018 Soil Environmental Quality Risk Control Standards for Agricultural Land [42], the screening value for Zn is set at 200 mg/kg, meaning that the Zn content in all the root soil samples from the Heyuan region was below the screening value. The Zn content in the rice grains from the Heyuan region ranged from 13.60 to 25.40 mg/kg, with an average content of 18.47 mg/kg. Referring to the Chinese Food Composition Table Standard Edition 6, the national average Zn content in rice grains is 15.40 mg/kg. The proportion of samples from the Heyuan region with a Zn content higher than the national average was 84.6%, indicating substantial potential for developing Zn-enriched rice. The bioaccumulation factor (BAF\({}_{\text{Zn}}\)) of the soil-rice system in the Heyuan region ranged from 0.10 to 0.67, with an average value of 0.28.  
In the Pearl River Delta region, the Zn content in the root soil ranged from 16.90 to 253.30 mg/kg, with an average of 79.78 mg/kg. Only one sample exceeded the screening value of 200 mg/kg. The Zn content in the rice grains from the Pearl River Delta region ranged from 11.33 to 30.37 mg/kg, with an average of 18.10 mg/kg, and 83.33% of the samples had a Zn content higher than the national average, indicating substantial potential for Zn development in rice. The BAFZn in the soil-rice system of the Pearl River Delta region ranged from 0.06 to 1.19, with an average value of 0.31.  
Compared to Heyuan, the Pearl River Delta region showed a broader range of Zn content in both rice grains and root soil, as well as a wider range of \(\text{BAF}_{\text{Zn}}\). However, the average and median values of the Zn content and \(\text{BAF}_{\text{Zn}}\) in both regions were quite similar. As shown in Figure 3, the distribution characteristics of the Zn content in both regions are similar. This suggests that, across the entire study area, there may be a similar relationship between the soil Zn content and rice grain Zn content.  
Figure 3 shows that there is no correlation between the soil Zn content and rice grain Zn content in the study area. Based on the Zn content in the soil, it is not possible to predict the Zn content in rice grains. Therefore, accurately predicting the Zn content in rice and identifying the Zn-enriched rice regions are crucial for the efficient and precise production of Zn-enriched rice.
## 2 Triplet Ordinal Cross Entropy Hashing
### Hashing Learning Problem  
The purpose of the hashing algorithm [3; 10; 11] is to learn the hashing function \(H(\cdot)\), mapping the high dimensional float point data \(x\) into the compact Hamming space as defined in Equation (1). \(B(x)\) represents the compact binary code of \(x\).  
\[B(x)=(sign(H(x)-0.5)+1)/2 \tag{1}\]  
With the assistance of the obtained hashing function \(H(\cdot)\), we can encode RS image content as compact binary code and efficiently achieve RS image search task according to their Hamming distances [1; 2; 3; 4; 5; 23; 24; 25]. Furthermore, to guarantee the quality of the RS image search result, we expect the triplet ordinal relation among RS images in the Hamming space to be consistent with that in the original space [29; 30]. To illustrate this requirement, a simple example is provided below. Here, \(x_{i}\), \(x_{j}\) and \(x_{k}\) separately represent RS image content information. In the original space, the image pair \((x_{i},x_{j})\) is more similar than the image pair \((x_{j},x_{k})\). After mapping them into the Hamming space, the Hamming distance of the data pair \((x_{i},x_{j})\) should be smaller than that of the data pair \((x_{j},x_{k})\). This constraint is defined as in Equation (2).  
\[\begin{array}{l}s.t.\quad\left|\begin{matrix}H(x_{i})-H(x_{j}) \big{|}\big{|}_{1}\leq\leq\big{|}|H(x_{k})-H(x_{j})\big{|}\big{|}_{1}\\ |x_{i}-x_{j}||_{2}^{2}\leq\big{|}|x_{k}-x_{j}|\big{|}_{2}^{2}\end{matrix}\right| _{1}\\ \end{array} \tag{2}\]  
The constraint in Equation (2) guarantees that the ranking order of the retrieval result in the Hamming space is consistent with that in the Euclidean space. Thus, the hashing algorithm, satisfying the triplet ordinal relation preserving constraint, can achieve RS image ANN search tasks [31; 32; 33; 34; 35].
## 3 Results and Discussion
### Overall Summary of the Association of VOCs with Spoilages  
The combined presentation of the associations between spoilages and VOCs by the different analyses would provide an overall picture of their relationships (Table S4, Supplementary Material). Notice that the linkage depended upon the analysis and supports the convenience of using diverse statistical methodologies to select putative VOC markers. Thus, only compounds related to each spoilage by several statistical techniques will be finally chosen. The VOCs most strongly associated with butyric (two samples and three techniques) were: methyl butanoate (V13), ethyl butanoate (V18), and butanoic acid (V62). Also related to butyric (two samples and two analysis) were methyl salicylate (V69), styrene (V36), and benzoic acid (V91). There were other compounds related to butyric spoilage by two analysis, but the presence in only one of the samples prevented their selection. The only VOC associated with the sulfidic spoilage by three statistical techniques was 2-propyl-1-pentanol (V53). However, there were also several compounds related to only this spoilage and suggested by 2 methods: dimethyl sulfide (V2), methanol (V6), 2-methylbutanal (V9), 2-methyl-2-butenal (V24), ethanol (V10), 2-mehtyl-3-buten-2-ol (V19), isopentanol (V33), and acetone (V3). Finally, VOCs associated with putrid spoilage by at least 2 statistical techniques were: D-limonene (V32) and 2-pentanol. Besides, 2-Methyl-3-buten-2-ol (V19) and ethyl-2-methylbonanoate (V20) were related to putrid only by the biomarker analysis.  
The other VOCs shown in Supplementary Table S4 were also related to spoilages, but the association was not exclusive, observed in only one analysis, or related by techniques not providing direct linkages.  
These results contrast with the main contributors reported by [PERSON] et al. (2018) for butyric spoilage (benzyl pentanoate, butanoic acid, nonanal, pentanoic acid, 1-propanol, dimethyl sulfide, methyl pentanoate, (Z)-3-hexen-1-ol, phenylethyl alcohol, methyl hexanoate, and octanal). Only butanoic acid, pentanoic acid, and methyl pentanoate (found in F282) were associated with the so-called butyric spoilage in this work. Furthermore, most VOCs related to zapatera fermentations by [PERSON] et al. (2018) (propionic acid, methyl propanoate, propyl propanoate, acetic acid, 3-methyl-1-pentanol, 1-octanol, \(\alpha\)-terpineol, 1-butanol, and cyclohexanoic acid) were not linked to any alteration in this work, except 1-butanol (identified in F282 and F283) and methyl propanoate (found only in F282), linked to the butyric spoilage. Moreover, cyclohexanoic acid, the typical current marker for zapateria (2018), was utterly absent in the studied samples. Therefore, one should deduce from these comparisons that the spoilages analysed in this study were different from those previously investigated, that the definitions of spoilages by industries are not still clear, or that appreciation of the off-flavours usually noticed in table olives can be produced by different combinations of components.
# Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks
## 2 Related Work
Aggregation of IoT devices allows coping with the flow table limits of SDN switches, so that it becomes possible to handle the vast number of devices that 5G networks will support in massive IoT scenarios. In case a set of suspicious devices is detected, the data flows of these devices are diverted to a quarantine network slice in which fine-grained detection, based on DPI, can be performed to set apart malicious devices from legitimate ones. By isolating suspicious devices, the operator can ensure that the QoS requirements of legitimate devices with a regular behaviour are fulfilled, whereas the performance of suspicious devices can still be guaranteed subject to the resource constraints in the quarantine slice. As in-depth traffic analysis is only performed when an abnormal behaviour is detected and only over a reduced subset of IoT traffic, the computational requirements of this detection solution is lower than previous solutions in which all the data flows are continuously sampled and deeply analyzed.
# Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
###### Abstract  
In the field of aeronautics, aircraft, as a critical aviation tool, exert a decisive influence on the structural integrity and safety of the entire system. Accurate prediction of the stress field distribution and variations within the aircraft structure is of great importance to ensuring its safety performance. To facilitate such predictions, a rapid assessment method for stress fields based on a multilayer perceptron (MLP) neural network is proposed. Compared to the traditional machine learning algorithm, the random forest algorithm, MLP demonstrates superior accuracy and computational efficiency in stress field prediction, particularly exhibiting enhanced adaptability when handling high-dimensional input data. This method is applied to predict stresses in the wing rib structure. By performing finite element meshing on the wing ribs, the angle of attack, inflow velocity, and node coordinates are utilized as input tensors for the model, enabling it to learn the stress distribution in the wing ribs. Additionally, a peak stress prediction model is separately established for regions experiencing peak stresses. The results indicate that the MAPE of the stress field prediction model is within 5%, with a coefficient of determination R\({}^{2}\) exceeding 0.994. For the peak stress model, the MAPE is within 2%, with an R\({}^{2}\) exceeding 0.995. This method offers faster computation and greater flexibility, presenting a novel approach for structural strength assessment.  
stress field prediction; neural network; finite element meshing; angle of attack; peak stress Article