esa-sceva/satcom-chunk-collection huggingface dataset:
# Fixed Wireless Access in Flexible Environment: Problem Definition and Feasibility Check
###### Abstract
Several factors can prevent the selection of the closest optical point, including:  
* Local power supply conditions (e.g., lack of a battery backup unit) may immediately degrade the availability of the traffic running through that connection point below the necessary requirement of service;
* Latency from the closest connection point may not be the best e2e;
* Optical connection could provide the required availability and latency, but all Gbit Ethernet ports may be allocated already (costly hardware upgrade required);
* Expected Line-of-Sight (LoS) problems (e.g., new building or growing trees) may be foreseen at the site of the closest connection point;
* Radio-frequency interference or frequency permitting problems (e.g., high/low sub-band conflict of radio transceivers) may occur. A single radio link may be insufficient to serve all the service traffic at the new planned location. In these complex scenarios, a potential solution is to assign the traffic of different services to different links. For this problem, a complex search algorithm is needed. This paper presents a rigorous problem description, defining both existing network load and expected traffic at the new site. It also includes a feasibility check of potential connection points. A feasible connection point is defined as one where a radio link can serve all new service traffic and the existing network can handle that traffic to its destination (or from its source). The feasibility check may also identify cases where planned traffic cannot be served, such as when all potential connection points fail to meet latency requirements. The feasibility check can be followed by a search algorithm to find an optimal solution, considering the use of multiple connection points. This paper is structured as follows: Section 2 introduces the related work; Section 3 defines our assumptions and the input parameters of the problem; Sections 4 and 5 present the proposed feasibility check and an example; Section 6 provides details of the radio-link dimensioning and simulation; and finally, Section 7 concludes the paper and summarizes the planned continuation of the current work.
## 3 Results and Discussion
The differences in macroscopic optoelectronic characteristics of NUV LED samples with identical epitaxial layers except for In composition, as summarized in Table 1, can be consistently understood by using the proposed model of local in-plane potential fluctuation in the QWs as depicted in Figure 7. At low currents, the injected carriers in the active region always have three different kinds of processes. The first process is localization of carriers at the valleys that mostly cause radiative recombination; the second process is the capture of carriers by point defects (i.e., NRCs) that cause SRH nonradiative recombination, and the last process is the tunneling of carriers from QWs through the point defects to the p-GaN layer that yields additional nonradiative recombination. At low currents, such as 0.5 mA, the radiative recombination rate increased with the sample's In composition due to the influence of local in-plane potential fluctuation (deeper valleys and, thus, higher carrier density) since more electron-hole pairs were available for radiative recombination. As the current increased to \(\sim\)150 mA, the IQEs of all samples increased due to the increment of the radiative recombination rate over the nonradiative recombination rate (caused by the SRH and tunneling processes). This observation is consistent with \(S\) values decreasing with reduced point defects owing to the increased In composition. In this current region, the overall IQE increased with the sample's In composition, caused by the decreasing point defects (which was also consistent with the calculated \(A\) coefficients, \(S\) values, and the PHEMOS patterns). The maximum IQE increased with the sample's In composition, which was due to reduced NRCs in potential valleys and the increased area of potential valleys in QWs.
## 4 Asymmetric Programming: A Reliability Enhancement Scheme for Metadata  
### Framework  
Asymmetric Programming is an address mapping strategy that introduces transparent management between the file system and hardware to protect the metadata from errors. The system architecture of Asymmetric Programming is shown in Figure 4. In the architecture, the host system and the storage system are connected through the host interface. Most widely used host interfaces, such as SATA II and USB (2.0 or 3.0 version), support outstanding requests. The storage system can serve multiple  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline RBER & 2.00 \(\times\) 10\({}^{-6}\) & 4.00 \(\times\) 10\({}^{-6}\) & 6.00 \(\times\) 10\({}^{-6}\) & 8.00 \(\times\) 10\({}^{-6}\) & 2.00 \(\times\) 10\({}^{-5}\) & 4.00 \(\times\) 10\({}^{-5}\) & 6.00 \(\times\) 10\({}^{-5}\) & 8.00 \(\times\) 10\({}^{-5}\) \\ \hline LSB pages & & & & & & & & \\ with 3-bit ECC & 3.66 \(\times\) 10\({}^{-9}\) & 5.78 \(\times\) 10\({}^{-8}\) & 2.89 \(\times\) 10\({}^{-7}\) & 8.99 \(\times\) 10\({}^{-7}\) & 3.23 \(\times\) 10\({}^{-5}\) & 4.51 \(\times\) 10\({}^{-4}\) & 2.00 \(\times\) 10\({}^{-3}\) & 5.50 \(\times\) 10\({}^{-3}\) \\ \hline MSB pages & & & & & & & & \\ with 3-bit ECC & 2.30 \(\times\) 10\({}^{-10}\) & 3.66 \(\times\) 10\({}^{-9}\) & 1.84 \(\times\) 10\({}^{-8}\) & 5.78 \(\times\) 10\({}^{-8}\) & 2.17 \(\times\) 10\({}^{-6}\) & 3.23 \(\times\) 10\({}^{-5}\) & 1.53 \(\times\) 10\({}^{-4}\) & 4.51 \(\times\) 1\"out-of-order\" requests and form a queue. The request queue scheduler is utilized to manage the queue to optimize the I/O performance by scheduling the requests in the queue.  
In MLC NAND flash memory storage systems, physical pages must be programmed sequentially from the least significant page address to the most significant page address. Programming pages with the \"out-of-order\" pattern is prohibited [30]. To handle this constraint, we present Asymmetric Programming, which works in the flash translation layer (FTL) and reorganizes the address mappings. The objective is to enhance the reliability of MLC NAND flash memory by providing a more enhanced metadata protection mechanism.  
Asymmetric Programming adopts two reliability techniques, namely _MSB allocation_ and _asymmetric garbage collection_, to enhance the reliability of metadata in MLC NAND flash memory storage systems. The MSB allocation is a reliability strategy that identifies the pages that contain metadata and allocates metadata pages to MSB pages that have lower bit error rates. Asymmetric garbage collection is another reliability strategy that aims to fully utilize the storage space when triggering garbage collection operations. This strategy uses a small buffer to reorganize the valid pages and assign the pages to free pages based on the importance of the data. In the following subsections, we will present these two strategies.
## 3 Results
### Electrochemical Tests
#### 3.2.2 Corrosion Rate  
The corrosion rate procedure offers insight into the effect of the environment on a material and can be determined through linear polarization curves, which relate potential to current. Figure 4 illustrates the linear polarization test performed to measure the corrosion rate of the composites immersed in artificial seawater, represented on a semi-logarithmic scale of current values.  
The 0.5% HEA B\({}_{4}\)C sample shows the most negative corrosion potential (E\({}_{\text{corr}}\)) and the highest corrosion current (I\({}_{\text{corr}}\)) of all the composites under study, as can be seen in Table 1. It was observed to be oxidized the most among all samples. To obtain the corrosion rate, first, the Tafel slopes (\(\beta\)c and \(\beta\)a), equivalent weight, density and surface were calculated.  
Figure 4: Polarization curve of studied samples in 3.5% NaCl solution.  
Figure 3: Comparison of corrosion potentials vs. time for 24 h of immersion in 3.5% NaCl solution of studied samples.  
As the HEA content of composites increases, their corrosion rate decreases, indicating a higher corrosion resistance. Of all samples, B4C.3 presented the lowest corrosion potential, corrosion current and corrosion rate values, confirming that increasing the HEA content implies better performance regarding corrosion resistance. All four of the samples in our study tend to passivate, probably due to the creation of a passive film on their surface.
# A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks
## 1 Introduction
### Contribution  
One of the key features of Blockchain is the fact that is easily applied to distributed systems where multiple entities maintain the same information without requiring a central authority. It has a massive potential to improve the performance of distributed systems. Particularly, it allows mitigating many types of attacks emanating from systems governed by a central authority and reduces communication overhead with central servers and authorities [8; 23; 24].  
In the same context, a new Blockchain-based Trust Management framework baptized BC-TMF is proposed in the current study. It aims to compute and share the trust metrics of the vehicles in a distributed fashion. It is a context-dependent framework where the trust metric of the vehicles depends on the trustworthiness of their alert messages disseminated in the network. These alerts report hazardous events on the road. The proposed BC-TMF is built on Blockchain technology to take benefit of its features. On one hand, the distribution and availability facilitate the update of the trust metrics of the vehicles. On the other hand, the integrity that blockchain technology offers allows saving the computed trust metrics in a dependable ledger. In so doing, the proposed BC-TMF will avoid bad-mouthing attacks. Moreover, the availability of the Blockchain allows sharing the computed trust metrics between the vehicles, which mitigates the cold start problem when a vehicle enters a new network and has to timely authenticate alerts received from unknown peers. To enhance the distribution of the proposed framework, the miners will be a set of vehicles selected on the road instead of RSUs, which seems more efficient in rural environments and in the earlier stages of VANETs deployment where few RSUs are deployed in the network due to their high costs. Unlike existing approaches, where the trust metrics of the vehicles are computed in the RSU, in the proposed approach the trust metrics of the vehicles are computed in a fully distributed manner in the vehicles due to their high potential to perceive the events on the roads. Furthermore, contrary to existing frameworks, in the proposed approach, the trust metric is inferred based only on direct observations without considering recommendations from other vehicles.  
The remainder of the paper is organized as follows. Section 2 presents the different details of the proposed BC-TMF. Section 3 is devoted to the performance evaluation and discussion. Finally, Section 4 concludes the paper and presents some future directions.
## 1 Introduction  
_Brassica_ vegetables are temperate climate crops that can be directly seeded or transplanted out in the field. The selection of vegetable species and cultivars is determined to a large extent by current market needs [1]. Increasing consumer awareness regarding the quality of vegetables and global food security prompts producers to constantly improve the functional properties of foods. An improvement in the quality of the edible parts of vegetables should be accompanied by increased productivity per unit area, whereas the environmental impact of vegetable production should be minimized [2]. This goal can be achieved through the use of organic and profitable raw materials [3].  
Broccoli (_Brassica oleraca_ var. _italica_ [PERSON]) is not very popular in Poland, although it is relatively easy to cultivate. This vegetable species can be frozen because freezing does not change its dark green color or taste [4, 5, 6]. Broccoli is appreciated by consumers due to its high nutritional value, including a high content of provitamin A (\(\beta\)-carotene), vitamin B1, minerals, and dietary fiber [4, 7], as well as potential anticarcinogenic properties [8, 9, 10]. Broccoli plants are a rich source of vitamin C whose concentration ranges from 54 to 119.8 mg per 100 g of raw broccoli. Raw broccoli is considered most nutritious [11, 12]. Notonly flower heads and florets, but also leaves and stalks can be eaten. Broccoli is available in grocery stores all year round, both fresh and frozen [5].  
Substrate (soil) affects plant growth and development, and it is one of the factors that determine crop yield and quality. According to estimates, 60% of the world's arable soils contribute to limiting plant growth, and both mineral deficiencies and excess may exert negative effects [13; 14]. Soil fertility is the key to adequate plant nutrition, since nutrients have to be supplied at the right time and in the right quantities [15; 16]. Nutrient supply and plant growth are often inhibited by unfavorable soil conditions, including pH and salinity, which influence the phytoavailability of minerals and the concentrations of toxic elements in the soil solution [17]. However, the soil's ability to supply essential nutrients to plants is affected not only by its properties. Soil must also support root growth, so that growing plants can take up sufficient amounts of available nutrients [18]. One of the contemporary challenges is to produce high-quality foods. Therefore, efforts are being made to maximize the use of crops and optimize their growing conditions.  
In view of the above, the present study was undertaken to determine the effect of soil type on the yield and nutritional value of broccoli florets, leaves and stalks. The research hypothesis postulates that soil type affects the biometric parameters and chemical composition of the edible parts of broccoli, and induces changes in the morphological and chemical parameters of broccoli plants. The aim of this study was to evaluate the influence of different soil types on the biometric parameters of broccoli plants and the mineral content of florets, leaves and stalks.
## 6 Experiment
### Experimental Setup
#### 6.1.3 Targeted Recommendation Algorithm  
In our experimental research on data poisoning attacks, we selected three types of collaborative filtering recommendation algorithms from the RecBole library. The core idea of these algorithms is to utilize the interaction data between users and items to make recommendations. However, each employs different technologies and model structures to enhance the accuracy and efficiency of recommendations:  
**Bayesian Personalized Ranking (BPR)**[36]: BPR optimizes pairwise ranking loss to improve recommendation accuracy by focusing on the relative order of user preferences rather than absolute ratings. Leveraging a Bayesian framework to model user-item interactions, it is particularly effective in handling implicit feedback data, emphasizing the relative preferences between items.  
**Deep Matrix Factorization (DMF)**[37]: This approach applies deep learning techniques to traditional matrix factorization methods, using deep neural networks to capture the complex nonlinear relationships between users and items.  
**Neural Matrix Factorization (NeuMF)**[38]: NeuMF is a neural network-based recommendation algorithm that combines matrix factorization with a multi-layer perceptron (MLP). It integrates linear matrix factorization and nonlinear MLP paths in parallel to learn complex user-item interaction patterns, enhancing recommendation accuracy.
## 2 System Description, Dynamic Model, and Controls
### Controller  
The controller used in this study is an integral sliding mode controller (ISMC). The reason for choosing this position controller is that it does not require a mathematical model of the system. The ISMC control input applies a switching gain to compensate for the perturbation and system dynamics [25]. The control input of the ISMC can be expressed as  
\[u=\ \rho*sat(s)+k\ *\sigma, \tag{2}\]  
Figure 3: Sphere-to-Tube dimension.  
Figure 2: CAD robot manipulator [23; 24].  
\[\text{where}\;\;sat(s)=\begin{cases}\frac{s}{|s|}\;,\;\;if\;\;|s|>\epsilon_{c}\\ \frac{s}{\epsilon_{c}}\;,\;\;if\;\;|s|\leq\;\epsilon_{c}\end{cases},\]  
where \(k\) is a constant, \(s\) is a sliding surface, \(\rho\) is the switching gain, \(u\) is the control input, and \(\sigma\) is the actual sliding variable. The sat function is used to minimize the chattering in the system. \(\epsilon_{c}\) is the boundary layer thickness. In the ISMC, the controller output does not require dynamic information because only the output feedback of the plant is required.
## 3 Method Development
### Geometry Decomposition  
In order to extend the single-beam Bernoulli-Euler solution to structures composed of several sections with attached masses, the structure is decomposed in simple regular beams. This decomposition must be defined in such a way that couplings only occur at the beam endpoints. Moreover, every pair of coupled beams must describe an angle in the set \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), \(270^{\circ}\). The principal axis of each beam must belong to a horizontal plane. Masses should only be attached at the beam endpoints.  
The following notation will be used: \(e_{i}\) denotes the \(i\)-th beam, \(l_{i}\) denotes the length of \(e_{i}\), each beam has two endpoints, the coordinate \(x_{i}\) will refer to the beam (local coordinate system), thus one endpoint of \(e_{i}\) is located at \(x_{i}=0\) and the other endpoint is located at \(x_{i}=l_{i}\). \(w_{i}(x_{i},t)\) represents the vertical displacement of the beam \(e_{i}\) at local coordinate \(x_{i}\in[0,l_{i}]\). \(E_{i}\), \(G_{i}\), \(I_{i}\) and \(J_{i}\) denote [PERSON]'s modulus, the shear modulus, the cross-sectional moment of inertia, and the polar moment of inertia of \(e_{i}\), respectively. Given a function \(f(x)\), it will be used \(f(x)|_{x^{\prime}}\) to denote the evaluation of \(f(x)\) at \(x=x^{\prime}\).  
Figure 1 shows a correct decomposition in 6 simple beams for the structure of a CNC _router_ type structure similar as RoutakitHD equipment [16]. Arrows on the beams indicate the local coordinate systems, i.e., the endpoint where \(x_{i}=0\) and the positive axis direction.  
This decomposition will allow computing the modes by sections, i.e., the mode for \(e_{i}\) associated with the \(k\)-th fundamental frequency will be given by \(W_{i}^{k}(x_{i})=g(\beta_{i}^{k},x_{i})\mathbf{c_{i}}\), where \(\beta_{i}^{k}\) is related to the fundamental frequency \(\omega^{k}\) by (6) (Notice that if some beam sections have different cross-section and/or different material, a single fundamental frequency \(\omega^{k}\) will lead to a set of parameters \(\beta_{1}^{k}\), \(\beta_{2}^{k}\),..., \(\beta_{m}^{k}\), since the parameters \(\mathrm{E_{i}}\), \(I_{i}\), \(\rho_{i}\), \(A_{i}\) in (6) could be different for each beam section. In the particular case that all the beam sections have the same material and cross-section, a single parameter will be obtained for each fundamental frequency \(\omega^{k}\), i.e., \(\beta_{1}^{k}=\beta_{2}^{k}=...=\beta_{m}^{k}\)). In this way, the set of functions  
\[\{W_{1}^{k}(x_{1})\text{, }W_{2}^{k}(x_{2})\text{, }...\text{, }W_{n}^{k}(x_{n})\}\]  
describe the \(k\)-th mode of the complete structure, associated with the frequency \(\omega^{k}\).  
The following subsections will explain how to compute the fundamental frequencies \(\omega^{k}\), the corresponding parameters \(\beta_{i}^{k}\) and the coefficient vectors \(\mathbf{c}_{i}^{k}\) for each beam element \(e_{i}\), by following the aforementioned methodology.  
Figure 1: Decomposition of a CNC router structure similar as RoutakitHD equipment [16].
## 2 Related Work
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Algorithm** & **Simulation** & **Real Implementation** & **Detection or Defence** & **Control or Data Plane** & **Advantages** & **Disadvantages** \\ \hline \hline [PERSON] et al. [26] & ✓ & ✗ & Defence & Data Plane & Capable of edge deformable controller in the data plane, the control plane is shared by the data plane. & The interface between SPF controller and programmable controller in the data plane, the control plane is shared by the data plane. & The interface between SPF controller needs to be defined. The gateway devices shall support both SDN and programmable controller. \\ \hline [PERSON] et al. [27] & ✓ & ✗ & Both & Data Plane & Fog computing enables the mitigation of DDoS attacks at the ingress of the network. & Defence mainly focuses on Mirai botnet and TCP protocol, other attack types might need validation. \\ \hline \hline [PERSON] et al. [28] & ✓ & ✗ & Both & Control Plane & Existing trustful users can still interact with the controller during DDoS attacks. & The controller rejects all the new flows under DDoS attacks. & The controller rejects all the new flows under DDoS attacks. & The controller rejects all the new flows under DDoS attacks. & A legitimate user without a high trust value will encounter more delays when the network is busy. \\ \hline [PERSON] et al. [29] & ✓ & ✓ & Both & Both & The real-time training keeps the flow rules that are generated by machine learning up-to-date. & The performance of LEDEM relies on the training dataset. The link between local and universal controllers needs to be defined. \\ \hline [PERSON] et al. [30] & ✓ & ✗ & Detection & Both & The proposed mechanism is able to detect not only DDoS attacks but also other types of attacks. & New-flow attacks against one network slice might saturate system resources, as it triggers a new flow record and the update in the detection pattern. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Countermeasures in SDN-based IoT networks. In Tables 1 and 2, we systematically analyse each algorithm. More specifically, we provide a comparative analysis in the light of simulation or real implementation, whether it puts forward a defence mechanism or not, the focus of the algorithm in terms of data and control plane and a summary of the advantages and disadvantages of existing algorithms.
# Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition
## 4 Experimental Results and Analysis
### Algorithm Comparison
#### 4.4.2 Comparison with Representative Stacked Autoencoders  
To verify the accuracy of FESSAE, we experimentally compared its performance to that of some representative stacked autoencoders that are representative, including the stacked autoencoder (SAE) [49], stacked sparse autoencoder (SSAE) [50], stacked denoising autoencoder (SDAE) [51], stacked pruning sparse autoencoder (SPSAE) [52], and SSAE combined with LASSO (SSAE and LASSO) [53]. The performance of FESSAE without sparse term is verified, and the method is referred to as ESAE. There are some main reasons why the autoencoders are considered as a deep learning method: (1) the proposed algorithm includes an autoencoder, so representative autoencoders are compared for fairness; (2) the autoencoder is a kind of deep network, which is more suitable for datasets with a small number of samples and features compared with other kinds of deep learning methods.  
Since the number of the samples and features is small or medium, the number of hidden layers and neurons cannot be large. For a fair comparison, the SP_EMNCM is not considered and all parameters are set to the same parameters for all the methods. The observed accuracy is presented in Table 13. Three datasets (PD, AD, and Vehicle) are used.  
As seen from Table 13, the FESSAE achieved the highest accuracy, showing that the proposed FESSAE is effective. Table 13 shows that the sparse constraints can substantially enhance the classifier accuracy. For all datasets, the proposed FESSAE achieved the best classification accuracy.