esa-sceva/satcom-chunk-collection huggingface dataset:
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 4 Testbed and Evaluations  
We divide our testbed into three different networks i.e., _Source, Intermediate_ and _Destination_ network(s). We use Mininet [32] to emulate the networks with POX [31] as the controller platform. In our testbed, OF-switch are also used to simulate the behavior of an edge router in an SDN network to filter the traffic as per policy. All the Mininet instances emulating different networks are connected via GRE tunneling. The role of each network in our testbed is discussed below.  
The _Source_ network is the one that generates both legitimate and attack traffic. We used three nodes in the source network out of which two generate attack traffic while one node is the legitimate one.  
The _Intermediate_ or inter-connecting networks are multiple Mininet networks connected via GRE tunneling. They are autonomous networks running their own topologies and also act as the transit networks to route the traffic between source and destination. They can be treated as different autonomous systems within the same ISP or different autonomous systems in different ISPs. Since they are Mininet emulated networks, they contain SDN controllers running on POX framework. The SDN allows us to communicate with these networks during the process of mitigation and to install the blocking flows to restrict the attack traffic passing forward, hence the load of mitigation is not solely on destination network, instead it is distributed on the whole network and it will gradually find its way towards the source of the attack.  
The _Destination_ network is also a Mininet network comprising a victim node that is the destination for both legitimate and attack traffic generating from the source network(s). Initially the destination host fulfills all the requests coming from the source network(s) without any distinction between legitimate and malicious traffic. However, once the destination network is made aware of the malicious traffic, it starts blocking the malicious traffic and subsequently informs the neighboring networks.  
For the most part of our evaluations (mentioned otherwise), each node in our testbed consists of 2.60 GHz Intel core i5 CPU, 8 GB RAM, 500 GB HDD and 1 Gbps Ethernet card. We use Scapy [33] to generate ICMP packets with varying payloads for both attack and legitimate traffic targeted towards the destination.  
Since detection is not within the scope of our work, we have simulated a node as a detection engine that feeds malicious flows to the destination network in order to mitigate the attack. It can reside in any of the above networks or it can be in a different network. Furthermore, there is no restriction that this node should also be running within an SDN network. It can be in a legacy network. All that is required from this node is that it speaks the same C-to-C protocol as defined in the above section to properly authenticate itself and provide the attack definitions.
## 2 System Identification Methods
### The Strategy of the Identification Models  
The strategy used in this study focuses on the modeling of the behavior of the structures equipped with a passive controller, i.e., TMD, under seismic loads. The experimental study is designed as presented in Section 3. The three models are designed in-between input and output experimental data, as shown in Figure 2 under seismic generated loads, and predicted response of third floor of the frame building, as output. The first model, i.e., SISO system model, is designed using one input (seismic signals \(S(t)\)) and predicted acceleration behavior (\(\overline{a}\left(t\right)\)) is the output (see Figure 2a). Second model uses the seismic signal and one time-delayed for the acceleration measurements (\(a\left(t\right)\)) of third floor as input (see Figure 2b). The third model utilizes the seismic signal, and one and two time-delays of the acceleration measurements of third floor as input and predicted acceleration response as output (see Figure 2c). Second and third models are considered a MISO system models.  
A shaking table is used to apply input excitation signals (Section 3) to the scaled three-story building equipped with TMD passive control. The models are designed using TMD with mass of 1.8 kg (TMD-1.80) as a training setting and TMD with mass 2.39 kg (TMD-2.39) as a testing setting. The output signals (acceleration) of two cases is filtered using Butterworth Low Pass filter, the verification of de-noising filter is provided by [PERSON] and [PERSON] [5]. The models process is shown in a flow chart format (see Figure 3).  
Figure 2: (**a**) Single-input single-output (SISO) and (**b**,**c**) multi-input single-output (MISO) models designed for the modeling of the structure behavior under seismic load.
# Error-Correction Coding Using Polynomial Residue Number System
## 1 Introduction
At the same time, in the BCH code it is not necessary to make any recalculations such as shown above with the generating polynomial. BCH codes are not arithmetical. In BCH codes, control symbols are determined by summing modulo two corresponding information digits. In this case, informational and control bits are not equal in relation to each other. If calculations are performed in BCH codes, then the corresponding arithmetical operations can be performed only on informational bits. However, these operations cannot be performed on control bits, as the result will lead to incorrect control bits. As BCH codes are not arithmetical, they cannot be used for detection and correction of errors that occur during calculations [64; 65]. However, the issues of building redundant codes of PRNS have not yet found wide application. This disadvantage hinders the expansion of the scope of application of codes of polynomial residue number system. Therefore, the objectives of the present article are:  
* To conduct studies on the error-correction abilities of redundant codes of polynomial residue number system with one and two control bases. * To develop an algorithm for detecting and correcting errors in redundant codes of PRNS based on the calculation of a positional characteristic (a polynomial interval) in which only modular operations are used, that is, without performing a reverse conversion from a code of PRNS to a positional code and then performing a division operation.
## 3 Preliminaries
### Disjunctive Graph  
To express the scheduling state more comprehensively and logically, we utilize a disjunctive graph [40] to represent the scheduling process of the panel block assembly line. Let \(N=\{O_{ij}|\forall i,j\}\cup\{S,T\}\) denote the set of all operation nodes, where \(S\) and \(T\) represent the virtual start and end nodes, respectively. Therefore, the disjunctive graph \(G=(N,\ C,\ D)\) is a mixed graph with \(O\) as its vertex set. Here, \(C\) represents a set of conjunctive arcs (directed arcs) that depict the adjacent operation relationships determined by the process, usually denoted by solid lines. On the other hand, \(D\) represents a set of disjunctive arcs (undirected arcs), denoted by dashed lines, representing the disjunctive arcs between operations that can be processed on the same workstation. By determining the direction of each disjunctive arc, a solution for the planar segmented assembly line scheduling instance can be obtained, resulting in a directed acyclic graph (DAG) [41]. Figure 2(a,b) illustrate an example of a disjunctive graph and its solution for a panel block assembly line scheduling instance. In Figure 2(a), which represents a scheduling instance with three blocks and three workstations, the black arrows depict the conjunctive arcs among operations within the same block, while the dashed lines represent the disjunctive arcs connecting operations that require the same workstation across different blocks. Figure 2(b) presents a complete solution to the scheduling problem, where each disjunctive arc has a direction, and each node is connected by at most two disjunctive arcs.  
Figure 3: Exemplary disjunctive graph for the panel block assembly line scheduling problem and its solution.  
Figure 2: Ship panel block assembly process.  
To enhance the reader's understanding of the scheduling process for the panel block assembly line, we present an illustrative example in Figure 4. As depicted in the diagram, when the input instance consists of five blocks and four workstations, the Gantt chart output reveals that the optimal sequence for block input should follow the order of 4-2-3-1-5.
## 5 Discussion
### Limitations and Outlooks for Future Study  
Our strategy was based on the GBRT model, a machine learning method relying on training sample datasets. The performance of the data-driven model usually depends on the quantity of the training set [85]. The number and distribution uniformity of sampling sites also affected the accuracy of estimation [31]. Due to limited transportation conditions, many depopulated zones distributed in the western region of the TRHR are inaccessible. There were very few field surveys taken in these regions, which might result in lower accuracy compared to other regions [41]. Our ground observations were captured only during the growth period of grassland in 2009. The lack of AGB ground observations increases the uncertainty of the estimated AGB results in the non-growing season. In future studies, the AGB observation data of non-growing season can be properly supplemented to reduce the uncertainty of the estimation results. It will be helpful to monitor the long-term spatiotemporal variation of AGB in the TRHR.  
Although the GBRT model achieves a higher accuracy, the physical mechanism of the GBRT to estimate AGB is unclear. Undoubtedly, ecosystem and physical models can better explain the spatial and temporal heterogeneity of ecosystem biomass variation, although they still have large uncertainties [86; 87]. In future work, we will focus on combining machine learning models, such as GBRT, with physical-based methods to establish a better grassland AGB inversion model for upscaling the ground observations to the target grid scale, which could be explained by the internal physical mechanisms, and obtain better accuracy.  
There was a time difference between satellite and ground observations, even if we made great efforts to select the MODIS data close to the field measurement time [30]. The spatial scale mismatch between the AGB observations and satellite-based remotely sensed data would also introduce uncertainties to upscaled AGB results.
# Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020
## 2 Materials and Methods  
### Study Area  
Inner Mongolia, situated between 37\({}^{\circ}\)24\({}^{\prime}\) N to 53\({}^{\circ}\)23\({}^{\prime}\) N and 97\({}^{\circ}\)12\({}^{\prime}\) E to 126\({}^{\circ}\)04\({}^{\prime}\) E, is located in northern China and covers an expansive area of approximately 118.3 \(\times\) 10\({}^{4}\) km\({}^{2}\). The topography is characterized as long and narrow, with elevations decreasing from the southwest to the northeast (Figure 1). Influenced significantly by the Asia monsoon, Inner Mongolia is a typical temperate continental monsoon climate. Consequently, the mean annual rainfall varies from 50 to 550 mm and diminishes from the northeast to the southwest [27; 28]. The annual mean temperature typically falls within the range of \(-\)4\(-\)10 \({}^{\circ}\)C and progressively increases from the northeast to the southwest [26; 27; 28; 29]. Grasslands dominate the Inner Mongolia landscape, constituting the primary vegetation types and covering approximately 74% of the entire region. Nevertheless, due to the temporal variability of precipitation and the effects of global warming, Inner Mongolia has experienced a heightened frequency of droughts over the past decades [30].
# Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network
## 2 Related Work
Inspired by this, a global authentication blockchain can be established in the UAV network to prevent unauthorized access by malicious external nodes. [PERSON], [PERSON] et al. [29] proposed a joint learning base data accumulation scheme combining drones and blockchain for remote areas where IoT devices face network scarcity and potential cyber threats. With the consent of the mining nodes, the federated learning is simply stored on the blockchain to obtain models, reducing the storage overhead of the blockchain. Although it does not fit the drone network reconfiguration application scenario, the idea of lightweight storage, and the two-stage consensus is worth addressing. Inspired by the above research results, the solution proposed in this paper: a blockchain-assisted adaptive reconfiguration method for a trusted hierarchical network of UAVs to build a lightweight authentication blockchain in the UAV network. The local state records of drone nodes are phased for data consensus, and the blockchain system implements decision consensus on the data consensus results. Finally, only the configuration information after the decision is stored on the blockchain. The drone nodes reconfigure the trusted network based on the blockchain. This scheme is based on the protocol architecture of ZRP, which ensures network scalability, serious blockchain to ensure the authenticity of node identity, periodic updates of the upper network to guarantee node trustworthiness and reasonable allocation of resources. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Security**} & \multirow{2}{*}{**Scalability**} & \multicolumn{3}{c}{**OverHead**} \\ \cline{3-6}  & & & **Computation** & **Communication** & **Storage** \\ \hline
[7; 8; 9; 10; 11; 12] & Low & Low & Low & - & Low \\ \hline
[13] & Low & Low & Low & Low & Low \\ \hline
[14; 15; 16; 17; 18] & High & Low & Middle & Middle & Low \\ \hline
[19; 20; 21; 22; 23; 24] & Low & High & Middle & Low & Low \\ \hline
[25; 26] & High & Middle & High & High & High \\ \hline
[27] & High & High & High & High & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of network reconfiguration methods.
# A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning
## 4 Results and Discussion
### Experimental Setup  
The proposed model was implemented in Python 3.7 with NumPy, OpenCV, TensorFlow [36], and Keras libraries. The models were implemented with the Keras framework with batch sizes of 64 and 300 epochs, we used the Adam Optimizer for optimization purposes, and the learning rate decreased according to the given threshold throughout the training process. We used an Nvidia GeForce GTX 1650 4 GB GPU for training the models.
## 2 Definitions and Known Results  
### Graphs, Expansion, Matrices  
In this subsection, we recall some definitions and notation of graphs, expansion, and matrices. Let \(G\) be a simple graph. We use operators \(V\) and \(E\) to refer to the vertex and edge sets of \(G\) as \(V(G)\) and \(E(G)\), respectively. We will frequently write \(G=(V,E)\) instead of \(G=(V(G),E(G))\). For a subset \(X\) of \(V(G)\) (\(E(G)\), resp.), \(\overline{X}\) denotes \(V(G)\backslash X\) (\(E(G)\backslash X\), resp.). Let \(X\) and \(Y\) be subsets of \(V\). \(N(X)\) denotes the neighbors of \(X\) (i.e., \(N(X)=\{u\in\overline{X}\mid\exists v\in X\text{ s.t. }\{u,v\}\in E\}\)). \(E(X,Y)\) denotes \(\{\{u,v\}\in E:u\in X,v\in Y\}\). For each \(v\in V\), \(d_{G}(v)\) (or simply \(d(v)\)) denotes the degree of \(v\) in \(G\). \(\Delta(G)\) denotes the maximum degree of \(G\). \(G[X]\) denotes the induced subgraph of \(G\) induced by \(X\). The _vertex boundary width_ of order \(i\) of \(G\), denoted by \(b_{v}(i,G)\), is defined as \(b_{v}(i,G):=\min_{\mathbb{S}\subseteq V,|S|=i}|N(S)|\). Similarly, the _edge boundary width_ of order \(i\), denoted by \(b_{e}(i,G)\), is defined as \(b_{e}(i,G):=\min_{\mathbb{S}\subseteq V,|S|=i}|E(\overline{S},\overline{S})|\).  
Let \(S\) be a subset of \(V\). The _edge expansion_\(\Phi(S)\) is defined as \(\Phi_{G}(S)=\frac{|E(S,\overline{S})|}{\min\{\mathsf{vol}(S),\mathsf{vol}( \overline{S})\}}\), where \(\mathsf{vol}(S)=\sum_{v\in S}d(v)\). Moreover, \(\mu(S)\) denotes \(\frac{\mathsf{vol}(S)}{\mathsf{vol}(V)}\). Since, in this paper, we mainly consider \(d\)-regular graphs, \(\mathsf{vol}(S)\) and \(\mu(S)\) can be regarded as \(d|S|\) and \(\frac{|S|}{|V|}\), respectively.  
Let \(M\) be the adjacency matrix of \(G\). For \(X,Y\subseteq V\) such that \(X\cap Y=\emptyset\), \(M[X,Y]\) means a matrix satisfying the following. The rows and columns are labeled by \(X\) and \(Y\), respectively, and each entry \((x,y)\) with \(x\in X\) and \(y\in Y\) is \(1\) if \(\{x,y\}\in E(G)\), \(0\) otherwise. We denote the rank over \(GF(2)\) of \(M[X,Y]\) as \(\textit{rank}(M[X,Y])\). For the rank over \(GF(2)\), the following is known.  
**Lemma 1** (Lemma 4.3 in [17]).: _Let \(A\) be a matrix over \(GF(2)\) such that \(A\) has at least \(p\) non-zero entries and each row and each column in \(A\) has at most \(q\) non-zero entries. Then, \(\textit{rank}(A)\geq\frac{p}{q^{2}}\) holds._
## 3 Results and Discussion
### THz in Sediment in Tagburos River and Pit Lake  
Tagburos River, which is the main tributary in the abandoned mine area, has measured THz concentrations in the sediments ranging from 1.8-119 mg kg\({}^{-1}\). Sediments found in the river were influenced by the geology of the surrounding area, as the exposed soils with Hg can be transferred into the river and eventually deposited in Honda Bay. High Hg concentration in the river sediments suggest that exposed mine waste calcines with elevated Hg concentration in the abandoned mine area are being carried to the river by erosion and surface runoff, and are eventually deposited in Honda Bay where they become a source of Hg contamination and enter the food chain. Rehabilitation programs,  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Location** & **n** & **Total Hg (mg kg\({}^{-1}\))** \\ \hline Palawan Hg mine, Philippines (This study) & & \\ Soils & 31 & 0.04–67.5 \\ Mine waste calcines & 7 & 52.7–924.2 \\ \hline Palawan Hg mine, Philippines [11,22] & & \\ Soils & 21 & 0.012–168.72 \\ Mine waste calcines & 11 & 3.68–660 \\ \hline Wuchuan Hg mines, Guizhou, China [5] & & \\ Soils & * & 0.33–320 \\ Mine waste calcines & * & 79–710 \\ \hline East Guizhou Hg mines, China [6] & & \\ Soils & * & 5.1–790 \\ Mine waste calcines & * & 5.7–4400 \\ \hline West-central Nevada Hg mines, USA [8] & & \\ Mine waste calcines & 36 & 1.9–2000 \\ \hline Puhipuhi Hg mine, Northland, New Zealand [7] & & \\ Soils & 4 & 35.3–1486 \\ \hline Almaden Hg mine, Spain [15] & & \\ Soils & 6 & 13–64 \\ \hline Usagre Hg mine, Spain [4] & & \\ Soils & 26 & 0.2–311 \\ \hline La Soterrán Hg mine, Northern Spain [3] & & \\ Soils & 56 & 1.73–502 \\ \hline Branalamosa Hg mine, Northern Spain [3] & & \\ Soils & 28 & 0.5–895 \\ \hline Maramuñiz Hg mine, Northern Spain [3] & & \\ Soils & 23 & 2.0–577 \\ \hline Global background for pristine soils [25] & & 0.045–0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total Hg concentrations in soils and mine waste calcines collected from selected Hg mines worldwide.  
such as revegetation, are recommended in this abandoned mine area, to avoid the erosion of soil particles with high Hg concentrations going to the river and depositing in Honda Bay.  
High THg concentration measured in the pit lake sediments ranged from 37.5-454.1 mg kg\({}^{-1}\). The highest concentration was measured near the exposed bedrock on the lake shore, as shown in the inset of Figure 3. These values are within the range of the measured THg in pit lake sediments (6.9-400 mg kg\({}^{-1}\)) in 2003 [11]. The high THg concentration at the lake bottom may be due to naturally occurring cinnabar in the bedrock, and deposition of eroded calcite stocked near the lakeshore.
###### Abstract  
The purpose of this study was to determine the profile of volatile compounds (aroma) and overall flavor in dessert yogurts prepared from cow and goat milk, using three different, commercially available starter cultures, in the presence or absence of probiotic bacteria and to correlate this to organoleptic evaluation results obtained using a consumer acceptability method. The extraction of volatile compounds was carried out by solid phase micro-extraction; separation and analysis by gas chromatography-mass spectrometry. Variations among the different classes of compounds (i.e., aldehydes, alcohols, ketones, volatile acids, hydrocarbons, and terpenes) were recorded for different treatments. The results showed that the main volatiles in the cow milk dessert yogurts without _Bifidobacterium_ BB-12 were: acetaldehyde, 2,3-butanedione, 2,3-pentanedione, 3-OH-2-butanone, 2-propanone, hexanoic acid and limonene). Respective volatiles in cow milk dessert yogurts with _Bifidobacterium_ BB-12 were: acetaldehyde, pentanal, hexanal, the same ketones, acetic acid and limonene). The volatiles in goat milk dessert yogurts without _Latchoacillus acidophilus_ LA-5 were: acetaldehyde, the same ketones, no carboxylic acids, limonene, camphene, \(\alpha\)- and \(\beta\)-pinene. Respective volatiles in goat milk dessert yogurts with _Latchoacillus acidophilus_ LA-5 were: aldehydes acetaldehyde, the same ketones, butanoic acid, \(\alpha\)-pinene and camphene varying in concentration in different samples. Based on the results of volatiles and organoleptic evaluation, it can be concluded that dessert yogurts from cow milk without probiotic bacteria strains using the mild and classic starter cultures, and dessert yogurts from goat milk with probiotic bacterial strains using the classic and acidic starter cultures are found to be more organolepically acceptable by consumers. In most cases, a positive correlation was found between dessert yogurt organolepically determined flavor and volatiles (aldehydes, ketones and carboxylic acids).  
desert yogurts; cow milk; goat milk; starter cultures; probiotics; volatile compounds; organoleptic evaluation +
Footnote †: journal: 20 December 2021
# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 3 Challenges Brought by Crowd Density
### Target Occlusion Problem  
In a crowded crowd, the occlusion situation between individuals is complex and changeable, including not only partial occlusion, but also complete occlusion, and even the alternating occurrence of continuous occlusion and instantaneous occlusion [15], especially in public places such as railway stations, subways, and popular squares. Occlusion causes the key features of the target to be hidden or changed, making it difficult for feature-based detection and recognition algorithms to extract complete and reliable visual information. For behavior recognition models, occlusion may cause the established target feature model to fail, and it is difficult to correctly associate the targets before and after occlusion, thereby causing target loss or incorrect association. In crowded scenes, the mutual occlusion between targets is often accompanied by dense interactions between targets, such as collisions and passing by, and these interactions further increase the complexity of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Crowd State** & **Density Range (p/m\({}^{2}\))** & **Crowd Level** \\ \hline Free Flow & \textless{}0.5 & Very Low (VL) \\ Restricted Flow & 0.50–0.80 & Low (L) \\ Dense Flow & 0.81–1.26 & Medium (M) \\ Very Dense Flow & 1.27–2.00 & High (H) \\ Congestion & \textgreater{}2.00 & Very High (VH) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Crowd states.  
occlusion problem. The algorithm needs to consider the interaction between targets and potential group behavior patterns [16] while dealing with occlusion.