esa-sceva/satcom-chunk-collection huggingface dataset:
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 6 Discussion
### Discussion on Countermeasures  
After describing how the attacker can adjust the attack patterns to circumvent existing defense mechanisms, we discuss the possible countermeasure. Existing flow table management methods for benign environments are unable to deal with this in adversary scenarios. The defense method should differentiate whether flow table overflow is caused by benign traffic or malicious attacks. For malicious attacks, we believe that accurately distinguishing between malicious flows from normal flows is the key point. To achieve this, we need to characterize every flow rule and acquire real-time information based on packet granularity. Since the controller can only acquire the flow-level information, we recommend considering to implement this security-aware flow table management mechanism on the data plane side.  
Existing defense methods can only react when the flow table is about to overflow, and lack the ability of early detection. Considering that the attack flows must use multiple rounds to trigger flow rules, they usually take a long time from the first probing packet to the maintaining phase. We can monitor (i) the packet intervals and patterns and (ii) the long-term flow duration and flow pattern before assigning large timeouts to identify suspicious flow behaviors. Once the switch identifies suspicious rules, it can proactively delete them or assign higher eviction priorities as a conservative approach.  
Figure 25: The traffic rate of benign flows: (**a**) the traffic rate of attack flows; (**b**) the traffic rate of benign flows.
# A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus
## 3 A Coordination Technique for Scalable BFT Consensus
### Step 3. Processing Equal/Unequal Transactions
#### 3.3.1. Step 3.1 Handling Equal Transactions  
The coordinator performs this step if all transactions from each node are equal. Algorithm 3 shows detailed steps for handling equal transactions. To check transactions' equality, a set of transactions from each node is converted into a hash function (see Lines 3-5) first, and their equality is compared. Due to the characteristic of the hash function, their hash values must be the same if all transactions are the same.  
```
1:Inputs:\(Txs=\{Txs_{0},Txs_{1},...Txs_{n}\}\)
2:Initialize:\(TxsList\leftarrow\{\}\)
3:for\(i\)=0, \(i\leq N(Node)\), \(i\)++do
4:\(TxsList_{i}\leftarrow\mathrm{Hash}(Txs_{i})\)
5:endfor
6:if All hashs of \(TxsList\) are \(\mathit{equal}\)then//Handle Equal Transactions
7:\(isConfirmed\), \(sig^{\prime}_{prime}\), \(pk_{prime}\leftarrow\)request\(Node_{prime}\) to confirm \(Txs_{0}\)
8:if\(isConfirmed==true\)then
9: request All Nodes to generate a new block with \(Txs_{0}\) and \(sig^{\prime}_{prime}\)
10:\(time_{p}\gets time_{c}\)
11:else
12: Terminate
13:endif
14:else//For Unequal Transactions
15: handle Unequal Transactions(\(Txs\))
16:endif
```  
**Algorithm 3**[_Coordinator_] Handling Equal Transactions  
If all transactions are the same, the coordinator requests the prime node \(Node_{Prime}\) to confirm transactions \(Txs_{0}\) and the prime node responses to the confirmation of transactions. This confirmation step is necessary for mutual trust between the coordinator and the prime node on integrity of transactions from the coordinator. The response from the prime node includes \(isConfirmed\), \(sig^{\prime}_{prime}\) and \(pk_{prime}\). Among the responses, \(sig^{\prime}_{prime}\) results from execution \(Signature(Txs_{0})_{sk_{prime}}\) of the prime node (see Lines 7-8).  
Figure 3: Steps for processing collected transactions.  
When the prime node confirms all equal transactions, the coordinator requests all nodes of blockchain network to generate a new block. Then, the controller receives the request and delegates the request to the BFT-Module to generate a new block. All nodes' \(time_{p}\) is updated with the current \(time_{c}\) for designating the starting period for the next round (see Line 10). If the prime node does not confirm transactions, this round is terminated and \(time_{p}\) remains at the previous time. In addition, when some of the transactions are different, the coordinator performs the _handle unequal transactions_ step presented in the next subsection.
## 2 Pharmacology of Piperine: In Vitro and In Vivo Studies
### Enzyme-Related Activity
#### 2.7.2 Other Enzymes  
Piperine decreases cyt-P-450, benzphetamine N-demethylase, aminopyrine N-demethylase, and aniline hydroxylase activities [81]. Also, the basal activity of TWIK-related acid-sensitive K\({}^{+}\) channel (TASK-1, -3), and TWIK-related spinal cord K\({}^{+}\) (TRESK) channels were inhibited by piperine in a dose-dependent manner [82]. It activates transient receptor potential cation channel subfamily V member 1 (TRPV1) receptor, a novel anti-epileptic target, indicating the anti-seizure effects [83]. [PERSON] et al. [84] recently reported the effects of piperine (5 and 10 mg/kg) on the testis development in the pubertal rat, where piperine increased the ratio of phospho-AKT1 (pAKT1)/AKT1, phospho-AKT2 (pAKT2)/AKT2, and phospho-ERK1/2 (pERK1/2)/ERK1/2 in the testis in rats, showing a stimulating effect to the Leydig cell development.  
Piperine increases GABA levels and inhibits neuronal NOS, leading to antianxiety activity in stressed mice [85]. Investigation of its effect on microsomal P450s showed diverse action, where P4501A and P4502B increased, and P4502E1 expression was suppressed in animals treated with piperine [86]. Acute acetaminophen poisoning results in increased AST and ALT levels in hepatotoxic rats. Pretreatment with piperine prevents the increased levels of these enzymes [87]. In the study which investigated the free radical scavenging properties of piperine in rat intestinal lumen model (exposed to hydrogen peroxide and cumene hydroperoxide), piperine significantly increased GSH level [88]. In MC3T3-E1 cells, piperine increases osteoblast differentiation through AMPK phosphorylation, by inhibiting of DNA binding-1, and runt-related transcription factor 2 (Runx2) [89]. Piperine inhibits phorbol 12-myristate 13-acetate (PMA)--induced NF-\(\kappa\)B, C/EBP, and c-Jun nuclear translocation, inhibits activation of the Akt and ERK, leading to inhibition of cyclooxygenase-2 expression in murine macrophages [90], and suppresses T cell activity and Th2 cytokine production in the ovalbumin-induced asthmatic mice [13]. In pilocarpine-induced epileptic rats, piperine exhibits anticonvulsant activity by upregulating caspase 3 and decreasing Bax/Bcl 2 [91]. Piperine lowers the serum levels of thyroxin, triiodothyronine and glucose concentrations while decreasing hepatic 5\({}^{\prime}\)D enzyme and glucose-6-phosphatase in adult male Swiss albino mice. The enzyme activities are on par with a standard antithyroid drug, propylthiouracil [92]. Piperine inhibits two carbonic anhydrases (CAs), human cytosolic isoforms hCA I and II [93]. These enzymes catalyze a physiological reaction of the conversion of CO\({}_{2}\) to the bicarbonate ion and protons and are involved in many physiological and pathological processes including carcinogenesis. Discovery of inhibitory activity of piperine against CAs shows its potential as an anti-convulsant, analgesic, anti-tumor, and anti-obesity agent.
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 4 Implementation of CoFi  
We have implemented the CoFi protocol based on the 802.11 MAC/PHY layer in ns-2 [31], a widely used packet level discrete event simulator. As mentioned above, we suppressed the original per-packet ACK in 802.11, and used per-batch ACK instead. The 802.11 style per-packet retransmission and the exponential backoff schemes are discarded in CoFi. This modified MAC layer interfaces the higher layers through the CoFi encoding/decoding queues (Figure 4).  
Figure 4: Implementation of CoFi in the ns-2 simulator.
## 3 Discussion
### Challenges in Terms of AM Adoption  
The third research question (RQ3) adopted in this paper concerns the challenges that make AM more difficult to adopt. This review shows that there are several challenges in AM adoption. One of the greatest challenges seems to be the perception of the inferior quality of AM-based spare parts. Most researchers still show AM as a stop-gap for a short-term supply of spare parts until the availability of conventionally manufactured parts. This could be because conventional manufacturing produces parts based on established processes, with established machines, and with the required quality assurance and control. Homogeneity in terms of material and the performance of the produced parts can be considered implicitly by the researchers as the benefits of conventional manufacturing. It is assumed that postprocessing may be needed to meet the accepted standards of quality parts [4], and this could add processing and quality control costs. Postprocessing could include heat treatment, surface treatment, secondary machining, assembly [21], and quality testing. In some cases, the integration of AM parts with other parts can also pose a challenge. Therefore, added costs and the limited number of material suppliers make it a challenge to adopt AM [40; 75]. There seems to be no challenge in terms of configuring or reconfiguring SCs. Another challenge is the constraints owing to the cost of the technology. Because of investment costs, the purchase of multiple machines may not be economical, and therefore, one way to counter this is to develop hub-based manufacturing for spoke-based demand centres. Another technical limitation is the design of the spare parts. When spare parts are designed for conventional manufacturing, the digitation of manufacturing can become difficult owing to the limitations of AM technology. [4]. Another challenge is the intellectual property rights (IPR) of these parts. Security and certification requirements exist in terms of handling digital data and producing spares.
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### Interference and Channel Model
#### 3.2.2 Doppler Shift Model  
LEO satellites are traveling at a high relative speed with respect to the observer, causing a high level of Doppler frequency shift leading to possible demodulation failure if shifts beyond \(\frac{B}{2S^{2}}\) are not compensated [49]. In order to obtain the Doppler shift between the observer and the satellite, the slant distance between the two points during time \(t\) is calculated as \(d(t)\). The emulator calculates the velocity \(v(t)\) by numerical differentiation. As such, the Doppler shift for an IoT device \(i\) is given by, \(f_{i}(t)=v_{i}(t)\ f_{\mathrm{o}}/c\). Figure 8 illustrates the impact of Doppler shift on the IoT-over-Satellite system using case II as an example. Compared to the same case without Doppler shift, the BER increases significantly when the transmit power exceeds 20 dBm.
## 4 Discussion
### Quality of the Dataset  
Regarding the quality of the dataset, 69.77% of the studies worked with balanced datasets with an average number of 23.10 \(\pm\) 16.74 subjects for each class. The use of balanced classes in training is important to ensure reliable results since unbalanced training classes tend to favor the majority class and can lead to skewed accuracy metrics. Among the studies that used balanced classes, those with 20 subjects per class predominated. This may provide a measure of the adequate number of subjects, an especially important piece of information in the case of EEG data, as they depend on the availability of the patients, and therefore they may be quite complicated to obtain.  
Furthermore, the EEGs of the subjects change with age, and hence, it should be corroborated that the classes exhibit similar demographic data so that the sets are statistically comparable. This also applies to the state of the disease of PD patients, since patients in more advanced stages of the disease are easier to distinguish from healthy controls than patients in the early stages of PD. It is striking that only 34.88% of the articles specified such information. This lack of specificity may be a limitation in many studies since the differences between the state of the disease of PD patients and their medication status may influence the results of the classification problem. This was reflected in [65], which classified PD patients in the ON state vs. OFF state of medication, and in those studies that evaluated cognitive impairment [32; 34; 45; 47; 53]. Consequently, those articles that did not specify the clinical setting of the patients [37; 40; 42; 43; 46; 51; 53; 55; 56; 58; 59; 60; 61; 62; 64; 66; 68; 69; 70], nor the status of the medication in the patients [32; 33; 35; 41; 50; 54; 63; 71] were excluded from the evaluation process of the current state of diagnosis of PD, because they did not allow the objective evaluation of the results of the models used. These exclusions, based on quality criteria, resulted in 15 admissible articles.  
Regarding the EEG recording parameters, although the electrode density did not directly affect the quality of the recorded signal, it did so to its spatial resolution, because a high density of electrodes increases the number of EEG signals and therefore the amount of information, favoring a more complete dataset that benefits the learning of the models. The number of electrodes considered was heterogeneous, although montages with 14 electrodes predominated, followed by 64 and 32 channels in the same proportion.
# Comparison of Basic Notch Filters for Semiconductor Optical Amplifier Pattern Effect Mitigation
## 2 Basic Optical Notch Filters' Configurations
The wavelength distance between consecutive maxima is given by \(FSR_{MRR}=\lambda_{amp}^{2}/(2\pi Rn_{eff})\). The spectral alignment of the transmission maximum, relative to the data carrier, \(\Delta\lambda_{max,MRR}\), which, analogously to the ODI, corresponds to a phase bias \(\Phi_{b,MRR}=-2\pi\times(\Delta\lambda_{max,MRR}/FSR_{MRR})\), can be varied within half FSR towards the blue or red sideband by tuning the MRR resonant wavelength through various appropriate mechanisms [40].
# A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising
## 2 Digital Filter as Bio-Inspired Optimization Problem
### Particle Swarm Optimization  
The particle swarm algorithm was initially proposed to optimize nonlinear continuous functions [19]. It uses a group of particles, where each particle remembers the best solution it has found (the best position of an individual), and the algorithm tracks the optimal location for the entire group. The effectiveness of the particle swarm algorithm depends on the choices made during the process. The algorithm begins with a group of particles that have random starting positions and velocities in the n-dimensional parameter space. This collection of particles is made up of \(n\) different individuals or models. During each iteration, a particle's movement is influenced by its previous best position \(p\_best\), and the global best is represented as \(g_{best}\). The updating of velocity, \(vel_{n}\), and position, \(pos_{n}\), for each particle is mathematically expressed as  
\[vel_{n}=\omega*el_{n}+C_{1}*r1_{n}*(p_{bestn}-pos_{n})+C_{2}*r2_{n}*(g\_best_{n}- pos_{n}) \tag{2}\]  
\[pos_{n}=pos_{n}+vel_{n} \tag{3}\]  
Here, inertia weight is represented by \(\omega\); learning factors are represented as \(C_{1}\) and \(C_{2}\), respectively; these are used to determine the convergence speed towards local (\(p\_best_{n}\)) and global optimal (\(g\_best_{n}\)) value for the \(n_{th}\) particle in the respective iteration. For the optimal solution, \(\omega\), \(C_{1}\), and \(C_{2}\) decrease linearly. The overall processing of the PSO algorithm is represented in Algorithm 1. Once the optimal spacing and shift are obtained, the filter response is evaluated.  
Figure 1: Decimation filter.  
```
Input: Minimization criteria function \(f(X),\ \text{the domain of sampling interval or}\) the spacing is represented as \(\Delta\) and shift represented as \(\delta\) Output: \(\Delta,\delta\) Initialize : \(pos_{0}\ \leftarrow\ random\ points\ in\ domain\ of\ \Delta\ \&\ \delta\) Initialize : \(vel_{0}\ \leftarrow\ random\ velocity\)\(p\_best\ \leftarrow\ pos_{0}\)\(g\_best\ \leftarrow\ pos_{best}[argmin\ (f(p\_best))]\) for i = 1, 2,... T do if g_best does not change for \(i_{max}\) iterations, then return \(\Delta,\ \delta\_{g\_best}\) end if update \(vel_{n}\) and \(pos_{n}\) according to Equations (2) and (3) Update \(ID\ \leftarrow\ is\ Smaller(f(X_{i}),f(p\_best))\)\(p\_best\ [updateID]\ \leftarrow\ X_{i}[updateID]\)\(g\_best-p\_best[argmin(f(p\_best))]\) end for
```  
**Algorithm 1**Particle Swarm Optimization
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 3 Energy Management
### Alternative Energy Sources
Instead, lightweight **Triboelectric Nanogenerators (TENG)** could be an alternative for converting these mechanical vibrations into electrical energy [32; 33]. Unlike mechanical converters, TENGs do not rely on electromagnetic conversion but use electrical charge transfer due to the triboelectric effect. Therefore, TENGs do not require permanent magnets and coils, which contribute significantly to the weight of traditional wind energy generators. A comparison of energy generators and sources discussed so far is presented in Table 2. It is worth noting that this represents only a subset of energy sources. There are several sources yet to be exploited or discovered. For instance, there have been discussions on the feasibility of **geothermal energy**[34] as well as **sublimation heat engines** based on the Leidenfrost effect for generating energy [35]. Sublimation heat engines propose the use of locally available dry ice on Mars's surface to convert temperature differences into rotational motion. Additionally, systems like **thermal wadis**, which have already been proposed for Moon missions may also find use in the Martian atmosphere [36]. Although there are many potential sources of energy native to the Martian environment, tapping energy from these sources for powering Mars missions and autonomous vehicles presents numerous challenges. In addition to the inherent inefficiencies associated with the energy converters, the harsh and not yet completely understood Martian environment imposes many restrictions. Thermoelectric generators (TEGs) are known to be highly inefficient energy converters, particularly at low temperature differences, where their efficiency can even drop below 1%. Furthermore, most of the materials used for building TEGs are not suited for subzero temperatures [37; 38], whereas the temperature on Mars can be as low as -91 \({}^{\circ}\)C at night. Hence, the choice of material for TEG and its effectiveness becomes a critical question. Similarly, the thin atmosphere and reduced atmospheric pressure raise questions about the possibilities of **wind energy generators**. Because of this concern, the possibility of wind energy generation has often been overlooked for Mars applications. Thus, more research and investigation need to be directed toward sustainable and efficient energy sources and generators for Mars. Nevertheless, it is improbable that a single energy source suffices all the energy demands on Mars. Instead, a combination of multiple energy sources complementing each other will provide a consistent and reliable energy system.
# Decision System Based on Markov Chains for Sizing the Rebalancing Fleet of Bike Sharing Stations
## 1 Introduction
By examining highly dynamic bike stations in the real-world Citi Bike New York system during specific time intervals of practical significance, a Markov Chain (MC)-based model of critical events was developed and validated. This model provides support information for the estimation of subsystem demand and facilitates the optimization of fleet size for operators implementing the rebalancing operation strategy that was developed in study [7]. One of the general challenges encountered when utilizing an MC is the issue of state space explosion. An MC relies on defining a set of states and the transition probabilities between those states. However, as the complexity of the system increases, the number of possible states grows exponentially, leading to a phenomenon known as state space explosion. This explosion in the number of states can quickly become computationally infeasible to handle, making it challenging to accurately model and analyze the system using traditional MC techniques. In this paper, the challenge of state space explosion in an MC is addressed by employing an effective approach that involves fixing the number of states through feature quantization. This approach is particularly suitable for tackling the state space explosion problem within the context of a BSS. The quantization technique is motivated by the understanding that the definition of hotspots in a BSS, as measured by bike counts, is relative rather than absolute. Specifically, it is scaled by the total capacity of bike stations and compared with the system-wide average. By quantizing the features and constraining the number of states, the computational complexity associated with the state space explosion can be mitigated, while still capturing the essential characteristics of the BSS dynamics. This paper is organized into several sections. The second section, Literature Review, provides an in-depth review of the existing literature and research related to the problem at hand. The Methodology section, which is the core of this paper, presents a detailed description of the proposed approach. The Results section presents the findings and outcomes of the research: it includes a detailed analysis of the data collected during the practical evaluation, as well as the results obtained from the theoretical evaluation. Our discussion of the results and methodology can be found in the Discussion section. Finally, the Conclusions section summarizes the key findings of this study and discusses their implications.
# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 3 Industrial Internet of Things Applications and Requirements
### IIoT-Applications
#### 3.1.2 Smart Factory Applications  
Smart industry comprises distributed automated systems and robotics. This future smart factory floor is possible using ML algorithms and AI technology. These automated devices are integrated with sensors, actuators, microchips, autonomous systems, and controllers. Relying upon CPS and IoT technologies for evolution industrial processes required ultra-reliable and low-latency communication (URLLC). For monitoring, managing and controlling such environment, IoT nodes can handle bounded latency of millisecond scale. These applications are characterized using latency, jitter, energy consumption, workload parameters. M2M and D2D are emerging supporting technologies for smart factory development. In the smart factory manufacturing process, machines will have high-level of automation and self-optimization attributes. These attributes will fulfill complex requirements of products. This has open issues regarding network communication technologies, number of devices, security, and cost.