esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Packet Loss Rate (PLR)  
The difference between data packets that are broadcast and received is used to calculate PLR. The number of packets lost is seen in Figure 10. Because there were four rogue nodes in BAODV that did not have a detection mechanism, the number of lost packets reached 89 percent. When under assault, OEAODV (our technique) had a lower packet loss rate than BAODV as well as the two intrusion detection methods, idsAODV and EAODV, and this was in comparison to the industry standard BAODV. This may also be determined by looking at Figure 8, which shows that the higher the PDR, the lower the rate of packets lost. When compared to other methods, Figure 10 shows that OEAODV requires a smaller number of packets that must be routed in order for network communication to take place, and as a result, it has a lower processing overhead.  
Figure 8: Routing overhead in the kilobit stream from nodes of each technique.  
Figure 9: Throughput of nodes from each technique.
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 3 2D Permutation-Aided OTFS Secure Transmission
### SLM Algorithm for PAPR Reduction  
To realize secure OTFS transmission, we need to carefully deal with the PAPR problem to avoid eavesdropping. For this reason, we apply the SLM algorithm to our OTFS transmission. In particular, it generates multiple sets of candidate sequences by multiplying the QAM symbols with the \(U\) groups of the phase rotation vectors and selects the time-domain signal with the lowest peak-to-average power ratio as the transmission sequence, which requires \(U\)ISFFT transforms and Heisenberg transforms. To reduce the computational complexity, we generate candidate sequences by multiplying the time-frequency domain signal sequence by the transition matrices. The principle of the SLM algorithm developed is described in Figure 3:Assuming that the OTFS system contains \(M\) symbols and \(N\) subcarriers, \(x\) represents the input signal in the DD domain, \(X\) represents the signal in the TF domain, \(Q_{1}\) is an \(N\times N\) IFFT matrix, and \(Q_{2}\) is an \(M\times M\) FFT matrix. Then, we have \(X=ISFFT(x)=Q_{1}xQ_{2}\). There are \(U\) different phase rotation vectors, and the representation is as follows:  
\[\gamma_{i}=\left[\begin{array}{cccc}P_{0}^{i}&P_{0}^{i}&\cdots&P_{0}^{i}\\ P_{1}^{i}&P_{1}^{i}&\cdots&P_{1}^{i}\\ \vdots&\vdots&\ddots&\vdots\\ P_{N-1}^{i}&P_{N-1}^{i}&\cdots&P_{N-1}^{i}\end{array}\right], \tag{15}\]  
where \(i=1,2,\cdots,U\), \(P_{b}^{i}=e^{i\varphi_{b}^{i}}\), \(\varphi_{b}^{i}\) follows a uniform distribution between \([0,2\pi]\). Suppose that the ISEFT transform is used to change \(X\) and \(X_{i}\) into \(S=x\) and \(S_{i}=R_{i}x\), respectively, where \(R_{i}\) is the phase transformation matrix corresponding to \(\gamma_{i}\). Therefore, \(X=ISFFT(x)=Q_{1}xQ_{2}\), \(X_{i}=ISFFT(x)=Q_{1}R_{i}xQ_{2}\). Given that \(T_{i}=Q_{1}R_{i}Q_{1}^{-1}\), we have  
\[T_{i}=Q_{1}R_{i}Q_{1}^{-1}=[p_{i},p_{i}^{(1)},p_{i}^{(2)},\cdots p_{i}^{(N-1)}], \tag{16}\]  
where \(p_{i}^{(k)}\) is obtained by circularly shifting \(p_{i}\) down by \(k\) elements. As shown in [25], by choosing the appropriate \(\gamma\), the complexity of SLM can be significantly reduced.
# Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance
## 2 Adjusting Image Acutance
### Assessing Image Acutance  
Motivated by the thresholded gradient magnitude maximization denoted Tenengrad [43] and edge acutance value (EAV) in [44], a new method named thresholded pixel-wise acutance value (TPAV) is proposed to assess the image acutance in hyperspectral hand biometrics.  
\[TPAV(I)=\frac{\sum g(x,y)}{C},\ T_{1}<g(x,y)<T_{2} \tag{1}\]  
Figure 1: Image acutance adjusting phase in hyperspectral hand recognition.  
where  
\[g(x,y)=\frac{\sum_{i}\sum_{j}\left|I(x,y)-I(x+i,y+j)\right|}{\sqrt{l^{2}+j^{2}}} \tag{2}\]  
\[T_{1}=Min(g(x,y))+a*(Max(g(x,y))-Min(g(x,y))) \tag{3}\]  
\[T_{2}=Max(g(x,y))-a*(Max(g(x,y))-Min(g(x,y))) \tag{4}\]  
\(C\) is the count of \(g(x,y)\) whose value is in the range of \(T_{1}\sim T_{2}\). \(I(x,y)\) is the value of pixel \((x,y)\). \(\left|\hat{u}\right|\leq 1\), \(\left|\hat{y}\right|\leq 1\), \(\left|\hat{u}\right|+\left|\hat{y}\right|>0\). The parameter \(a\) is a small positive constant, which is set as 0.05 in the following experiments empirically. This means outliers in the image will not be counted when measuring the image acutance. TPAV both takes advantage of Tenengrad [43] and EAV [44] into consideration. Tenengrad sets a threshold for reducing the sensitivity of the algorithm to noise when it assesses the acutance of an image. However, only horizonal and vertical edges in an image are considered to count as acutance in Tenengrad. EAV applied an additional 45\({}^{\circ}\) and 145\({}^{\circ}\) directional edges in 8 neighbors of a pixel, which is more reasonable for acutance assessment [44]. To this end, TPAV combines the threshold and more directional edges to effectively assess the acutance of an image for local feature analysis. In the following Figure 2 the comparison of acutance evaluation values was demonstrated for a ROI hand image (from a single band (in a hyperspectral dorsal hand vein database) with both rotation and noise). When the image rotates, the acutance value of Tenengrad was dramatically changed (+40.4% from 6.992 to 9.816) due to the direction limitation (see Figure 2b). Also, the acutance value of EAV increased significantly (+90.5% from 12.214 to 23.265) due to noise (refer to Figure 2c). However, the TPAV did not change a great deal under rotation (-7.1% from 15.226 to 14.140) or with noise (+9.6% from 15.226 to 16.685), which validates its robustness in both rotation and noise.
Head stability can also change depending on the visual condition. Previous research highlights the differential effects that focusing on a target in front of them during overground gait can have on children with CP; while children with CP GMFCS I reduced their head movements in all planes of motion in a similar manner to TD children indicating increased visual attention, children with CP GMFCS II remained unchanged [6]. Though it is currently unclear, it is likely that head instability could further compromise existing visual, vestibular, and proprioceptive sensory deficits that are common in children with CP [14; 15; 16; 17] due to the organs of the visual and vestibular systems being located within the head [18]. These sensory systems are critical for controlling spatial orientation of the head and trunk, stabilizing vision during rotational movements and accelerations of the head, and gauging the relative alignment of neighboring body segments within the environment. There is limited consensus on the most appropriate ways to measure head instability in children with CP, and whether this should differ between diagnosis. Previous research has investigated head instability in small numbers of children with only diplegic CP or hemiplegic CP compared to TD children [10; 11], or the combined group effects of children with varying diagnosis of CP [5; 9; 12]. This ignores potential group differences between children with hemiplegic and diplegic CP, which is particularly important since both groups demonstrate different development rates [19] and impairment characteristics [20] in their gait. It is unknown, for example, whether children with bilateral lower limb impairment (dilegic CP) are less able to attenuate accelerations of the head compared to children with unilateral lower limb impairment (hemiplegic CP). Impaired attenuation of accelerations between the trunk and head segments was previously reported in a CP cohort combining children with hemiplegia, diplegia, and dystonia compared to TD children [21]. Reporting these groups separately could better inform future tailored treatment strategies. Understanding of head instability is limited to typical level straight-line gait [6; 7; 8] with little focus on the interaction between instability of the head and how this might affect functional performance of the child, except for basic spatiotemporal parameters (e.g., gait speed, cadence, or step length). To increase the complexity of typical gait, the current study used a targeted stepping task (stepping into two successive floor-based targets) to replicate the functional challenges of navigating a cluttered or busy environment. In this way, we hope to elucidate more functionally relevant links between head stability and accurate movement control. The aim of this study was to explore the differences in head stability between children with diplegic and hemiplegic CP and TD children when completing the stepping task. It was hypothesized that children with diplegic and hemiplegic CP show greater head instability compared to TD children when stepping into both targets.
## 5 The Proposed Method
### Majority Voting Ensemble Learning  
Ensemble learning refers to methods for making predictions that combine several inducers. It is often used in supervised machine learning applications. An inducer, also known as a basic learner or weak learner, is a machine learning algorithm that takes a set of labeled examples as its input and produces a model. The model can then be used to make predictions for new unlabeled samples. Any type of machine learning approach can be employed as an ensemble inducer (e.g., decision trees, neural networks, linear regression models, etc.). The predictions of these models are then integrated to generate a final prediction. The core concept of ensemble learning is that by combining multiple models, the faults of an individual inducers can be compensated by the other inducers, which creates a strong learner out of several weak learners [43].  
Ensemble members can be of the same or various types and they may or may not be trained using the same training dataset [44]. When all individual learners in an ensemble are of the same type, the ensemble is said to be homogeneous. For example, a \"neural network ensemble\" contains only neural networks [45].  
In the case of classification, the combination of the results from all of the base learners can be accomplished using majority voting, which has three types: (1) unanimous voting, in which all of the classifiers agree on the prediction; (2) simple majority, in which more than half of the classifiers predict the same class; (3) plurality voting, in which the prediction receives the most votes, regardless of whether the total number of votes exceeds 50% of the classifiers [46].  
In this work, we trained homogeneous ensemble learning using the MHOANN with the cost sensitivity fitness function as the ensemble members and the training dataset after applying sampling with replacements. Subsequently, majority (plurality) voting was implemented to generate the final predictions using the testing dataset.
# Creating a Parallel Corpus for the Kazakh Sign Language and Learning
## 3 Materials and Methods
### Word Order in KSL  
The arrangement of words plays a crucial role in the grammar of any spoken language. Unlike spoken languages, where words are pronounced sequentially due to the constraints of the speech apparatus, sign languages differ in this aspect. They are not entirely sequential, as both hands can be used simultaneously to produce different symbols, reducing the need for strict word order. In this study, we cannot definitively claim that sentence structure in sign languages serves the same grammatical function as in spoken languages. This question is central to our research. Given the importance of sentence structure, it is necessary to examine whether it functions as a grammatical tool in both signed and spoken languages or whether sign languages exhibit unique structural characteristics due to their visual-spatial nature. Based on these questions, the aim of this study is to analyze the distribution of key sentence elements (subject, object, and verb) in simple declarative sentences in KSL and explore the possibility of identifying its basic sentence structure. This study also emphasizes the importance of a reliable methodology. KSL is used by individuals with hearing and speech impairments in Kazakhstan and some other countries of the former Soviet Union,including Russia and Belarus. Until recently, linguistic research on Russian Sign Language (RSL) was virtually nonexistent, with only a few studies emerging in recent years [22]. The word order in KSL has not yet been systematically examined, but [23] suggests that it is flexible. Word order is a phenomenon that can be observed without specific constraints. However, determining what constitutes \"basic word order\" and whether a language has a default word order is a more complex issue. Before delving into KSL, we will first discuss the concept of basic word order in both spoken and sign languages. Additionally, we will outline the methodology used for collecting and analyzing RSL data. Following this, we will present the findings of our research. Finally, we will conclude with a discussion on the issue of basic word order in KSL.
## 3 System Model
### Design Objectives  
Based on the above, our objective was to design an incentive mechanism that improves resource utilization while minimizing the platform's costs, formulated as follows:  
\[\begin{split}(\mathbb{P}_{1})&\min_{\Phi_{j}}\ U_{S}= \frac{1}{\sqrt{\sum_{j\in\mathcal{J}}r_{j}I_{j}s_{j}}}+\xi\sum_{j\in J}r_{j}I_{ j}p_{j},\\ &\text{s.t.}\ \left\{\begin{array}{l}1\leq r_{j}\leq\overline{ \theta},\\ 0<s_{j}\leq s_{\text{max}},\forall j\in\mathcal{J}.\end{array}\right.\end{split} \tag{6}\]  
where these two constraints, respectively, limit the range of local training rounds and the required data size.  
Moreover, an incentive mechanism that can motivate the truthful data contribution of edge servers should guarantee the following properties:  
* Individual rationality (IR). A contract satisfies IR if each type of edge server can receive a non-negative payoff by choosing the contract item, \(\phi_{j}\), designed for that type, i.e., \(U_{j}(c_{j},\phi_{j},r_{j})\geq 0,\forall j\in\mathcal{J}\)[30; 37].
* Incentive compatibility (IC). A contract satisfies IC if each type of edge server receives its maximum payoffs by choosing the contract item, \(\phi_{j}\), designed for that type, i.e., \(U_{j}(c_{j},\phi_{j},r_{j})\geq U_{m}(c_{m},\phi_{m},r_{m}),\forall j\
eq m\in \mathcal{J}\)[30; 37].
* Computational efficiency (CE). The contract mechanism can be terminated in polynomial time [33].  
The IR constraints provide the necessary incentive for making edge servers sign the contract. The IC constraints dictate that edge servers can maximize their utilities when choosing the contract item designed for their true types. When IC conditions are satisfied, each edge server's type is revealed to the platform, which is called \"self-reveal\". The contract item is feasible if it satisfies both IC and IR constraints [20; 37]. However, in this work, as strategic edge servers can adjust their local training rounds, simply imposing IC solely on each edge server's total utility may result in a decrease in the unit-round utility of edge servers that conduct more local training rounds, which is unfair [38; 39; 40]. Hence, we relax the incentive compatibility of the edge servers' total utility into incentive compatibility for their unit-round utility [33].  
**Definition 1** (\(\mu\)-Incentive compatibility).: _A contract satisfies \(\mu\)-IC if each type of edge server can receive its maximum unit payoff by accepting the contract item, \(\phi_{j}\), designed for that type, i.e.,_  
\[u_{j}\big{(}c_{j},\phi_{j}\big{)}=p_{j}-c_{j}s_{j}\geq u_{m}(c_{m},\phi_{m}), \forall j\
eq m\in\mathcal{J}. \tag{7}\]  
If a contract satisfies IR, it also satisfies \(\mu\)-IR since \(r_{j}>0\). Therefore, \(\mu\)-IR can be regarded as an equivalent transformation constraint of IR, expressed as follows:  
**Definition 2** (\(\mu\)-Individual rationality).: _A contract satisfies \(\mu\)-IR if each type of edge server can receive a non-negative unit payoff by accepting the contract item, \(\phi_{j}\), designed for that type, i.e.,_  
\[u_{j}\big{(}c_{j},\phi_{j}\big{)}=p_{j}-c_{j}s_{j}\geq 0,\forall j\in \mathcal{J}. \tag{8}\]
## 3 Research Methodology
### Security Implications of OTP\({}^{3}\)
#### 3.3.3 Data Plane  
The data plane is designated for transfers during the operational stage of the system. Following the receipt of permission to initiate data transmission, the paired modules must independently generate a new and non-repeating OTP password, based on their SN (refer to Step 3 in Figure 4). Throughout the data transmission process, the paired modules utilize the OTP keys generated by the OTP password (refer to Step 4 in Figure 4) to perform encryption and decryption operations, respectively (refer to Step 5 to 7 in Figure 4). It is anticipated that there will be a proliferation of IoT devices in the future, and the designed data plane of OTP\({}^{3}\) allows devices to directly exchange data through the zero-trust access virtual tunnel (refer to Figure 1). This approach enables the proposed OTP\({}^{3}\) IoT cybersecurity system (refer to Figure 2) to distribute computing and storage resources to the endpoint devices, facilitating the switching of datagrams at the network edge to mitigate data transmission delays. This concept represents a novel realization of fog computing [1]. Consequently, the data plane is imperative for the proposed OTP\({}^{3}\) mechanism and the OTP\({}^{3}\) IoT cybersecurity system.
# A Hybrid Routing Protocol Based on Naive Bayes and Improved Particle Swarm Optimization Algorithms
## 2 Related Works
### Multi-Hop Algorithm between Cluster Heads  
In clustering algorithms such as LEACH, it is generally assumed that all nodes have the ability to communicate directly with the base station. Therefore, after being selected, the cluster head node directly sends the collected node data in the cluster to the \(BS\) in one hop. This method is suitable and efficient when the network size is small, but with the expansion of the network monitoring area, the energy consumption of cluster heads far away from the \(BS\) will make the nodes consume energy rapidly and then affect the network performance [17]. Therefore, researchers have proposed a multi-hop hybrid routing structure between cluster heads. [PERSON] et al. [18] proposed an energy approximation cluster head selection method that continuously appoints a node as the cluster head until it approaches the average residual energy in the cluster and adopts the multi-hop method between cluster heads to reduce the excess energy consumption overhead of remote cluster heads and prolong the network life.  
In terms of multi-hop routing protocols, the minimum energy consumption (MEC) routing protocol proposed by [PERSON] et al. [19] takes the minimum energy consumption of the transmission path as the main consideration in the design of the routing protocol without considering the residual energy of nodes outside the minimum energy consumption path in the network, so that the nodes on the minimum energy consumption path will die prematurely due to heavy forwarding tasks. SOR [20] is an energy-aware self-organizing routing protocol that introduces a payment strategy to encourage nodes to undertake data transfer tasks, and sensor nodes make routing decisions according to local information. The protocol realizes the combination of energy efficiency and balancing. [PERSON] et al. [21] proposed a low-energy routing protocol that combines near-source data aggregation and congestion control. According to the different data distribution densities in the WSN, the node combination and the forwarding path of data fusion are selected to reduce the network's energy consumption and control congestion. [PERSON] et al. [22] proposed a load balancing clustering algorithm based on a fuzzy C-means (FCM) algorithm for cluster head selection and applied the bat-inspired routing algorithm to multi-hop between cluster heads to determine the best low-energy routing path. PSO, bat, and other swarm intelligence algorithms and machine learning classification algorithms can better solve nonlinear classification problems in cluster head and path selection, and are very suitable for dynamic network applications in probabilistic channels.
## 2 Materials and Methods
### Quantitative Method Validation  
To verify linearity, the standard solutions containing 16 reference substances at seven different concentrations were injected into the triple quadrupole tandem mass spectrometer and analyzed. Calibration curves of the reference standards were constructed by plotting the integrated peak area versus the corresponding concentrations. The limit of detection (LOD) and the limit of quantification (LOQ) of the 16 ginsenosides were determined by injecting a series of standard solutions until the basis of response at the signal-to-noise ratio (S/N) was about 3 times for LOD and 10 times for LOQ. To determine precision, the samples were analyzed six times within the same day. Reproducibility was evaluated by extracting and analyzing six replicates of the same batch of sample with the established method. To determine stability, the same sample solution was analyzed at 0, 2, 4, 8, 12, and 24 h. Furthermore, a recovery test was used to evaluate the accuracy of this method. Known amounts of ginsenoside standards were added into a 0.1 g of sample six times; the six mixtures were extracted and analyzed.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 1 Introduction
A third type of method that satisfies the principle of unconditional security is the one proposed by [PERSON] and [PERSON] [8], which is based on the ability of imitation of speech signals. The secret message imitates a voice message of non-sensitive content. The main disadvantage of this method consists in the necessity to have a database of non-sensitive content messages. As an enhancement to this limit, authors of [12] recently proposed an adaptation between a (secret) voice signal and a super-Gaussian signal, which is generated in situ in the randomization system. In this way, it is not necessary to have a database to create the signal to be imitated. As a limitation, this method only contemplates scrambling but not steganography. According to the comments above, even though there are works in literature framed within hidden communication of speech signals, to date, as far as it is known, there is not an available solution that combines the advantages of the existing methods and overcomes the disadvantages. The characteristics of our proposal are:  
1. The proposed system combines scrambling and steganography. 2. The scrambling block allows a voice signal to imitate a super-Gaussian noise. The output signal (scrambled) is highly similar to the super-Gaussian noise. The key that permits descrambling the signal is a system out. 3. The hiding of the scrambled signal within the host signal is done through an adaptive substitution technique, which allows control of the amount of signal distortion through the parameter bits to hold (\(BH\)). 4. The final output signal (stego) is highly similar to the host signal and does not generate suspicion of the existence of a secret message within it. Without knowledge of the seed used to generate the super-Gaussian signal, as well as the value of BIH, a non-authorised user can not reveal the secret content.