esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The platform is also intended to be a long-term solution that patients can integrate into their daily lives, offering a more sustainable approach than alternatives that may be episodic or limited in duration. Moreover, by enabling home-based training, COGNIVITRA seeks to reach a broader population, including those with limited access to clinical facilities. One of COGNIVITRA's core features is its ability to enhance communication between patients and care providers through integrated real-time data monitoring and reporting systems. This allows healthcare providers to monitor patient progress remotely, making adjustments as necessary to optimize outcomes. By maintaining continuous interaction,particularly for elderly patients, COGNIVITRA helps reduce the sense of isolation that can accompany home-based care, contributing to better mental health and overall patient satisfaction. Finally, COGNIVITRA has significant economic implications for healthcare institutions. By enabling home-based training, it reduces the frequency of in-person visits, leading to cost savings for both providers and patients. The platform's ability to monitor patient progress remotely also allows for early intervention, potentially preventing more serious cognitive decline and reducing long-term healthcare costs. In this way, COGNIVITRA contributes to a more efficient and cost-effective approach to cognitive rehabilitation. Finally, gathering data from a real-world setting, with the tasks being performed in both home and clinical settings, can be included among the strengths of the study. In contrast, the low number of patients in the analysis can be included among the weaknesses of the study. Therefore, it is essential to conduct prospective studies with larger groups of participants and longer follow-up periods to validate and build upon our results.
# Emerging Optical Techniques for the Diagnosis of Onychomycosis
## 2 Spectroscopy
### Raman Spectroscopy (RS)
Other vibrational bands observed in Raman spectra of nail keratin are: (i) the phenylalanine (Phe) bands assigned to the symmetric ring breathing mode (1004 cm\({}^{-1}\)) and C-H in-plane bending mode (1030 cm\({}^{-1}\)); (ii) the amide I broad band (\(\sim\)1655 cm\({}^{-1}\)) with major components' contribution from \(\alpha\)-helix C = O stretching (\(\sim\)1652 cm\({}^{-1}\)) and \(\beta\)-sheet C = O stretching (\(\sim\)1670 cm\({}^{-1}\)); (iii) the amide II band (\(\sim\)1450 cm\({}^{-1}\)) assigned to the deformation of -CH, -CH\({}_{2}\) and -CH\({}_{3}\); and (iv) the amide III broad band (\(\sim\)1350 cm\({}^{-1}\)) attributed to C-N stretching and -CH\({}_{2}\) deformation and scissoring [24]. In onychomycotic nails, the ratio of the peak area of the \(\alpha\)-helical band divided by the peak area of the \(\beta\)-sheet was found to be lower than the corresponding value in normal nails [23]. The secondary structure of \(\alpha\)-keratin forms coiled-coil domains that unravel under onychomycosis, inducing a less dense coil-loose protein structure. Nevertheless, this finding seems to be inconsistent and possibly specific to a causative agent. Finally, it is worth noting that in Nd:YAG laser-treated nails, the \(\beta\)-sheet band dominated over the \(\alpha\)-helical structures, contrary to the outcomes for the non-treated onychomycotic nails [21].
# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 2 Review of the State-of-the-Art Technology
### Detection through UAS Radio Frequency Signals
The experiment has consisted of the study of the performance of these models under different signal-to-noise ratio levels. The results had a correct detection accuracy obtained of 98.9% at 10 dB signal-to-noise ratio level. Next, we detail some commercial RF detection systems. DJI has created a system to detect their own drones. AeroScope [28] can identify them by monitoring and analyzing their electronic signal to gain critical information such as flight status, paths, and other information in real time. There are two types of AeroScope systems: stationary (designed for continuous protection of large-scale sites, up to 50 km range) and portable (designed for temporary events and mobile deployments, up to 5 km range). Dedrone provides a complete airspace security system [29], including RF sensors, able to detect and localize drones by their RF signals. There are two types of these sensors: the DedroneSensor RF-160 forms the basis of the sensor network and is used in initial risk analysis, whereas the DedroneSensor RF-360 can locate and track drones. The main characteristics of these sensors can be seen in Table 7.  
Finally, DroneShield provides the DroneSentry-X product [30], which is a portable device that is compatible with vehicles. It provides 360\({}^{\circ}\) awareness and protection using integrated sensors to detect and disrupt UAVs moving at any speed. It has a nominal UAV detection range greater than 2 km, and it detects UAV RF signals, operating on consumer and commercial industrial, scientific, and medical (ISM) frequencies.
## 5 Conclusions  
In robotic control systems, nonlinearity and disturbances such as friction and differential signal may severely limit the performance of control. In this paper, a sliding mode variable structure control architecture is proposed for the normal adjustment of a designed robotic drilling end-effector. By using computation torque control and by adopting a third-order nonlinear integration chain differentiator for obtaining the speed and acceleration signals from the position signals, this sliding mode control scheme is developed with good dynamic quality. The new control law ensures global stability of the entire system and achieves both stabilization and tracking within a desired precision. A real-time control experiment platform is developed in xPC target environment based on MATLAB RTW to verify the proposed control scheme and simulation results. The experimental results prove that the proposed sliding mode control strategy is effective and robust with regard to external disturbances. The proposed controller is successfully tested on the normal adjustment of the robotic drilling end-effector. This research provides a solution for the more accurate control of robotic drilling on the curved surface of an airplane.  
Conceptualization and methodology, L.Z.; formal analysis, L.Z, M.L.W., and H.H.; writing--original draft preparation, L.Z.; supervision, J.S.D.; Funding acquisition, L.Z.  
This research was funded by National Natural Science Foundation of China, grant number 51765031 and Natural Science Foundation of Gansu Province, China, grant number 1508 RJZA075.  
The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.  
The authors declare no conflict of interest.  
The abbreviations and symbols adopted throughout the paper are listed:  
SMCSliding mode controllerPIDProportional-Integral-Derivative  
\(\mathbf{D}\)Positive definite symmetric inertia matrix  
\(\mathbf{H}\)Centrifugal and Coriolis force vector  
\(\mathbf{G}\)Gravity vector  
\(\theta_{1}\)Rotation angle of joint 1  
\(\theta_{2}\)Rotation angle of joint 2  
\(d_{3}\)Linear displacement of prismatic joint 3  
\(I_{abi}\)Product of inertia in respect to a pair of orthogonal axes \(a\) and \(b\), \(a\), \(b\) denote \(x\), \(y\) or \(z\), \(i\) denotes  
joint number  
\(m_{i}\)Mass of Joint \(i\)  
\(c_{ai}\)Centroid coordinate on axis \(a\), \(a\) denotes \(x\), \(y\) or \(z\), \(i\) denotes joint number  
\(\tau_{1}\)acts on joint 1  
\(\tau_{2}\)Torque acts on joint 2  
\(f_{3}\)Force acts on prismatic joint 3  
\(\tau_{d}\)Disturbance torque vector
# Inverse Properties in Neutrosophic Triplet Loop and Their Application to Cryptography
## 2 Main Results
\((\mathbb{Z}_{p},+\cdot)\) is a non-associative commutative NTL. 2. The following are equivalent. 1. \((\mathbb{Z}_{p},*)\) is a CIPNTL. 2. \((\mathbb{Z}_{p},*)\) is an IPNTL. 3. \(a^{2}\equiv 1\) mod \(p\). **Proof. **  
1. \((\mathbb{Z}_{p},*)\) is a groupoid by the definition of \(*\). **Commutativity**\(x*y=ax+ay=ay+ax=y*x\). So, \((\mathbb{Z}_{p},*)\) is commutative. **Neutrality**\(x*neut(x)=x\Leftrightarrow ax+a\ neut(x)=x\Leftrightarrow a\ neut(x)=x-ax=(1-a)x\Leftrightarrow neut(x)=a^{-1}(1-a)x\). Similarly, \(neut(x)*x=x\Leftrightarrow neut(x)*x=a^{-1}(1-a)x\). **Opposite**: \(x*anti(x)=neut(x)\Leftrightarrow ax+a\ anti(x)=neut(x)\Leftrightarrow ax+a\ anti(x)=a^{-1}(1-a)x \Leftrightarrow anti(x)=a^{-1}(1-a)x-ax=a^{1}[a^{-1}(1-a)-a]x\Leftrightarrow anti(x)=[a^{-2}(1-a)-1]x\). Similarly, \(anti(x)*x=neut(x)\Leftrightarrow anti(x)=[a^{-2}(1-a)-1]x\). So, \((\mathbb{Z}_{p},*)\) is a NTL. So, \((\mathbb{Z}_{p},*)\) is an NTL. **Non-Associativity**: \(x*(y*z)=ax+a(ay+az)=ax+a^{2}y+a^{2}z\) and \((x*y)*z=a(ax+ay)+az=a^{2}x+a^{2}y+az\). So, \(x*(y*z)\
eq(x*y)*z\). \(\therefore\): \((\mathbb{Z}_{p},*)\) is a non-associative commutative NTL. 2. Going by 3. of Lemma 4, it suffices to only show that \((\mathbb{Z}_{p},*)\) is a RIPL. \((\mathbb{Z}_{p},*)\) has the RIP if and only if \((y*x)*anti(x)=y\Leftrightarrow(ay+ax)*anti(x)=y\Leftrightarrow a(ay+ax)+a\ anti(x)=y \Leftrightarrow a^{2}y+a^{2}x+a[a^{-2}(1-a)-1]x=y\Leftrightarrow a^{2}y+[a^{- 1}(1-a)-a+a^{2}]x=1y+0x\Leftrightarrow a^{2}\equiv 1\bmod p\). **Remark 4**. : _In Theorem 8, \(a^{2}\equiv 1\bmod p\Leftrightarrow p|a^{2}-1\Leftrightarrow\exists k\in \mathbb{Z}\ \
i a^{2}-1=pk\Leftrightarrow a=\sqrt{pk+1}\) for some \(k\in\mathbb{Z}\) with \(a<p\). Hence, with the requirements that \(a^{2}=pk+1\) and \(a<p\), \(k=p-2\), so that \(a=p-1\)._  
**Example 5**. : \((\mathbb{Z}_{p},*)\) _where \(x*y=(p-1)(x+y)\), for any prime \(p\) is a non-associative commutative CIPNTL and IPNTL._
# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
## 5 Experimental Analysis  
### Identification of Regimes  
#### 5.1.1 Model Calibration  
The first step of the developed regime identification models associated with the four detector stations was to determine the model parameters. To this end, the models were implemented and calibrated using the whole study data set. The determined model parameters \(\pi\) and \(A\) are listed in Tables 2 and 3, respectively. The statistical means of the traffic measures for each identified regime are depicted in Table 4.  
The parameter \(\pi\) describes the probability distribution of the initial state. As seen from Table 2, for each detector station, the values of four of the five states are 0 or close to 0, while the value of the other state is 1.0. This might be because the start time of the traffic flow when the data were collected from each detector station was at 00:00, when the traffic on the road is under a free-flow condition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station ID** & **State 1** & **State 2** & **State 3** & **State 4** & **State 5** \\ \hline
400976 & 0 & 1.0 & 0 & 0 & 0 \\
400081 & 0 & 1.0 & 0 & 0 & 0 \\
400329 & 1.0 & 0 & 0 & 0 & 0 \\
400825 & 1.0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Determined \(\pi\) in four regime identification models.  
Figure 3: Locations of the study detector stations.  
Another significant model parameter \(A\) describes the transition probability distribution from one state to another. The digital numbers in the last five columns of Table 3 are the determined probabilities. Take Station 400976, for example. The transition probability between the same states was much higher than that between the different states. This indicates that the variations of traffic conditions usually occured after a certain time period. For State 1, its more likely next state is State 2, meaning the traffic evolved from slightly crowded conditions to completely free flow conditions at times. For State 2, the transition probability from it to another state was very small. Occasionally, it transformed into State 1 (slightly crowded) or State 5 (very congested). The traffic goes through from free-flow conditions to very congested conditions, which might be because of the occurrence of traffic crashes. For State 3, its next possible states were State 5 and State 4, implying the traffic evolved from transition conditions to more congested conditions. For State 4, it is possible to transit to the other four states. For State 5, it might change to State 2, State 3, or State 4, meaning that the traffic congestion has been gradually dissipating. For the other stations, similar patterns could be identified. However, there were some slightly differences in the cases where different traffic states were transformed into each other.
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 3 Motivation of the Proposed Work
### Design of Hybrid Trust Model
#### 4.3.2 State Transition between Two Trust Models  
The hybrid trust model will adaptively choose either the beta trust model or the new trust model based on the occurrence of success or failure. As the two trust models use different formulas to evaluate trust value on the same input values of \(s\) and \(f\) (the total number of successes and failures, respectively), they may give different trust values. When we switch from the beta trust model to the new trust model on consecutive failures, we can use the current values of \(s\) and \(f\) to determine base penalty and then apply the new trust model to evaluate trust value. However, when we switch back to the beta trust model, the current trust value (from the new trust model) will be lower than the value based on the beta trust model (see Equation (1)). To continue using the beta trust model, we should adjust the parameters of the beta trust model so that the beta trust model has the same trust value as our new trust model. We call this procedure _state transition_.  
One way to solve the state transition problem is to adjust the number of failures \(f\) in the beta trust model. Let \(T_{New}\) and \(T_{Beta}\) be the trust values in new trust model and the beta trust model, respectively. By \(T_{New}=T_{Beta}\),  
\[T_{New}=\frac{s+1}{s+f^{*}+2}. \tag{15}\]  
By Equation (15), we obtain the adjusted \(f^{*}\) as:  
\[f^{*}=\frac{s+1}{T_{New}}-s-2. \tag{16}\]
# Effect of Ultrasonic Treatment on Taste and Flavor Quality of Japonic Rice
## 3 Results
### Properties of Textures  
Figure 3 illustrates the effect of ultrasound treatment time on the textural characteristics of cooked Japonica rice. The hardness of the cooked rice significantly increased with increasing ultrasound treatment time (\(p<0.05\)). This increase was attributed to the mechanical force of the ultrasound, cavitation effect, and strong shock wave, which can break down the swollen starch granules and partially depolymerize straight-chain starch. The release of linear straight-chain starch and the improvement of lipid dispersion facilitated the complexation reaction between straight-chain starch and lipid to form a complex [18], increasing the hardness. After an ultrasound treatment time of 30 min, JJ830 hardness increased by 84.95%. In contrast, LJ66 hardness increased by only 23.94%, indicating that the hardness of JJ830 increased the most with increasing ultrasound time. The difference in hardness between the varieties became significant due to the variability in the body composition of the cooked rice. For example, JJ830 exhibited the highest hardness owing to its high-branched amylose content (75.65%) and the lowest ratio of amylose and amylopectin (0.15), while LJ66 exhibited the least hardness due to its low-branched amylose content (68.89%) and the high ratio of amylose and amylopectin (0.27). Cooked rice adhesion decreased with increasing ultrasound treatment time because ultrasound treatment broke the C-O-C bond of the \(\alpha\)-1,6 glycosidic bond, which contributed to the branching point of the branched starch [18]. As the concentration of branched starch decreased, the adhesion progressively decreased. When resistant starch and straight-chain starch contents increased, cooked rice chewingability increased, with no significant effect on elasticity and cohesion. This observation is in line with that of earlier research [19]. In summary, high-branched amylose content and low rectilinear ratio varieties facilitated resistant starch production after ultrasound treatment. At 20 min of ultrasound treatment, cooked rice exhibited moderate hardness and viscosity, chewy, minimal gritty perception, and good textural quality.  
Figure 3: The effect of ultrasound treatment on cooked Japonica rice texture and quality. Effect of ultrasound treatment on cooked rice hardness (**a**); Effect of ultrasound treatment on cooked rice springiness (**b**); Effect of ultrasound treatment on cooked rice adhesiveness (**c**); Effect of ultrasound treatment on cooked rice cohesiveness (**d**); Effect of ultrasound treatment on cooked rice chewiness (**e**). Different letters under the same parameter indicate the significant differences (\(p<0.05\)).
## 4 DeepFogSim: The Considered System Model
### Models of the Per-Node Computing Power and Energy
#### 4.5.1 Power and Energy Wasted by the Convolutional Processor  
The power wasted by the convolutional processor of \(\text{Fog}(j,m)\) in Figure 6 is composed of a static part \(\mathcal{P}_{CON}^{(IDLE)}(j,m)\) (Watt), with \(1\leq j\leq m_{m}\) and \(1\leq m\leq M\), and a dynamic one:  
\[\mathcal{P}_{\text{CON}}^{(DYN)}(j,m)=K_{\text{CON}}^{(j,m)}(|\mathcal{T}_{m}| \varepsilon_{jm}\,f_{ij})^{\gamma_{CON}^{(j,m)}},\quad 1\leq j\leq m_{m},\ 1\leq m \leq M, \tag{8}\]  
where [41]:  
1. \(\gamma_{CON}^{(j,m)}\) is a dimensionless power exponent that depends on the power profile of the underlying CPU;
2. \(\varepsilon_{jm}\) (CPU cycle/bit) is the so called processing density of the computation operations performed by the underlying CPU when a single layer of the CDNN must be executed. It depends on the number of convolutional neurons that compose a single layer of the considered CDNN and the average number of summations/multiplications performed by each neuron [15];
3. \(f_{jm}\) (bit/s) is the processing frequency of the \((j,m)\)-th convolutional processor;
4. \(|\mathcal{T}_{m}|\) is the number of layers of the underlying CDNN to be executed by \(\text{Fog}(j,m)\). This term accounts for the fact that the workload to be processed by the \((j,m)\)-th convolutional processor scales (more or less) linearly with the number \(|\mathcal{T}_{m}|\) of convolutional layers of CDNN to be executed by the \(\text{Fog}(j,m)\)[15]; and
5. \(K_{CON}^{(j,m)}\), measured in \(\text{(Watt/(CPU cycle/s)}^{\gamma_{CON}^{(j,m)}})\), is a power scaling factor that depends on the power profile of the CPU supporting the \((j,m)\)-th convolutional processor.  
In order to compute the (average) energy \(\mathcal{E}_{CON}^{(j,m)}\) (Joule) consumed by the \((j,m)\)-th convoulutional processor during each sensing interval, we note that: (i) the \((j,m)\)-th convolutional processor must remain turned ON for a time \(T_{EXE}^{(1,M)}\) equal to the execution time required by the Cloud node of Figure 3, i.e., the _maximum_ time needed for the processing of the data generated by the server in a sensing period; and (ii) since the \((j,m)\)-th convolutional processor works at the processing speed \(f_{jm}\) (bit/s) and the workload to be executed is \(v_{I}(j,m)\) in (1), the resulting processing time is \(T_{CON}(j,m)\) in (3). Hence, we have the following relationship for \(\mathcal{E}_{CON}(j,m)\):  
\[\begin{split}\mathcal{E}_{CON}(j,m)&\triangleq \mathcal{E}_{CON}^{(IDLE)}(j,m)+\mathcal{E}_{CON}^{(DYN)}(j,m)\equiv\mathcal{P} _{CON}^{(IDLE)}(j,m)\times T_{EXE}^{(1,M)}+\mathcal{P}_{CON}^{(DYN)}(j,m)\times T _{CON}(j,m)\\ &=\mathcal{P}_{CON}^{(IDLE)}(j,m)\times T_{EXE}^{(1,M)}+v_{I}(j,m )K_{CON}^{(j,m)}(|\mathcal{T}_{m}|\varepsilon_{jm})^{\gamma_{CON}^{(j,m)}} \times T_{ij}^{\gamma_{CON}^{(j,m)}-1},\end{split} \tag{9}\]  
where \(T_{EXE}^{(1,M)}\equiv\sum_{k=1}^{M}\max_{1\leq j\leq m_{k}}\{T_{EXE}(j,k)\}\) is the full (i.e, worst case) execution time of the supported CDNN of Figure 1a.
## 4 Proposed Method
### Specific Procedures  
The specific steps to construct a prediction interval for bus travel time are as follows:  
(1) Division of time period and road segment. In order to more accurately classify bus drivers on multiple routes into different types according to their driving styles, this study considers both spatial and temporal characteristics. First, divide the time periods. Divide a day into three time periods: morning peak hour (7:00-9:00), off-peak hour (9:00-16:00), and evening peak hour (16:00-19:00). The purpose of time division is to ensure that bus drivers have similar patterns in their travel time within the same time period. Second, divide the predicted bus route into sub-segments. The segment between two bus stops with multiple routes having duplicate segments is used as the basic prediction unit.  
(2) Construct the original sample data set \(D_{HC}\) of bus travel time using the hierarchical clustering method. Perform hierarchical clustering on all drivers on sub-road segments according to their driving styles, and use the travel time data sets of different categories of drivers as the original sample data set \(D_{HC}\), which includes n sub-sample data sets \(D_{HC1}...D_{HCn}\), each representing the historical travel time data set of a category of drivers over a period of time.  
(3) Resampling of the original sample data set. The bootstrap method is used to resample each subsample set in data set \(D_{HC}\)\(B\) times (where \(B\) = 30) with replacement, resulting in \(B\) bootstrap sample sets.  
(4) Prediction of travel time points based on ANN. In this step, select a three-layer neural network, including the input layer, the hidden layer, and the output layer. The factors (inputs) considered in this study are the following: \(X_{1}\) day of the week, \(X_{2}\) road segment number, and \(X_{3}\) departure time. The resampled sample data set is trained to obtain \(B\) trained ANN models, and then the test data set is used to predict the \(B\) point prediction results and their squared residuals \(r^{2}(x_{i})\).  
(5) Training the \(b+1\) neural network model. According to Equation (7), construct a squared residual data set \(D_{r^{2}}=\left\{\left(x_{i},r^{2}(x_{i})\right)_{i=1}^{n}\right.\) to train the \(b+1\) neural network model. Use the test sample data set and the above-trained models to obtain the prediction value, prediction model variance, and variance of the errors.  
(6) Constructing travel time prediction intervals for sub-segments. Construct a travel time prediction interval for the corresponding road segment based on Equations (9) and (10).