esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Influence of Dispersion on an OFDM-OCDMA System
Therefore, when the OFDM signal has been carried onto the optical carrier wave, each subcarrier signalof OFDM respectively represents an optical carrier wave, and will cause the signals carried on each subcarrier to reach the receiver at different times after transmission through the optic fiber. Figure 8 shows that if no cyclic prefix is added, the receiver will lack part of the signal when it retrieves the signal. Furthermore, if the receiver retrieves other OFDM signals, an intersymbol interference (ISI) effect is generated, greatly reducing the system's effectiveness. The different colors mean different frequencies of modulated OFDM signal. By adding a cyclic prefix in the OFDM technology, the OFDM signal data loss at the receiver can be compensated and the influence of ISI on the signal can be suppressed. In a related study, scholars utilized a single optical carrier wave to transmit OFDM data, and they deduced the cyclic prefix time length required by OFDM signals to suppress dispersion-induced ISI [21]. However, because the present system principally utilizes multiple optical carrier waves to transmit data, the method of adding a cyclic prefix alone cannot be used to suppress ISI and dispersion-induced interference. In general, the use of DCF to compensate for dispersion was adopted in the proposed system. In addition, because most of the dispersion is suppressed by the DCF, the length of the cyclic prefix added to the OFDM signal in this system architecture only needs to be identical to the length used in general OFDM technology (e.g., 1/4 of the inverse fast Fourier transform (IFFT) size).
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
###### Abstract  
Software-defined networking (SDN) enables dynamic management and flexible network control by employing reactive rule installation. Due to high power consumption and cost, current OpenFlow switches only support a limited number of flow rules, which is a major limitation for deploying massive fine-grained policies. This bottleneck can be exploited by attackers to launch saturation attacks to overflow the flow table. Moreover, flow table overflow can occur in the absence of malicious attackers. To cope with this, researchers have developed many proposals to relieve the load under benign conditions. Among them, the dynamic timeout mechanism is one of the most effective solutions. We notice that when the SDN controller adopts dynamic timeouts, existing flow table saturation attacks can fail, or even expose the attackers, due to inaccurate inferring results. In this paper, we extract the common features of dynamic timeout strategies and propose an advanced flow table saturation attack. We explore the definition of flow rule lifetime and use a timing-based side-channel to infer the timeout of flow rules. Moreover, we leverage the dynamic timeout mechanisms to proactively interfere with the decision of timeout values and perform an attack. We conduct extensive experiments in various settings to demonstrate its effectiveness. We also notice that some replacement strategies work differently when the controller assigns dynamic timeouts. The experiment results show that the attack can incur significant network performance degradation and carry out the attack in a stealthy manner.  
software-defined networking (SDN); OpenFlow; saturation attack; flow table overflow; flow table management; side-channel attack
## 2 Research Method
### Method 3 of 4, Regression Fits for Raeder's Tabulated Results: Integration
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Species** & **Linear Regression Equation** & **R-Squared** \\ \hline
1 & CO & \(\overline{Q}=7.840000+0.003098\)\(T_{e}\) & 0.99916 \\ \hline
2 & CO\({}_{2}\) & \(\overline{Q}=24.431818-0.005240\)\(T_{e}\) & 0.98317 \\ \hline
3 & H\({}_{2}\) & \(\overline{Q}=12.512727+0.000955\)\(T_{e}\) & 0.99128 \\ \hline
4 & H\({}_{2}\)O & \(\overline{Q}=107.955455-0.023073\)\(T_{e}\) & 0.98272 \\ \hline
5 & K & \(\overline{Q}=362.090909-0.068000\)\(T_{e}\) & 0.99326 \\ \hline
6 & O\({}_{2}\) & \(\overline{Q}=4.010000+0.000767\)\(T_{e}\) & 0.99787 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Proposed linear regression models for the average electron–neutral collision cross section (\(\overline{Q}\), in Å\({}^{2}\)) in the third method as a function of the electron absolute temperature (\(T_{e}\), in K), based on fitting 11 published data points for each species. The obtained non-dimensional goodness-of-fit R-squared (\(R^{2}\)) value for each proposed linear regression model is also provided. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Limit Value** & \(T_{e}\) **= 2000 K** & \(T_{e}\) **= 3000 K** \\ \hline Smallest \(u_{e}\) (eV) & 0.0005 \\ \hline Largest \(u_{e}\) (eV) & 6.0000 \\ \hline Smallest \(v_{e}\) (m/s) at smallest \(u_{e}\) & 13,262 \\ \hline Largest \(v_{e}\) (m/s) at largest \(u_{e}\) & 1,452,785 \\ \hline Smallest \(x\) (-) at smallest \(u_{e}\) & 0.00258 & 0.00172 \\ \hline Largest \(x\) (-) at largest \(u_{e}\) & 34.81355 & 23.20904 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mapping the limits of the monoenergetic electron kinetic energy (\(u_{e}\)) to the monoenergetic electron thermal speed (\(v_{e}\)); to the non-dimensional energy ratio (\(x\)) at \(T_{e}\) = 2000 K; and to the non-dimensional energy ratio (\(x\)) at \(T_{e}\) = 3000 K for the integrations implied in method 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Value at Peak \(g\) (=0.270671 [-])** & \(T_{e}\) **= 2000 K** & \(T_{e}\) **= 3000 K** \\ \hline \hline \(x\) (-) at peak \(g\) & 2.0000 \\ \hline \(u_{e}\) (eV) at peak \(g\) & 0.34469 & 0.51704 \\ \hline \(v_{e}\) (m/s) at peak \(g\) & 348,211 & 426,469 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Shift of the peak \(g\) weight function when expressed as dependent on the monoenergetic electron kinetic energy (\(u_{e}\)) or on the monoenergetic electron thermal speed (\(v_{e}\)), as the electron absolute temperature increases from 2000 K to 3000 K.
## 1 Introduction
On the other hand, [PERSON] et al. [15] used the thermoelectric effect for indirect body cooling, where the role of a TEM was to cool a liquid distributed in the garment, while the reduction in skin temperature resulted from the circulation of the liquid. In this case, during the tests, a temperature reduction of about 2.7 \({}^{\circ}\)C was observed. TEM-based personal cooling systems (PCSs) offer similar or even higher maximum reductions in skin temperature compared with other types of active PCSs described in the literature (Table 1), such as liquid-based [16; 17] or PCM-based systems [18; 19; 20] without TEMs. At the same time, the total weight (including batteries) of TEM-based solutions may be much lower for comparable operating times. It is related to both the low mass of active components and to the high possible value of the coefficient of performance (COP), which helps to reduce the required battery capacity. A low total weight of the system is, in turn, important in terms of the metabolic cost for the user who carries the PCS [18]. The above observations apply especially to PCSs with flexible TEMs that are in direct thermal contact with the skin [12; 14] and, to some extent, to other PCSs where TEMs are used to cool down a circulating liquid [15; 21; 22]. Importantly, the electronic sub-system already on board for TEM control may be used for several additional purposes. For example, microcontroller modules usually offer communication interfaces. The TEM controller may thus become a node of an IoT system with little or no additional hardware; it is only necessary to develop suitable software extensions. If low-power communication technology is used, energy-storage requirements will not considerably increase, as most of the power will still be consumed by the TEMs. Moreover, the ultra-wideband (UWB) technology has embedded features that enable, i.e., node tracking and real-time data collection. This makes it particularly well-suited for the integration of TEM-based protective clothing within smart work environments oriented toward improved worker safety, but such a concept has not been considered so far. For the above reasons, a model of active clothing with Peltier-based PCS was developed and integrated within a Next-Generation Internet-of-Things (NGIoT) reference architecture. The integration enables a seamless functioning of the PCS within a larger Cloud-Edge-IoT ecosystem, leveraging artificial intelligence (AI) methods. Moreover, by using the interoperability mechanisms of the NGIoT platform, AI can use measurements from other sensors for an intelligent control of the PCS. In the considered case, this includes a locally installed weather station and a low-cost smartwatch for heart-rate monitoring [23]. The aim of this contribution is to discuss selected results of the PCS validation tests, as well as its expected functioning in the Smart Safety of Workers pilot study within the ASSIST-IoT reference architecture (an EU Horizon 2020 project) [24].
## 3 Control System Design
### Automatic Braking Using a Seventh-Degree Polynomial  
We assume that the vehicle's longitudinal displacement during braking varies as shown in Equation (1). Therefore, the expressions for the velocity, acceleration, and jerk can be written as in Equations (2)-(4).  
\[x(t)=b_{7}t^{7}+b_{6}t^{6}+b_{5}t^{5}+b_{4}t^{4}+b_{3}t^{3}+b_{2}t^{2}+b_{1}t+ b_{0} \tag{1}\]  
\[v(t)=7b_{7}t^{6}+6b_{6}t^{5}+5b_{5}t^{4}+4b_{4}t^{3}+3b_{3}t^{2}+2b_{2}t+b_{1} \tag{2}\]  
\[a(t)=42b_{7}t^{5}+30b_{6}t^{4}+20b_{5}t^{3}+12b_{4}t^{2}+6b_{3}t+2b_{2} \tag{3}\]  
\[j(t)=210b_{7}t^{4}+120b_{6}t^{3}+60b_{5}t^{2}+24b_{4}t+6b_{3} \tag{4}\]  
At the moment that braking begins, the vehicle has \(x(0)=0\), \(v(0)=v_{0}\), \(a(0)=0\), and \(j(0)=0\). Similarly, at the moment that braking ends, \(t_{f}\), the vehicle has \(x(t_{f})=x_{f}\), \(v(t_{f})=0\), \(a(t_{f})=0\), and \(j(t_{f})=0\). By substituting the above constraints into Equations (1)-(4), we can derive Equations (5)-(9).  
\[b_{0}=0,\ b_{1}=v_{0},\ b_{2}=0,\ b_{3}=0 \tag{5}\]  
\[b_{4}=\frac{5\big{(}7x_{f}-4t_{f}v_{0}\big{)}}{t_{f}^{4}} \tag{6}\]  
\[b_{5}=\frac{-3\big{(}28x_{f}-15t_{f}v_{0}\big{)}}{t_{f}^{6}} \tag{7}\]  
Figure 2: Overall control framework.  
\[b_{6} =\frac{2\Big{(}35x_{f}-18t_{f}v_{0}\Big{)}}{t_{f}^{6}} \tag{8}\] \[b_{7} =\frac{-10\Big{(}2x_{f}-t_{f}v_{0}\Big{)}}{t_{f}^{7}} \tag{9}\]  
To ensure comfort during the braking process, the following objective function in Equation (10) must be minimized. Through calculations, the time of the vehicle's stop, \(t_{f}\), can be derived as shown in Equation (11).  
\[J=\int_{0}^{t_{f}}j(t)^{2}dt \tag{10}\]  
\[t_{f}=\frac{7x_{f}}{3v_{0}} \tag{11}\]  
The maximum braking acceleration, \(a_{\max}\), occurs at time \(t_{a}\), and the stopping distances, \(x_{f}\), are given by Equations (12) and (13). Here, \(x_{s}={v_{0}}^{2}/(2a_{\max})\) represents the stopping distance at maximum deceleration. It is evident that the required minimum braking distance using a seventh-degree polynomial is approximately 1.78 times that of the stopping distance at maximum deceleration and 1.25 times that of the stopping distance using a fifth-degree polynomial.  
\[t_{a}=\frac{14x_{f}}{15v_{0}}=\frac{2}{5}t_{f} \tag{12}\]  
\[x_{f}=\frac{3888v_{0}^{2}}{4375a_{\max}}\approx 1.78x_{s}\approx 1.25x_{f5} \tag{13}\]
## 3 Implementation of Proposed Method
### Highlight and Shadow Segmentation  
It is well known in the machine learning community that the interference of background information may affect the accuracy of network classification if the original sonar image is directly used as the input of the network. Conversely, the huge amount of computation brought by the large image size will increase the network training and prediction time. Based on these reasons, the original sonar image is firstly segmented before input into the network. This can make the network pay attention to the key regions in the sonar image that affect the classification decision to improve the accuracy and efficiency of object classification.  
The image segmentation algorithm based on Markov random field uses Gibbs field and maximum posterior probability MAP to achieve image segmentation, which has good robustness to noise images. For each sonar image, the image segmentation algorithm of the Markov random field (MRF) [16; 17; 18; 19] is used in this paper. Hammersley-Clifford theorem [20] proves that the joint probability of Markov random fields obeys Gibbs distribution, so it can be obtained:  
\[p(x|\beta)=\frac{1}{Z(\beta)}\exp(-H(x|\beta)), \tag{1}\]  
where \(Z(\beta)\) is the regular constant and \(H\) is the energy function defined by potential function \(V_{c}\) and a nonnegative scalar constant \(\beta\):  
\[H(x|\beta)=\sum_{c}V_{c}(x_{c}|\beta), \tag{2}\]After that, a conditional iterative model (ICM) algorithm is applied to optimize the noise and model parameters to obtain the region of highlight and shadow. Morphological expansion and corrosion operations [21] are used to reprocess the segmentation results, and then the small connected regions were removed to obtain a relatively complete binary image of highlight and shadow. The segmentation result of a measured object in a synthetic aperture sonar (SAS) image is shown in Figure 2. Figure 2a is the original sonar image, Figure 2b is the corresponding highlight region of the object, and Figure 2c is the shadow image of the object. Through the above operations, the highlight and shadow regions of the underwater object are completely segmented.
# Extraction of Dietary Fibers from Plant-Based Industry Waste: A Comprehensive Review
## 4 Dietary Fibers Extraction
### Cellulose Extraction
\begin{table}
\begin{tabular}{c c} \hline
**Plant Resource** & **Reference** \\ \hline \multicolumn{2}{c}{**Tomato pomace**} \\ \hline Method & Alkaline and bleaching treatment for cellulose production and extraction of cellulose nanocrystals with acid hydrolysis. \\ \hline Conditions & Acid hydrolysis-optimized conditions: 45 \({}^{\circ}\)C temp; 30 min duration. \\ \hline Observations & The obtained crystallinity was 97\% and the particle average diameter was 104 nm. The results showed that tomato pomace, as food waste, could be used for the extraction of cellulose nanocrystals, which is an environmentally friendly material. \\ \hline \multicolumn{2}{c}{**Peanut shell**} \\ \hline Method & Alkaline and bleaching treatment for cellulose production. \\ \hline Conditions & For alkaline treatment: 0.5 M sodium hydroxide; 90 \({}^{\circ}\)C temp. \\ \multicolumn{2}{c}{Washing with nitric acid in ethanol and finally bleaching with sodium hypochlorite (10\%).} \\ \hline Observations & A total of 0.39 g/g of cellulose (dry wt%) was extracted from the tested material. \\ \hline \multicolumn{2}{c}{**Mengkuang leaves (_Pandanus lectorius_)**} \\ \hline Method & Alkaline and bleaching treatment for cellulose production and extraction of nanocrystals with acid hydrolysis. \\ \hline Conditions & For alkaline treatment: 4\% sodium hydroxide; 125 \({}^{\circ}\)C temp; and 2 h duration. \\ \multicolumn{2}{c}{Bleaching treatment: 1.7 \(w\)/\(w\)/\(w\)\({}_{0}\) NaClO\({}_{2}\); pH 4.5; 125 \({}^{\circ}\)C; and 4 h duration.} \\ \multicolumn{2}{c}{Acid hydrolysis: 60 wt\% H\({}_{2}\)SO\({}_{4}\); 45 \({}^{\circ}\)C temp; and 45 min duration.} \\ \hline Observations & The raw material contained 37.3\% cellulose. After alkaline treatment and bleaching treatment, cellulose content was 57.5\% and 81.6\%, respectively. \\ \hline \multicolumn{2}{c}{**Rice husk**} \\ \hline Method & Alkaline and bleaching treatment for cellulose production and extraction of nanocrystals with acid hydrolysis. \\ \hline \multicolumn{2}{c}{Conditions} & For alkaline treatment: 4 wt\% sodium hydroxide; 2 h duration. \\ \multicolumn{2}{c}{Bleaching treatment: buffer solution of aqueous chlorite (1.7 wt%), acetic acid and distilled water; 4 h duration; and 100-130 \({}^{\circ}\)C temp.} \\ \multicolumn{2}{c}{Acid hydrolysis: 10 M sulfuric acid 50 \({}^{\circ}\)C temp; 40 min duration.} \\ \hline Observations & The raw material contained 35\% cellulose. After alkaline treatment and bleaching treatment, cellulose content was 57\% and 96\%, respectively. \\ \hline \end{tabular}
\end{table} 
Table 3: Studies of cellulose extraction from various material sources.
## 2 Theoretical Models of Light Propagation and Light Scattering
### Analytical Solutions for the RTE and PDE  
We employed analytical solutions of the steady-state RTE and PDE for a 3D infinite slab to verify our numerical results and investigate the diffusion approximation. We considered the slab with two \(z\)-planes (\(z=0.0\) cm and \(2.5\) cm with a width \(d_{slab}=2.5\) cm) under  
Figure 2: (**a**) Refractive indices of the alumina suspension at different volume fractions from 1% to 20%. (**b**) Reflectance as a function of the scattering angle at different refractive indices of 1.336 (\(\eta=0.01\)) and 1.419 (\(\eta=0.20\)) with the critical angles of 48.4\({}^{\circ}\) and 44.8\({}^{\circ}\).  
the Robin boundary condition (Equation (3)). We focused on calculating points inside the medium on the line perpendicular to the \(z\)-planes.  
The analytical form of the fluence rate for the PDE is given by [44]  
\[\Phi_{PDE,slab}(z,I)=\frac{1}{4\pi D}\sum_{s=-N_{s}}^{N_{s}}\biggl{[}\frac{1}{r_ {s}^{+}}e^{\mu_{s}r_{s}^{+}}-\frac{1}{r_{s}^{-}}e^{\mu_{s}r_{s}^{-}}\biggr{]}, \tag{4}\]  
where \(I=(x,y)\), \(N_{s}\) is the number of the summation with \(s\) (\(s=0,\pm 1,\pm 2,\cdots,\pm N_{s}\)); \(r_{s}^{+}\) is a distance between the \(s\)-th positive source and calculating position (\(z,I\)); \(r_{s}^{-}\) is a distance between the \(s\)-th negative source and calculating position; and \(\mu_{e}=\sqrt{\mu_{a}/D}\). \(r_{s}^{+}\) and \(r_{s}^{-}\) are given by  
\[r_{s}^{+} = \sqrt{(z-z_{s}^{+})^{2}+l^{2}},\hskip 28.452756 ptr_{s}^{-}=\sqrt{(z -z_{s}^{-})^{2}+l^{2}},\] \[z_{s}^{+} = 2s(2z_{b}+d_{slab})+z_{0},\hskip 28.452756 ptz_{s}^{-}=2s(2z_{b}+d_ {slab})-2z_{b}-z_{0}, \tag{5}\]  
where \(z_{b}=\gamma(n)D\) is an extrapolated length and \(z_{0}=\min[\Delta r,1/\mu_{s}^{r}]\) is a small positive valued.  
The analytical solution (Equation (4)) is constructed by summing analytical solutions for an infinite medium over the different distances to treat the boundary condition based on the method of images. We considered the analytical form for the RTE in the same way as that for the PDE  
\[\Phi_{RTE,slab}(z,I)=\sum_{s=-N_{s}}^{N_{s}}\Bigl{[}\Phi_{RTE,inf}(r_{s}^{+})- \Phi_{RTE,inf}(r_{s}^{-})\Bigr{]}, \tag{6}\]  
where \(\Phi_{RTE,inf}\) is the analytical solution of the RTE for an infinite medium, derived by [PERSON] and [PERSON] [45]. Equation (6) treats the boundary condition under the diffusion approximation, while \(\Phi_{RTE,inf}\) is the exact solution of the RTE. We modified the open-source code of \(\Phi_{RTE,inf}\) for using the phase function calculated from the DST instead of the Henyey-Greenstein (HG) phase function, originally used.
# Bridging the Appearance Domain Gap in Elderly Posture Recognition with YOLOv9
## 3 Materials and Methods
### Methodology
#### 3.3.2 Data Augmentation  
In order to improve the generalisation ability of the model and to avoid overfitting, several data augmentation techniques were applied during the training phase (see Table 2). These included random hue adjustments between \(-15^{\circ}\) and \(+15^{\circ}\), saturation changes between \(-25\%\) and \(+25\%\), brightness changes between \(-15\%\) and \(+15\%\), and exposure adjustments between \(-10\%\) and \(+10\%\). Random noise affecting up to 0.1% of the pixels was also added to simulate real-world image imperfections. Greyscale transformations were applied to 15% of the images, and a blur of up to 2.5 pixels was introduced to make the model robust to varying image quality.  
To ensure the robustness of the training process, the following steps were taken:  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Value** \\ \hline Resize & True \\ imgsz & 640 \\ epochs & 100 \\ total images & 1433 \\ training images & 1254 \\ validating images & 120 \\ testing images & 59 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training parameters.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Outputs per training example & 3 \\ Hue & Between \(-15^{\circ}\) and \(+15^{\circ}\) \\ Saturation & Between \(-25\%\) and \(+25\%\) \\ Brightness & Between \(-15\%\) and \(+15\%\) \\ Exposure & Between \(-10\%\) and \(+10\%\) \\ Blur & Up to 2.5 pixels \\ Noise & Up to 0.1\% of pixels \\ \hline \hline \end{tabular}
\end{table}
Table 2: Augmentation parameters.  
Data collection and annotation: The images extracted from the videos recorded by the six cameras in the virtual environment (used in the four experiments) and the images from the real-world video (used in the HYBRID-VIRTUAL and HYBRID-REAL experiments) were uploaded to Roboflow. The proportion of real data in the HYBRID dataset was 0.036. Each image was annotated for the three postures.
2. Data augmentation: Augmentation techniques, such as rotation, flipping, and scaling, were applied to the dataset to improve model generalisation. The specific augmentation parameters are listed in Table 2.
3. Model training: The YOLOv9 model was trained using the annotated and augmented dataset. Training was performed on a machine equipped with CUDA 11.8 to take advantage of GPU acceleration for faster training.  
The methodology ensures that the model is well equipped to accurately detect and classify the three postures in different scenarios within the virtual environment. The remaining question is whether the application is consistent when tested in the real world.
## 3 Test Results
### Construction Noise  
In typical conditions, field noise ranges from 60 to 80 dB. Figure 11 displays noise during hammering, peaking at 110 dB close to the pile driver and increasing with hammering depth. Figure 11b shows noise from the PGP pile; drilling emits the most noise, then pile pressing, and drilling produces the least. Noise is higher near the pile driver and drilling generates around 100 dB at 1 m. PGP pile construction is slower than the driven pile, and the noise is mainly generated by the machine engine. When the drilling rod rotates and disturbs the soil during drilling, it produces much noise, less than 90 dB at 10 m.  
Figure 11: Comparisons of maximum noise during pile construction. (**a**) Driven pile; and (**b**) PGP pile.  
Figure 10: Comparison of PGA changes with distance during construction. (**a**) Vertical direction; and (**b**) Horizontal direction.  
During PGP pile construction, noise is over 10 dB lower compared to driven piles at the same spot. PGP pile noise diminishes notably with distance, while driven piles maintain 100+ dB at 20 m. PGP piles produce lower decibel noise, with minor attenuation. The maximum level at the same distance can be 8-15% lower than driven piles. The noise limit during the construction pile driving stage is 85 dB (Central urban area), and it is obvious that driven piles are not very compliant with regulations. If the distance from the PGP pile is 17 m, the noise can be controlled within a limited range.