esa-sceva/satcom-chunk-collection huggingface dataset:
# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
## 3 Approximating and Eliminating the Airlight--Reduced DCP (AEA-RDCP)  
### Aea-RDCP  
In this paper, we introduce a novel algorithm called Approximating and Eliminating the Airlight--Reduced Dark Channel Prior (AEA-RDCP) designed to address the issue of mistaking sunlight-reflecting sea areas for fog in image-based fog intensity measurement systems. The AEA-RDCP algorithm builds upon the initial steps of the DCP methodology and aims to provide accurate estimations of fog density and visibility. A comprehensive flowchart outlining the proposed algorithm's processes is presented in Figure 5.  
To explain the proposed method, we first apply the initial Dark Channel Prior (DCP) process to images captured with a standard camera. This process involves calculating DC values, which are essential for estimating fog density and visibility. By focusing on DC values, the algorithm reduces the complexity and computational demands. A critical step in the AEA-RDCP method is excluding the sky region from analysis. The DCP algorithm often struggles to differentiate between bright objects, like clouds or sunlight, so removing the sky region mitigates this issue and improves fog detection accuracy.  
Figure 4: Example of an image with significant atmospheric light influence in the sea region.  
Next, when extracting the DC image from the sea area, the algorithm processes pixels influenced by atmospheric light. These pixels, which exceed a set threshold, are darkened to prevent distortion in the DC percentage calculation due to reflected sunlight. After minimizing the influence of atmospheric light, the algorithm calculates the ratio of DC values to assess fog conditions. A higher proportion of DC values below 100 indicates clear weather, while a lower proportion signals the presence of fog. This method enables real-time fog monitoring, and, by excluding atmospheric light, it improves the accuracy of visibility estimation, helping to prevent maritime accidents.
## 3 Results and Discussion
### GC-FID and SBSE-GC-MS Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Compounds** & **Quantitative Ion** & **Linear Equation** & **R2** & **Threshold ug/L** & **Content ug/L** \\ \hline
1 & Acetaldehyde \({}^{\text{b}}\) & - & & 1200 [17] & 151,980 \(\pm\) 2200.54 \\
2 & Ethyl acetate \({}^{\text{b}}\) & - & & & 32,600.00 [17] & 537,920 \(\pm\) 3221.14 \\
3 & 1,1-ietshowethane \({}^{\text{b}}\) & - & & 2090.00 [17] & 96,040 \(\pm\) 2376.11 \\
4 & 3-methylburraldehyde \({}^{\text{b}}\) & 44 & Y = 0.0364x + 0.0412 & 0.9997 & 179,000 [17] & 421.45 \(\pm\) 21.25 \\
5 & Ethyl 2-methylburralue \({}^{\text{b}}\) & 57 & Y = 0.135x + 0.04631 & 0.9996 & - & 115.43 \(\pm\) 9.88 \\
6 & Sec-butanol \({}^{\text{b}}\) & - & & 50,000.00 [21] & 138,820 \(\pm\) 1356.89 \\
7 & Isobutyl acetate \({}^{\text{b}}\) & 88 & Y = 0.1356x + 0.0305 & - & - & 45.67 \(\pm\) 6.98 \\
8 & Ethyl butyrate \({}^{\text{a}}\) & 71 & Y = 0.3334x + 0.004 & 0.9999 & 80.50 [17] & 529.76 \(\pm\) 36.98 \\
9 & \(N\)-propanol \({}^{\text{b}}\) & - & & 53,952.63 [17] & 520,420 \(\pm\) 3654.76 \\
10 & Ethyl isovalerate \({}^{\text{a}}\) & 88 & Y = 0.4286x + 0.007 & 0.9999 & 6.90 [17] & 88.92 \(\pm\) 4.76 \\
11 & Isobutanol \({}^{\text{b}}\) & - & & 28,300.00 [17] & 147,310 \(\pm\) 3761.56 \\
12 & Ethyl valerate \({}^{\text{a}}\) & 88 & Y = 0.4443x + 0.0174 & 0.9999 & 28,300.00 [17] & 180.74 \(\pm\) 13.14 \\
13 & Isoamyl acetate \({}^{\text{a}}\) & 70 & Y = 0.4885x + 0.0021 & 0.9999 & 93.93 [17] & 7099.8 \(\pm\) 437.81 \\
14 & \(N\)-butanol \({}^{\text{b}}\) & - & & 2733.35 [17] & 6080 \(\pm\) 4657.7 \\
15 & Isoamyl alcohol \({}^{\text{b}}\) & - & & 179,1903.83 [17] & 10,0350.54 \(\pm\) 6543.65 \\
16 & Ethyl hexanoate \({}^{\text{a}}\) & 88 & Y = 0.988x + 0.0394 & 0.9999 & 55.33 [17] & 2283.89 \(\pm\) 111.43 \\
17 & Isoamyl butyrate \({}^{\text{a}}\) & 71 & Y = 0.2916x + 0.0016 & 0.9999 & - & 8.31 \(\pm\) 2.11 \\
18 & \(N\)-amyl alcohol \({}^{\text{a}}\) & 55 & Y = 0.0105x + 0.0027 & 0.9999 & 64,000.000 [17] & 421.2 \(\pm\) 27.44 \\
19 & Dimethyl disulfide \({}^{\text{a}}\) & 126 & Y = 0.118x + 0.04361 & 0.9999 & 0.18 [19] & 4.2 \(\pm\) 0.02 \\
20 & Ethyl lactate \({}^{\text{b}}\) & - & & & 128,000.00 [17] & 442,370 \(\pm\) 4356.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative results of volatile compounds in JPYQL.
## 4 Results and Case Study
### User Review Quality Filters  
We evaluated the user review quality filters models in terms of accuracy. We achieved 98.49% accuracy for training and 89.38% for model evaluation for \"Spam Detection Classifier\". The overall performance of the \"Relatedness Classifier\" model was 95% for training and 90% for testing.
# Implementing YOLO Convolutional Neural Network for Seed Size Detection
## 3 Aim of the Article  
The aim of this study is to thoroughly investigate the utility of the You Only Look Once v8 (YOLO) method, a specialized convolutional neural network, for detecting seed sizes in images. Figure 1 presents a block diagram of the actions performed by the program for detecting the actual width, height, and area of seeds. To achieve this, a database containing images of coffee and white bean seeds was created, considering industrial and agricultural conditions on a production line. The scope of the work included taking a series of seed photographs with a reference element, which was a coin of known dimensions. The database was created under various seed arrangements and numbers of seeds in the images. The YOLO algorithm was used to classify the seed type and determine the seed area in the image (segmentation). Subsequently, using image processing techniques, simple operations were performed to determine the length and width of the seed. Based on the surface area of the reference element, a 1 PLN coin, calculations were performed to determine the actual values of the seed's surface area, width, and length. An error analysis of the segmentation was then conducted, and the actual errors in the dimensions and surface area of the seeds were estimated. The developed algorithm can serve as the foundation for a vision system used in seed sorting machines. Its application can significantly increase precision and efficiency, enabling the automation of the seed selection process.
## 3 Link Quality Estimation Routing Protocol Design
### Link Quality Based Routing Protocol  
LQER protocol integrates the approach of minimum hop count field and (\(m,k\)), which makes the routing energy-aware and low loss rate. Thus the whole network may obtain a longer lifetime and a better link quality.  
```
choose the path and transmit the data packet update the information in the link table if data packet successful transmitted then shift all bits in \(k\)-sequence towards left and add 1 to the rightmost else do shift and add 0 to the rightmost end if
```  
**Algorithm 2** Link Quality Table Maintenance Algorithm  
```
1:receive the routing data 2:list all the nodes that are 1 hop count less than the current node 3:choose the node in the list that have largest value of \(\frac{m}{k}\) 4:broadcast routing data 5:finish
```  
**Algorithm 3** Link Quality Estimation Routing Algorithm  
The algorithm of link quality table maintenance and link quality estimation based routing is described in Table 2 and Table 3. We take the example in Figure 1 to illustrate the LQER. Suppose minimum hop field has been established and the minimum hop count of node S is 10. Its forwarding node set includes A, B, C and D with the same hop count 9. The forwarding node set of node B includes E, F and G with hop counts 8. When node S has data packet to send to the sink, it first chooses the node from its forwardingset, the one with the largest value of \(\frac{m}{k}\), which means the best link quality in history. Not like MHFR, the forwarding node is selected randomly from forwarding set. In this example, node B has the largest value of \(\frac{m}{k}\) and is chosen to forward the packet. The same way is adopted by node B to choose its forwarding nodes. Obviously, node F is the victor. In this way, all the data packets can be forwarded through the shortest and most effectively reliable path and will not generate any extra information. When a node is forwarding the data, all the neighbors with 1 hop less can hear the message. But only the node designated in the message will forward the data. Thus the number of nodes participating in data forwarding is the least and the energy consumption is optimized.
## 6 Performance and Comparative Analysis
### Computation Costs  
The computation cost means the total time it takes to complete the different crypto operations during the key agreement and authentication phases. Normally, it measures in milliseconds (ms). We specified the expected unit time costs of several activities that were completed during a simulation [49]. The computation cost for one XOR operation is 0.002 ms, one hash operation is 0.0005 ms, concatenation is 0.0004 ms, random number generation is 0.0052 ms, and one arithmetic operation is 0.0004 ms.  
Table 3 indicates the assessment outcomes of computational expenses between our proposed scheme and other associated schemes. Comparative analysis shows that our proposed scheme, including the key exchange procedure, consumed less envisioned computational time compared to other present schemes. Our proposed scheme reduced computation costs by up to 37.68% compared to [42].
# Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection
## 4 Experiments  
### Settings  
Dataset and Evaluation Metrics. Our experiments utilize the MS COCO dataset, a publicly available and reputable dataset comprising 80 distinct object categories. It consists of 115 k images for training (\(train\)2017) and 5k images for validation (\(val\)2017). Training is conducted on the \(train\)2017, while ablation experiments and comparable results are generated using the \(val\)2017. The performance assessment utilizes standard COCO-style average precision (AP) metrics, incorporating varying intersection over union (IoU) thresholds ranging from 0.5 to 0.95. AP\({}_{s}\), AP\({}_{m}\), and AP\({}_{l}\) represent the AP of small, medium, and large objects. Moreover, AP\({}^{b}\) and AP\({}^{m}\) denote the AP of the bounding box and mask in the instance segmentation task.  
Implementaion Details. To maintain experimental comparison fairness, all experiments are conducted utilizing PyTorch [56] and mmdetection [57]. In our configuration, input images are resized to ensure their shorter side measures 800 pixels. We train detectors with 8 Nvidia V100 GPUs (2 images per GPU) for 12 epochs. The initial learning rate is 0.02. And it is reduced by a factor of 0.1 after the 8 th and 11 th epochs, respectively. The backbones utilized in our experiments are publicly available and have been pretrained on ImageNet [58]. The training process incorporates linear warming up during the initial stage. All remaining hyperparameters remain consistent with the configurations outlined by mmdetection. Unless stated otherwise, all baseline methods incorporate FPN, and the ablation studies utilize Faster R-CNN based on ResNet50.
# \"In Vivo\" Validation of 3D-Printed Innovative Surgical Template for Lumbar Spinal Arthrodesign.
## 3 In Vitro Tests  
Several in vitro tests were performed to check the feasibility of the templates and the effectiveness of the procedure before beginning the in vivo tests. Several vertebrae belonging to thoracic and lumbar spine segments were acquired by CT (Computerized Tomography) exams and were 3D-modelled and rapidly prototyped in Acrylonitrile-Butadiene-Styrene (ABS) using a fused-deposition-moulding (FDM) technology (Shareboat Dual Extrusor, with a 0.8 mm of thickness and 30 mm/s of print speed) that allows physical models of the vertebrae to be created in a few hours at low cost and with a geometric error lower than 0.3 mm. The procedure was used to build templates to create a physical demonstrator of the surgery. Finally, real screws were inserted in the vertebra utilizing the templates, and the obtained trajectories investigated by simply cutting the verteb along planned trajectories planes. The results showed the perfect match between planned surgeries and in vitro realised ones.
## 2 The Proposed Method
### Bezier Curve  
Assume that the vertical projection histogram curve consists of a set of points \(\{CP_{m}\}\ \ 1\leq m\leq M\), where \(M\) is the number of total points in the curve. For every point in curve, a point group of \(CP_{m}\) including \(2L+1\) points can be expressed as follows:  
\[\left\{CP_{m+j}^{\text{s}}\right\}\ \ \ 1\leq m\leq M\ \ \ -L\leq j\leq L \tag{1}\]  
The point \(CP_{m}\) is the center point of the sub points set \(CP_{m+j}^{\text{s}}\), which means there are \(L\) points before \(CP_{m}\), and also \(L\) points after \(CP_{m}\) (see Figure 5). If we can construct a smooth curve that fit all the sub points well, and the curvatures in the fitted curve can be calculated precisely and easily, then all the curvatures of all sub points set can construct a new \"curvature curve\", which can be used to estimate the local maximums (normally means the positions of scratches). In this situation, the key problem turns to find a curve fitting algorithm to the points group \(CP_{m+j}^{\text{s}}\).  
In order to address this problem, a curve fitting method based on Bezier curve and Monte Carlo sampling is proposed.  
A Bezier curve, which is widely used in many applications, is a kind of parametric curve, in which a group of discrete \"control points\" defines a smooth, continuous curve by means of a formula.  
A degree \(n\) Bezier curve can be formulated:  
\[P(t)=\sum\limits_{i=0}^{n}P_{i}B_{i,n}(t)\ \ \ t\in[0,1] \tag{2}\]  
where \(P_{i}\) means the control points of the curve, \(n\) means the degree, and \(t\) is the parameter to control the smoothness of the curve. The basis function, \(B_{i,n}(t)\), is the Bernstein polynomials of degree \(n\) defined explicitly by:  
\[B_{i,n}(t)=C_{n}^{i}t^{i}(1-t)^{n-i}=\frac{n!}{i!(n-i)!}t^{i}(1-t)^{n-i} \tag{3}\]  
In this study, the cubic Bezier (see Figure 6), that is \(n=3\), was used after considering both the accuracy and efficiency. In a cubic Bezier curve, four points are required, including two points which are not on the curve (control points) while the other two are on the ends of the curve (end points).  
The problem mentioned in the last section of finding the optimal curves of every sub point set \(CP_{m+j}^{\text{s}}\), turned to finding four optimal control points that define the corresponding Bezier curve. This is a typical proximity point optimization problem, which some scholars have already studied in depth from a mathematical perspective [21; 22]. Giving \(2L+1\) points, an obvious way to find a group of four optimal control points is to perform an exhaustive search of the whole space, but this method was not a good choice. In this study, the Monte Carlo sampling method was used to optimize the location of the control points quickly.  
Figure 5: Sub points set centered at \(CP_{m}\).
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
## 4 Test Protocol
### Ts-Df-Stc Scheme  
For the MA phase, the description of the signal transmissions from the end nodes T\({}_{1}\) and T\({}_{2}\) to the relay node R can be done as in the TS-DF scheme, i.e., Equations (2), (29) and (30). The aforementioned ZF estimation and bit-level NC also follow. The corresponding average BER (i.e., \(\overline{P}_{\text{e},\text{r}}\) where \(i=1,2\)) is therefore the same as Equation (34). During the BC phase, the relay node performs full-rate space-time block coding for the resultant composite bit sequence and the end nodes perform the corresponding sequence decoding and signal recovery, as in the PS-DF-STC case.  
It is straightforward to show that the instantaneous SNR of an estimate of \(x_{\text{r}}\) at T\({}_{i}\) is:  
\[\begin{array}{ll}\gamma_{\text{ri}}&=\frac{P_{\text{r}}\sum\limits_{m=1}^{M} \left|h_{\text{r}m}\right|^{2}}{\sigma_{\text{e}}^{2}+\sigma_{\text{e}}^{2}} \\ &=\frac{2\alpha\eta M\left(Po^{2}+(1+\alpha)\sigma_{\text{a}}^{2}\right)\sum \limits_{m=1}^{M}\left|h_{\text{r}m}\right|^{2}}{\left(1-\alpha^{2}\right) \left(\sigma_{\text{a}}^{2}+\sigma_{\text{e}}^{2}\right)}.\end{array} \tag{39}\]  
From Equations (22) and (39), we have:  
\[\begin{array}{ll}f_{\gamma_{\text{ri}}}(x)=&\frac{\left(1-\alpha^{2}\right) ^{M}\left(\sigma_{\text{a}}^{2}+\sigma_{\text{c}}^{2}\right)^{M}}{\Gamma(M) \left(2\alpha\eta M\alpha^{2}\right)^{M}\left[Po^{2}+(1+\alpha)\sigma_{\text{ a}}^{2}\right]^{M}}x^{M-1}\\ &\times\exp\left(-\frac{\left(1-\alpha^{2}\right)\left(\sigma_{\text{a}}^{2}+ \sigma_{\text{c}}^{2}\right)}{2\alpha\eta M\alpha^{2}\left(Po^{2}+(1+\alpha) \sigma_{\text{a}}^{2}\right)}x\right);\ x\geq 0.\end{array} \tag{40}\]  
Following the same procedure as in Equation (9), the average BER associated with \(\gamma_{\text{ri}}\) is obtained as:  
\[\begin{array}{ll}\overline{P}_{\text{e},\text{r}i}=&\frac{\sigma\ \Gamma(M+1/2)\left(\alpha\eta\left(Po^{2}+(1+\alpha)\sigma_{\text{a}}^{2} \right)\right)^{1/2}\left(\left(1-\alpha^{2}\right)\left(\sigma_{\text{a}}^{2 }+\sigma_{\text{c}}^{2}\right)\right)^{M}}{\sqrt{2\pi M}\ \Gamma(M)\left(2\alpha\eta M \alpha^{2}\left(Po^{2}+(1+\alpha)\sigma_{\text{a}}^{2}\right)+(1-\alpha^{2}) \left(\sigma_{\text{a}}^{2}+\sigma_{\text{c}}^{2}\right)\right)^{M+1/2}}\\ &\times_{2}F_{1}\left(1,M+\frac{1}{2};M+1;\frac{\left(1-\alpha^{2}\right) \left(\sigma_{\text{a}}^{2}+\sigma_{\text{c}}^{2}\right)}{2\alpha\eta M\alpha ^{2}\left(Po^{2}+(1+\alpha)\sigma_{\text{a}}^{2}\right)+(1-\alpha^{2})\left( \sigma_{\text{a}}^{2}+\sigma_{\text{c}}^{2}\right)}\right).\end{array} \tag{41}\]  
The corresponding end-to-end BER is obtained using Equation (17).
# Efficient Lattice-Based Cryptosystems with Key Dependent Message Security
## 3 Compact Public-Key Cryptosystem with KDM Security
### A Generic Transformation  
In this section, we make a useful transformation to sampling \(s\leftarrow\chi\). There is no loss of security, and it is ensured that the secret can be placed in the message space. The transformation lemma follows.  
Lemma 5: _For modulus \(q\), arbitrary \(s\in R_{q}\) and the error distribution \(\chi\), there is a deterministic polynomial-time transformation \(T\), which maps \(A_{s,\chi}^{*}\) to \(A_{\phi,\chi}^{*}\) where \(\phi\leftarrow\chi\), and maps\(U\Big{(}R_{q}^{*}\times R_{q}\Big{)}\) to itself._  
Proof: The transformation \(T\) to access the distribution \(D\) over \(R_{q}^{*}\times R_{q}\), possibly \(A_{s,\chi}^{*}\) or \(U\Big{(}R_{q}^{*}\times R_{q}\Big{)}\). Then, we prove it in two steps.  
_The first step_. Transformation \(T\) generates the sample \(\Big{(}\overline{u},\overline{b}\Big{)}\in R_{q}^{*}\times R_{q}\) by drawing from the distribution \(D\). When \(D=A_{s,\chi}^{*}\), we have \(\overline{b}=\overline{a}\cdot s+t\cdot\overline{x}\), where \(\overline{x}\leftarrow\chi\).  
_The second step_. To transform samples from \(D\) into samples from a different distribution, the sample \((a,b)\in R_{q}^{*}\times R_{q}\) from \(D\) will be transformed into \((a^{\prime},b^{\prime})\in R_{q}^{*}\times R_{q}\), where \(d^{\prime}=-\overline{a}^{-1}\cdot a\), \(b^{\prime}=b+a^{\prime}\cdot\overline{b}\).  
Especially \(a^{\prime}\in R_{q}^{*}\) is uniform due to \(\overline{a}\gets R_{q}^{*}\) being invertible modulo \(q\) and \(a\) chooses from \(U\Big{(}R_{q}^{*}\Big{)}\). If \(D=U\Big{(}R_{q}^{*}\times R_{q}\Big{)}\), then \((a^{\prime},b^{\prime})\) is also subject to \(U\Big{(}R_{q}^{*}\times R_{q}\Big{)}\). If \(D=bA_{s,\chi}^{*}\), then \(b=a\cdot s+t\cdot e\), so we have  
\[b^{\prime}=b+a^{\prime}\cdot\overline{b}=a\cdot s+t\cdot e+a^{\prime}\cdot( \overline{a}\cdot s+t\cdot\overline{x})=a^{\prime}\cdot(t\cdot\overline{x})+t \cdot e, \tag{3}\]  
where \(\phi=t\cdot\overline{x}\), therefore, \((a^{\prime},b^{\prime})\) is subject to \(bA_{\phi,\chi^{\prime}}^{*}\), as desired. \(\square\)  
Definition 3: (The RLWE\({}^{*}\) assumption-Hermite normal form). As in the previous definition, for all security parameters \(\lambda\in\mathbb{N}\), the RLWE\({}^{*}\) assumption suggests that, for any \(\ell=\text{poly}(\lambda)\), we have  
\[\left\{(a_{i},a_{i}\cdot s+t\cdot e_{i})\right\}_{i\in[\ell]}\approx\left\{(a_ {i},u_{i})\right\}_{i\in[\ell]}, \tag{4}\]  
in which \(s\) is sampled from the noise distribution \(\chi\), and other parameters remain unchanged.
# Future Internet Architectures on an Emerging Scale--A Systematic Review
## 3 Evolutionary and Clean Slate Approaches
### The Clean-Slate Approach
#### 3.2.1 Named Data Networking (NDN)
The authors of [38] identified three main issues in this design: string matching, longest prefix matching, and the identification of unbounded names. They also proposed solutions to address these issues and improve the execution of the NDN reference implementations and forwarding structures. By addressing these issues, NDN can provide efficient and scalable packet forwarding, which is essential for its use in various applications. It is notable that NDN offers several significant advantages, such as an improved efficiency of the application layer through the named data, which is particularly useful for its current network applications, such as streaming media services [32] that rely on the internet to deliver their data services. By bypassing the need for host addresses, and instead accessing content based directly on its name [38], NDN eliminates the limitations of traditional network architectures and allows for a more efficient utilization of high bandwidth. Additionally, NDN allows for the implementation of a router cache for frequently accessed content, further enhancing the efficiency of its data retrieval. Overall, these features make NDN a promising technology for addressing the challenges of data communication in the evolving internet landscape.