esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Bacterial, Viral, and Parasitic Diseases in Fish
### Piscine Streptococcus  
Piscine Streptococcus is a significant bacterial disease that affects various species of fish, particularly in aquaculture settings. Caused by Streptococcus bacteria, notably _Streptococcus iniae_ and _Streptococcus agalactiae_, this disease poses a significant threat to fish populations worldwide. Piscine Streptococcus manifests through a range of symptoms, including skin lesions, hemorrhaging, exophthalmia, and can ultimately lead to high mortality rates if left untreated [91]. Specifically looking at _S. iniae_, the bacterial type II strain survives in phagocytes, explaining its ability to rapidly and dangerously impact the organism [92]. Killed and inactivated vaccines are predominantly used as a precautionary measure, aiming to produce antibodies against the weakened version of the disease to prevent severe symptoms when exposed to the true bacteria [93]. Currently, DNA vaccines are being formulated in China against the _S. iniae_ and _S. agalactiae_ strains, encoding the bacteria's surface protein into plasmid vectors [94; 95; 96]. Live, attenuated vaccines are also being produced, primarily utilizing serial passing and chemical agents such as acrilavine dye and novobiocin, the latter of which has been found to be highly protective against the pathogenic _S. agalactiae_[10; 97]. The _S. iniae_ live-attenuated vaccine, developed through serial passage in the presence of rifampicin, resulted in the attenuation of the rifampicin-resistant strain E1-r250 in tilapia, with no mortality observed at the specified dosage. However, when administered via injection, it provided significant protection against its virulent, wild-type parent [98]. Additionally, there are papers dissecting the strains of _S. iniae_ and _S. agalactiae_ to advance genomic and serotype understanding and provide a better comprehension of the signaling pathways involved, which can be applied in future disease control [94; 99].
## 5 Evaluation
### Overfitting Mitigation  
To address overfitting in the model training, we employed various techniques. First, we utilized the dropout layer and empirically determined the dropout rate to mitigate overfitting. For mild overfitting, a dropout rate between 0.2 and 0.5 is recommended, and for severe cases, a rate of 0.8 is beneficial. Therefore, we applied a rate of 0.8 to ensure a robust prevention of overfitting. Additionally, as the complexity of the model increases, it becomes more prone to overfitting. Hence, we adjusted the number of layers and parameters in the model. Finally, we incorporated the BatchNormalization() layer into the deep learning model to further mitigate overfitting. The BatchNormalization() Layer normalizes the output value of the activation function to distribute it appropriately. If the weights are not appropriate, the model may become sensitive to even small changes in the input data, which may lead to overfitting. In this case, BatchNormalization() improves the learning speed and suppresses overfitting by maintaining good weight values and appropriately distributing activation values without depending on the initial weight value. Finally, it is worth noting that existing overfitting mitigation methods, including the aforementioned mitigation strategies, can be also leveraged to our proposed framework.  
To validate the overfitting mitigation strategies applied in our framework, we measured the trend of training and validation accuracies in phase 2 for the IoT Intrusion data set while varying the epochs until 100. Figure 4 shows the result after adopting the aforementioned mitigation methods.  
Figure 4: Training and validation accuracies after adopting overfitting mitigation.  
Typically, a model is likely to be overfitted when the slope of training and validation accuracy gradually decrease, reaching a floor. In our observation, we noted a smooth increase in both training and validation accuracy, with a consistent plateau observed from epoch 20 onwards, maintaining a stable shape. This is interpreted as the effective contribution of the overfitting technique mentioned above.
## 2 Methodology
### Transforming the Problem into Solving a SCP  
The SCP is a classical problem in combinatorial optimization. Given a set \(U\) (known as the universal set) and a collection \(S=\{S_{1},S_{2},\ldots,S_{m}\}\) consisting of a number of subsets of \(U\), the objective is to find a subset \(C\) of S such that the union of all subsets in \(C\) is equal to the universal set \(U\), and the size of \(C\) (number of elements) is as small as possible. The idea of solving this problem is to select some columns from a 0-1 sparse matrix \(A=(a_{ij})_{m\times n}\) with m rows and n columns to cover all rows while minimizing the sum of the costs of the selected columns. The corresponding mathematical formulation is as follows:  
\[x=min\sum_{j=1}^{n}c_{j}x_{j}. \tag{1}\]  
\[s.t.\sum_{j=1}^{n}a_{ij}x_{j}\geq 1,i=1,2,\ldots,m. \tag{2}\]  
\[x_{j}e\{0,1\},j=1,2,\ldots,n. \tag{3}\]  
Figure 1: Technology roadmap.  
In the above formula, \(c_{j}\) represents the cost of column \(j\). In this study, we set the cost of each column to be the same (i.e., \(c_{j}=1\)); \(x_{j}\) is taken as 0 or 1, with 0 indicating that column \(j\) is not selected and 1 indicating that column \(j\) is included in the solution; \(a_{ij}\) represents the values of the ith row and jth column of the 0-1 sparse matrix \(A=(a_{ij})_{m\times n}\), where \(a_{ij}=0\) means that the jth column does not cover the ith row, and \(a_{ij}=1\) means that the jth column covers the ith row.  
ArcGIS (The version used in this study is 10.7.1), as the mainstream geographic information software, is widely used in data processing [22], and can realize the batch processing of remote sensing images through the model builder. The specific process of transforming the remote sensing data optimization screening problem into solving the SCP is as follows:  
1. We obtain all image datasets intersecting the target region by spatial queries and performing a simple conditional filtering to obtain the initial datasets.
2. The effective range of each view image is utilized so they cut one another and the same items are removed to obtain the fragments that do not overlap each other as a candidate sets.
3. The spatial join of the cropping result and the set of fragmented geometric elements is performed to obtain the fragmentation information contained in each view image.
4. The data are preprocessed to simplify the research problem.  
After the above steps, the remote sensing data optimization screening problem has been transformed into solving the SCP. At this time, the columns refer to the data of each view, and the rows covered by the column refer to the fragments contained in the images of each view.
## 4 Comparison between Two Models
### Acceleration Responses of the Main Structures  
#### 4.3.1 Acceleration Time Histories of the Main Structures  
Under harmonic excitation, a comparison of the horizontal acceleration time histories of the station structures for the two models is presented in Figure 11, showcasing representative frequencies of 12 Hz and 21 Hz. It is observed that at lower input frequencies, the acceleration time histories at the same measurement points of the two models are quite similar; however, as the input frequency increases, significant differences in the peak acceleration at the measurement points between the two models emerge. Overall, the acceleration response at the vault is larger than that at the arch bottom, and the acceleration response at the station hall is larger than that at the platform. Meanwhile, the lining acceleration amplitudes of the two models have different patterns of variation depending on the frequency and direction of excitation, which is further explained in Section 4.3.2.
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 3 Methodology
### Preprocessing the Wi-Fi Data
#### 3.2.3 Removing a Pseudo-MAC Address  
A few mobile device manufacturers use random MAC address technology to protect user privacy. This way, there are few rules to follow and affect the subsequent traffic information. The second hexadecimal character of an actual MAC address can only be \"0\", \"4\", \"8\", or \"c\". Based on this rule, pseudo-MAC addresses can be effectively removed.
## 4 Results  
Figure 9 shows an overview of all tested material combinations (see Table 1) without additional treatment or interlocking structures. It can be noted, that peeling resistance varies by about an order of magnitude between the weakest and the strongest material combination.  
In the conducted experiment, the combination of materials with the highest interface strength is GF30-PA6 and NinjaFlex with a peeling resistance of about 9.0 kN/m and fractures occurring exclusively on the adhesive interface. However, since the second strongest combination (ABS and NinjaFlex) showed a substrate fracture on all peeling tests, interface strength in this case can only be deduced to be stronger than the determined value of about 6.0 kN/m. The specimens made out of XT and NinjaFlex showed adhesive fracture at about 5.0 kN/m, while the specimen produced from XT-CF20 and NinjaFlex failed at about the same load of 5.1 kN/m, however, while partly destroying the substrate. The Combination of PLA and NinjaFlex showed a purely adhesive fracture at a load of about 2.4 kN/m. Both combinations containing TPU-93 showed adhesive failure at very low loads of about 0.7 kN/m (ABS) and 0.1 kN/m (PLA).  
Figure 8: Depiction of roll peeling tests.  
Additional measures and their influence on interface strength of PLA are summarized in Figure 10. Some values are identical to the ones in Figure 9 and are only shown again for clarity. It can be seen, that both plasma pretreatment as well as interlocking features have significant influence on the bonding strength, while surface smoothing leads to a reduction of peel resistance.
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 3 System Model and Assumptions
### Multicast Engine  
As the multicast engine, Bidirectional Multicast RPL Forwarding (BMRF) [18] is used. BMRF combines the best features of RPL multicast on the one hand and those of Stateless Multicast RPL Forwarding (SMRF) on the other hand, with a minimal increase in memory usage. As most important new functionalities, BMRF includes bidirectionality (the sources of multicast traffic can be located inside the network), dynamic group registration and the ability to have multiple senders (multipoint-to-multipoint).  
An additional feature is that BMRF can offer a choice between Link Layer (LL) broadcast and LL unicast for which the decision for each more is based on a threshold, mainly determined by the more's number of interested children and the CCR. When BMRF is in unicast mode (and thus always uses LL unicasts to forward a multicast packet, independent of the threshold), BMRF is a well-designed and well-functioning implementation of RPL unicast with some additional features. When BMRF is in broadcast mode (and thus uses LL broadcast to forward multicast packets), BMRF behaves similar to SMRF, but adds some useful extensions.  
The performance of the different modes of BMRF was tested in [19]. Since for our situation reliability is the key performance metric, BMRF will be used in unicast mode, which offers very good reliability and energy consumption on Radio Duty Cycling (RDC) enabled networks.
## 4 The Influence of Structural Parameters on Synchronization and Stability
### Parameters Influence on the Synchronization
Moreover, the absolute value of the stable phase difference (ranging from \(-180^{\circ}\) to \(180^{\circ}\)) between an exciter and CR 1 declines as the friction coefficient corresponding to CR 1 is increased (\(2\alpha_{1}\) remaining almost unchanged when fixing \(f_{R1}\)), whereas that between two arbitrary CRs grows as the difference value between two corresponding friction coefficients is increased. According to Equation (45), only when \(H_{i}>0\) (\(i=1,2,3\)) can the stability criterion of synchronous operation be satisfied, and as a result, a stable solution of the phase differences be obtained. Figure 9 shows the dependence of the stability coefficients as a function of the dimensionless parameter \(r_{l}\). It is seen from Figure 9 that even with variations in \(r_{l}\), all elements (\(H_{1}\), \(H_{2}\) and \(H_{3}\)) are positive, which accounts for the ongoing and ever-present values of stable phase differences during the entire process of varying \(r_{l}\). Moreover, one of the noticeable features in Figure 9 is the significant growth in three stability coefficients when \(r_{l}\) is increased, even as a set of friction coefficients is changed to another one. As mentioned before, the greater the stability coefficient \(H_{i}\), the stronger the stability of synchronous operation. Therefore, a greater value of \(r_{l}\) should be chosen to improve the stability of synchronous operation. The stability coefficients in a synchronous state are largely influenced by \(r_{l}\), whereas the clinging ability coefficients of three CRs are unaffected by \(r_{l}\) and are determined by the three friction coefficients, as shown in Figure 10. It is observed that all the clinging ability coefficients are very tiny-far less than one with four different sets of friction coefficients. This indicates that three CRs will cling to the surface of cavities quite tenaciously when rotating around their centroids synchronously and steadily. Furthermore, it is worthwhile to mention that from a comparison between two different sets of three identical friction coefficients (Figure 10a,d) or among three non-identical friction coefficients, one can realize that a large \(f_{Ri}\) makes for better cling ability.
## 3 System Building Blocks
### Modeling
#### 3.2.2 Filtering
It is noteworthy that \(p_{a},p_{b},p_{c}\in\Pi\) and that they have XYZ and UV coordinates. Let \(\lambda_{a},\lambda_{b},\lambda_{c}\in[0,1]\) be the respective barycentric coordinates; then, the XYZ coordinates of this point are given by \((x_{j,i},y_{j,i},z_{j,i})=\lambda_{a}(x_{x},y_{a},z_{a})+\lambda_{b}(x_{b},y_{ b},z_{b})+\lambda_{c}(x_{c},y_{c},z_{c})\). Therefore, this completes all coordinates of \(\Delta\) points. After the resampling, we perform a filtering process that consists of modifying the Z value of the target points. This is achieved through a neighborhood comparison between that point and the neighborhood of equivalent points in the exemplar. In this step, both the target and sample are regular grids at the same sampling frequency. This phase is divided into two parts: the Predictor Definition and the Correction Process. The filter is a Nearest Neighbor Predictor whose input is a neighborhood of a target point containing \(k\times k\) Z-values (centered at this point), and the output is the respective normalized Z-value of the central point of the closest neighborhood from exemplars (normalization is explained below). We create one predictor per \(FFP\) region, and each one is trained by using all neighborhoods in all exemplars that belong to the respective \(FFP\) region. The definition of the \(FFP\) regions is given by a Voronoi Diagram. For each exemplar, a Voronoi Diagram of all \(FP\)s is created (Figure 6 on the right), and for each region, in turn, all points inside it are used to train the respective predictor. Once the points per region are defined, it is necessary to achieve a normalization of each neighborhood by subtracting the average and dividing by its variance. This guarantees that all neighborhoods can be compared, since all of them are at the same scale and depth translation. The next step is the correction process, which consists of modifying the \(\Delta\) points position according to the predictor. For each point \(p\in\Delta\), (i) we determine its respective region (\(FFP\)), (ii) we obtain its neighborhood and normalize it (with the respective mean and variance), and finally (iii) we apply the projection to the base of the PCA. We use the normalized and reduced neighborhood in the prediction process. The predictor returns the normalized Z-value of the central point according to the best-matching neighborhood. Therefore, we take this value and multiply it by the variance and add it to the average of the \(p\in\Delta\) neighborhood. The normalization of the exemplar and target neighborhood ensures that we can compare them irrespective of scale (division by variance) and translations in depth (subtraction by the mean). In addition, it is noteworthy that a neighborhood of the exemplar is normalized with its mean and variance, but the process of denormalization is performed by using the mean and variance of the neighborhood of the target that is being corrected. Thus, we transfer the neighborhood feature of the exemplar to the target, with the invariance mentioned above. Figure 7 illustrates this step.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
### Background  
The proposed Cramer-Rao lower bounds (CRLBs) on position and orientation estimation apply to estimators that use distance estimates as the primary measurement mechanism. This work was originally motivated by emerging TWR technologies for urban air mobility (UAM) applications, but the results apply to any technique that generates multiple range estimates for a given target. These results apply to several different types of TWR systems and are primarily enabled by previous work in the field of time-of-arrival (ToA) estimation.  
Figure 1: Example multi-antenna two-way ranging (TWR) system with a 4-antenna ground user \(A\) and 4-antenna aerial user \(B\). By measuring the distance between each antenna pair (4 of which are depicted above using different colors to denote unique links), we can estimate the relative position and orientation of each user, enabling a wide variety of distributed airborne applications. We derive bounds on the performance of these estimators as a function of the number of antennas, platform geometry, and geometric dilution of precision (GDoP).
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 4 Information Data of Indirect Online Measurement on Tool Wear
### Power  
Spindle power is a comprehensive feature, and related research shows that the change of spindle power is related to tool wear [90]; it can fuse other data information to accurately predict tool wear. [PERSON] et al. [97] found that there was a linear relationship between spindle power and tool wear state. [PERSON] et al. [89] proposed a threshold update strategy for tool condition monitoring. In this study, the classification success rates for turning and drilling are 96% and 93%, respectively. [PERSON] et al. [98] monitors power consumption by electrical features to drill holes and further explain tool wear. [PERSON] et al. [99] studied the relationship between tool wear and extra power input. [PERSON] et al. [93] uses neural network (NN) technology to predict the remaining service life of the tool based on the spindle power. It is reported that this method has a low calculation cost and can be used for real-time predictions in the machining process.  
At present, the power information data are mainly fused with other information data, to become the supplementary information data of the neural network system, in order to improve the information reliability and help the tool monitoring system achieve good results [26].  
The installation diagram of a power sensor is shown in Figure 4.
## 3 Results and Discussion
Recordings of LDF signals were associated with several problems such as anatomical features preventing accurate positioning of the probe in interested area and holding it for a long time, as well as some level of inconvenience for patients causing movements artefacts. This affected the length of registered signals. The means of every signal (Table 2) were obtained to analyze for statistically significant differences by ANOVA. The results of analysis are shown in Figure 4b. Statistically significant differences were observed between common bile duct and remained areas of interest. The average perfusion in common bile duct significantly exceed the values of this parameter in the other areas, as the fluorescence spectra does. Usually, it is expected that fluorescence intensity decreases as blood perfusion increases and more optical radiation is absorbed, especially in the range of 400-600 nm. However, experimental measurements showed that common bile duct has higher fluorescence intensity as well as blood perfusion. This can relate with the influence of the factors mentioned earlier. Nevertheless, both methods used shows the sensibility to the area of interest and its pathology. So far, laser Doppler flowmetry method has not been widely used in surgery, since there are a number of methodological difficulties for that. The most important one is the difficulty in assessing the effect of probe pressure on recorded signal. In addition, a few researchers of abdominal organs microcirculation use equipment of different manufacturers. Nevertheless, earlier studies confirm the fact that the use of laser Doppler flowmetry in multimodal optical devices for examining the gastroduodenal area is a useful addition in postoperative observation of patients with gastrointestinal diseases, since no other methods provide a continuous evaluation of perfusion [23; 24]. An example of the analysis of pathophysiological changes in blood flow is known in studies of benign and malignant neoplasms in the stomach in vivo [25]. The examples show that in the long term an evaluation of blood perfusion of the tissue can become an additional diagnostic tool for assessing tissue viability, which will allow a surgeon to separate tissues by their state of vitality during operations more accurately. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Area** & **Mean** & **Standard Deviation** \\ \hline Common bile duct & 16.82 & 6.24 \\ Gallbladder & 12.46 & 5.03 \\ Liver & 11.34 & 5.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average values of LDF signals. Figure 4: Typical LDF signals in the areas of interest (**a**) and Box-Whisker diagram of mean perfusion (**b**). * Confirmed statistically significant differences. The level of significance was estimated using a one-way analysis of variance (\(p\) < 0.05)
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 6 Experimentation and Result Analysis
### Result Analysis under Various Vehicle-Density Ratios
#### 6.3.3 Test Case 3: High Density (50 Vehicles)  
Fifty vehicles were arbitrarily scattered using uniform distribution among deployment area A within the 1 km square range. The server in VANET is at the origin (0,0) to disseminate data packets to all vehicles. The mean route reliability score is presented in Figure 7(b). Here, we observed that JMA achieves the best route reliability in the fast-run range. However, compared algorithm starts to achieve the better routes in the late mid-run range, and fails to sustain the route in the long-run range. Figure 7(a) shows the best route discovery of JMA. Here, JMA converges slowly in the fast-run range, and later increases the convergence rate, sustains the best route score in late fast-run range, and retains the same until the long-run range.  
Figure 6: Low Vehicular Density (10 vehicles). (**a**) Best route discovery achieved using JMA; (**b**) Mean route reliability results achieved across 1000 Monte Carlo trails.  
Thus, the overall results of high-density vehicles concludes that JMA achieved 32.65%, 24.56% and 21.43% better performance compared to the GA, PSO and cuckoo search algorithms, respectively. The outcome clearly shows the superiority of the proposed JMA compared GA, PSO, and CS algorithms.