esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Selection and Labeling  
In this study, image selection and labeling of the meniscal region of interest (ROI) areas were performed by a radiologist with 12 years of experience and an orthopedic and traumatologist with 12 years of experience. Considering that sagittal slices contain the most information on meniscal tears and because they better meet the clinical requirements [16; 25], only sagittal slice images were used in this study. In image selection, images including both the anterior and posterior horns in the sagittal planes were preferred. Still, images showing the medial region of the meniscus were also included in the dataset. These labels produced by experienced radiologists and orthopedists were used as the gold standard (Figure 3). This study created separate ROIs for image segmentation for the anterior and posterior horn regions. A total of 1548 segmentation areas were labeled from 849 images. Of these labels, 35.70% (n = 549) belonged to the lateral meniscus (LM) and 63.30% (n = 999) to the medial meniscus (MM).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Valid**} & \multicolumn{2}{c}{**Test**} \\  & & **Image** & **Labeling** & **Image** & **Labeling** & **Image** & **Labeling** \\ \hline \multirow{2}{*}{Healthy} & LM & 65 & 115 & 19 & 34 & 9 & 16 \\  & MM & 60 & 115 & 17 & 33 & 9 & 17 \\ \hline Meniscus & LM & 151 & 268 & 43 & 77 & 22 & 39 \\ Injury & MM & 318 & 577 & 91 & 165 & 45 & 82 \\ \hline \multicolumn{7}{l}{MM: medial meniscus, LM: lateral meniscus.} \\ \end{tabular}
\end{table}
Table 1: Distribution of the internal dataset to the training, validation, and test groups.
## 2 Materials and Methods
### Chi-Square Automatic Interaction Detector  
This CHAID model was first introduced by [PERSON] for use in qualitative and classified quantitative variables [70]. As a modeling approach, this algorithm is suitable for establishing the relationship between a dependent parameter and several independent parameters. The CHAID model is mainly characterized by the following: (1) finding the influential parameters in the final result by applying a chi-square test of independence; (2) useful in the combination of effective variable groups [71]. This implies that CHAID employs the chi-squared independence test to examine the significance of independent parameters within a classification in comparison to the dependent parameters [72]. The chi-square statistic is expressed as follows:  
\[X^{2}=\sum\frac{\left(O_{ij}-E_{ij}\right)^{2}}{E_{ij}} \tag{4}\]  
where \(O_{ij}\) is the observed value, and \(E_{ij}\) is the predicted value. There are three stages in the CHAID model; merging, splitting, and stopping. The merging phase involves the application of the chi-square test to test the significance of each independent parameter. Each pair of dependent and independent parameters, as well as the probable tables, are subjected to this test. For the splitting stage, it initiates with the comparison of the calculated \(p\)-values of each independent parameter with the independent parameters that have the least \(p\)-value, followed by their selection as the node separator. In situations where no variable has a significant \(p\)-value, there will be no splitting stage and the final node will be determined as the node that precedes no branching [68]. The last stage (the stopping stage) begins with a repeat of the combination and analysis stages of all subsets. The process is terminated after all the subsets have been analyzed [71].  
The formation of different parts in the CHAID model is represented by a classification tree diagram, where each dependent parameter is represented by a root, and the independent parameters are associated with significant \(p\)-values and are directly related with the root [72; 73; 74]. The weakness of this algorithm is that it cannot generate the best feasible divisions from the current parameters. More information on CHAID has been provided by [70; 71; 72; 73].
## 6 Simulations and Discussion
Also, as shown in Figure 14b, when the nodes number is 25 and the packet arrive rate is set to 0.02 per slot, in both cases, with the increase of _macMaxCSMA_, the packet discard probability shows continuous decay. The proposed protocol also achieves a lower packet discard rate. Figure 14: Packet delay and throughput with nodes number for different _macMaxCSMA_(\(m\)). (**a**) Packet discard rate; (**b**) Network throughput. Figure 15: Comparison of network throughput and packet discard probability for one CCA and two CCA. (**a**) Network throughput; (**b**) Packet discard rate. Figure 16 shows the average queue delay in the network. When the packet rate is less than nine packets per second, the network maintains a few queues in both schemes. With the increase of the packet arrive rate, in the standard 802.15.4; the average queue length grows rapidly. In contrast, in the traffic adaptive slot allocation algorithm, the average queue length grows slowly until the packets rate reaches 20 packets per second. Therefore, the proposed method can effectively release the queue accumulation and tolerate a heavier traffic.
# A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems
## 3 Background
### Graphs and Agents  
Graphs are also used to represent the dependency between the transactions and data items of a database. A graph-based approach is presented in [20] with cold and warm start modes. A cold start mode conducts offline recovery of the database, which increases the downtime. A warm start mode conducts online data recovery with less downtime but possible degradation in performance. Transactions are grouped into malicious and non-malicious types. The data structure used in this approach is a graph where the edges represent dependencies between the nodes of the two groups. This graph structure helps in finding which of the non-malicious transactions are affected by the malicious transactions.  
Another approach, presented in [21], uses agents and graphs. Agents receive and forward messages. Graphs are used to represent the dependency between transactions. All messages pass through a single controller agent. Since graphs are not necessarily connected, multiple agents scan multiple graphs for affected transactions and return pointers to a malicious or affected state. This approach can isolate only the affected part of the database.  
Graph- and agent-based methods have a disadvantage that is mainly related to graph traversal. The damage assessment process should go back and forth while scanning, which increases the running time of the database recovery as the graph gets bigger.
## 2 System Architecture, Theoretical Methods, and Experiments  
The architectural diagram of the intelligent sound-assisted water leak identification system is shown in Figure 1. In this study, an acoustic and vibration diagnostic system for structural deterioration was constructed first, including an acoustic wave sensing unit, an acoustic and vibration diagnostic module, and a communication module for signal transmission between the acoustic wave sensing unit and the acoustic and vibration diagnostic module; then, construction equipment was used to collect more than ten thousand pieces of data. After multiple pieces of audio data were processed into noise, the audio was dissociated by the Mel-scale Frequency Cepstral Coefficient (MFCC). Finally, a CNN was used to create a database to identify whether the sound was ambient or from a leakage. In addition, a mobile version of the cloud-based intelligent water leakage signal diagnostic and management platform was also developed, as shown in Figure 2.  
Figure 1: AI-IoT-Based AI Leakage Detection and Localization System.
### X-ray Computed Tomography (XCT)  
X-ray Computed Tomography (XCT) illuminates a specimen with X-ray photons, which interact principally with electrons in the target. Thus contrast in XCT images is generated by local differences in mass density and mean atomic number. In a typical cone beam system, such as the one used in this study, the rotating sample is exposed to an X-ray beam from a point source and the transmitted X-rays are captured by a detector. The 2D radiographs produced during the image acquisition are then'reconstructed' into 3D tomography images using specially designed reconstruction algorithms.  
More details of the working principle of XCT imaging and recent advances can be found in [36; 37] and the references therein. Although both CLSM and XCT can be used to obtain 3D images of a suitable sample, they are different in terms of the acquisition methods, image processing, volume of investigation in 3D, and image resolution. An important difference is that the samples need to be optically transparent for confocal imaging, whereas XCT imaging can be used for optically opaque samples.  
The fields of application of CLSM and XCT imaging, therefore, vary significantly, in that the former is most widely used in biosciences, whereas the latter is also prominently used in physical and materials science applications. XCT is frequently used for the non-destructive characterization of materials, for example metal composites and alloys [38; 39]. In this study, we used micro-CT, a high resolution industrial XCT, for evaluating the quality of the resin pore casts.
## 4 Results and Discussion
### Stabilization of Graphite
#### 4.1.4 Determination of Particle size Distribution  
The particle size distribution was analyzed for the graphite dispersions at a pH value of 9.0-9.5 for PVP and at a pH value of 10.75-11 for TX-100 (see Figure 4). The dispersion with TX-100 contained the minimum suitable dispersant content (see Figure 9a). The dispersion with PVP was even investigated for a dispersant content of 15 m%, which is slightly lower than the minimum dispersant content (see Figure 9b). The standard deviation resulted from three single measurements.  
It can be seen that the graphite particles mixed with PVP are predominantly dispersed as nanometeric particles, although the dispersant content is slightly lower than that iden  
Figure 8: Viscosity as a function of TX-100 content for aqueous dispersions containing 10 m% graphite nanoparticles at varying pH values (**a**) pH = 9 (see Table 10) and (**b**) pH = 10.75-11 (see Table 12).  
Figure 9: Particle size distribution of the aqueous dispersions containing 10 m% graphite nanoparticles and (**a**) 15 m% PVP (see Table 13) and (**b**) 40 m% TX-100 (see Table 14).  
tified in the rheological characterization. The obtained particle size distribution is not larger than the required threshold range between 1 um and 5 um. The majority of particle fractions are even below 1 um. However, the graphite dispersion dispersed with TX-100 shows a fraction of giant agglomerates of the order of several microns. Thus, these results confirm that PVP is more effective than TX-100 in dispersing graphite nanoparticles in an aqueous medium. The resulting particle size distribution meets the requirements of the inkjet printing process.
## 4 Conclusions  
In this paper, the design, fabrication, and measurement of a floating patch antenna are proposed. The design procedure is eased by selecting air as the dielectric for the patch, as well as a simple patch shape; the most critical aspect is the positioning of the feeding pin rod that connects the microstrip line to the patch so that there is a good impedance matching able to maximize the radiation at the resonance frequency. The manufacturing processes are standard PCB operations, and the whole fabrication procedure is very easy and highly tolerant of manufacturing errors. This antenna is able to radiate at 2.4 GHz with a 10.41% fractional bandwidth, 9.63 dBi gain, 96.41% radiation efficiency, and total efficiency of 93.08%, together with a wide beamwidth and good polarization purity. Thanks to the microstrip and through-wire feeding system, this antenna presents a high integration capability with other planar circuits; at the same time, this device is low-cost, lightweight, robust, and easy to manufacture. The main novelties of this design are its ease of design and manufacture, the total absence of a dielectric, the use of the through-wire technique to feed the patch and fasten the floating patch, the extreme robustness and error tolerance of the prototype, and the simultaneous achievement of good values in all of the performance indexes. The proposed antenna can be profitably used for wireless applications in the 2.4 GHz band.  
[PERSON], [PERSON] and [PERSON] conceptualized the design; [PERSON] and [PERSON] designed, simulated, and optimized the device; [PERSON]. ([PERSON]) and [PERSON] ([PERSON]) manufactured the device and characterized it in the laboratory; [PERSON] wrote the manuscript; all authors revised the manuscript; [PERSON] and [PERSON] supervised the whole work. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministerio de Ciencia e Innovacion of the Spanish Government through the Subproject C44 of the Coordinated Research and Development Project PID2019-103982 RB under Grant MCIN/AEI/10.13039/501100011033.  
Not applicable.  
All data are available upon reasonable request.  
The authors declare no conflict of interest.
# Securing SCADA Energy Management System under DDs Attacks Using Token Verification Approach
## 4 Trusted Encrypted Validator Module (TEVM) Based on Token Authentication
### Generating Token  
Tokenization [33] has been widely applied in various sectors, such as network communication, information security, and credit card and third-party authentication. With the help of tokenization, an internet-connected device can be projected to a token, thus becoming a reference (identification code) without external meaning or use, which can be applied in sensitive data protection, safe storage, auditing, identification, and authentication, as well as services. When other devices apply for a token through TBAS in the future, they will follow the same rule to deliver information to TBAS to obtain a legitimate token. After receiving an application, TBAS examines the seven items, generates the token if all items are legitimate, and sends a reply. If one of the seven items is incorrect, a message of incorrect information will be returned with a request for reapplication. The required items to apply for a token include the IP address (source) of the device, the applic  
Figure 3: The authentication flow.  
address, the IP address (destination), socket port, hostname, and TEVM mac address as shown in Table 1. Then, based on the abovementioned seven pieces of information, TBAS generates a group of tokens through RSA-2048 and SHA-256. In addition, tokens carry an extra information packet that includes the generated time and effective period of the token, the applicant's IP and mac address, and information regarding the privilege and type of token. Both CS and TEVM are required to examine the legality of a token before combining the token and the application and sending a reply, as well as after receiving the other side's information. If all nine items (token hash value and other eight information items) are legitimate as shown in Table 2, the token is legitimate; otherwise, the token is illegitimate.
# Anticipatory Classifier System with Average Reward Criterion in Discretized Multi-Step Environments
## 1 Introduction
They compared it with XCSG (where the prediction parameters are modified by applying the idea of gradient descent) and ACXS (maximizing the average of successive rewards) in large multistep problems (Woods1, Maze6, and Woods14). In this paper, we introduce the average reward criterion to yet another family of LCSs-anticipatory learning classifier systems (ALCS). They differentiate from others so that a predictive schema model of the environment is learned rather than reward prediction maps. In contrast to the usual classifier structure, classifiers in ALCS have a state prediction or _an anticipatory part_ that predicts the environmental changes caused when executing the specified action in the specified context. Similarly, as in the XCS, ALCSs derive classifier fitness estimates from the accuracy of their predictions; however, anticipatory state predictions' accuracy is considered rather than the reward prediction accuracy. Popular ALCSs use the discounted criterion in the original form, optimizing the performance in the infinite horizon. Section 2 starts by briefly describing the psychological insights from the concepts of imprinting and anticipations and the most popular ALCS architecture-ACS2. Then, the RL and the ACS2 learning components are described. The default discounted reward criterion is formally defined, and two versions of undiscounted (averaged) criterion integration are introduced. The created system is called AACS2, which stands for _Averaged ACS2_. Finally, three testing sequential environments with increasing difficulty are presented: the Corridor, Finite State Worlds, and Woods. Section 3 examines and describes the results of testing ACS2, AACS2, Q-learning, and R-learning in all environments. Finally, the conclusions are drawn in Section 4.
# Maximizing the Capacity of Edge Networks with Multicasting
###### Abstract  
Edge networks employ local computing and caching resources to process data, thus alleviating the bandwidth pressure on backbone networks and improve users' quality of experience. System capacity is one of the key metrics to evaluate the performance of edge networks. However, maximizing system capacity in edge scenarios faces challenges due to the dynamical user sessions and the changing content popularity. This study reports on the influence of multicast communication on the capacity of edge caching networks. When large amounts of content are requested simultaneously or over a short period, one-to-one unicast transmission will consume copious network resources due to repetitious transmission. To solve this problem, this study used the one-to-many multicast scheme to realize cooperative transmission between edge servers. First, multiple copies of the content are distributed to multiple small base stations (SBSs) based on the content's popularity and the SBSs' cache sizes. Second, a multicast tree is constructed by using, as the root node, the SBS that stores the content. Third, the content is transmitted along the path of the multicast tree to each end-user. Finally, a simulation platform is constructed to analyze the performance of the two transmission schemes. The results of simulating on edge caching networks show that multicast communication responds well to users' requests even when the requested content requires sudden transmission, is highly popular or is requested often within short time. This system's capacity has been significantly improved compared to the classical methods.  
content copy; cooperative transportation; multitasking; edge caching networks +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Strengths and Limitations  
The proposed approach offers several notable strengths. The joint model's dual focus on discrete and dimensional emotions provides a comprehensive understanding of emotional states, surpassing the capabilities of traditional models. The use of multiple feature encoders in parallel enhances feature extraction, capturing a wide range of emotional information. The adaptive weighting schemes effectively balance classification and regression tasks, improving overall model performance. However, the model's complexity and computational demands pose limitations, potentially restricting its real-time applicability. Additionally, class imbalance in the dataset remains a challenge, suggesting a need for further exploration of data augmentation and cost-sensitive learning techniques. While the model demonstrated strong performance on the IEMOCAP dataset, its generalization to diverse linguistic and cultural contexts requires additional validation.
# Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach
## 3 Proposed Signal Processing Methodology
### Pre-Processing Algorithms
#### 3.1.1 Spike Subtraction Algorithm for Detection of Abnormal Events  
The spike subtraction algorithm restores each data point of the vector by its median estimated over a fixed window length. Accordingly, median filtering ensures all the outliers are eliminated. The performance of the spike subtraction algorithm is presented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **De-Noising** & **Filtering** & **Decomposition** & \begin{tabular}{c} **Prior Knowledge** \\ **to Failures** \\ \end{tabular} & \begin{tabular}{c} **Automated Defect** \\ **Detection** \\ \end{tabular} \\ \hline [PERSON] et al. [18] & No & \begin{tabular}{c} Yes–Wavelet \\ Transform \\ \end{tabular} & \begin{tabular}{c} Yes–Singular Value \\ Decomposition \\ (SVD) \\ \end{tabular} & No & No \\ \hline [PERSON] et al. [19] & No & \begin{tabular}{c} Yes–Packet \\ Wavelet Transform \\ \end{tabular} & \begin{tabular}{c} Yes–Empirical \\ Mode \\ Decomposition \\ (EMD) \\ \end{tabular} & No & No \\ \hline [PERSON] [20] & No & \begin{tabular}{c} Yes–Hidden \\ Markov Model \\ (HMM) \\ \end{tabular} & \begin{tabular}{c} Yes–Dynamic \\ Principal \\ Component \\ Analysis (DPCA) \\ \end{tabular} & No & No \\ \hline Proposed method & \begin{tabular}{c} Yes–Stationary \\ Wavelet Transform \\ \end{tabular} & Yes–Wiener Filter & \
\begin{tabular}{c} Yes–Spectral \\ Kurtosis \\ \end{tabular} & No &
\begin{tabular}{c} Yes–change \\ detection using SK \\ estimates \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of signal processing technniques using the NASA IMS bearing data.  
Figure 1: IMS bearing test set up and sensor’s configuration.  
Despite there are spikes subtracted (arrows), there are still lots of noise buried in the signal. Therefore, an appropriate de-noising algorithm is required to improve the signal-to-noise (SNR) ratio that is discussed in the next section.
## 2 Methodology
### Key Technical Innovations
#### 2.2.2 Loss Function [30]  
To solve the gradient imbalance problem of traditional IoU in dense defects, we introduce Unified-IoU loss function. Unified-IoU balances the model convergence speed and high-precision detection performance by dynamically adjusting the scaling ratio of the bounding box and the weight allocation strategy. The core innovations of Unified-IoU are as follows.  
First, we incorporate a dynamic bounding box scaling mechanism. Dynamically adjust the IoU loss weights by scaling the dimensions of the prediction box and the real box. Scaling down the bounding box reduces the IoU value of the high-quality prediction box and increases its loss weight, prompting the model to focus on high-precision detection; scaling up the bounding box reduces the loss weight of the low-quality prediction box, accelerating model convergence. The scaling ratio is controlled by the dynamic hyperparameter \"_ratio_\", and its adjustment strategies include linear, cosine, and fractional decay.  
Linear decay:  
\[ratio=-0.005\times epoch+2 \tag{12}\]  
Cosine decay:  
\[ratio=0.75\times cos\bigg{(}\frac{\pi\times epoch}{300}\bigg{)}+1.25 \tag{13}\]  
Second, we incorporate the Dual-attention mechanism. Combine the FocalLoss idea to design a bidirectional weight assignment, assigning higher weight to the low-confidence prediction frame (focusing on the difficult samples) and extra weight to the high-IoU prediction frame (improving the performance of dense scene detection). The final loss function incorporates geometric metrics and dynamic weights:  
\[UlloU_{loss}=(1-p)\times IoU_{loss} \tag{14}\]  
\[IoU_{loss}=1-\frac{P\cap P_{gt}}{P\cup P_{gt}} \tag{15}\]  
where \(p\) is the confidence of the predicted box, and \(IoU_{loss}\) is the original IoUloss. \(P\) is the predicted box, and \(p_{gt}\) is the Ground Truth. Unified IoU solves the problem of the traditional IoU loss in which low-quality samples dominate the gradient and achieves a balance between high accuracy and high efficiency.