esa-sceva/satcom-chunk-collection huggingface dataset:
# A Unified Framework for Automatic Detection of Wound Infection with Artificial Intelligence
## 3 Results
### Detection of Wound Infection  
For the detection of wound infection, 380 images (including 108 images of wound infection) were used for training, while we validated the performance on 100 images (including 28 images of wound infection). We also adopted fivefold cross-validation to assess the outcomes of the four models.  
Table 1 shows the classification accuracy, recall, precision, F1 score, and AUC for each method. Our algorithm achieved an accuracy of 79.5%, recall of 77.1%, precision of 82.7%, F1 score of 79.4%, and AUC score of 83.3%, whereas the other three methods achieved lower AUC scores (kernel support vector machines, 44.4%; random forest, 67.1%; gradient boosting classifier, 66.9%). For detailed comparisons among the four methods, we show the receiver operating characteristic (ROC) curves in Figure 5. The ROC curve is a probability curve, and AUC characterizes the degree of separability. One model with a higher score is better at detecting the aimed outcome.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Model** & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **Specificity** & **False Positive** & **False Negative** & **AUC** \\ \hline Our model & 79.5\% \(\pm\) 4.2\% & 77.1\% \(\pm\) 6.1\% & 82.7\% \(\pm\) 7.8\% & 79.4\% \(\pm\) 3.9\% & 82.2\% \(\pm\) 3.5\% & 17.8\% \(\pm\) 6.5\% & 22.9\% \(\pm\) 6.0\% & 83.3\% \(\pm\) 2.8\% \\ \hline Kernel support vector machines & 41.1\% \(\pm\) 2.1\% & 40.8\% \(\pm\) 8.7\% & 48.2\% \(\pm\) 3.3\% & 38.3\% \(\pm\) 3.5\% & 56.2\% \(\pm\) 2.6\% & 43.8\% \(\pm\) 7.3\% & 59.2\% \(\pm\) 8.8\% & 44.4\% \(\pm\) 4.5\% \\ \hline Random forest & 58.6\% \(\pm\) 5.9\% & 64.8\% \(\pm\) 12.4\% & 44.5\% \(\pm\) 13.6\% & 43.1\% \(\pm\) 11.4\% & 19.2\% \(\pm\) 7.5\% & 80.8\% \(\pm\) 2.5\% & 35.2\% \(\pm\) 12.3\% & 67.1\% \(\pm\) 7.3\% \\ \hline Gradient boosting classifier & 63.4\% \(\pm\) 4.2\% & 65.2\% \(\pm\) 7.4\% & 54.5\% \(\pm\) 6.4\% & 58.7\% \(\pm\) 3.3\% & 65.2\% \(\pm\) 5.3\% & 34.8\% \(\pm\) 4.6\% & 34.8\% \(\pm\) 7.4\% & 66.9\% \(\pm\) 4.9\% \\ \hline \end{tabular} AUC: area under the receiver operating characteristic curve.  
\end{table}
Table 1: Comparison of performance of our model, kernel support vector machines, random forest, and gradient-boosting classifier for detection of wound infection.
## 3 Results
### Ablation Studies  
For our ablation studies, we tested the effect of the number of modalities, number of layers, and node features on our proposed MGIN framework. As shown in Table 4, we compared the performance of MGIN with the use of different modalities. The accuracy (0.8167 \(\pm\) 0.0749), F1-score (0.8192 \(\pm\) 0.0683), and AUC (0.8269 \(\pm\) 0.0754) were highest when utilizing both nback and emoid fMRI modalities. Additionally, we explored the effect of the number of hidden layers in our proposed MGIN method. Our experiments illustrated the optimum number of hidden layers is two with an accuracy of 0.8167 \(\pm\) 0.074 and AUC of 0.8269 \(\pm\) 0.075. Furthermore, our experimental results demonstrated the effect of various types of node features on the MGIN framework, where we found that the connection profile comprehensively captures the structural information of the brain network and retains extensive information on pairwise connections with an accuracy of 0.7012 \(\pm\) 0.041, and it outperforms structural features like degree or positional features such as eigen decomposition.
## 3 ABER Analysis of the SISO TOW Link
### SISO TOW Link with IM/DD L-PPM  
The PPM modulation format is more power effective than OOK, but at the expense of an increased bandwidth requirement and higher complexity. Additionally, PPM eliminates the requirement of OOK for a decision threshold dependent on the input power [2; 57]. In \(L\)-PPM, each word of \(N\) bits is mapped into one of the \(L=2^{N}\) symbols and transmitted through the channel, while each \(L\)-PPM symbol has the form of a pulse transmitted in one of the \(L=2^{N}\) consecutive time slots with duration \(T_{s}=NT_{b}/L\), where \(T_{b}\) is the bit duration, with the remaining slots being empty. Information is encoded within the position of a pulse of constant power along with (\(N\)-1) empty slots, while the position of the pulse corresponds to the decimal values of the \(N\)-bit input data [64]. Assuming a SISO \(L\)-PPM TOW link we can omit, for the sake of brevity, the index \(m\), and thus, the BER as a function of \(h\), can be defined as [56]  
\[P_{e}^{L-PPM}(h)=\frac{1}{2}\text{erfc}\Bigg{(}\sqrt{\frac{\eta^{2}h^{2}}{8N_{0} }L\log_{2}L}\Bigg{)}. \tag{19}\]  
Next, in order to estimate the ABER of the TOW link with the \(L\)-PPM scheme under the joint impact of skin-induced attenuation and NZB pointing errors, we average (19) over \(h\). After substituting (10) and (19) into (15), we get  
\[P_{e,\mu 0}^{L-PPM}=\frac{\psi^{2}\exp\!\left(-\frac{1}{2}\alpha(\lambda)\delta \right)^{\psi^{2}-1}}{2\sqrt{\pi}(1-\psi^{2})(A_{0\delta})^{\psi^{2}}}\Bigg{(} \frac{\eta\sqrt{L\log_{2}L}}{\sqrt{8N_{0}}}\Bigg{)}^{\psi^{2}-1}\Gamma\Bigg{(} \frac{2-\psi^{2}}{2}\Bigg{)}. \tag{20}\]  
Alternatively, by substituting (12) into (20), the latter can be expressed as a function of the average electrical SNR, \(\mu\)  
\[P_{e,\mu 0}^{L-PPM}=\frac{\psi^{2}\Big{[}\mu\big{(}\psi^{2}+2\big{)}\Big{]}^{ \frac{\psi^{2}-1}{2}}\Bigg{(}\frac{\sqrt{L\log_{2}L}}{8}\Bigg{)}^{\frac{\psi^{ 2}-1}{2}}\Gamma\Big{(}\frac{2-\psi^{2}}{2}\Big{)}}{2\sqrt{\pi}\psi^{\psi^{2}- 1}(1-\psi^{2})(A_{0\delta}g)^{\psi^{2}}\big{(}\overline{P_{s}}\big{)}^{\frac{ \psi^{2}-1}{2}}}. \tag{21}\]
# Measuring the Performance of Wastewater Treatment in China
## 3 Empirical Analysis and Results
### Comparing Overall Efficiency Results from Year 2011 to 2015  
This section compares overall efficiency during the period 2011-2015. Table 5 lists the rank and efficiency score results throughout the 5 years. Seven regions exhibited efficiency for all years. Beijing, Tianjin, Shanghai, Jiangsu, Shandong, and Guangdong are all economically developed and have stronger economic growth. These six regions have better technological facilities on discharged wastewater treatment. Another best region, Tibet, is the origin of the river in China. There are not many heavy industries and there are fewer people in this large region. Inner Mongolia, Hainan, and Ningxia are characterized by small populations, less sewage, no large-scale factories, and low industrial wastewater pollution. Hainan regressed to the worst group. Zhejiang, Fujian, Hebei, and Liaoning are developing regions with fluctuating efficiency from time to time. Chongqing began at the 27 th place in the ranking and advanced to 24 th, 23 rd, 22 nd, and 11 th over the years 2011 to 2015. Shanxi, conversely, regressed from 16 th to 21 st, 25 th, 27 th, and 29 th. Guangxi and Jiangxi were always the worst performing regions on wastewater discharge and economic efficiencies during the years 2011-2015.
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 5 Result and Performance Analysis  
The performance analysis is described in three subsections, namely, the probability analysis, throughput analysis and routing time. All the simulations are performed in MATLAB. The simulation parameters are shown in Table 2.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 6 Experimental Results and Discussion
### Sensitivity Analysis of Object Detection Method  
In this section, we conduct an ablation experiment to compare the effects of Shuffle Attention, transfer learning, and smaller anchor boxes on the network training strategy we have constructed. In this section, we use 585 images as the training set and 1614 images as the validation set.
## 3 Overview of HO Management in 5G UDSC Network
### 5G Small Cells Architecture
Once the user equipment develops the potential to take benefit from the services and capabilities of network, its registration over the network must be initiated. The basic of registration process relies on the broadcasting of control messages amid UE, gNB and AMF, where gNB stands for NG NodeB and AMF is used for access & mobility management function. The registration of UE ensures that it can be controlled, handled and monitored over the network and reachable. Registration conditions including initial, periodic, mobility and emergency registration are essential to initiate the registration procedure. When the device is switched on, UE starts the initial registration to connect with the network. In periodic registration, network regularly monitors the UE to begin a new registration process. It enables the UE located in the registration area to determine whether its registration is eradicated without alerting the network or not [50]. UE performs the mobility registration whenever subscriber changes its position, and its linking tracking area cell is not available in the radio access list. Lastly, UE utilizes emergency registration while using emergency services and so. When the UE is switched on, it requires to select target cell and needs to launch the RRC connection with the gNB. Multiple input access operations are present to perform target cell synchronization along with the requirement to uncover public land mobile network (PLMN). Throughout the registration procedure, there exists a continuous exchange of NAS signal among UE and AMF, which is then captured and forwarded to gNB by RRC protocol [51]. By similar approach, the signal is moved towards the AMF through next generation application protocol (NGAP) in gNB. All these transmission and transactions enable the UE to receive the registration of 5G services.
## 4 Simulations Results and Discussion with Performance Evaluation
### Performance Trade-Off  
In this subsection, the performance of the existing and the proposed protocols are compared. To avoid the repetition, parameters are explained as general because of similar behaviors in the proposed protocols. To achieve the data reliability in data transmission, multiple copies of data packets are generated. Existing protocols experience the least delay with high EC, a high number of active nodes and high PRR. Meanwhile, proposed algorithms achieved reliable data delivery with the least EC and a minimum number of active nodes and affordable PRR. In the proposed protocols, the delay is compromised over EC. Moreover, performance trade-off and achievements with compromised parameters of existing and proposed protocols are shown in Table 10.  
In the proposed protocols, a proactive approach is used to search the best shortest path, but a trade-off occurs between the EC and the delay. If the proposed protocols dissipate minimum EC to prolong the network lifespan, the network must compromise on delay, i.e., the network must pay the cost of delay. The BFS-SPF-Three and SPF-Three selects the single efficient shortest path with minimum EC which results in collision of data packets and delay. Retransmissions deliver the data packets as soon as possible which results in the least number of active nodes with minimum EC. Therefore, the lifetime of the network increases. However, the network must compromise on delay to minimize the EC.
# A Study of Constitutive Model of Rock Damage under the Joint Effect of Load and Moisture
## 3 The Mechanical Test of Sandrock Containing Water  
### Experimental Overviews  
Samples were processed into a dimension of \(\Phi\)50 mm \(\times\) h100 mm, with groups and numbers shown in Figure 1a,b. The mineral composition of this batch of sandstone samples was obtained by D8 ADVANCE X-ray diffractometer analysis, including 35.5% of quartz, 23.4% of plagioclase, 35.9% of calcite, and 5.1% of kaolinite and chlorite, and the test results are shown in Figure 1c.  
Before the immersion experiment, some tests were performed to obtain basic physical and mechanical properties of the initial state of the rock samples, indicating an average natural water content of 0.45%, and an average natural density of 2.44 g/cm\({}^{3}\). The average uniaxial compressive strength is 67.03 MPa. The average compressive strengths under various confining pressure conditions of 10 MPa, 20 MPa, and 30 MPa are 129.08 MPa,  
Figure 1: Experimental preparation. (**a**) Standard sample used in the uniaxial compression experiment; (**b**) Standard sample used in the triaxial compression experiment; (**c**) Mineral composition of sandstone samples used in the study.  
174.99 MPa, and 210.49 MPa, respectively. The calculated internal friction angle of the rock sample is \(\varphi=37.27^{\circ}\) with a cohesion \(C=22.33\) MPa.  
All groups of samples were dried in an XMTD-8222 constant temperature oven, the oven was heated to 105 \({}^{\circ}\)C, and maintained at a constant temperature for 48 h, followed by colling till room temperature. The water content of the rock samples was measured after 0.75 h, 1.5 h, 6 h, 24 h, and 720 h of natural soaking.  
The mechanical tests of rock samples are all completed by an RMT-301 rock and concrete mechanical test system, as shown in Figure 2. The detailed experimental scheme is as follows:  
1. Single Axis Compression Test: The test samples are numbered from Ai-\(j\), with \(i=1\)-6 (1 refers to the sample in dry status, sample 2 to 6 refer to the samples submerged for 0.75 h, 1.5 h, 6 h, 24 h, and 720 h, respectively) and \(j=1\)-3 (three samples in one test group). The test adopted the displacement control, with the load increased at a rate of 0.5 mm/min until sample failure.
2. Conventional Three-axis Compression Test: The test samples are numbered from Bi-\(j\), with \(i=1\)-6 (1 refers to the sample in dry status, sample 2 to 6 refer to the samples submerged for 0.75 h, 1.5 h, 6 h, 24 h, and 720 h, respectively) and \(j=1\)-9 (The surrounding pressure is categorized into three sections, with 3 samples in each section). The test adopted the stress control, with the surrounding pressure increased at a rate of 0.05 MPa/s, along with the axial pressure. The initial pressure settings are 10 MPa, 20 MPa, and 30 MPa, respectively. During the later phases, the control is switched to the displacement control, with a constant surrounding pressure and an increasing axial pressure at a rate of 0.5 mm/min.
### Objective and Organization of This Paper  
Though many approaches are published for RFI mitigation in SAR data, a thorough summary of current technique status and discussions about future trends is lacking in existing literatures [11; 12]. By providing a comprehensive survey of the RFI mitigation techniques applicable for the SAR system, this review can serve as a reference for future work on the implementation of most suitable RFI mitigation schemes for an air-borne or space-borne SAR system.  
The review is structured as follows. Section 2 introduces the proactive efforts of frequency allocation and management, illustrating major RFI sources and their interfering mechanism. Typical interference models adopted in existing RFI mitigation techniques are presented as well. In Section 3, a comprehensive literature review of state-of-the-art techniques for RFI mitigation in SAR system is provided. Discussions on the RFI signal models, as well as their pros and cons are given. Further, to deal with the deficiency of current methods, Section 4 outlines possible directions for future development of the field and the final section concludes the review.
## 5 Security Analysis
### Perfect Forward Secrecy Attack  
In our proposed scheme, the RCRS secret key Xs is not related to the RCRS session key SK. The session key is not created by the RCRS internal key Xs. In addition, session keys are randomly created by each legitimate participant from each session. Using the session key for a limited time and then encrypting each key with an irreversible hash function, the attacker cannot find the correct rules and guess the correct session key. That is, the key for each conference session is only related to a random number, and it is impossible to find the rules at any time. Therefore, we confirm that the proposed TCVK scheme provides perfect forward secrecy.
# Human Action Recognition Based on 3D Convolution and Multi-Attention Transformer
## 2 Related Work
### Action Recognition Based on Transformer Model  
With the tremendous success of the Transformer model in Natural Language Processing, it has been widely applied in the field of action recognition [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. For example, in order to model spatiotemporal features, [PERSON] [18] improved motion detection and enhanced understanding of human motion in adjacent frames by utilizing spatial and temporal attributes. [PERSON] [19] proposed a relative position embedding (RPE-STDT) model based on spatial and temporal decoupling for action recognition. This model replaces absolute position embedding with relative position embedding, effectively captures token sequences, reduces computational costs, and optimizes recognition performance. [PERSON] [20] proposed the STTFormer network, which utilizes a spatiotemporal tuple self-attention mechanism module to capture the relationships between different joints in consecutive frames. [PERSON] [21] proposed the SkateFormer network, which partitions joints and skeletons based on different types of skeletal-temporal relationships, and performs a self-attention mechanism within each partition, alleviating the requirement of a large amount of resources to obtain correlations between joints. [PERSON] [22] proposed the TranSkeleton action recognition method that can effectively capture remote dependencies and subtle time structures. In addition, [PERSON] [23] used k-NN attention instead of the original self-attention, optimizing the training process by ignoring irrelevant or noisy labels in the input sequence, achieving efficient recognition. This improved the attention mechanism to make the original Transformer model more suitable for action recognition tasks. [PERSON] [24] proposed ActionFormer, a single-stage temporal action location model based on anchor-free. It uses a lightweight decoder to classify actions at every moment and estimate corresponding action boundaries. [PERSON] [25] employed the attention mechanism and adopted a two-stage training strategy for mutual enhancement of the two motions during training. [PERSON] [26] proposed the Recurrent Vision Transformer (RViT) method for spatiotemporal feature learning to perform video behavior recognition. However, the above methods are all based on a single scale for action recognition, ignoring that the feature scale changes during the recognition process, resulting in uneven attention allocation and limited recognition performance. Therefore, we introduce a multi-scale Transformer model under the two-stream network to model spatial features. Compared with single-scale methods, the attention allocation is more balanced and accurate, and can better identify actions of different granularities.
# Optimal Longitudinal Texture on Concrete Pavement to Reduce Lateral Vibration of Vehicles
## 1 Introduction
The Cooper Tire and Rubber Company research team investigated various tire patterns by applying a new lateral stress theory [10]. According to this theory, lateral vibration occurs when the lateral forces--which occur when the pavement intning comes in contact with the tire groove--are unbalanced. [PERSON] studied the vibrations induced according to tire patterns in longitudinal timing sections [9]. The study showed that lateral vibrations decreased as the distance between the tire groove and intning groove increased, and they suggested that rather than the groove position of tires, their width, shape, and edge shape should be modified. Uniroyal Tire published the Tread Element Trapping theory about lateral vibrations on pavement textures [11]. They also estimated that lateral vibrations were caused by the interaction between the tire groove and pavement texture and presented a prediction model for lateral vibration by plotting the number of times when the tire groove and intning coincide with each other (coincidence index). In 2010, [PERSON] proposed a prediction model for lateral vibrations on pavement surfaces for complex tire patterns [12]. The study discovered that the size and frequency of reaction force change with the relative position of tire grooves when they come in contact with the pavement texture. Thus, they claimed that lateral vibrations can be reduced by creating different tire grooves according to the groove width and depth on the surface. In their study, the issue of lateral vibrations showed a high correlation with straight-grooved tires and a low correlation with zigzag-patterned tires. Other studies have been conducted on lateral vibration caused by tire and pavement texture [13; 14; 15]. However, there is not much literature on lateral vibration between longitudinal texture and tire. In a lot of literatures, only individual studies on tire treads or textures have been conducted. The above discussion indicates that the lateral vibrations experienced by vehicles vary according to their tire tread profile and the longitudinal texturing specifications. Therefore, the present study was conducted to derive a longitudinal texturing specification that can minimize the lateral vibrations in vehicles resulting from expressways with longitudinal texture. To that end, the degree of lateral vibrations was evaluated by driving vehicles with tires widely used in domestic vehicles on the sections with longitudinal thinning sections where complaints were raised or field tests were performed. The results were revalidated using laboratory testing equipment for driving simulation and by performing finite element analysis under the same conditions. Finally, a longitudinal texturing specification that can minimize lateral vibrations and can be practically applied in the field is proposed.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 6 Result and Discussions  
In our simulation, all the nodes have equal energy, which is deployed randomly in the network. The network contains 120 RPL routers and one DODAG root. The data transfer interval is 60 s. We have taken the sky mote for our simulations [37]. The simulation has been conducted in the COOJA simulator [38]. The data transfer rate is one packet per minute. All results presented in the figures below are averaged over 10 simulation runs and error bars show the 95% confidence intervals. The simulation parameters are given in the Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Operating System & Contiki 2.7 \\ Simulator & COOJA \\ Initial Energy & 1500 mA \\ Routing Protocol & RPL \\ Simulation Time & 1 h \\ Network area & 300 m \(\times\) 300 m \\ Topology & Random \\ Node Type & Skymote \\ Number of Nodes & 120 \\ MAC Layer & 802.15.4 \\ Data Transmission Interval & 60 s \\ Physical Layer & Two Ray Ground Propagation Model \\ RPL Parameter & MinHopRankIncrease = 256 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.