esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Evaluations
### Evaluation Method
#### 4.3.2 Evaluation Model  
In order to test the detection rate and escape rate of the adversarial model, we not only used the most popular XSS detection software (SafeDog and XSSChop), but also trained the LSTM model for evaluation. To get a better LSTM model, the paper tuned the Size, Iter, Window, and Negative parameters in Word2 Vec. Through the control variable method, only one parameter was modified at a time, and the effects of different parameters on the recall rate, accuracy, accuracy, and F1 value of the LSTM detection model were compared. The experimental results of the Word2 Vec parameter tuning are shown in Figure 4.  
\begin{table}
\begin{tabular}{p{85.4 pt}|p{142.3 pt}} \hline \hline System & Ubuntu 16.04.4 LTS \\ RAM & 16G \\ CPU & i7-7/700 CPU @ 3.60 GHz \\ GPU & NVIDIA GeForce GTX 1060 6 GB \\ \hline Versions of Python and Extension Library & Python 3.6.7 keras-rl==-0.4.2 gym==0.9.5 requests==2.18.4 tensorflow==1.13.1 keras==20.9 gensim==3.2.0 h5 py=2.9.0 sklearn=0.20.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment environment.  
The experiment comprehensively considered the recall, precision, accuracy, F1 value, and training time to adjust the parameters. Finally, we decided to set the training parameters of Word2 Vec as follows: \"size\" to 60; \"windows\" to 10; \"negative\" to 20, and \"iter\" to 70.  
To evaluate the LSTM model trained in the experiment objectively, we compared the model with the traditional machine learning algorithm based on ADTree and AdaBoost proposed by [PERSON]. Their method used the same XSS malicious sample dataset as this paper. The paper also selected the SafeDog and XSSChop for comparative experiments. The comparing results are shown in Table 2.  
From the result, the accuracy of the LSTM-based XSS attack detection model was 99.5%; the recall rate was 97.9%; and the F1 value was 98.7%. The performance in terms of accuracy, recall, and F1 was superior to the traditional machine learning algorithms ADTree and AdaBoost. The accuracy of the LSTM model in this paper was slightly lower than SafeDog and XSSChop, but the accuracy rates of the three were all more than 99.5%. Besides, the LSTM detection model trained in the experiment was superior to the website SafeDog and XSSChop in terms of recall rate and F1 value. In summary, the LSTM-based detection model trained in the experiment had obvious advantages in accuracy, recall rate, and F1 value, which proves that the model can effectively detect cross-site scripting attacks.
## 2 Materials and Methods  
### Experimental Site and Treatments  
The field experiment was conducted in a long-term experimental vegetable field in Nanjing, Jiangsu Province, China (32\({}^{\circ}\)01\({}^{\prime}\) N, 118\({}^{\circ}\)52\({}^{\prime}\) E), with an average annual temperature and precipitation of 17.5 \({}^{\circ}\)C and 1107 mm, respectively. The test soil pH was 7.9, the bulk density was 1.3 g cm\({}^{-3}\), the organic carbon was 18.8 g C kg\({}^{-1}\), the total nitrogen was 2.3 g N kg\({}^{-1}\), the cation exchange capacity (CEC) was 25.3 cmol kg\({}^{-1}\), and it contained 3.1% of clay particles, 66.4% of silt particles, and 30.5% of sand particles. Soil physicochemical properties were measured using the methods described in a previous study [3].  
Five experimental treatments were established, which were no fertilizer or biochar addition (CK); synthetic fertilizer application (SN); SN plus 20 t ha\({}^{-1}\) biochar amendment (NB); substituting 50% of chemical N fertilizer with organic manure (NM); and NM plus 20 t ha\({}^{-1}\) biochar amendment (NMB). The N application rate was 240 kg N ha\({}^{-1}\) crop\({}^{-1}\) as urea; the phosphate (P) application rate was 120 kg P ha\({}^{-1}\) crop\({}^{-1}\) as superphosphate; the potassium (K) application rate was 240 kg K ha\({}^{-1}\) crop\({}^{-1}\) as potassium chloride. The organic manure was applied at 120 kg N ha\({}^{-1}\) crop\({}^{-1}\) containing 1.1% TN, 0.13% P, 0.83% K and 28.3% organic matter. The organic manure purchased from Nanjing Mingzhu Fertilizer Co., Ltd. (Nanjing, China). Biochar purchased from Henan Sanli New Energy Co. (Shangqiu, China), which was produced by pyrolyzing wheat straw at 500 \({}^{\circ}\)C under a continuous carbonization furnace for 4 h. Usually, biochar produced at low temperatures (e.g., 400 \({}^{\circ}\)C) is more appropriate for agricultural soil improvement, while at high temperatures (e.g., 600 \({}^{\circ}\)C), it is more suitable for the remediation of organic and inorganic polluted environments [17]. Therefore, choosing 500 \({}^{\circ}\)C as the pyrolysis temperature can take into account the product characteristics and application needs. The usually recommended pyrolysis time for biochar is 2-6 h. Choosing 4 h as the pyrolysis time can ensure that the biomass is fully decomposed, and, at the same time, avoid the degradation of product quality that may be caused by pyrolysis for too long. The properties were as follows: carbon content 467 g C kg\({}^{-1}\); nitrogen content 5.9 g N kg\({}^{-1}\); pH 9.4, CEC 24.1 cmol kg\({}^{-1}\); and ash content 20.8%. Crop rotations, species, tillage, and pest controls followed local practices.
# Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon
## 4 Experiments and Results
### Test Result from Narrow Alley to Alley  
In order to verify the performance in an area where the LTE environment is poor, the test was performed with the same route as shown earlier in Figure 14b. This test area had only one available BS as seen in Figure 13a, and also had poor GPS signal conditions as shown in Figure 19a. The estimated initial location from GPS was at origin because it did not receive satellite signal at the beginning of the test. In the case of the kNN and the PF, localization was almost impossible in areas where the LTE environment was very poor with a few BSs while the SC shows a stable and accurate performance as seen in Figure 19b-d. In test scenario 2, there was only one available BS; therefore, 24 more steps were needed to stabilize the performance than in test scenario 1 as shown in Figure 20. The RMSE of the GPS is 18.52 m and the kNN is 53.04 m, respectively. The PF shows RMSE at 31.73 m while the SC-based fingerprinting method shows it at 7.85 m except for the initial phase error. The test results for the above two scenarios are summarized in Table 1, and they can confirm that our proposed method shows better or similar performance with GPS even in a poor LTE environment. Especially, the availability of the RF coverage is definitely improved by utilizing the accumulated RSS pattern visible in the results of scenario 1 and scenario 2 with different LTE environmental conditions. This implies that it is possible to maintain a high performance with only a small number of BSs using the proposed fingerprinting method even in urban areas.  
Figure 19: _Cont._  
Figure 18: Localization error on test scenario 1: (a) Localization error according to the walking distance; (b) CDF of the localization error.
# A Bioinspired Humanoid Foot Mechanism
## 2 Materials and Methods
### An Underactuated Humanoid Foot Mechanism  
The proposed foot mechanism aims to enhance robot mobility and efficiency by enabling body support in all of the stances acquired in Section 2. In order to do so, a three-segment design was considered, based on the diagram in Figure 4. During the gait, the foot motion is ruled by four main centers of rotation, which correspond to the ankle joint, the heel bone (or calcaneus), the navicular tuberosity, and the metatarsophalangeal (MTP) joint, as shown in Figure 4. The first segment of the foot can be associated to the calcaneus-talus complex, which includes the heel and ankle, and can be approximated to a rigid body. The motion of the central segment of the foot is enabled by the active stiffening behavior of the intrinsic foot muscles in the plantar arches, which connect the calcaneus to the MTP joint. The plantar fascia and several ligaments support the plantar arches, which store mechanical energy during weight bearing by deforming. This behavior can be reproduced with the elasticity of a compliant body. The motion of the last segment of the foot can be approximated to a flexion/extension movement of the toes, which rotate around the MTP joint and the interphalangeal joints. The MTP joint is usually described as a condyloid joint, while the interphalangeal joints resemble revolute joints.  
In order to replicate the behavior of the human foot, a new compliant foot mechanism based on the kinematic scheme in Figure 5 is here presented. The proposed mechanism is based on a compliant mechanism in the plant, which can be approximated with an RPRR (revolute-revolute-prismatic-revolute) architecture, with revolute joints in points B, C, and D, and a link of varying length between B and D. Furthermore, a second compliant mechanism is added in the MTP joint in point E, with a rigid revolute joint controlled by a torsional spring.  
Figure 4: Human foot mobility [23], with a focus on the main joints of the foot: the ankle joint (**A**), the heel bone (or calcaneus (**B**)), the navicular tuberosity (**C**), and the metatarsophalangeal (MTP) joint (**D**/**E**).  
Figure 5: Kinematic scheme of the proposed foot mechanism based on the corresponding points (A to E) in the foot mobility diagram in Figure 4: (1) hindfoot; (2) midfoot; (3) forefoot; (4) compliant body.  
The mechanism works mainly on the sagittal plane, even if the compliant part can be designed to balance 3D loads as well. When analyzing the planar behavior of the proposed foot design, its degrees of freedom can be evaluated from its rigid mechanism equivalent with the Chebychev-Grubler-Kutzbach criterion as two, controlled respectively by the plantar compliant element and by the torsional spring of the MTP joint. An exemplar design of the proposed foot mechanism is shown in Figure 6.
# Biomechanical Analysis of Gait Compensation Strategies as a Result of Muscle Restriction
## 3 Results
### Joint Motion in C-Restriction  
In the ankle joint, the majority of subjects exhibited (a) higher plantar flexion at the beginning of the loading response phase, (b) higher dorsal flexion in the terminal stance phase, and (c) lower dorsal flexion during the swing phase (see these characteristics in Figure 3a). At the speed of 3.5 km/h, (a) seven (\(p<0.01\)), (b) eight (\(p<0.01\)), and (c) seven (\(p<0.01\)) subjects presented these characteristics, respectively. Furthermore, at the speed of 4 km/h, (a) six (five with \(p<0.01\) and one with \(p<0.05\)), (b) nine (\(p<0.01\)), and (c) seven (six with \(p<0.01\) and one with \(p<0.05\)) subjects showed the same characteristics.  
In the knee joint, all subjects showed higher knee flexion, mainly in the (a) loading response and (b) terminal stance phases, and (c) from the initial to middle swing phase (see Figure 3c). At the speed of 3.5 km/h, (a) eight (six with \(p<0.01\) and two with \(p<0.05\)), (b) seven (\(p<0.01\)), and (c) seven (\(p<0.01\)) subjects presented this characteristic, respectively. Furthermore, at the speed of 4 km/h, (a) eight (\(p<0.01\)), (b) seven (\(p<0.01\)), and (c) seven (\(p<0.01\)) subjects showed this characteristic.  
In the hip joint, the subjects mainly exhibited (a) higher hip extension in the middle stance (see Figure 3e), (b) higher hip extension in the terminal stance (see Figure 3e), (c) higher hip flexion in the initial swing and (d) higher hip flexion in middle swing. At a walking speed of 3.5 km/h, (a) four (\(p<0.01\)), (b) six (five with \(p<0.01\) and one with \(p<0.05\)), (c) five (four with \(p<0.01\) and one with \(p<0.05\)), and (d) six (\(p<0.01\)) subjects presented these characteristics, respectively. At the speed of 4 km/h, the same trend was observed in (a) ten (eight with \(p<0.01\) and two with \(p<0.05\)), (b) nine (\(p<0.01\)), (c) seven (\(p<0.01\)), and (d) eight (\(p<0.01\)) subjects, respectively.  
Figure 2: Human gait cycle.  
Figure 3: Joint motion of a representative subject, showing the typical motion seen in the majority of the subjects.
# Research on Three-Phase Wireless Power Transfer System
## 4 Conclusions  
In this paper, a three-phase overlapping winding wireless power transfer system suitable for three-phase power supply is proposed. The magnetic field of the proposed coupling mechanism is analyzed in detail by finite element analysis software. The results show that the three-phase overlapping coupling mechanism has a higher transmission efficiency than the single coil. Experiments show that the three-phase over-lapping structure can minimize the leakage flux and increase the coupling coefficient, so that the upper limit of the three-phase coil alternating transmission power is higher, which is conducive to improving the cost, power density, and operational stability of the system. In addition, the circuit topology is verified on the MATLAB 2018b/Simulink platform, and the experimental platform is built. The experimental results are consistent with the simulation, which proves the effectiveness and feasibility of the system control.  
Conceptualization, X.Z. and J.G.; methodology, X.Z.; software, J.G.; validation, X.Z., J.G. and X.W.; formal analysis, X.W.; investigation, L.W.; data curation, X.Z.; writing--original draft preparation, J.G.; writing--review and editing, X.Z.; visualization, P.G.; supervision, X.Z. and L.W.; project administration, P.G.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 52077153 and the Tianjin Natural Science Foundation under Grant 20]CYBJC00190.  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors declare no conflicts of interest.
# Deep Pixel-Level Matching via Attention for Video Co-Segmentation
## 4 Experiments  
In our evaluation experiments, we evaluated our framework on two video co-segmentation datasets: ObMiC dataset [15] for ablation study and DAVIS 2016 dataset [23] for state-of-the-art methods comparison. We implemented our network in the Caffe framework [22]. In the training step, we set the learning rate to 0.001. The momentum and the weight decay are set to 0.9 and 0.0005. And the batch size is set to three during the training step. We also used the weight pre-trained by ImageNet [24] as the initial weight of our baseline during the training step in all experiments in this paper. In the testing step, two frames in an input frame-pair should come from different video sequences, but the length of each video sequence in a video-pair is different. Thus, we randomly selected several frames from a shorter video in the video and each selected frame forms frame-pairs with two or three frames obtained from the longer video. In this way, we can make sure that two input frames are from different videos. Each selected frame in the short video has several binary masks as the results, and the results of the same selected frame took an average operation to generate the final result of this selected frame.  
**Evaluation metric.** In this paper, we employ two kinds of metrics to evaluate the segmentation performance of our network. One metrics is the pixel-wise accuracy (Pixel Acc.), the other is the intersection-over-union (IoU) accuracy [5]. On the one hand, the pixel-wise accuracy is computed in the accuracy layer in the Caffe framework, and it is defined as  
\[\textit{Pixel Acc.}=\frac{n_{Predict}}{N_{All}} \tag{3}\]  
where \(n_{Predict}\) denotes the number of pixels, which are assigned by the correct labels in the segmentation result, and \(N_{All}\) denotes the number of all pixels in this segmentation result. On the other hand, the IoU accuracy is a metric that is widely employed to evaluate the performance of segmentation approaches, and it is defined as  
\[IoU=\frac{Seg\bigcap GT}{Seg\bigcup GT} \tag{4}\]  
Figure 3: The process of transforming \(f_{a}\) to \(v_{a,1}\) and \(v_{a,2}\) in the tile layers.  
where \(Seg\) denotes the segmentation result, and \(GT\) denotes ground truth segmentation. mIoU denotes the average IoU scores.
# Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces
## 3 Continuation of Existing in Services and Replacement Possibilities
### SIP-Based Service Invocation  
There are IN vendors where the service logic can be invoked on its legacy CAP/INAP interfaces from the 2G/3G domain, while it can also be invoked on a SIP-based (Session Initiation Protocol) interface from the IMS domain. It requires both maintenance of the existing IN and development of the SIP-based service invocation.  
Alternatively, IMS AS vendors may decide to implement replacement services. Typically, such implementations are integrated to MMTEL AS in order to enable strong MMTEL interworking, assuming a microservice architecture. For example, an integrated MMTEL/SCC AS may provide parallel alerting of multiple devices with a single subscription, with shared MMTEL data for all devices and their terminating SIP sessions controlled by IN services. Furthermore, an IMS native Selective Ringback Tone service may allow the calling party to hear a customized ringback tone while the called party is alerted. That is, instead of the existing ringback tone, the calling party hears a melody or a greeting specified by the called party while waiting for the receiver to respond to the call. The called party can select a melody or greeting based on the identity of the calling party.
## 2 Void Classification  
Voids are non-conductive cavities in the soldered joint, and they can pose a reliability risk, especially for power electronics where higher currents are present. Voids are formed during the soldering process; their presence can decrease heat dissipation from the product to the heatsink, which results in the non-uniform thermal stress, lower lifetime, or thermal breakdown of the products. Voids can be classified into several categories: macrovoids, microvoids, shrinkage voids, voids in microvoids, Kirkendall voids, and pinhole voids. The typical macrovoid diameter varies between 100 \(\upmu\)m and 300 \(\upmu\)m. Voids with diameters below 50 \(\upmu\)m are considered to be microvoids. Voids with diameters between 50 \(\upmu\)m and 100 \(\upmu\)m are sometimes referred to as microvoids as well, or sometimes as macrovoids, and there is no clear agreement on how to classify these void sizes. Shrinkage voids or solidification voids are the consequence of the reduction in volume that occurs when a solder changes its state from liquid to solid [19].  
By applying a vacuum while the solder is in the liquid phase, most of the voids can be sucked out of the soldering joint [20,21]. More critical are non-wetted areas, due to residues on the substrate or components, which drastically reduce the contact area for electrical and thermal transfer. Reasons for the existence of these residues can be contamination, inadequate handling of the substrate and components, inadequate cleaning, or inappropriate process parameters [22, 23, 24].  
De-wetting refers to the failure of molten solder to establish a proper metallic bond with the base metal. This condition occurs when the solder initially wets the surface but then retracts, leaving behind irregularly shaped solder mounds separated by areas covered with a thin solder film and exposed base metal. Notably, the surface finish remains unexposed. A common cause of de-wetting is oxidation on the surface to be soldered. Oxide layers act as a barrier between the solder and the base metal, and prevent effective adhesion. When oxidation is widespread, non-wetting can occur across the entire oxidized surface [25,26].
# City Architectural Color Recognition Based on Deep Learning and Pattern Recognition
## 3 Research Methodology
### Analysis of the K-Means Clustering Algorithm  
At the core of the K-means clustering algorithm lies the process of partitioning data into K-groups [31]. Initially, K-objects are randomly selected as cluster centers, and the algorithm proceeds to calculate the distances between the data points and K. The greater the similarity between the center of the cluster (K) and the surrounding data, the greater the distance, and vice versa.  
To compute the distance, this paper employed the Euclidean distance (5) measure, which quantifies the distance between a cluster center and the distributed data. The formula for determining the distance of a data object from the cluster center within the space is illustrated in Equation (5):  
\[(x,y)=\sqrt{\left(\left(x_{1}-y_{1}\right)^{2}+\left(x_{2}-y_{2}\right)^{2}+ \ldots+\left(x_{n}-y_{n}\right)^{2}\right)}=\sqrt{\sum_{i=1}^{n}\left(x_{i}-y _{i}\right)^{2}} \tag{5}\]  
where \(x\) is regarded as the data object, \(y\) is regarded as the cluster center, and \(n\) is regarded as the dimension of the data object. The clusters generated by this classification calculate the distance to the other uniformly worthy minimum points within the cluster, treating the center of mass as the reference point until it no longer changes.  
Figure 3: Logistic regression gradient training graph.
## 3 Feature Selection
### F-Score  
F-score [55] is a technique that measures discrimination. Given training vectors \(\mathbf{x}_{k}\), \(k=1,\ldots,m\), if the number of positive and negative instances are \(n_{+}\) and \(n_{-}\), respectively, then the F-score of the \(i^{th}\) feature is calculated as in Equation (41)  
\[F(i)=\frac{\left(\overline{\mathbf{x}}_{i}^{(+)}-\overline{\mathbf{x}}_{i}\right)^{2} +\left(\overline{\mathbf{x}  
where \(\overline{x}_{i},\overline{x}_{i}^{(+)},\ \text{and}\ \overline{x}_{i}^{(-)}\) are the averages of the \(i^{th}\) features of the whole, positive, and negative data, respectively, and \(x_{k,i}^{(+)}\) and \(x_{k,i}^{(-)}\) are the \(i^{th}\) features of the \(k^{th}\) positive and negative sample, respectively. F-score indicates the discrimination between the positive and negative sets; therefore, the larger the F-score, the more likely this feature is to be more discriminative. Thus, we could consider this score as a criterion for feature selection. A disadvantage of this method is that it cannot reveal shared information between features [55]. In the example, both features have low values of F-score; however, the set of them classifies the two groups precisely.  
In spite of this drawback, F-score is simple and generally quite effective. We order all features based on F-score and then use a classifier to train/test the set that includes the feature with the highest F-score. Then, we add the second highest F-score feature to the feature set before training and testing all of the dataset again. The procedure is repeated until all features are added to the feature set.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 3 Activity-Based Congestion Management (ABC)
### Activity AQM  
An activity AQM takes acceptance decisions for packets. If the current queue size \(Q\) exceeds a computed drop threshold \(T_{drop}\), the packet is dropped, otherwise it is forwarded. The drop threshold is computed as follows:  
\[T_{drop}(A)=\max\left(Q_{min},Q_{base}-\gamma\cdot(A-A_{avg})\right). \tag{2}\]  
We explain the components of that formula. \(Q_{min}\) prevents packet dropping in the absence of congestion. \(Q_{base}\) is a configured baseline value around which packets are dropped. \(A\) is the packet's activity and \(A_{avg}\) is a moving average of the activity of recently accepted packets. We utilize the UTEMA method [24] with memory \(M_{AA}\) for the computation of that average. The drop threshold is proportional to the packet's activity so that the loss probability of a packet increases with its activity. The parameter \(\gamma>0\) allows to tune that effect.