esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The QoE assessment for television devices that uses adaptive streaming technology might be similar to algorithms that assess pure OTT applications. Continuous clustering can be used to list devices with a decreased level of video playback continuously. The STB devices work similar to OTT applications, but the main difference is that there is no possibility to set the intermediate level of bitrate to omit the bitrate switches. The STB player does not have the button to set the quality level, usually available in pure over-the-top applications. QoE assessment was made on a TV system that uses Linux-based STBs. However, the solution can be applied to other TV platforms based on Android or other software devices. Currently used STBs will always tend to play the highest possible bitrate. Hence the bitrate switches will occur in the impaired network environment. Grade 1 or 2 can be considered a significant decrease in the QoE level. The number of devices with impairments is relatively low. The elimination of problems affecting those devices should be considered the highest priority for TV operators due to decreased customer satisfaction. Proactive monitoring based on the proposed solution will be an asset in increasing the television video experience. The VSBCT parameter is highly correlated with the outcoming cluster. The SCI and SCTI parameters might not be meaningful since STBs always try to play the highest bitrate, and several fluctuations appear during the playback. The fluctuations trigger the events with positive and negative SCI and SCTI interchangeably.  
The limitations of the work are connected to the data-driven approach. It is necessary to generate logs that describe the current level of bitrate and the occurrence of stallings either as bitrate with 0 kbps or separate logs. The proposed system cannot infer this based on the player's buffer state or network parameters from the ONT's perspective. If the pure OTT applications are available on the set-top box, it may not be possible to assess the QoE of content served by providers such as YouTube, Netflix, Disney, or others due to the lack of logs on the system associated with applications that complement TV features. However, the proposed approach allows real-time QoE assessment for many connected devices playing content related to linear channels or nPVR, catchup, and timeshift functions offered by the TV provider.  
Figure 16: Localization of devices with increased impairment.  
Another limitation is that it is hard to compare with related work at the current research stage due to the limitations regarding gathering data from customers. Mentioned algorithms in the introduction are frequently compared with RMSE, SROCC, or PCC parameters. However, preparing the assessment gathered from TV system users is in progress. Unfortunately, it is a long process since the data will be gathered from the customers calling the call center or second line of support and compared with the output returned by the QoE assessment system. Along with data collection, the assessment system may be improved, and the outcome will be compared with the current and future state-of-the-art works.
## 6 Discussion
### Different Tracking Options  
As shown in Figure 16, there are three types of trackers available for PV modules. When mounted on a two-axis tracker, a PV module can receive direct light perpendicularly. Alternatively, a one-axis tracker can track the more dominant sun movement along the east-west direction, while sacrificing the accuracy on the north-south dimension and saving on equipment. Depending on the relative angle between the rotation pivot and the ground, one-axis trackers have two options: horizontal and tilt (Figure 16).  
Although all three trackers have been commercialized at some point, one-axis horizontal is the only type that is still being applied in utility-scale PV projects. While the input power gain compared with the fixed tilt by different trackers varies depending on the latitude (Figure 17), the general conclusion holds that the additional yield when choosing one-axis tilt tracker and two-axis trackers over one-axis horizontal trackers cannot justify the much greater capital costs and long-term maintenance fees [15].  
Figure 16: Three types of PV trackers defined by tracking dimension.  
Therefore, we consider it less valuable to conduct a detailed analysis of the impact of module efficiency on system LCOE in configurations adopting one-axis tilt trackers or two-axis trackers. However, it is understandable that in those systems, area-related non-module costs account for a higher percentage of the total system spending, and a certain module efficiency rise can impose a stronger leverage in lowering system cost and therefore LCOE. In fact, most of the two-axis tracker shipments in history were for systems adopting concentrator PV (CPV) modules composed of multi-junction solar cells with module efficiencies up to 30%, although the module efficiency testing condition for CPV modules is different from that for flat-plate PV modules [22].
## 4 Discussion  
The new technique presented here was able to reproduce the actual opening and closing movements of the patient's mouth, since the positions of the targets attached to the teeth were tracked using an optical scanner. A method was also proposed for confirming the CR-MI discrepancy and transferring the altered vertical dimension to the mechanical articulator. This can be used to fabricate the patient's prosthesis based on the observed trajectory, which allowed the actual maxillomandibular relationship to be maintained despite changes in the vertical dimension even when the patient had a CR-MI discrepancy. In the conventional technique, the facebow and semiadjustable articulator were manufactured based on the assumption that the patient's face and mandibular movements are symmetrical, and so errors can occur when the patient exhibits asymmetry. The occurrence of horizontal displacement during opening and closing trajectories is difficult to reproduce on the articulator. Our novel technique can overcome these limitations of the traditional method since it reproduces the detailed mandibular movements of the patient using digital software.  
Another disadvantage of the conventional method is that converting between the digital cast and the physical cast is difficult on the digital articulator embedded in the CAD software. However, in the proposed technique, a 3D printed jig can be used to mount the patient's actual plaster cast in addition to the 3D printed cast. This approach can be very useful in cases that require an existing gypsum cast for the production of different kinds of prosthesis, such as a removable dental or implant prosthesis.  
However, our technique also has some limitations. While it is only necessary to attach targets on the anterior teeth for target tracking, which is performed using an optical scanner, the retractor required to keep the targets exposed can interfere with the patient's natural jaw movement. It is important that the targets are in the same position without falling out even during jaw movement. If the position of the targets changes while tracking jaw motion, errors are generated and clinicians have to start over again. There are also limitations associated with the need to have access to optical scanner equipment. In order to fit exactly into the existing plaster casts without any damages, the bite jig needs to be  
Figure 9: Mean deviation at the closing state was smaller than that of the 4 mm opening state (mean + standard deviation).  
flexible. However, there is a possibility that errors may occur due to the flexibility of the bite jig at the same time. It is necessary to confirm that the bite jig should be in the correct position when mounting the casts. Additionally, when flexible materials become too thick, occlusal errors may occur due to the elasticity of the material.  
Future studies should assess the accuracy and reproducibility of our new method for altering the vertical dimension and the associated mounting procedure, and consider its compatibility with existing CAD software. The results obtained could lead to this technique having wide clinical applications.
## 2 Plasma Shaped Cavity and Axial Microwave Injection  
In Figure 1, the \"plasma-shaped\" cavity is shown with its main dimensions. The cavity has diameter \(d=48.5\) mm and length \(L=150\) mm, which are typical dimensions for a second generation ECRIS plasma chamber [10]. As said, the cavity's shape has been obtained by taking into account the electron trajectories as they move under the influence of a _minimum-B_ magnetic field profile which is used for the plasma confinement in ECRISs [11].  
As first step of this numerical study, we compare the \"plasma-shaped\" S-parameters (i. e., the number of modes excited inside the cavity) with those of a standard cylindrical cavity usually employed in ECRISs setups. The adopted cylindrical cavity has a diameter \(d~{}=63.5\) mm and a length \(L=150\) mm: these dimensions, comparable with those adopted for the \"plasma-shaped\" cavity, are the dimensions of the CAESAR Ion Source plasma cavity currently working at the INFN-LNS. Both cavities, initially fed through a standard WR62 axial waveguide, have been simulated with comparable settings: both consist of a vacuum solid with lossy metal boundary conditions ad with the same mesh quality (e.g., curvature tolerance, number of mesh refinement steps). For both cavities, we consider finite conductivity walls with \(\sigma=5.8\times 10^{7}\) S/m. The two geometries with the applied mesh are visible in Figure 2.  
Figure 1: “Plasma-shaped” cavity with its main dimensions: diameter \(d\) and length \(L\).  
The two structures have been analyzed in terms of S-parameters. In particular, the \(|S_{11}|\) has been evaluated in the frequency range of 14-14.5 GHz. The frequency interval has been selected in order to employ the future \"plasma-shaped\" cavity with the CAESAR ion source setup. The \(|S_{11}|\) curve for the two geometries is shown in Figure 3. In the case of cylindrical plasma cavity, very few and bad adapted modes are found inside the chosen frequency range (i.e., not optimal power transfer from the feeding waveguide to the cavity), while, in the case of the \"plasma-shaped\" cavity, more modes can be found in the same frequency range. In particular, the mode found at the frequency of 14.304 GHz presents a good waveguide-to-cavity coupling (\(|S_{11}|\simeq\)\(-\)15 dB) and the related electric field profile has a maximum around the cavity center, where the plasma usually forms, as seen in Figure 4.  
The above configurations, with the same mesh quality, can be simulated in around 20 and 30 min, respectively, with CST Studio Suite on a workstation i7 with 28 cores and 192 GB of ram.  
Figure 3: \(|S_{11}|\) curve, inside the operational bandwidth of 14–14.5 GHz, for the standard cylindrical cavity and the “plasma-shaped” one.  
Figure 2: (**a**) Standard cylindrical cavity and (**b**) “plasma-shaped” cavity models. Applied mesh and axial feeding waveguides are visible.
# NBTI and Power Reduction Using an Input Vector Control and Supply Voltage Assignment Method
## 4 Experiment and Discussion  
### Experimental Setting  
The efficiency of the proposed IVC and SVA combination method is evaluated on selected ISCAS'85 and ISCAS'89 benchmark circuits. The circuits are synthesized by the Synopsys Design Compiler tool, and the synthesized netlists contain only INV, NAND, and NOR gates. The delay and power information of these basic gate cells is extracted by HSPICE simulation with the PTM 65 nm transistor model. Some key parameters are: \(\mid V_{dd}\mid\) = 1.1 V, \(\mid V_{th}\mid\) = 0.18 V for both nMOS and pMOS transistors, \(T\) = 300 K, \(t_{ox}\) = 1.2 nm, and \(T_{clk}\) in Equation (1) is 0.01 s. The circuit lifetime is set to be ten years. The timing constrains of each circuit are chosen as the postaging delay at a ten-day time node. According to Equations (1)-(6), the NBTI-induced delay degradation is dependent on many parameters. Therefore, the related parameters in calculating the timing constrain are set as follows: the duty cycle in active mode is set to be 0.95, as in the maximum dynamic stress (MDS) method [9]; the input vector in standby mode is selected using an ILP formulation for NBTI mitigation only; and the \(R_{AS}\) is set to be 1:9. By the above method, we can obtain the specific timing constrains for each circuit. In order to obtain the switching probability for each gate in Equation (9), which is necessary to calculate the dynamic power, we implement a 30,000 times logic simulation to generate different input patterns for the circuit and count to the number of switching activities for each gate to obtain the probability. The ILP problems are solved by LINGO 11.0 software [34]. All the experiments are implemented in C++ platform on a DELL T7500 workstation, with Intel Xeon E5620 2.4 GHz (two quad-core processors), 2 GB RAM, and the 64-bit operating system of Windows 7 Enterprise.  
Figure 6: Degradation and leakage minimization result by different ILP formulations. (a) c880 circuit; (b) c3540 circuit.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 2 Mathematical Model
### Kinetic Equations  
Generally, the ROV's motion is represented by the six-DOF nonlinear equation. Here, the following is a matrix representation of the nonlinear dynamic of the ROV in the BF frame [22]:  
\[M\ddot{w}+C(v)v+D(v)v+G(\eta)=\tau+\tau_{d} \tag{5}\]  
where \(M\) is the global inertial matrix, \(C(v)\) denotes the coriolis and centripetal matrix, \(D(v)\) denotes the damping matrix, \(G(\eta)\) denotes the matrix of restoring force and moments, \(\tau\) denotes the thruster forces and moments, and \(\tau_{d}\) denotes the external disturbance forces and moments.
## 3 Results and Discussion
The optimal expansion ratios of the TC-C cycle, C-TC cycle, C-CT cycle and CT-C cycle are 2.41, 2.47, 2.56 and 2.52, respectively, and the corresponding maximum COPs are 0.746, 0.748, 0.800 and 0.800, respectively. When the expansion ratio is less than 1.9 for open-loop low-pressure cycles, the COP of the TC cycle is higher than that of the TC-C cycle and C-TC cycle. As the expansion ratio increases, the COP of the C-TC cycle and TC-C cycle surpasses the TC cycle, and the COP of the TC-C cycle is smaller than that of the C-TC cycle. When the supplied-air temperature is higher than about 282 K, the COP of the TC cycle is highest; otherwise, the COP of the C-TC cycle is highest. Similarly, the COP of the CT cycle is higher than that of the C-CT cycle and CT-C cycle when the expansion ratio is less than 1.6. As the expansion ratio increases, the COP of the C-CT cycle and CT-C cycle gradually exceeds the TC cycle. The CT cycle has the highest COP when the supplied-air temperature is higher than 285 K. The COP of the C-CT cycle is slightly higher than that of the CT-C under other conditions. In addition, air refrigeration systems such as domestic air conditioners also have requirements for the supplied temperature. The supplied temperature is generally required to be greater than 283.15 K to ensure the comfort of human body, which requires a relatively low expansion ratio. This makes the open-loop cycle unable to operate at the optimal expansion ratio. At this case, the performance of CT-C cycle is the best (COP is 0.722). The COP of TC cycle is somewhat lower than that of CT-C cycle, but it has a simpler configuration with one-stage compression. The expansion ratio should not be too large to prevent ice in the system when the supplied-air temperature is lower than 273.16 K. Open-loop low-pressure cycles can achieve the optimal pressure ratio, but their COP is lower than that of open-loop high-pressure cycles. The C-CT cycle and CT-C cycle have the highest COP of 0.798 when the supplied-air temperature is above 273.16 K. Alternatively, before being supplied to the compartment, the cold air from expander can be mixed with the indoor warm air. The temperature at expander outlet can be lower than 273.16 K in this situation, and the cooling capacity can be increased by increasing the expansion ratio.
# Electropolishing--A Practical Method for Accessing Voids in Metal Films for Analyses
## 2 Device under Test  
The samples used within this work are microelectronic test chips that are referred to as poly-heaters. A detailed description of their inner architecture and of the experimental setup used for actuating them can be found elsewhere [51]. Poly-heaters are provided with integrated active heating (utilizing an electrically resistive, polycrystalline silicon layer) and are specifically designed for studying thermo-mechanical fatigue of metallizations. Primarily, the chips consist of a 120-\(\upmu\)m-thick silicon substrate and a 20-\(\upmu\)m-thick copper metallization layer. Upon heating, the poly-heater's Cu metallization layer gets significantly strained due to the mismatch in the coefficients of thermal expansion (CTEs) of Si (2.56 ppm/K) and Cu (17 ppm/K). This leads to a corresponding mechanical stress that drives microstructural degradation processes, such as void and crack formation. Figure 1a shows a light microscopy image of a poly-heater. Depending on the purpose of their function one can distinguish between two different types of Cu plate structures: First, there is the structure of interest in terms of thermo-mechanical degradation, which is the 700 \(\upmu\)m \(\times\) 490 \(\upmu\)m large Cu plate in the center of the chip. It is located directly above the actively heated region that is available in two different layouts, resulting in different temperature distributions in the Cu, as will be shown in Section 4. Secondly, there are eighteen outer Cu plates that act as electrical contact pads for heating (the two larger rectangular pads of size 1300 \(\upmu\)m \(\times\) 280 \(\upmu\)m) and for performing measurements (the remaining sixteen smaller pads). For example, some of these measurement pads are connected to the central Cu plate (see Figure 1a) and allow for a measurement of its electrical resistance, serving as a parameter quantifying its degradation.  
For later degradation void analyses (Section 4) two poly-heater specimens have undergone 1000 heat cycles between 100 \({}^{\circ}\)C and 400 \({}^{\circ}\)C, with a heating duration of 200 \(\upmu\)s and a repetition frequency of 1 Hz. These temperatures refer to the ones measured using the poly-heaters' integrated temperature sensor [51], which is located below the central Cu plate at a distance of 245 \(\upmu\)m from its edge.
# Concealed Conduit Routing in Building Slabs
## 2 Materials and Methods
### Pipe Path Routing
#### 2.2.4 Pipe Path Computation  
The pipe path is calculated utilizing [PERSON]'s algorithm. To do so, the proposed method treats the feasible space as a graph. Its voxels become vertices and the connectivity relations of its voxels are regarded as edges. Since this graph is created by using the voxels of the feasible space, the edges are parallel to \(x\)-, \(y\)-, or \(z\)-axes, and thus the resultant pipe path will be composed of straight lines or 90-degree bends.
* Branch scheduling If the pipe consists of multiple branches, the proposed piping method constructs these branches one after another. At first, we compute the Manhattan distance between each pair of the terminals, and the two terminals generating the longest Manhattan distance are designated as the source and the destination. Then, a shortest path is created to connect them by using [PERSON]'s algorithm. The resultant path is the initial skeleton of the pipe. In the following step, one of the unconnected terminals is randomly selected and treated as the source, and the shortest path from this unconnected terminal to the current skeleton is computed. Once constructed, the new branch is included in the skeleton, and the above calculation is repeated to generate the remaining branches. Once all the terminals are connected, the routing process of the pipe is finished.
* Cost function of path-finding [PERSON]'s algorithm may produce many bends and elbows in the resultant pipe path since the feasible space is built upon a regular grid. To reduce the number of bends in the pipe path, we employ a cost function \(C(\)-\()\) to control the forward directions in the pipe path. Assuming that the current position of the routing is at voxel \(v_{i}\) and the cost of the pipe is \(C(v_{i})\), then the cost for selecting the neighboring voxel \(v_{i+1}\) as the next position is computed by the following formula: \[\begin{array}{l}C(v_{0})=0,C(v_{1})=1.0,\\ C(v_{i+1})=C(v_{i})+p(v_{i},v_{i+1}),i\geq 1.\\ p(v_{i},v_{i+1})=\left\{\begin{array}{c}1,\ \{v_{i-1},v_{i},v_{i+1}\} \ \textit{are collinear},\\ 1+\omega,\ \textit{otherwise}.\end{array}\right.\end{array}\] (2) where \(v_{0}\) and \(v_{1}\) are the first 2 voxels in the pipe path, \(C(v_{i+1})\) is the new cost, and \(p(v_{i},v_{i+1})\) is the incremental value to the current cost. The incremental value is decided by the direction of the pipe path. If the pipe path marches straightforwardly, the incremental value is 1. Otherwise, the incremental value is 1 + \(\omega\), where \(\omega\) a positive number, which is used to penalize the bend caused by the new voxel \(v_{i+1}\). In this research, \(\omega\) is set to 9 to benefit straight pipe paths.
## 4 Results and Discussion
### Thinning Defect Detection
As with phase contrast, it can be seen that SNR of good quality is shown according to thinning depth increases. Additionally, it can be seen that as the modulation frequency increases from 0.1 Hz to 0.01 Hz, the SNR improves. Considering the phase contrast results comprehensively, the low modulation frequency band is more advantageous for quantitative investigation than the high modulation frequency band.
# A Payload Based Malicious HTTP Traffic Detection Method Using Transfer Semi-Supervised Learning
## 1 Introduction
The main contributions of this paper are summarized as follows. (1) We propose a deep learning framework for malicious HTTP traffic detection. To ensure high generalization ability under the condition that the target system has very limited number of labeled samples, we leverage the labeled samples of a public dataset to train the initial detection model, and then fine-tune the initial detection model to adapt to the target system based on a transfer semi-supervised learning model. To the best of our knowledge, this is the first solution of combining deep learning with transfer learning and semi-supervised learning for by augmenting the payload dataset malicious HTTP traffic detection. (2) We propose a HTTP payload data augmentation method based on a novel keyword avoidance perturbation mechanism. To increase the diversity of the training samples, we generate pseudo labeled samples by imposing noises to the original labeled HTTP traffic samples while keeping the key portion unchanged. The pseudo labeled and original labeled samples are then utilized collaboratively in the semi-supervised learning algorithm. (3) We conduct cross-dataset validation on two real datasets to evaluate DeepPTSD. The results show that DeepPTSD has competitive performance under the small data condition. The remainder of this paper is organized as follows. Section 2 reviews the related work. Section 3 details the proposed method. Section 4 reports the experiment results. Section 5 concludes the paper.