esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In the latest version of the criterion, known as the 2018 version, [PERSON] and [PERSON] [6] updated the guideline for selecting the disturbance factor, including revisions primarily to the lowest disturbance factor values, while maintaining their concerns regarding the uncertainty associated with the use of empirical tools. Although the updated [PERSON] and [PERSON]'s guideline provides the lowest and highest values for the disturbance factor to be used at the design stage of slopes and tunnels, it lacks a clear definition for the continuous range of this factor between the boundary values. In this respect, the quantitative guidance remains quite subjective. Recent studies, such as those by [PERSON] et al. [7] and [PERSON] et al. [8], have attempted to address this limitation by deriving continuous relationships for the disturbance factor. However, both studies--despite their valuable contributions--are based on similar approaches involving the wave velocity (\(V_{p}\)) and acoustic wave velocity (\(C_{p}\)) of the rock mass,respectively, and present notable practical limitations. For instance, changes in \(V_{p}\) or \(C_{p}\) before and after disturbance can only be observed after excavation begins. Therefore, the continuous disturbance relationships proposed in these studies are not applicable during the initial design phase of slope excavations. The following section presents a detailed discussion of the limitations of these two key studies, supported by various empirical relationships used in conjunction. A practical and empirically based approach is still needed to continuously define the disturbance factor based on rock mass properties while considering the method of excavation and its productivity, without compromising the practical value of the Hoek-Brown failure criterion. The EXCASS system, proposed by [PERSON] et al. [9], is designed to select the optimum excavation method based on the excavation power index (\(EPI_{opt}\)) in conjunction with the Excavation Performance Rating (EPR) to adjust the power of excavation either positively or negatively. With its flexible structure and practical applicability in excavation method selection, the EXCASS system is capable of predicting the degree of disturbance on the excavation surface even at the preliminary design stage. In this study, a practical quantitative tool is proposed for selecting the disturbance factor on an excavated slope face for design purposes by utilizing the EXCASS system.
## 2 Problem Formulation
### Gliding Phase Constraints  
In general, the three fundamental forms of path constraint that the gliding phase trajectory planning should satisfy are normally the heating rate \(\dot{Q}\), dynamic pressure \(q\), and load factor \(n\). Path constraints are given by [14]:  
\[\dot{Q}=k\rho^{0.5}v^{3.15}\leq\dot{Q}_{\rm max} \tag{2}\]  
\[q=\frac{1}{2}\rho v^{2}\leq q_{\rm max} \tag{3}\]\[n=\sqrt{D^{2}+L^{2}}/g_{0}\leq n_{\rm max} \tag{4}\]  
where \(k\) is a heating rate constant relative to the material of the head of the vehicle; \(k=5\)\(\times\)\(10^{-5}\); \(\dot{Q}_{\rm max}\), \(q_{\rm max}\), and \(n_{\rm max}\) are the maximum heating rate, dynamic pressure, and load factor respectively; \(g_{0}\) is the gravitational acceleration at sea level; and \(D\) and L are the drag and lift accelerations, respectively.  
The following control constraints on the amplitude and rate of the angle of attack (AoA) and bank angle, which are likewise handled as one part of the path constraints, should also be satisfied by the trajectory planning during the gliding phase:  
\[\alpha\in[\alpha_{\rm min},\alpha_{\rm max}],\ v\in[v_{\rm min},v_{\rm max}], \ \big{|}\dot{a}\big{|}\leq\dot{a}_{\rm max},\ \big{|}\dot{v}\big{|}\leq\dot{v}_{\rm max} \tag{5}\]  
where \(\alpha_{\rm min}\), \(\alpha_{\rm max}\), \(v_{\rm min}\), and \(v_{\rm max}\) are the minimum and maximum AoA and bank angle, respectively. \(\dot{a}_{\rm max}\) and \(\dot{v}_{\rm max}\) are the maximum AoA rate and bank angle rate, respectively.  
The altitude and speed of the vehicle at the end of the glide phase are regarded as the terminal constraints:  
\[h_{f}=h^{*},\ v_{f}=v^{*} \tag{6}\]  
where the superscript \((^{*})\) means the terminal constraint.
## 2 Materials and Methods
### Soil Leaching  
#### 2.2.1 Inorganic Acid Leaching  
A single factor experimental study with different inorganic acids (HCl, HNO\({}_{3}\), H\({}_{2}\)SO\({}_{4}\), H\({}_{3}\)PO\({}_{4}\)) was firstly carried out to compare their effects on the remediation efficiency, and deionized water was used as a comparison. All elements, including organic and inorganic acids, are AR-grade, purchased from Sinopharm Chemical Reagents Co., Ltd (Beijing, China). Firstly, a batch of 1.0 g soil was mixed with 20 mL different acid at a same concentration (0.5 mol\(\cdot\)L\({}^{-1}\)) in a 50 mL glass bottle, stirring at 180 \(\pm\) 10 rpm for 24 h. Then, the mixtures were separated by centrifuging at 6000 rpm for 15 min, and the supernatant was filtered with a 0.45 um filter membrane. The contents of Pb were determined by Inductively Coupled Plasma Massspectrometry (ICP-MS). The remediation efficiency, \(\eta\), was defined by the ratio of leaching amount to total lead, as shown in Equation (1)  
\[\eta=\frac{m_{1}}{m_{0}}\times 100\% \tag{1}\]  
where \(m_{0}\) (mg) is the amount of Pb in the primary soil, and \(m_{1}\) (mg) is the amount of Pb in the leaching solution.  
Subsequently, an orthogonal experiment was carried out for exploring the importance of factors, and the specific experimental conditions are shown in Table S1. Afterward, single-factor experimental studies with different concentrations of HCl (0.1, 0.2, 0.3, 0.5 and 0.7 mol\(\cdot\)L\({}^{-1}\)) and stirring times (0.5, 1, 2, 4, 8, 12, 24, 24 and 48 h) were carried out under the solid-to-liquid ratio of 20:1.
# Development Status of Key Technologies for Optoelectronic Integrated Circuit Manufacturing
## 3 Manufacturing of Optoelectronic Integrated Circuits
### Integration Method
#### 3.2.1 Monolithic Integration
Extremely covered symmetric Ge-Si superlattices with many thin 8-15 atom monolayers of interleaved Ge and Si atoms enable high stacking heights [40]. Amber wave systems disclosed structures with optically active layers embedded in Si wafers such that the outermost epitaxial layer exposed to CMOS processing equipment is always Si or other CMOS-compatible material (SOI or SiO\({}_{2}\)), and optical signals can be transmitted between the embedded optoelectronic layer and the external waveguide using normal incidence (through the Si substrate or top Si cap layer) or in-plane incidence (edge coupling) [41]. [PERSON] announced a transceiver with a light pipe with an inclined reflective surface that generates a first light signal in the chip based on the light source signal, transmits the first light signal from the chip through the light pipe, receives the second light signal from the light pipe, and converts the second light signal through the electrical signal of the photodetector [42]. Huazhong University of Science and Technology used quantum well mixing technology to achieve on-chip photonic integration of passive and active optoelectronic devices. They subsequently prepared InP-based monolithic integrated optical phased arrays [43]. The U.S. Naval Laboratory provided building blocks for on-chip chemical sensors and other highly compact photonic integrated circuits that combine interband or quantum cascade lasers and detectors with passive waveguides integrated into III-V or other components on Si [44]. In 2019, Acacia Communications Co., Ltd., proposed a structure and the fabrication method of a monolithic Si coherent transceiver with integrated lasers and gain elements in which an InP chip was bonded to a Si chip in a notch formed in the Si chip [45]. [PERSON] et al. [46] used Si-based CMOS technology to monolithically integrate Si-based optoelectronic modulators and a quadrature hybrid coupler to produce a monolithic integrated sideband modulator with a high sideband rejection ratio, simple driving, and large operating bandwidth. With the continuous development of monolithic integration technology, smaller size, lower power consumption, and lower cost are primary development directions. However, in submicron-scale etching technology, the connection between photonic and electronic devices, process compatibility, thermal isolation and electrical isolation, the integration of light sources, optical transmission loss and coupling efficiency, and optical logic devices remain challenging.
## 3 Performance Evaluation  
### Experimental Environment  
#### 3.1.1 Performance Monitor  
The performance monitor package provides real-time system performance utilities. This program receives performance data from computer components. The performance monitor window allows us to view, in real-time, detailed information about the hardware components (CPU, disk, networks, and memory) and system resources (including descriptors and modules) used by the operating system, services and running applications. In addition, we can use the resource monitor to manage processes, start and stop services, analyze process deadlock, view thread wait chains, and determine the files  
Figure 3: Two inputs and two rules based on the Mamdani and [PERSON] inference engine.  
responsible for the blocking processes. When using the performance monitor, the CPU utilization is displayed in different fields. The \"% User Time\" shows the CPU utilization that occurred during execution at the application level. This field indicates the time taken to start the virtual processors. The \"% Idle Time\" indicates the percentage of time during which the CPU or CPUs were in idle mode, and the system did not have a failed request for disk I/O. The \"% Processor Time\" gives the summation of all the others and hence is the absolute CPU utilization as shown in Figure 4. In the \"Memory\" section, almost the same information is displayed that can be obtained with the help of the \"Task Manager\", but it is ordered in relation to the entire system. The histogram of physical memory displays the current organization of this memory, dividing the entire amount of memory into reserved, used, modified, in standby mode and free. Here \"Pages/sec\" shows memory utilization. To get disk utilization, monitoring the \"Average transfer rate/sec\", \"Disk Queue Length\", and \"Disk Time (%)\" pointers are more efficient pointers.
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 5 Security Analysis
### Security of Verification Objects
As the function used in Algorithm 1 is \(prf\), we introduce another algorithm Algorithm 1\({}^{*}\), which uses a random function \(rrf:\{0,1\}^{*}\rightarrow\{0,1\}^{S}\) to replace the pseudo-random function \(prf\). We denote Algorithm 1 as \(f_{0}\) and Algorithm 1\({}^{*}\) as \(f_{1}\). In essence, the \(prf\) and \(rrf\) are modeled as \(\mathcal{D}\)-accessible random oracles. \(\mathcal{D}\) emulates the game \((\widehat{Game1})\) and \((\widehat{Game2})\) for \(\mathcal{A}\). According to whether adversary \(\mathcal{A}\) succeeds in the game, \(\mathcal{D}\) determines whether \(x=0\) or \(x=1\), so that it accepts the algorithm \(f_{x},\ x\in\{0,1\}\). Specifically, if \(\mathcal{A}\) succeeds, then \(\mathcal{D}\) determines \(x=0\); otherwise, \(x=1\). \(\mathcal{A}\) chooses two keywords \(w_{1}\) and \(w_{2}\) to \(\mathcal{D}\). \(\mathcal{D}\) uses the algorithm \(f_{x}\) and chooses a bit \(b\in\{0,1\}\) randomly. The choice \(\mathcal{D}\) made influenced whether \(\mathcal{A}\) can succeed. However, the chances of \(b=0\) and \(b=1\) are uniformly \(\frac{1}{2}\). Then \(\mathcal{D}\) returns \(VO_{w_{b}}\) to \(\mathcal{A}\). \(\mathcal{A}\) outputs a bit \(b^{\prime}\) and \(\mathcal{D}\) outputs a guess \(x^{\prime}\) for \(x\). As defined above, \(\mathcal{A}\) has a non-negligible advantage \(\epsilon\) to succeed in games \((\widehat{Game1})\) and \((\widehat{Game2})\) (i.e., \(b^{\prime}=b\)), we can easily conclude that \(\mathcal{D}\) also has a non-negligible advantage \(\epsilon\) to determine the guess \(x^{\prime}=x=0\). This conclusion can also prove that \(\mathcal{D}\) can distinguish the output \(VO_{w_{b}}\) of \(f_{0}(w_{b})\) (using the pseudo-random function \(prf\) under the key \(k\)) from \(f_{1}(w_{b})\) (using the real random function \(rrf\)) with the non-negligible advantage \(\epsilon\). Since \(VO_{w_{b}}\) contains \(S\times l\) elements after padding, for each element \(c\), assume that the advantage that \(\mathcal{D}\) distinguishes \(prf_{k}(c)\) from \(rrf(c)\) is \(\epsilon^{\prime}\), we have  
\[\epsilon=\prod_{S\
eq l}\epsilon^{\prime}\Rightarrow\epsilon^{{}^{\prime}}= \sqrt[(S\times l)]{\epsilon} \tag{13}\]  
Since \(\epsilon\) is non-negligible, the advantage \(\sqrt[(S\times l)]{\epsilon}\) is also non-negligible. Therefore, if \(\mathcal{A}\) wins games \((\widehat{Game1})\) and \((\widehat{Game2})\) with a non-negligible advantage \(\epsilon\), then \(\mathcal{D}\) has the non-negligible advantage \(\sqrt[(S\times l)]{\epsilon}\) to distinguish the output between the pseudo-random function \(prf\) and the real random function \(rrf\), which contradicts Definition 3. So that the security of verification objects can be proofed.
## 7 Stage 2: ML Recognition
### Task 2: Local Damage Detection  
In Task 2, the local detection state was based on the floor level where only TS signals collected on the same floor were used for training and testing the ML models. From Table 6, the local damage detection achieved over 95% accuracy. Similar to the observations in Task 1, EVC achieved the highest accuracy compared to the individual classifiers in all cases, confirming the stable and robust performance of the EVC. At the floor level, using features extracted from the ARI(7,1) model still slightly outperformed the other two complex models especially for floors 1 and 2; however, all three models had the same performance for floor 3 with EVC (over 98% accuracy).  
Figure 12: Confusion matrices of ARI(7,1) with EVC.  
In addition, the example confusion matrix for floor 3, presented in Figure 12b, indicates the accurate and unbiased performance of EVC using ARI(7,1) features under a slightly imbalanced label distribution (41% UD vs. 59% D) in the local damage detection Figure 9. These promising results are attributed to more significant local stiffness changes of floor 3 due to brace loosening where the effective area of the braces for floor 3 was less than half that for the other floors, Figure 4a. Therefore, from these results, it is inferred that the ARIMA coefficients capture the local stiffness changes between floors giving more confidence for their potential in practical applications.
## 4 Design of TLQoS
### Routing Potentials  
TLQoS defines potential fields on the network over which diverse types of packets are routed to the BC. Let us consider a node, \(n_{i}\) with potential, \(P\) generates a packet. To reach the \(BC\), the packet needs to be forwarded to a neighbor \(n_{j}\), \(n_{j}\in NB(n_{i})\). To choose a suitable \"candidate\" from \(NB(n_{i})\), we define a force acting on the packet at \(n_{i}\), as  
\[F(n_{i},n_{j})=\frac{P(n_{i})-P(n_{j})}{c_{n_{i},n_{j}}} \tag{1}\]  
Here, \(c_{n_{i},n_{j}}\) denotes the link cost from \(n_{i}\) to \(n_{j}\). Hence, the force is the potential difference between a node \(n_{i}\) and each one of its neighbors. The packet is now directed to a neighbor \(n_{j}\in NB(n_{i})\) for which the \(F(n_{i},n_{j})\) is maximum. In particular, a packet follows the direction of the steepest gradient to reach to the destination. To provide differentiated services, we assign four potential fields to a node \(n_{i}\). We define a set of potential fields to a node \(n_{i}\) as \(\mathbb{P}(n_{i})=[P^{h}_{n_{i}},P^{d}_{n_{i}},P^{r}_{n_{i}},P^{t}_{n_{i}}]\). Here, \(P^{h}_{n_{i}},P^{d}_{n_{i}},P^{r}_{n_{i}},P^{t}_{n_{i}}\) denote the hop-count potential, delay potential, reliability potential and temperature potential respectively.
## 3 The Proposed Approach  
### Fuzzy Neural Network (FNN)  
In this paper, a FNN with four layers is employed and the architecture of the four-layer FNN is shown in Figure 1.  
In the first layer, the output of each node can be calculated as follows:  
\[\mu_{ij}=\exp\left[-(x_{i}-c_{ij})^{2}/\sigma_{ij}^{2}\right] \tag{1}\]  
where \(i=1\):\(k\), \(j=1\):\(n\), \(x_{i}\), \(\mu_{ij}\), \(c_{ij}\), and \(\sigma_{ij}\) are the inputs of FNN, membership function, center (mean), and width (variance) of the membership function of the \(j\)th fuzzy set of the \(i\)th input variable \(x_{i}\), respectively. \(k\) is the dimension of input vector and \(n\) is the number of fuzzy sets, which can be interpreted as the number of neurons in the hidden layer.  
In the second layer, the output of each node is labeled as \(\mathbf{\Pi}\), which can be obtained through the product of all input signals for each node, described as follows:  
\[z_{j}=\prod_{i=1}^{k}\mu_{ij}= \mu_{ij}(x_{1})\times\mu_{2j}(x_{2})\times\cdots\times\mu_{kj}(x _{i}) \tag{2}\]  
Figure 1: The architecture of a four-layer fuzzy neural network.  
In the third layer, two parameters of \(a\) and \(b\) can be calculated as follows:  
\[\begin{array}{l}a=\sum\limits_{j=1}^{n}\omega_{j}z_{j}\\ b=\sum\limits_{j=1}^{n}z_{j}\end{array} \tag{3}\]  
where \(\omega_{j}\) is the connection weight between the second layer and third layer. Eventually, the overall output of FNN can be represented by \(y=a/b\).  
In the FNN, three kinds of parameters, including center (\(c_{ij}\)), width (\(\sigma_{ij}\)), and connection weight (\(\omega_{j}\)), have a significant impact on its performance. The training of these parameters has a great influence on the performance of the FNN. For the \(k\times n\) means and variances of the membership layer (\(c_{ij}\) and \(\sigma_{ij}\), \(i=1:k\), \(j=1:n\)), and the \(n\) weights of the output layer (\(\omega_{j}\), \(j=1:n\)), there are \(2 kn+n\) parameters of the four-layer FNN. In order to express conveniently, a state vector \(X(t)\) consisting of the parameters at a given time step \(t\) can be given as follows:  
\[X(t)=[c_{11},\cdots,c_{1n},\cdots,c_{k1},\cdots,c_{kn},\sigma_{11},\cdots, \sigma_{1n},\cdots,\sigma_{k1},\cdots,\sigma_{kn},\omega_{1},\omega_{2},\cdots,\omega_{n}] \tag{4}\]  
In this work, an evolutionary algorithm, namely, a particle swarm optimization algorithm (PSO), is employed to adaptively adjust these parameters. The PSO treats the parameters of the FNN as a particle and many particles can generate a swarm to find the optimal parameters. Then, the particle swarm is updated by guiding the current swarm toward the best overall particle. The specific steps can be described in the following parts.
## 2 Methodology  
Upon initialization, the depth camera module captures both RGB images and 3D point cloud data, which are then forwarded to the deep learning and point cloud processing modules, respectively.  
As shown in Figure 1, in the deep learning module, the input RGB image is passed through the enhanced YOLOv8n network for object detection, whereby the object is identified and classified into four pose-based categories. During this process, a bounding box is generated around the object, and the coordinates of the bounding box center are extracted. These coordinates are then normalized and converted into 3D coordinates, in cluding horizontal and vertical positions along with the corresponding depth value. The resulting 3D coordinates are subsequently passed to the point cloud processing module for further analysis.  
In the point cloud processing module, the point cloud data captured by the depth camera are first preprocessed, including voxel grid downsampling to reduce point cloud density and background point removal through segmentation. Then, based on the 3D coordinates obtained from the deep learning module, a KD-Tree is used to efficiently locate and extract the target point cloud. Next, the target point cloud is aligned with a template point cloud selected based on the preliminary pose classification result, yielding a homogeneous transformation matrix. This matrix is applied to the corresponding ideal grasp pose of the template point cloud, resulting in the final grasp pose for the target object in the camera coordinate system.  
In the grasp control module, the transformation matrix obtained from hand-eye calibration is used to perform coordinate transformation on the grasp pose, resulting in the final grasp pose. Then, inverse kinematics is applied to calculate the corresponding joint angles, which are sent to the robot controller to execute the target grasp.
## 6 Experimental Results
### Detection Speed in FPS
In order to handle the new bottleneck, we adopted multi-threads to accelerate the post-processing stage. \"Post\({}_{mt}\)\" represents the speed of post-processing by adopting 10 threads. It can be seen that more or less 8 times acceleration for post-processing could be achieved. Aided by multi-threads, post-processing is no more the bottleneck, and the overall detection speed (\"All\({}_{mt}\)\") can be generally decided by the part of \"Net\". Next, we set different values on _batchsize_ to see the variation in FPS. We selected four neural architectures, respectively, from HRSID and SSDD to do the testing. Experimental results are given in Table 9. The entries filled by n/a are due to \"out of memory failure\". We can see that on both datasets, FPS (\"Net\") and FPS (\"All\({}_{mt}\)\") can benefit from a greater _batchsize_. FPS (\"Post\({}_{mt}\)\") is not so sensitive to _batchsize_. When _batchsize_ is set to 1, it can be seen that FPS in all the three columns decreases significantly. Running the first batch is time costly due to the fact that GPU has a warm-up time to start the pipeline. Thus, setting a larger _batchsize_ can share the costs of the warm-up and thus increase the overall efficiency of inference. On the other hand, the scale of the neural network decides the maximal _batchsize_ for a given GPU and a given set of images. Usually, a lightweight neural network wins a greater _batchsize_. Note that this is another advantage of our Darts_Tiny, as it aims at generating lightweight neural networks.