esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 3 Results
### Performance Comparison with other Algorithms
#### 3.3.1 Evaluation of Visible GPS Satellite Number Algorithm  
However, there is no ambiguity state like semi-outdoors or intermediate in our datasets. We focus on these complex scenarios, but the final detection states only include indoors and outdoors. Therefore we did not use the same experimental parameters. For SatProbe, we used different visible GPS satellite numbers as thresholds for IO detection to obtain the optimal performance of the algorithm in our datasets. Figure 15 shows the experimental results.  
As shown in Figure 15, when the threshold value set to 6, SatProbe achieved the highest accuracy in Dataset_2 (0.8066) and Dataset_3 (0.9720), and the second highest accur  
Figure 15: The accuracy of indoor and outdoor detection using different GPS number as a threshold on four datasets.  
and Dataset_4 (0.7972). Since lack of fuzzy state as semi-outdoor, only depending on the number of GPS satellites, thus resulting in the performance of IO transition is very poor.  
Next, we evaluated the IO transition delay when the threshold is 6. Figure 16 described the cumulative distribution probability of time delay on Dataset_2 and Dataset_4. To achieve more than 80% cumulative probability of indoor to outdoor transition on Dataset_2 and Dataset_4, the IO transition delay must be greater than 8 s and 8 s, respectively. To achieve more than 80% cumulative probability of outdoor to indoor transition on Dataset_2 and Dataset_4, the IO transition delay must be greater than 18 s and 14 s, respectively. It is difficult to detect the transition from outdoor scenes to indoor scenes in a short time only depending on the number of satellites. Such a long transition delay is difficult to accept for seamless indoor and outdoor positioning.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 2 Methods
### Pose Parameters of Pallet Estimation  
The pallet pose parameters include the center coordinates of the pallet detection surface \({}^{C}P\) and the inclination angle \(\theta\) of the pallet to be measured, which is shown in Figure 1. These parameters are calculated from the depth information of the pallet foot, which is extracted from the RGB-D camera depth data. In order to reduce the influence of the angle between the sensor and the pallet, the center grids of the pallet feet are selected as the detection sampling area, as shown in Figure 1.  
In the camera coordinate system {C}, the calculation formula of the pallet pose parameter is shown as Equations (13) and (14), respectively. \({}^{C}P\) is calculated by averaging the corresponding point cloud data in the detection grid of the center pallet feet. \(\theta\) is calculated by fitting the slope of all point clouds within the detection grid of the pallet feet in the x-y plane of the camera coordinate system {C}. The average filter is used to smooth the successive detection results, and the estimated value of the results will be updated every cycle. The calculation formula is shown in Equations (15) and (16). Note that when dynamically detecting pallets, the detection results of each loop will be transformed to world coordinates before estimation.  
\[{}^{C}P=\begin{bmatrix}{}^{C}\overline{x}_{p}&{}^{C}\overline{y}_{p}&{}^{C} \overline{z}_{p}\end{bmatrix} \tag{13}\]  
\[\theta=\begin{array}{c}\arctan\left(\frac{N\sum{}^{C}x_{n}{}^{C}z_{n}-\sum{} ^{C}x_{n}\sum{}^{C}z_{n}}{N\sum{}^{C}x_{n}{}^{2}-\left({}^{C}x_{n}\right)^{2} }\right)\end{array} \tag{14}\]  
\[{}^{C}\hat{p}=\frac{{}^{C}P^{N}+{}^{C}P^{N-1}+\cdots+{}^{C}P^{1}}{N} \tag{15}\]  
\[\hat{\theta}=\frac{\theta^{N}+\theta^{N-1}+\cdots+\theta^{1}}{N} \tag{16}\]  
\({}^{C}P
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 2 Related Works
### Data Processing  
In recent years, various data transformation methods have been proposed because raw data collected typically cannot be directly used for fault diagnosis. Researchers have developed several approaches to transform data to meet the requirements of different models. A method to transform input data into tensors to meet the data requirements of CNN models is crucial, as CNNs are commonly used for image-based fault diagnosis where data need to be structured appropriately as tensors [9]. A signal-to-image transformation method specifically for CNN-based fault diagnosis converts raw signal data into image-like representations, leveraging the CNN's ability to extract spatial features [10]. These transformation methods address specific data formatting requirements for different applications, ensuring that the input data are appropriately structured for effective use in fault diagnosis models. However, further research is needed to design input data formats tailored to diverse application scenarios. Each method aims to optimize data representation for enhanced model performance and accuracy in fault diagnosis tasks.
## 4 Analysis And Assessment
### Results and Discussion of Single and Multi-GNSS PPP
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**CEDU**} & \multicolumn{3}{c}{**DARW**} & \multicolumn{3}{c}{**JFNG**} & \multicolumn{3}{c}{**KARR**} & \multicolumn{3}{c}{**KZN2**} \\ \cline{2-13}  & **N** & **E** & **U** & **N** & **E** & **U** & **N** & **E** & **U** & **N** & **E** & **U** & **N** & **E** & **U** \\ \hline UC3-C & 16 & 23 & 108 & 14 & 47 & 84 & 27 & 34 & 45 & 15 & 64 & 35 & 24 & 19 & 35 \\ CIF3-C & 15 & 19 & 97 & 17 & 50 & 81 & 26 & 34 & 42 & 16 & 70 & 33 & 13 & 24 & 37 \\ ECIF2-C & 17 & 20 & 110 & 5 & 38 & 107 & 30 & 29 & 43 & 6 & 58 & 52 & 14 & 19 & 48 \\ CIF2-C & 18 & 29 & 131 & 8 & 36 & 115 & 21 & 22 & 31 & 4 & 55 & 73 & 11 & 25 & 45 \\ UC3-BDS & 33 & 18 & 108 & 18 & 29 & 164 & 13 & 55 & 165 & 23 & 29 & 64 & 29 & 53 & 147 \\ CIF3-BDS & 33 & 17 & 119 & 21 & 33 & 162 & 11 & 48 & 115 & 23 & 28 & 74 & 26 & 56 & 151 \\ CIF2-BDS & 33 & 17 & 119 & 19 & 34 & 159 & 11 & 48 & 115 & 23 & 28 & 74 & 25 & 56 & 149 \\ CIF2-GPS & 18 & 33 & 139 & 8 & 41 & 136 & 20 & 18 & 24 & 4 & 54 & 84 & 11 & 24 & 49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracies for different combined models with the daily solution in North (N), East (E), Up (U) directions (mm). Figure 10: The positioning errors in different combined models for the daily solution at different stations. (**a**) The positioning errors at CEDU; (**b**) The positioning errors at DARW; (**c**) The positioning errors at JFNG; (**d**) The positioning errors at KARR; and (**e**) The positioning errors at KZN2. Table 7 shows the mean accuracy, mean precision, mean convergence time, and median convergence time for different combined models with the daily solution at different stations. The results show that CIF2-C needs more mean convergence time than CIF2-GPS. It is inconsistent with the usual result \"CIF2-C usually needs less convergence time than CIF2-GPS\". In Figure 11, CIF2-C needs more mean convergence time than CIF2-GPS only occurs at KARR. Mean convergence time can not reflect convergence characteristics of different models. Median convergence time can be used as the statistic of convergence time. In Table 7, CIF2-C needs less median convergence time than CIF2-GPS in consistency with the general rule \"convergence time in a multi-system PPP is less than that in a single system PPP [38]\".
## 3 Results  
There were 100 potential candidates for the study. Of these, 25 were excluded from the study because of the following reasons: incomplete informed consent (\(n=9\)), no full MI-PLIF procedure (\(n=9\)), other LIF procedure (\(n=2\)), other C-arm fluoroscopy used (\(n=4\)), or when more than two levels of vertebrae were fused (\(n=1\)). A total of 75 patients participated in the study. Of these, 30 patients underwent a full MI-PLIF surgery with the assistance of the AIRO CT device, while 45 patients had surgery with the assistance of C-arm fluoroscopy devices.  
Figure 1: Schematic representations of iCT procedures (**left**) with the usage of iCT and lateral C-arm fluoroscopy and C-arm fluoroscopy procedures (**right**) with biplanar C-arm fluoroscopy.  
The iCT group consisted of 11 males and 19 females with a median BMI of 27 and a median age of 60 years. The fluoroscopy group included 21 males and 24 females with a median BMI of 26.5 and a median age of 60 years. The indications for MI-PLIF surgery in the iCT group consisted mainly of listhesis (\(n=16\)) or a combination of multiple lumbar disorders (\(n=7\)). However, some patients in the iCT group were offered MI-PLIF surgery because of discopathy (\(n=3\)), hernia (\(n=2\)), and severe osteoarthritis of discs (\(n=2\)). Indications for MI-PLIF in the C-arm fluoroscopy group were listhesis (\(n=16\)), multiple spinal disorders (\(n=18\)), discopathy (\(n=4\)), stenosis (\(n=3\)), hernia (\(n=1\)), facet arthrosis (\(n=1\)), or another lumbar spine disorder (\(n=1\)). A total of 29 (96.7%) patients in the iCT group had a single-level lumbar spinal fusion. Only one individual had two levels of vertebrae fused (3.3%). A total of 38 (84.4%) patients of the fluoroscopy group had a single-level fusion, while 7 (15.6%) patients had a multi-level fusion. There is no significant difference between both groups in terms of age, BMI, gender, indications, and number of levels to be fused.  
Surgeon 1 performed 21 surgeries (iCT: \(n=19\); C-arm fluoroscopy; \(n=2\)), surgeon 2 performed 18 surgeries (iCT: \(n=10\); C-arm fluoroscopy; \(n=9\)), and surgeon 3 performed 36 surgeries (iCT: \(n=1\); C-arm fluoroscopy; \(n=35\)). A summary of the basic characteristics of the study population in both groups is given in Table 1.
# Decision Tree-Based Adaptive Reconfigurable Cache Scheme
## 3 Proposed Adaptive Reconfigurable Cache Scheme Based on Decision Tree  
### Overview of Adaptive Reconfigurable Cache  
The decision tree-based adaptive reconfigurable cache scheme consists of a performance monitor, an adaptive controller, and an L2 cache with reconfigurable associativity. The performance monitor is used to collect information including the instruction-per-cycle (IPC) value in the current reconfiguration period, the total number of memory access,and the L2 cache miss rate, which is needed for the decision tree model to make decisions. Based on the above statistics, the adaptive controller decides the optimal associativity of the L2 cache through the decision tree model and controls the cache to tune the associativity. Figure 1 shows the architecture of the proposed adaptive reconfigurable cache scheme.  
Applications have time-varying behavior of memory access at run time, but the behavior of applications can be considered stable for a short time interval. Based on this feature, the application run time is divided into reconfiguration periods of the same length, and within each reconfiguration period, the application runs with the current optimal associativity. The reconfiguration period is further divided into a decision-making period and a stable-running period. The division scheme of run time is shown in Figure 2. During the decision-making period, the information within current runtimes, such as cache miss rate, total number of memory access, and instructions per cycle (IPC) is collected by the performance monitor. Then, the adaptive controller uses the above information to determine the optimal associativity. At the end of the decision-making period, the adaptive controller tunes the associativity so that the application runs with the optimal associativity during the stable-running period.  
In this study, the average access time (AMAT) is used as a metric to evaluate the system performance, and CPU clock cycle (C.C.) is used as the unit for the access time, hit time, miss penalty and other parameters covered subsequently in this paper. Equation (1) demonstrates how to calculate the average memory access time (AMAT):  
\[AMAT=Hit\ Time+\ Miss\ Rate\times\ Miss\ Penalty \tag{1}\]  
where _Hit Time_ is the latency of cache hit, _Miss Rate_ is the percentage of the cache miss number, and _Miss Penalty_ is the latency of cache miss.  
Figure 1: Architecture of adaptive reconfigurable cache.  
Figure 2: Division of application running times.
## 4 Extensions of RUMVRHF Smoother
### Extension to Posteriori State Estimation  
We first introduce an approach for extending the proposed recursive algorithm to a posteriori state estimation. By denoting the a posteriori estimates for the overall horizon \([k-N\;\;k]\), sub-horizon \([k-N\;\;k-h]\) and separated sub-horizon \([k-h+1\;\;k]\) as \(\hat{x}_{k-h|k}\), \(\hat{x}_{1,k-h|k}^{+}\) and \(\hat{x}_{2,k-h|k}^{+}\), respectively, the a posteriori overall estimate and its error covariance at time \(k-h\) can be represented as follows [1]:  
\[\hat{x}_{k-h|k}^{+} = U_{p1,k-h}\hat{x}_{1,k-h|k}^{+}+U_{p2,k-h}\hat{x}_{2,k-h|k^{ \prime}}^{+}, \tag{87}\] \[P_{k-h}^{+} = \left\{\Big{(}P_{1,k-h}^{+}\Big{)}^{-1}+\Big{(}P_{2,k-h}^{+} \Big{)}^{-1}\right\}^{-1} \tag{88}\]  
where \(P_{1,k-h}^{+}\) and \(P_{2,k-h}^{+}\) are denoted as the state error covariance matrices of the estimates \(\hat{x}_{1,k-h|k}^{+}\) and \(\hat{x}_{2,k-h|k}^{+}\), respectively, and \(U_{p1,k-h}\) and \(U_{p2,k-h}\) are obtained from  
\[U_{p1,k-h} = P_{2,k-h}^{+}\Big{(}P_{1,k-h}^{+}+P_{2,k-h}^{+}\Big{)}^{-1}, \tag{89}\] \[U_{p2,k-h} = P_{1,k-h}^{+}\Big{(}P_{1,k-h}^{+}+P_{2,k-h}^{+}\Big{)}^{-1}. \tag{90}\]  
For the sub-horizon \([k-N\;\;k-h]\), the a posteriori local estimate \(\hat{x}_{1,k-h|k}^{+}\) and its error covariance matrix \(P_{1,k-h}^{+}\) can be updated from the predicted local estimates \(\hat{x}_{1,k-h|k}\) and its error covariance matrix \(P_{1,k-h}\) as [1]\[\hat{x}^{+}_{1,k-h|k} = \hat{x}_{1,k-h|k}+P_{1,k-h}C^{T}_{k-h}\Big{(}C_{k-h}P_{1,k-h}C^{T}_{ k-h}+R_{k-h}\Big{)}^{-1} \tag{91}\] \[\times\Big{(}y_{k-h}-C_{k-h}\hat{x}_{1,k-h|k}\Big{)},\] \[P^{+}_{1,k-h} = P_{1,k-h}-P_{1,k-h}C^{T}_{k-h}\Big{(}C_{k-h}P_{1,k-h}C^{T}_{k-h}+ R_{k-h}\Big{)}^{-1}C_{k-h}P_{1,k-h}, \tag{92}\]  
respectively.  
Because the recursive equations in Theorem 2 are equivalent to the conventional information filter formulation in [1], the posteriori information estimate \(\hat{w}^{+}_{k-h|k}\) and its posteriori information matrix \(S^{+}_{k-h}\) can be obtained as \(\hat{w}^{+}_{k-h|k}=(P^{+}_{2,k-h})^{-1}\hat{x}^{+}_{2,k-h|k}\) and \(S^{+}_{k-h}=(P^{+}_{2,k-h})^{-1}\), respectively. Thus, the a posteriori local estimate \(\hat{x}^{+}_{2,k-h|k}\) and its error covariance matrix \(P^{+}_{2,k-h}\) can be computed from the predicted \(\hat{w}^{+}_{k-h|k}\) and \(S^{+}_{k-h}\) as follows:  
\[\hat{x}^{+}_{2,k-h|k} = P^{+}_{2,k-h}\hat{w}^{+}_{k-h|k}=\Big{(}S^{+}_{k-h}\Big{)}^{-1} \hat{w}^{+}_{k-h|k}, \tag{93}\] \[P^{+}_{2,k-h} = \Big{(}S^{+}_{k-h}\Big{)}^{-1}, \tag{94}\]  
where \(S^{+}_{k-h}\) and \(\hat{w}^{+}_{k-h|k}\) are calculated from (83) and (84) with boundary conditions \(S^{+}_{k}=0\) and \(\hat{w}^{+}_{k|k}=0\).
## 4 System Performance Discussion
### Parameter Discussion on System Performance
#### 4.2.1 Switch Time  
The switching time refers to the time consumed when the switch is changed from status 1 to state 2. For different conditions, \(T_{on}\) and \(T_{off}\), can be used to indicate the starting point and ending point, respectively. For SPDT switches, the switching time is 0 under ideal conditions, and two-way signals can be seamlessly switched at any time. In the nonideal case, the signal is changing during the switching time, which will lead to the control signal error result during the sampling process. In order to eliminate the error of the control signal, \(T_{on}\) and \(T_{off}\) should be smaller than the sampling interval to ensure the correctness of the control signal. With a stable symbol rate, this goal is achieved by selecting switch components with appropriate switching time.  
The switching process of the SPDT switch can be regarded as a linear change process in a very short time. Setting \(v_{1}(t)\) as input signal and \(v_{2}(t)\) as the control signal, \(v_{3}(t)\) and \(v_{4}(t)\) as two outputs, and the threshold value of control signal as 0.5, if \(v_{2}(t)\) is larger than 0.5, the main input signal outputs from the \(v_{3}(t)\) path. If the switch is switched at \(t=T\) instant, the two outputs are:  
\[v_{3}(t)=\left\{\begin{array}{l}10^{-\frac{ISQ_{2}}{20}}v_{1}(t),t\geq T+T_ {off_{2}}\\ (10^{-\frac{ISQ_{2}}{20}}-10^{-\frac{ISQ_{2}}{20}})(1-\frac{t-T}{T_{off_{2}}} )v_{1}(t)+10^{-\frac{ISQ_{2}}{20}}v_{1}(t),\textit{others}\end{array}\right. \tag{24}\]  
\[v_{4}(t)=\left\{\begin{array}{l}10^{-\frac{ISQ_{1}}{20}}v_{1}(t),t\geq T+T_ {on_{1}}\\ (10^{-\frac{ISQ_{1}}{20}}-10^{-\frac{ISQ_{2}}{20}})\frac{t-T}{T_{on_{1}}}v_{1 }(t)+10^{-\frac{ISQ_{1}}{20}}v_{1}(t),\textit{others}\end{array}\right. \tag{25}\]  
If \(v_{2}(t)\) is smaller than 0.5, the main input signal outputs from the \(v_{4}(t)\) path. If the switch is switched at \(t=T\) instant, the two outputs are:  
\[v_{3}(t)=\left\{\begin{array}{l}10^{-\frac{ISQ_{2}}{20}}v_{1}(t),t\geq T+T_ {on_{1}}\\ (10^{-\frac{ISQ_{1}}{20}}-10^{-\frac{ISQ_{2}}{20}})\frac{t-T}{T_{on_{2}}}v_{1 }(t)+10^{-\frac{ISQ_{2}}{20}}v_{1}(t),\textit{others}\end{array}\right. \tag{26}\]  
\[v_{4}(t)=\left\{\begin{array}{l}10^{-\frac{ISQ_{1}}{20}}v_{1}(t),t\geq T+T_ {on_{1}}\\ (10^{-\frac{ISQ_{1}}{20}}-10^{-\frac{ISQ_{1}}{20}})(1-\frac{t-T}{T_{off_{1}}} )v_{1}(t)+10^{-\frac{ISQ_{1}}{20}}v_{1}(t),\textit{others}\end{array}\right. \tag{27}\]  
It can be concluded that to prevent any error in the control signal, \(T_{on}\) or \(T_{off}\) should be less than the sampling time interval. In the case of fixed symbol rate, we want to achieve this goal by selecting components that have a suitable switching time.
# Target Identification via Multi-View Multi-Task Joint Sparse Representation
###### Abstract  
Recently, the monitoring efficiency and accuracy of visible and infrared video have been relatively low. In this paper, we propose an automatic target identification method using surveillance video, which provides an effective solution for the surveillance video data. Specifically, a target identification method via multi-view and multi-task sparse learning is proposed, where multi-view includes various types of visual features such as textures, edges, and invariant features. Each view of a candidate is regarded as a template, and the potential relationship between different tasks and different views is considered  
although the multi-task method (MTT) takes advantage of the interdependence between particles, the robustness of multi-task is not good due to the existence of outliers.  
To identify targets more robustly, researchers have proposed a multi-task and multi-perspective learning method to optimize the target identification problem [18; 19]. In this paper, we propose a target identification method based on multi-task multi-view sparse learning (MVMT).  
The main contributions of this paper are summarised as follows:  
1. A multi-view multi-task method based on sparse representation, namely MVMT, is proposed for target identification. Compared with the previous related method, the proposed MVMT can not only use the learning based on sparse representation, but also introduces a variety of complementary perspective features to enhance the expression features of the targets;
2. Each perspective of each particle is regarded as a separate task, and the potential connections between different perspectives and different particles are considered in a multi-task learning framework;
3. To capture the outlier tasks that frequently occur in the particle sampling process, the coefficient matrix is decomposed into two cooperative parts to enhance the robustness of multi-task learning, and the posterior probability of outliers is set to zero to ensure that samples will not be taken in the resampling process.
# The Use of Radio and Telemedicine by TMAS Centers in
## 4 Discussion
### What Needs to Be Done?
#### 4.1.1 Integration of Artificial Intelligence (AI) in Telemedicine  
The integration of artificial intelligence (AI) has provided telemedicine with the ability to offer a more personalized approach to diagnosis and treatment. The establishment of AI-based telemedicine systems for maritime vessels has numerous benefits. An AI-based marine doctor system enables prompt and efficient medical assistance to seafarers and crew members who encounter health complications [25]. Physicians can make informed decisions based on AI algorithms that analyze patient histories, laboratory results, and imaging studies [36]. Medical professionals will be able to make more accurate diagnoses, provide better treatment plans, and improve patient outcomes. It is important to address concerns regarding data privacy and ethical considerations regarding AI in telemedicine. AI has the potential to revolutionize telemedicine and enable healthcare providers to reach remote and underserved populations with high-quality medical care.
## 5 Results
### Measurement of Processing Time  
Through the analysis of the process described in Figure 6, four necessary stages were established to process the sample and measure the processing time.  
The first stage, called device preparation, encompasses the set of steps required to connect the equipment to the compressed air network, place the filter medium, and enable the valves and connections required for the test.  
The second stage corresponds to the preparation and introduction of the sample. In this phase, the sample is introduced into the equipment for analysis. This involves opening valves, preparing the device, and all necessary actions to have the system enabled and ready to start the test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **KS Statistic** & \(p\)**-Value** \\ \hline M1 & 0.16 & 0.549 \\ M2 & 0.18 & 0.396 \\ M3 & 0.16 & 0.549 \\ M4 & 0.16 & 0.549 \\ M5 & 0.18 & 0.396 \\ M6 & 0.16 & 0.549 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result of the KS statistic and \(p\)-value for the 6 samples.  
Figure 6: Result of the average times measured at each stage of the measurement process.  
The third stage is the measurement, which extends from the opening of the start valve until the end of the test. During this phase, the corresponding measurements are taken, recording the data necessary for subsequent analysis.  
The fourth and final stage is the cleaning. This stage begins with the removal of the wine from the internal circuit of the equipment and continues with the cleaning of all parts and components involved, in order to leave the device ready to be used again.  
The results presented in Figure 6 show an average reduction of 52% in the total processing time, considering all stages of the process. However, if the measurement stage, which does not depend on the device and is subject to the sample being analyzed, is excluded, the reduction in processing time increases to 66.2%. This significant improvement in processing time increases the laboratory's capacity to handle higher demand for analyses or allows the current laboratory operators to be reassigned to other complementary tasks.  
The cleaning stage is the most impacted by the change to a device without a pressure tank, with a time reduction of 86.2%. The sample preparation and injection stage experiences a 64.9% reduction, while device preparation is reduced by 55.2%. On the other hand, the measurement stage does not show statistically significant differences.