esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Small Spherical Robot Set Up and Modeling  
### The Mechanism of a Small Spherical Robot  
A small spherical robot was designed in our laboratory based on the flexible movement characteristics of sea turtles in water, as shown in Figure 1. The robot was composed of two hemispheres that were joined with a mid-plate [19]. The upper hemisphere was divided into two parts, namely the water storage compartment and the sealed compartment. Water could enter the storage compartment through the four holes distributed around the water storage cabin when the small spherical robot dives into water, which helps with reducing the buoyancy of the robot. A sealed cabin was utilized to load the electrical system. The lower hemisphere was the vector propulsion system, which was bounded by two separate quarter spherical shells. The lower hemisphere could be opened or closed by servo motors on both sides [20]. A stereo camera, an inertial measurement unit (IMU), 12 pressure sensors, and an acoustic communication module were attached to the small spherical robot. For convenience, we designed a detachable battery pack that was fixed to the middle partition of the robot. The battery pack included three 7000 mAh lithium batteries, which were sealed in a cabin. The prototype of a small spherical robot is shown in Figure 2. In addition, Table 2 lists the technical parameters of the robot.  
An advantage of the small spherical robot lies in its vector propulsion mechanism, as shown in Figure 3. It can be seen that four sets of propulsion units comprised the propulsion system [19]. Each unit consisted of a hip, knee, and ankle joint, which could rotate around a self axis. As a result, the vector propulsion was very flexible. In order to ensure that the center of gravity and the centroid coincided, an \"H\"-shaped propulsion strategy was adopted [21]. The \"H\" shape propulsion strategy is shown in Figure 4; the strategy ensures that the hip and knee joint are kept still and only the ankle joint can be adjusted. In this situation, the driving force \(\mathbf{\tau}\in\mathbf{R}^{3\times 1}\) is composed of three forces / moments: surge force \(\tau_{u}\), have force \(\tau_{w}\), and yaw moment \(\tau_{r}\).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Items** & **Parameters** \\ \hline Dimension (Width \(\times\) Length \(\times\) Height) & 30 cm \(\times\) 60 cm \(\times\) 30 cm \\ Mass in air & 6.5 kg \\ Processors & NVIDIA Jetson Tk1 \\ STM32F407 \\ Max thrust & 3.8 N \\ Motor & A2212 \\  & Pressure sensor(MS5803-14 BA ) \\  & IMU(3 DM-GX5-45) \\ Sensors & Stereo camera \\  & Acoustic communication module(Micron Sonar) \\ Power & 7.4 V rechargeable Ni-MH batteries (13,200 mAh) \\ Operation time & Average 100 min \\ \hline \hline \end{tabular}
\end{table}
Table 2: The technical specification of the robot.  
Figure 1: Mechanical structure of the turtle-inspired small spherical robot. (**a**) The two separate quarter spherical shells are closed. (**b**) The two separate quarter spherical shells are open.  
Figure 3: Mechanical structure of the vector propulsion mechanism.  
Figure 2: Prototype of the small spherical robot.
# Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Methodology** & **Detection Procedure** & \begin{tabular}{c} **Advantages of Our Suggested** \\ **Method** \\ \end{tabular} \\ \hline \multirow{7}{*}{[25]} & \multirow{7}{*}{Deep learning} & Using DL methods to & \\  & & distinguish the & \\  & & features of behavior & \\  & & of FDI attack via the & \\  & & historical & \\  & & measurement data & \\  & & and utilizing the & \\  & & obtained features for & \\  & & detecting the FDI & \\  & & attack & \\ \hline \multirow{7}{*}{[22]} & \multirow{7}{*}{\begin{tabular}{c} Chi-square detector \\ and cosine similarity \\ matching are used \\ \end{tabular} } & The outcomes & \\  & & described that the & \\  & & detection method on & \\  & & the basis of & \\  & & the basis of & \\  & & matching are used & Chi-square is not able \\  & & to diagnose the tested & \\  & & FDIAs & \\ \hline \multirow{7}{*}{[33]} & \multirow{7}{*}{\
\begin{tabular}{c} Kalman filter \\ \end{tabular} } & Utilizing the & \\  & & mathematical & This paper does not depend on \\  & & technique in smart & \\  & & grids for detecting the & \\  & & FDI attacks & \\ \hline \multirow{7}{*}{[34]} & Cooperative & Investigating attacks & Current and voltage \\  & vulnerability factor & on voltage & measurements have been \\  & & measurements & considered in this work \\ \hline \multirow{7}{*}{[35]} & \multirow{7}{*}{Kullback-Leibler} & Able to detect various & \\  & & attacks; it faces & \\ \cline{1-1}  & & difficulty to detect & \\ \cline{1-1}  & & FDI attacks on certain & \\ \cline{1-1}  & & state variables & \\ \hline \multirow{7}{*}{[36]} & \multirow{7}{*}{
\begin{tabular}{c} A Discordant \\ Element Approach \\ \end{tabular} } & Investigating attacks & Current and voltage \\ \cline{1-1}  & & on current & measurements have been \\ \cline{1-1}  & & measurements & considered in this work \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Published literature of FDI attack detection in smart electrical systems. of the FDI attack displayed that various malicious activities strongly impact the ISMG: in descending order of absolute terms, attack on data exchanges influence smart sensors, controller units, and smart loads are identified as the most influencing. Other activities, containing changing the loads below 10% showed overall lower impacts but it causes power losses and increasing cost. The wide environmental influence has been related with most of the power generation activities and affirms the required to produce the initial power production as cleaner, less energy-intensive and more resource-effective. Affordable incremental novelties that can transform the procedure business have been conducted in the context of primary power generation, which mainly has been assumed depending on green energy resources. The investigations of the FDIA detection procedure toward raised sustainability of power systems contains strategic and important subjects [38].
## 3 Description of Information Technologies for Order Picking and Formation of an MCDM Model
### Order-Picking Technologies
#### 3.2.3 Pick-to-Voice Technology  
Pick-to-voice technology implies two-way communication between the order picker and the management system [5]. Employees receive voice instructions from the control system and respond verbally using headphones and microphones connected to a control unit carried by the worker on his belt [34]. Order picking by voice recognition is shown in Figure 4.  
Order picking by voice recognition (pick-to-voice) is done in the following way:  
* The central computer receives a work order from the control system and these instructions are transformed into voice commands that are placed by priority in a series of tasks for order picking.
* The order picker receives verbal instructions through headphones to go and collect the first item from the order.  
Figure 4: Order picking by voice recognition [35].  
Figure 3: Order picking by light detection [33].  
* The order picker confirms the location of the item by reading the address from the shelf. If the address matches the expected response, the system further instructs the order picker on how many items to pick up. The order picker confirms the pickup by repeating the number.
* If the location on the shelf is empty or the number of items is less than the number required, the order picker reports that, and the system generates an order to fill in the location.
* When each operation is completed, the system gives instructions for the next work order.
* If the order picker does not hear or does not understand the command, they can request the command be repeated.
* According to the experiences of users of this technology, the training time of order pickers is reduced from a few days to a few hours.  
The voice recognition system is particularly effective where it is not necessary to have both hands free to handle items. Voice recognition is mostly used for ordering, but the technology is also applicable for other activities, such as goods receiving, storage, replenishment, sorting, and loading. The advantages of this technology are fewer order picking errors, increased productivity, reduced number of complaints, and shortened training time for order pickers.
## 4 Experiments
### Results and Discussion
#### 4.3.3 Computational Efficiency  
Table 5 compares the CPU time of the investigated methods for two key processes in Grassmann clustering: affinity calculation and the clustering process. We report the average CPU time based on 100 repetitions of the experiment. In terms of affinity calculation, Procrustes distance demonstrated greater efficiency in CPU time compared to other metrics, including Euclidean distance, tangent norm, and principal angle. Overall, the four intrinsic methods outperformed both extrinsic and semi-intrinsic approaches. Notably, the two intrinsic spectral clustering methods using Procrustes distance achieved the best CPU time among the 11 methods. In contrast, PK-KSC was the most computationally expensive method for affinity calculation, requiring 670 times more CPU time than _m_NN-SC with Procrustes distance, the fastest method in our experimental setting.  
For the clustering process, the three semi-intrinsic methods exhibited the longest CPU times, taking approximately 1000 times longer than the other methods. Aside from the semi  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Measure**} & **Clustering** & **Euclidean** & **Tangent** & **Principal** & **Procrustes** \\  & **Methods** & **Distance** & **Norm** & **Angle** & **Distance** \\ \hline \multirow{6}{*}{NMI} & _k_-means & - & 0.730 & 0.726 & 0.769 \\ \cline{2-6}  & FC-SC [1] & 0.755 (\(\sigma=1\)) & - & 0.753 (\(\sigma=1\)) & 0.777 (\(\sigma=1\)) \\ \cline{2-6}  & _m_NN-SC [2] & 0.756 (\(\sigma=100\), \(m=6\)) & - & 0.767 (\(\sigma=5\), \(m=8\)) & **0.829** (\(\sigma=3\), \(m=9\)) \\ \cline{2-6}  & PK-KSC [3] & & & 0.747 & \\ \cline{2-6}  & CCK-KSC [4] & & & 0.806 & \\ \hline \multirow{6}{*}{Accuracy} & _k_-means & - & 69.367 & 68.704 & 70.586 \\ \cline{2-6}  & FC-SC & 66.127 (\(\sigma=2\)) & - & 66.049 (\(\sigma=2\)) & 68.719 (\(\sigma=50\)) \\ \cline{2-6}  & _m_NN-SC & 67.315 (\(\sigma=0.1\), \(m=7\)) & - & 72.253 (\(\sigma=5\), \(m=8\)) & **74.568** (\(\sigma=10^{3}\), \(m=6\)) \\ \cline{2-6}  & PK-KSC & & & 65.911 & \\ \cline{2-6}  & CCK-KSC & & & 72.361 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Clustering performances of 11 Grassmann clustering methods combinations for AMC dataset.  
intrinsic methods, the remaining eight methods had similar CPU times. This significant difference in computation time for the semi-intrinsic methods is largely attributed to the Karcher mean computation, which involves iterative forward and inverse mapping between the Grassmann manifold and the tangent space.
# MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers
## The Proposed Fuzzy Keyword Search Scheme Based on MinHash
### A New Retrieval Method
If \(\hat{J}<J_{\text{max}}\), information \(\left|J_{w_{i}},\left\{\text{FID}_{b_{i}},\text{OPE}(\text{ek},\text{Score}_{i _{i}})\right\}\right|\) associated with the leaf node has a higher correlation than information \(\left|J_{\text{max}},\left\{\text{FID}_{\hat{J}},\text{OPE}(\text{ek},\text{ Score}_{i_{i}})\right\}\right|\)  
Figure 3: Illustration of the fingerprint index tree. associated with \(J_{\rm max}\) stored in the U. Then, information \([J_{w_{i},r}\left\{\mathrm{FID}_{i},\mathrm{OPE}(\mathrm{ek},\mathrm{Score}_{i}) \right\}]\) will be entered into U in an increasing order. This will result in a less relevant document to be remove from U, maintaining k documents in it. For the new set of documents in U, \(J_{\rm max}\) is reset, and traversal continues until all the leaf nodes are covered. It is very clear that following this method, some parts of the entire index tree will not be traversed, saving some traversal operations as well as time. The efficiency of search is therefore improved, which is very significant for massive datasets. Algorithm 1 is the pseudo-code of the search method. ```
Input: a: fingerprint index tree \(G_{W}\) b: search for trapdoor \(T_{W}\) Output: a sorted top-k ciphertext document \(C^{\prime}=(c_{1},c_{2},\ldots,c_{k})\) Step 1: Initialize the set U of capacity k to be null Step 2: WHILE (the number of documents in U is less than k)  Calculate the Jaccard distance \(J_{w_{i}}\) between the \(S_{i}\) and the trapdoor \(T_{W}\)  Document is inserted in U in an increasing order of the values of \(J_{w_{i}}\)  END WHILE Step 3: Assign the maximum value of \(J_{w_{i}}\) in U to \(J_{\rm max}\) Step 4: FUNCTION SearchTree(a)  Access node a B = the first child node of node a WHILE (b exists)  Calculate the Jaccard distance \(J_{w_{i}}\) between the \(S_{i}\) and the trapdoor \(T_{W}\)  Let \(J=[J_{w_{i},r}\left\{\mathrm{FID}_{i},\mathrm{OPE}(\mathrm{ek},\mathrm{Score}_{i })\right\}]\)  IF b has not been accessed & \(J\leq J_{\rm max}\)  IF b is a leaf node & \(J_{w_{i}}<J_{\rm max}\)  Node b is inserted in U in an increasing order of the values of \(J_{w_{i}}\)  Assign the maximum value of the \(J_{w_{i}}\) in U to \(J_{\rm max}\)  ELSE SearchTree(b)  END IF  END IF  B = the next child node of node a  END WHILE  END FUNCTION Step 5: FOR each \(\mathrm{FID}_{i}\in\mathrm{U}\)  Sort \(J_{w_{i}}\) as well as \(\mathrm{OPE}(\mathrm{ek},\mathrm{Score}_{i})\) from small to large  Get \(\mathrm{FID}^{\prime}=(\mathrm{FID}_{1},\mathrm{FID}_{2},\ldots,\mathrm{FID}_{ k})\)  END FOR  Find cipher document set \(C^{\prime}\)= \((c_{1},c_{2},\ldots,c_{k})\) that correspond to \(\mathrm{FID}^{\prime}=(\mathrm{FID}_{1},\mathrm{FID}_{2},\ldots,\mathrm{FID}_{ k})\) Step 7: RETURN \(C^{\prime}=(c_{1},c_{2},\ldots,c_{k})\) Step 8: DECRYPTION \(C^{\prime}=(c_{1},c_{2},\ldots,c_{k})\)
```  
**Algorithm 1**_The New Method for Fingerprint Index Tree Retrieval_
## 4 Illustrative Examples
### Example: Two Variables and Constant-Stress Loading  
There are 3000 samples from a prototype of a semiconductor. The degradation path model depends on the temperature, \(S_{a,i}\), and the voltage stress, \(S_{b,j}\). Therefore, the generalized Eyring model for the accelerated model is assumed between the parameter and the stress relationship. Both the maximum and the normal use conditions of the temperature and the voltage stress are specified as 130 \({}^{\circ}\)C and 45 \({}^{\circ}\)C and 4.4 V and 3.8 V, respectively.  
According to Lemma 1 in [PERSON] et al. [6], 20 units are allocated to the V&V tests under the normal use condition, the high-temperature operating life (HTOL) under the maximum temperature and the normal use voltage, the voltage limit tests under the normal use temperature and maximum voltage, and the biased highly accelerated stress test (HAST) for the combination of the maximum stress levels. In other words, 80 units among the 3000 samples are used to estimate the prior information for the acceleration model parameters. Thus, the prior information can be obtained by using the degradation data from the above tests with 80 units. Based on the estimated results, four units are added to the V&V tests under the normal use condition and 457, 557, and 92 units are allocated for the voltage limit test, the HTOL, and the HAST, respectively. A total of 1810 units are tested under the middle-stress levels, which are calculated based on model assumption  
Figure 5: Strategic approach: single variable and constant-stress loading case.  
4 in [PERSON] et al. [6]. In the semiconductor area, qualification tests should be used to verify the various item requirements. In particular, the HTOL and the HAST are included in the qualification tests along with 231 and 75 units, respectively. Therefore, the units for the tests and the reliability information can be more effectively applied during the qualification. Figure 6 shows an overview of this test.  
According to Equation (2), the drifts of the mean degradation path model at the combination of the normal use conditions and the maximum stress levels are \(\eta_{00}=\exp(\delta_{0})\) and \(\eta_{MM}=\exp(\delta_{0}+\delta_{1}+\delta_{2}+\delta_{3})\), respectively. Then, the drift ratio between the combination of the maximum stress levels and the normal use conditions, \(\eta_{MM}/\eta_{00}\), is calculated as \(\exp(\delta_{1}+\delta_{2}+\delta_{3})\). In the example by [PERSON] et al. [6], the \(\delta_{1}\), \(\delta_{2}\), and \(\delta_{3}\) are equal to 5, 6, and \(-3\), respectively, and it is known that the drift ratio is calculated as 2980.95. This means that the degradation increment at the maximum stress level is approximately 2981 times greater than the normal use condition. Note that the term \(\delta_{3}\) means the negative curvature effect between the temperature and the voltage in this example.
## 2 NDN Background and Related Work
### Mobility in Named Data Networking  
In NDN, the mobility of UAVs can be classified based on their applications into two distinct types: producer and consumer mobility. The latter is more easily managed due to inherent features of the NDN architecture design, like in-network caching, multi-path forwarding, and consumer-driven communication. For instance, when a consumer changes its PoA, it can resend Inputs from its new location without updating the location or establishing a new connection. This Input can be satisfied either by intermediate routers that have previously cached the data or by the producer. On the other hand, the NDN architecture faces difficulties in effectively supporting producer mobility. This is mainly because Inputs are forwarded based on the content's name, not the producer's location. When a producer moves to a new PoA, consumer Inputs are initially sent toward the producer's previous location due to the outdated FIB of the relevant routers. As a result, the Inputs cannot reach the newly moved producer until these routers' FIBs are updated to reflect the producer's new location. The time taken to update all relevant FIBs can cause significant delay in communication and increase packet loss, which is unacceptable in scenarios that require low-latency communication.
# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
## 6 Performance Evaluation
### Throughput  
As shown in Figure 9, with the continuous increase of the number of nodes, the throughput of the three algorithms shows a trend of decline. The throughput of the C-PBFT algorithm is higher than that of PBFT and TLPBFT, because C-PBFT elects committees in each region to participate in the consensus, reducing the number of consensus nodes, thus reducing the communication overhead, and the advantage becomes more obvious as the number of nodes increases. Therefore, in the networking of vehicles, the C-PBFT algorithm can maintain high efficiency and stability.  
Figure 7: Comparison of communication overhead between C-PBFT and PBFT.
# Fault Localization Using TrustRank Algorithm
## 2 Related Work
### Program Execution Network  
Program execution network can simulate complex data flow during program execution, establish impact propagation and interaction relationships between program entities, and understand program dynamic behavior. The idea of a program execution network comes from a complex network model, which has been widely used by researchers due to its robustness and applicability. Studies have shown that complex networks are powerful tools for researchers to understand complex software programs [27], as they can analyze the importance of program entities and understand program context relationships. It is worth mentioning that with the support of the complex network model, a program execution network can comprehensively consider the entire relationship of large programs.  
[PERSON] et al. [28] proposed a dynamic changing network that combines dynamic call relationships during program execution with complex networks to construct a call network with methods as nodes and call relationships as edges. [PERSON] et al. [29] also used software network technology and analyzed fault localization by measuring network metrics. In order to analyze the impact relationship of faults, [PERSON] et al. [30] constructed a new method based on counting different function call contexts to analyze the propagation of program faults. In order to reduce the impact range of fault nodes in software networks, [PERSON] et al. [31] constructed a directional call network, proposed their own influence propagation relationship method, and analyzed the local centrality of fault nodes in software networks. This paper uses a software execution network to represent data flow behavior during program execution and identifies and locates fault program statements' positions by leveraging complex network analysis capabilities.
## 4 Evaluation
### Sensitivity to Parameter Values
#### 4.3.2 Hallucination  
The bottom part of Figure 2 shows how changes in the hallucination parameter \(\tau_{H}\) affect performance. As described in Section 2.3.1, larger \(\tau_{H}\) values decrease hallucination and thus the number of activations. Decreased hallucination affects both occurrence and establishment of patterns, as there is little tolerance for pattern variations. With more hallucination, both measures increase and then remain stable; again, precision is not affected significantly, as the merging algorithm of SymCHMMerge reduces the growing number of activations on higher layers.  
Figure 2: Sensitivity of the model to changes of the hallucination parameter \(\tau_{I}\)**(top)** and the inhibition parameter \(\tau_{H}\)**(bottom)**. When one parameter was varied, all others remained fixed.
# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
###### Abstract  
Rice is an important food crop and a large producer of green-house relevant methane. Accurate and timely maps of paddy fields are most important in the context of food security and greenhouse gas emission modelling. During their life-cycle, rice plants undergo a phenological development that influences their interaction with waves in the visible light and infrared spectrum. Rice growth has a distinctive signature in time series of remotely-sensed data. We used time series of MODIS (Moderate Resolution Imaging Spectroradiometer) products MOD13Q1 and MYD13Q1 and a one-class support vector machine to detect these signatures and classify paddy rice areas in continental China. Based on these classifications, we present a novel product for continental China that shows rice areas for the years 2002, 2005, 2010 and 2014 at 250-m resolution. Our classification has an overall accuracy of 0.90 and a kappa coefficient of 0.77 compared to our own reference dataset for 2014 and correlates highly with rice area statistics from China's Statistical Yearbooks (R\({}^{2}\) of 0.92 for 2010, 0.92 for 2005 and 0.90 for 2002). Moderate resolution time series analysis allows accurate and timely mapping of rice paddies over large areas with diverse cropping schemes.  
rice; China; MODIS; time series; SVM; OCSVM; agriculture; change detection Article