esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Industrial Application  
The test bed of our research methodology is a shop floor producing low-voltage breakers. These products are composed of an electrical set (a coil and an electronic switch) and a mechanical drive enclosed in an iron case (Figure 4). The experiment is conducted on assembly activity for trip coil production (Figure 4). The ergonomic assessment is conducted considering the following criticality indices in the form of ergonomic parameter inputs:  
* Trunk bending angle;
* Neck bending/rotation angle;
* Left or right elbow angle;
* Spine;
* Number of repetitions of the same movements. The entire assembly process is described in Figure 5, where the frames of the EO\({}_{\text{i}}\)s of the worker are shown. In the first step, the analysis focuses on the set-up activity. This activity is divided into fourteen EOs. This section of the study provides an accurate ergonomic assessment method by introducing posture estimation algorithms based on  
Figure 4: Assembly activity for Trip coil. bi-dimensional (2D) video. To monitor the postures of the employees, we developed an ergonomic evaluation algorithm able to provide the values of the joint angles as an output. Figure 5: _Cont._  
Starting with the video of the whole working cycle, each EOi is isolated by identifying the portion of the video where a single repetition of the operation can be isolated. This process generates a set of frames (images) for each elementary operation of the work cycle. Each frame set is used as an input for the detection of the 3D human pose for each EOi. Figure 6 shows an example of human pose computation, including the predicted 2D keypoints for EOi. In the same figure, the key frames of the acquired image are shown. From these frames, we can see the joint angle based on the information acquired from the human skeleton incorporated with keypoint prediction. These predictions are the coordinates (see Figure 6) of 17 keypoints. Starting with these coordinates, an algorithm processes the predictions for each frame of each EOi. The outputs of this phase are the joint angles computed in the worst case for each EOi. Table 3 reports the values of the indicators arising from the ergonomic assessment for each EOi. Figure 5: Frames of elementary operations. Before running the fuzzy inference engine, it is necessary to normalize the inputs regarding the maximum values of their respective ranges. Table 4 shows the values normalized regarding five ergonomic parameters. As an example, let us consider the elementary operation EO\({}_{1}\), for which the input parameters for the fuzzy inference engine are as follows:  
Right elbow = 0.571  
Right shoulder = 0.761  
Neck bending = 0.655  
Spine = 0.137  
Repetition of the same movements = 0.630  
According to the membership functions, the fuzzy system assigns a label and a membership degree to each input. All of the rules in which the variables have these labels are considered by the decision logic.
## 4 Remote Sensing Platforms Used in Peatland Research
### Airborne-Based Remote Sensing
#### 4.2.1. Peatland Classification and Mapping  
Airborne-based remote sensing has proven effective in classifying and mapping peatland vegetation types. Studies by [PERSON] et al. (2021) and [PERSON] et al. (2019) demonstrated the ability to distinguish between different peatland vegetation communities using airborne hyperspectral and LiDAR data for the mapping and continuous monitoring of wetland ecosystems [42; 44]. Both studies demonstrate the effectiveness of integrating remote sensing products, such as hyperspectral bands, spectral indices, and topographic information, for accurate classification and mapping of wetland habitats. The research by [PERSON] et al. (2021) emphasises the potential of combining hyperspectral and LiDAR data to improve classification accuracy [42], while [PERSON] et al. (2019) highlights the advantages of remote sensing over traditional ground surveys for mapping peatland degradation [44]. Furthermore, a study by [PERSON] et al. (2017) used LiDAR data for boundary delineation in peatland complexes [24].  
The studies by [PERSON] and [PERSON] (2022) and [PERSON] et al. (2018) focus on assessing vegetation traits and water balance in peatland vegetation communities [45; 98]. [PERSON] and [PERSON] (2022) quantify throughfall, interception loss, and streamflow across various vegetation communities within a maritime raised bog, highlighting the importance of shrub vegetation in quantifying net precipitation [98]. [PERSON] et al. (2018) developed a methodology using airborne hyperspectral data to calculate the fractional coverage of various plant functional types within peatland ecosystems, showcasing the effectiveness of the multiple endmember spectral mixture analysis (MESMA) approach [45].  
These studies highlight the potential of airborne sensors to provide detailed information on vegetation composition and structure, facilitating peatland management and conservation efforts.
## 4 Calculation and Discussion for Eccentrically Compressed Steel Piers
### Seismic Response Analysis
At the same time, the key data comparison of the horizontal bidirectional seismic response calculation results obtained by the two kind of models is given in Table 7, including the maximum displacement and residual displacement, \(\delta_{\rm m}\) and \(\delta_{\rm r}\), respectively, in the X and Y directions, and the first-order frequency \(f\). As can be seen from the table, the results obtained by the improved fiber model are very close to those obtained by the hybrid model, while the error is within an acceptable range. In Figure 16, the calculation results of the local deformation of the hybrid model at the same time point are given. It can be seen that the local deformation is within the height of the \(L_{\rm red}\) at the bottom of the pier, and the increase in the eccentricity aggravates the degree of local deformation. Figure 14: Calculation results of the horizontal bidirectional seismic response analysis: (**a**) b2; (**b**) b3; (**c**) b4; (**d**) b13\(-\)10; and (**e**) b3\(-\)d.  
The above results further verify that the proposed equivalent hysteresis model and improved fiber model have a high calculation accuracy and good applicability for eccentrically compressed steel piers. Thus, the improved fiber model can be directly used in engineering to analyze the seismic response of eccentrically compressed square-section steel piers under different rarely met earthquakes, according to the flow chart in Figure 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Pier**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{\(\delta_{\text{m}}\)} & \multicolumn{2}{c}{\(\delta_{\text{r}}\)} & \multirow{2}{*}{\(f\)} \\ \cline{3-3} \cline{5-6}  & & **X** & & & & \\ \hline b2 & Fiber model & 8.234 & 3.699 & 8.125 & 2.395 & 1.452 \\ b2 & Hybrid model & 6.722 & 3.328 & 6.043 & 1.987 & 1.473 \\ b3 & Fiber model & 5.762 & 3.148 & 4.483 & 1.254 & 1.341 \\  & Hybrid model & 5.293 & 3.093 & 3.991 & 1.152 & 1.350 \\ b4 & Fiber model & 8.345 & 4.181 & 5.870 & 1.791 & 1.457 \\  & Hybrid model & 7.868 & 3.946 & 5.778 & 1.716 & 1.468 \\ b13\(-\)10 & Fiber model & 6.219 & 4.168 & 3.852 & 0.0180 & 2.553 \\ b3\(-\)d & Hybrid model & 6.024 & 3.614 & 3.774 & 0.0108 & 2.560 \\ b3\(-\)d & Fiber model & 6.742 & 3.094 & 6.445 & 0.520 & 1.310 \\  & Hybrid model & 6.532 & 2.957 & 6.347 & 0.106 & 1.316 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the seismic response analysis calculation results. Figure 16: Local deformation calculation results of the hybrid model: (**a**) b3 pier; (**b**) b3\(-\)d pier. Figure 15: Comparison of the shear force–displacement curves of the b3 pier: (**a**) X direction; (**b**) Y direction.
## 3 Proposed Error Compensation Method
### Pattern Detection and Pose Estimation
Since we applied robust image processing algorithms such as pattern detection and ellipse fitting to increase the performance of 3D position and orientation, the proposed system becomes more reliable on data preparation for the neural network used to compensate the error position.
# Developing a Hybrid Morphological Analyzer for Low-Resource Languages
## 3 Methodology
### Dataset Creation
#### 3.1.1 Pre-Processing  
The Kannada dictionary file in non-unicode Kannada text format was converted into Unicode format (UTF-16) using the online tool Sanka [34]. Certain words containing an aspirated phoneme were represented using an unaspirated phoneme followed by _an 'S'_ in the dictionary. For example, _aBiruci_ was represented as _absinuci_. These corrections were manually identified and incorporated before converting to WX notation. Special symbols like comma, colon, and full stop were eliminated. The dictionary containing words and their meanings was sliced so that only the words were retained, ignoring the descriptive meaning part. The segregation of homonyms categorized as nouns and verbs was considered under noun and verb paradigms. The mis-categorization of words was handled manually. Certain borrowed words, which, though present in the dictionary were not used in written Kannada language, were ignored, for example, the words such as _GeVArv_ and _walAS_. These words are omitted in the morphological analyzer that was developed. The words with zero-width non-joiners cause problems in conversion to WX notation, and they were resolved case by case. Also, the words containing _pZA_ and _rYa_, which were omitted in the modern Kannada character chart, though prominent in old Kannada language texts, were omitted. The segregation of homonyms and misclassifications was rectified manually and included for the data to be considered while developing the analyzer. It was observed that the excluded words from the raw dictionary file were roughly 5% of the dictionary data. Table 2 gives the analysis of dictionary data before and after pre-processing.  
Figure 1: Steps involved in developing the proposed morphological analyzer.  
A patch was written in Python 3 to transliterate given Kannada Unicode input text into its equivalent WX text as a morphological analyzer was built for Kannada using WX notation. The steps involved in pre-processing are shown in Figure 2.
# Flexible-Link Multibody System Eigenvalue Analysis Parameterized with Respect to Rigid-Body Motion
###### Abstract  
The dynamics of flexible multibody systems (FMBSs) is governed by ordinary differential equations or differential-algebraic equations, depending on the modeling approach chosen. In both the cases, the resulting models are highly nonlinear. Thus, they are not directly suitable for the application of the modal analysis and the development of modal models, which are very useful for several advanced engineering techniques (e.g., motion planning, control, and stability analysis of flexible multibody systems). To define and solve an eigenvalue problem for FMBSs, the system dynamics has to be linearized about a selected configuration. However, as modal parameters vary nonlinearly with the system configuration, they should be recomputed for each change of the operating point. This procedure is computationally demanding. Additionally, it does not provide any numerical or analytical correlation between the eigenpairs computed in the different operating points. This paper discusses a parametric modal analysis approach for FMBSs, which allows to derive an analytical polynomial expression for the eigenpairs as function of the system configuration, by solving a single eigenvalue problem and using only matrix operations. The availability of a similar modal model, which explicitly depends on the system configuration, can be very helpful for, e.g., model-based motion planning and control strategies towards to zero residual vibration employing the system modal characteristics. Moreover, it allows for an easy sensitivity analysis of modal characteristics to parameter uncertainties. After the theoretical development, the method is applied and validated on a flexible multibody system, specifically using the Equivalent Rigid Link System dynamic formulation. Finally, numerical results are presented and discussed.  
Keywords:modal analysis; flexible multibody systems; linearized models +
Footnote †: journal: Applied Mechanics and Physics Letters
# A Review on Internet of Things for Defense and Public Safety
## 5 Building IoT for Tactical and Emergency Environments
### IoT Standardized Protocols
#### 5.1.2 Service Discovery Protocols  
Resource management mechanisms are able to register and discover resources and services in a self-configured, efficient and dynamic way. Such protocols include CoAP resource discovery, CoAP Resource Directory (RD), and DNS Service Discovery (DNS-SD) which can be based on mDNS (Multicast DNS). A detail description of their characteristics can be seen in [125].
## 4 Simulations  
This paper studies the malfunction probability of current differential protection devices in power systems. The communication type is optical fiber. The relevant parameters used in the simulation studies are shown in Table 1[19, 21, 28]. The simulation tries to analyze the effects of key factors on the malfunction probability of optical fiber current differential protection. A comparison of the malfunction probabilities of protection in normal and abnormal communication conditions will also be provided.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Name** & **Value** & **Description** \\ \hline \(f_{\text{max}}\) & Consistent delay & 12 ms & If the measured consistent delay \(t\) is larger than \(t_{\text{max}}\), \\  & threshold & & the relay will refuse to operate when internal fault occurs. \\  & & & (1) Considering the uncertainty of environment impacts and \\  & Normal distribution & & communication condition variations, the consistent delay \(t\) \\ \(\sigma_{1}\) & standard deviation of & 1/6 ms & follows the Normal distribution. \\  & consistent delay & & (2) Assuming 99.7\% of consistent delay is less than 1 ms, \\  & & & then according to the probability theory the consistent delay \\  & & & fluctuation standard deviation is 1/6 ms. \\  & & & (1) If the measured bit error rate \(w\) is larger than the \(w_{\text{max}}\), \\  & Communication bit & & the relay will refuse to operate when internal fault occurs. \\  & error rate threshold & 2 \(\times\) 10\({}^{-6}\) & (2) The value of communication bit error rate threshold is relative \\  & & & fuzzy, it may be related to the types of relay protection and the \\  & & & distribution of the communication bit error. Here we select a \\  & & & typical value 2 \(\times\) 10\({}^{-6}\). \\ \(I_{sp}\) & Current start threshold & 0.5 \(\times\)\(I_{N}\) & Protection operates when the differential current is larger than \(I_{sp}\). \\  & for start criterion & & \\  & Normal distribution & & \\  & standard deviation of & & \\ \(\sigma\) & differential current & 0.26 \(\times\)\(I_{N}\) & The selected value 0.26 \(\times\)\(I_{N}\) is calculated from the reliable \\  & caused by & & coefficient of current different protection. \\  & inconsistent delay & & \\ \(k\) & Braking coefficient & 0.6 & Braking coefficient in braking ratio criterion. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the simulation.  
Figure 11: Refuse-operation probability for consistent delays and bit errors.
multi-level supervised network (MLSN), consisting of a backbone network and several sub-networks; it utilizes low-level and high-level information to re-identify pedestrians. There are also some methods that utilize a deep CNN for learning on top-level feature maps [2], and directly design a more suitable metric loss function to learn the distance between two high-level features [3]. Although there are already many multi-level feature learning methods related to pedestrian re-identification, how to better obtain and utilize multi-level information remains a challenging task.  
To alleviate these issues, this article considers designing a weakly multi-feature fusion pedestrian re-recognition model, introducing a multi-feature fusion mechanism to extract feature information from different layers into the feature space and fuse them into the deep and shallow joint features, achieving the goal of fully utilizing the rich information in the image and improving the performance and robustness of the pedestrian re-recognition model. Secondly, by matching the target character with unprocessed surveillance videos, it is possible to know that the identity of a person appears in the video, without the need to annotate the identity of a person in any frame of the video during the training process. By replacing the accurate annotations with broad annotations, the annotation of the training image is simplified. The pedestrian identities that have appeared in the video are placed in a package, and each package is assigned a video-level label. This greatly reduces the annotation work, transforming this weakly supervised pedestrian re-recognition challenge into a multi-instance and multi-label learning problem, as shown in Figure 1b. Therefore, for a video, there may be multiple video-level tags. The Re-ID task learns by comparing the pedestrian images obtained from the videos with query images during the inference process, so images are not organized in the form of packets during the application, as shown in Figure 1c. Extensive experiments were conducted to demonstrate the feasibility of the proposed weakly supervised model, and the performance of the proposed method compared to relevant methods was validated in the weakly labeled datasets.
## 3 Identification Scheme with Privacy-Preserving
### Identification of Multiple Secondary Users
At the public classifier, all 1-bit decisions are fused together according to a logic rule, denoted as  
\[Y=\sum_{i=1}^{K}D_{i} \begin{cases}\geq n,&H_{0}\\ <n,&H_{1}\end{cases}  \tag{9}\]  
where \(H_{0}\) and \(H_{1}\) represent the inference drawn by the central classifier about whether or not an image is from a specific known camera model, respectively. The threshold \(n\) is defined as an integer that denotes the \"\(n\)-out-of-\(K\)\" voting rule [25]. We can see that the OR rule applies when \(n=1\) and the AND rule applies when \(n=K\). Considering that the OR rule determines an image to be captured by camera model \(C_{0}\) as long as one of the auxiliary users makes a decision of 1-bit, this may lead to a false judgment. Meanwhile, the AND rule requires all secondary users to make the same decision, which can easily lead to a missed detection. Furthermore, considering application in large-scale datasets, we discuss the computational time and memory required when using different numbers of secondary users in the distributed scheme. As shown in Table 1, with the increase in the number of secondary users, the scale of the distributed framework grows, leading to an increase in both computational time and memory usage. For example, under the same conditions, when using three secondary users, the system's computational time and memory usage are 1.54 h and 10.91 GB, respectively; whereas, with 12 secondary users, the computational time and memory usage increase to 6.17 h and 44.11 GB, respectively. Since the distributed scheme with three auxiliary users was sufficient for the scale of the classical datasets we used, and considering the computational overhead, we only use three auxiliary users in this paper, i.e., \(K=3\). Based on \(K=3\), setting \(n=2\) strikes a balance between the OR and AND rules, reducing computational complexity and improving system efficiency, while maintaining good identification performance. Therefore, we set \(n=2\) in this paper, i.e., the public classifier identifies an image as captured by camera model \(C_{0}\) only when the decisions of at least two secondary users indicate that the image is from the known camera model \(C_{0}\).
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 3 Methodology
### Spatial-Temporal Graph Neural Network
#### 3.3.1 Graph Learning Layer  
In wargaming, the positional data of the pieces exhibit significant sparsity. At the same time, the movement of these pieces is not arbitrary, it is governed by various factors such as occupied points, opposing units, and terrain types. To effectively model this complex behavior, we introduce a graph learning layer that constructs a graph data structure, one that is capable of adaptively extracting a sparse graph adjacency matrix. This matrix reflects the inherent characteristics and potential relationships within the data.  
While distance measures such as dot product and Euclidean distance are commonly used to quantify the similarity between node pairs, these measures are typically symmetric or bidirectional. However, in multivariate wargame piece prediction, the relationships among pieces are often unidirectional, as the state change of one piece can influence the states of others in a directed manner. To capture these unidirectional relationships, we use a graph learning layer, which is defined by the following equations:  
\[A_{t}=\text{softmax}(\tanh(S_{t}^{\prime}W_{1})\tanh(W_{2}^{T}S_{t}^{\prime T })),\quad\forall t\in\{1,\cdots,T\}, \tag{7}\]  
\[A_{t}[i,-idx]=0,idx=argtpk(A_{t}[i,:]),\quad\forall i\in\{1,\cdots,N\}, \tag{8}\]  
where \(S_{t}^{\prime}\) represents the spatial feature embedding of the pieces at time \(t\), and \(W_{1}\) and \(W_{2}\) are learnable parameter matrices that are optimized during training. Originally proposed by [37], Equation (7) generates an asymmetric graph adjacency matrix \(A_{t}\). Equation (8) then selects the top-\(k\) nodes as neighbors for each node, and it is implemented using the \(argtpk(\cdot)\) function.  
This graph learning layer is designed to capture the dynamic spatial dependency relationships of the pieces. It adjusts the connections between nodes in the graph structure based on the input features of each situational graph. This dynamic adjustment of node relationships over time enables the model to capture temporal dependencies and spatial correlations effectively.