esa-sceva/satcom-chunk-collection huggingface dataset:
# Micro Vibration Measurement with Microscopic Speckle Interferometry Based on Orthogonal Phase
## 1 Introduction
These two methods both require additional signal modulation devices (analog-to-digital converters or signal generators) to modulate the frequency of the reference beam to be consistent with the vibration frequency to achieve the measurement. In this paper, we propose a new vibration measurement method based on orthogonal phase and microscopic speckle interferometry. The vibration with frequency from 0.5 Hz to 20 Hz is measured and the measurable frequency range can be greatly improved when using a high frame rate camera. The maximum measurable amplitude can reach 60 nm. Compared with the heterodyne method, homodyne method, etc. mentioned above, our method exhibits two main advantages: 1. It can realize real-time full-field measurement without scanning; 2. The calculation process and measurement equipment are very simple, and there is no need to introduce stroboscopic light source or heterodyne device. Additionally, compared with the traditional ESPI technology, our method does not require additional signal modulation devices.
## 5 Experimental Setup  
In order to validate the developed numerical algorithm, an experimental program focused on measuring the free rocking response was designed and conducted. All the experiments were conducted in the Structural Laboratory of the Faculty of Civil Engineering in Rijeka. The experimental setup is shown in Figure 8. Each block was initially tilted at an angle of \(\varphi_{0}\) and released from rest in such a position (Figure 8b). This followed the procedure previously suggested by [PERSON] et al. (2014). The rocking response was monitored by a contactless optical measuring system ZEISS Aramis 4M (ZEISS, Baden-Wurttemberg, Germany), with two cameras, each with resolution \(2400\times 1728\) pixels, and a measuring frequency of 150 fps (Figure 8a).  
The experiment is performed on a rigid base to focus entirely on validating the contact treatment in the developed algorithm, without introducing the effects of the elasticity of the  
Figure 8: Experimental setup: (**a**) top view of the optical measuring system and measuring volume, (**b**) front view of the measuring volume.  
beam. Therefore, the experimental validation is performed for the case when the base in the numerical code is rigid, which was obtained by setting the beam Young modulus to a high value.  
The mass and dimension of the tested blocks are given in Table 1. Each block has a rectangular base of dimensions \(b\times b\), mass \(m\), height \(h\), angle between the block's vertical edge, and the diagonal \(\alpha\) and length of the semi-diagonal \(s\). The rocking of a block was physically constrained so that only the rotation of the block was possible, while any sliding and/or jumping of the block was prevented (as shown in Figure 9a, according to [14]). This was obtained by using tape strips, marked with blue and green in Figure 9a, which are attached to the block and base. The tape strips do not allow the slipping of the block in the \(x\) direction, but since they posses no flexural stiffness rotation is allowed.  
Since the developed algorithm assumes a contact point between the bodies, the experiment were designed so that the contact between the block and the body beneath it occurs along 1.5 mm of the surface near the block's edge. In this way, the assumption used in the algorithm is validated.
# Novel Numerical Analysis of a Self-Circulating Bearing
## 2 Methods
The analytical solution for the pressure was used in the three inner layers of the porous media journal, as shown in Figure 2b. The passive reservoir was simplified for the current model. The reservoir pressure was not updated with each iteration of fluid within the porous bushing but instead held to a representative pressure distribution correlating with experimental results [19]. The fluid film between the shaft and the porous media bushing was modeled as an unwrapped bearing using a finite difference method with one radial grid, 200 divisions in the circumferential direction, and 60 divisions in the axial direction. This unwrapped grid was applied at each of the three inner layers of the porous medium. The supply pressure from the passive reservoir's representative equation was applied at each grid location. A convergence study for the selected grid size was performed to confirm that it was adequate. For stability and the ability to use larger time steps, the implicit discretization  
Figure 3: Pictorial description of superposition theorem used for closed-form solution within (**a**) ‘first problem’ and (**b**) ‘second problem’. of the Reynolds equation was used. Numerical stability was achieved for the parameters used; however, if the injection velocity magnitudes were too large, instabilities could occur. One method to prevent this would be to lower the relaxation coefficient; however, this is only valid for a steady-state solution, as it disrupts the time step. The steady-state solution was obtained once the pressure solution converged below 0.1% for the error criterion, which was obtained using Equation (9). The loads in the radial and tangential direction were determined with Equations (10) and (11), and the bearing load and attitude angle were calculated using Equations (12) and (13). \[error=max\left[\frac{\left(P_{b}\right)_{ij}-\left(P_{old}\right)_{i,j}}{ \left(P_{old}\right)_{i,j}}\right] \tag{9}\]  
\[W_{t}=\int_{0}^{L}\int_{0}^{2\pi}P_{b}Rcos(\theta)d\theta dz \tag{10}\]  
\[W_{r}=\int_{0}^{L}\int_{0}^{2\pi}P_{b}Rsin(\theta)d\theta dz \tag{11}\]  
\[W=\sqrt{w_{t}{}^{2}+w_{r}{}^{2}} \tag{12}\]  
\[\phi{=\tan^{-1}\left(-\frac{w_{r}}{w_{t}}\right)} \tag{13}\]  
The feeding parameter, Equation (14), was used for a comparison of the results. A larger feeding parameter implies a large injection velocity. The feeding parameter can be modified by either the porous medium permeability or the porous medium thickness, \(H\). \[\Lambda=12 KR^{2}/(c^{3}H) \tag{14}\]
## 2 Background
### Overview of the Cell BE  
The Cell Broadband Engine (Cell BE) is a heterogeneous multiprocessor that was developed by Sony, IBM, and Toshiba in 2000. Although it has been long time from the first release of the Cell BE, it has 256 GFLOPS (Giga Floating Operation Per Second). It still provides good performance as a single chip processor compared to one of today's high-performance commercial processors, Intel Core i7 series (Intel Core i7 975 has theoretical performance 221.44 GFLOPS) [24]. In addition, the Cell BE is appropriate to show heterogeneous program models. The Cell BE consists of one Power 64 architecture processor, referred to as a _Power Processor Element_ (PPE), and eight co-processors, referred to as _Synergistic Processor Elements_ (SPEs). The Cell BE also includes Directed Memory Access (DMA) controller and high bandwidth data bus, referred to as an Element Interconnection Bus (EIB). These various components are presented in Figure 1. The Cell BE processor incorporates a Single InstructionMultiple Data (SIMD) execution unit, high power and area efficiency, large memory bandwidth, a large bandwidth on-chip coherent bus, and a high-bandwidth flexible I/O [25].  
The PPE is a dual-threaded, dual-in-order issue 64 bits Power-architecture processor. It has a 32 KB instruction cache and a 32 KB data cache, as well as a 512 KB L2 cache. In addition, the PPE has an _AltiVec_ vector extension unit and floating point and integer SIMD instruction set.  
The SPEs are composed of a Synergistic Processor Unit (SPU), a 256 KB local store, and a Memory Flow Control (MFC). The execution performance of the SPEs affects much of the overall computational performance of the Cell BE. The SPU contains a 128 bit wide dual-issue SIMD unit fully pipelined to all precisions, with the exception of the double precision vector unit. The SPE can access main storage with an effective address (EA) translation by MFC and asynchronously transfer data to local storage, which has both narrow (128 bits) and wide (128 bytes) features.  
The Element Interconnect Bus (EIB) is a coherent bus that can transfer up to 96 bytes/s. It consists of four 16 bytes rings, each of which is only capable of unidirectional data transfer, clockwise or counter-clockwise, each ring supporting up to three simultaneous data transfers. The Cell BE employs dual channel _Rambus_ XDR DRAM, which is capable of transferring 12.8 GB/s per 32 bits memory channel. It is therefore capable of supporting total bandwidth of 25.6 GB/s [26].
# Hysteresis-Based Current Control Approach by Means of the Theory of Switched Systems
## 2 Theoretical Background and Proposed Control Law
### Proposed Switching Control Law  
Throughout the remaining paper, it is supposed that the input \(\underline{u}(t)\) of the switched system in (2) is predefined in advance, e.g., by a limited set of voltage vectors. Using (3) and (4), the subsystems can be represented as  
\[\underline{\hat{x}}(t)=\underline{\underline{A}}\Big{(}\underline{\bar{x}}(t )-\underline{x}_{\text{eq,s}}\Big{)}+\underline{\underline{A}}^{-1} \underline{\underline{B}}\,\underline{u}(t)\quad\forall s\in\mathcal{S} \tag{8}\]  
with unique equilibria \(\underline{x}_{\text{eq,s}}\). A quadratic function  
\[V_{s}(\underline{\xi}_{\text{eq,s}}(t))=\underline{\xi}_{\text{eq,s}}^{T}(t) \underline{\underline{M}}\,\underline{\xi}_{\text{eq,s}}(t)\quad\forall s\in \mathcal{S}, \tag{9}\]  
with the weighting matrix \(\underline{\underline{M}}\) from (5), is assigned to each subsystem. These functions do not necessarily have to be Lyapunov functions, i.e., \(\dot{V}_{s}(\underline{\xi}_{\text{eq,s}}(t))<0\,\forall\underline{\xi}_{ \text{eq,s}}(t)\) is not required. In this paper it is shown that convergence is also given with \(\dot{V}_{s}(\underline{\xi}_{\text{eq,s}}(t))>0\) for certain systems and reference trajectories.  
Then, it is proposed to extend the switching sequence \(s(t)\) from [19] and define a new stabilizing control law of the form  
\[s(t)=\left\{\begin{array}{ccc}\arg\Bigl{(}\max_{s\in\mathcal{S}}(h_{s}( \underline{\bar{x}}(t),\underline{\bar{x}}_{\text{ref}}(t)))\Bigr{)},&\forall &\underline{\bar{x}}(t)\
otin\mathcal{R}^{n}_{\delta}\\ s(t_{\delta}),&\forall&\underline{\bar{x}}(t)\in\mathcal{R}^{n}_{\delta}\end{array}\right. \tag{10}\]where \(t_{\delta}\) denotes the time when \(\underline{\tilde{x}}(t)\) enters \(\mathcal{R}^{n}_{\delta}\). The so-called quality functions \(h_{s}(\underline{\tilde{x}}(t),\underline{\tilde{x}}_{\text{ref}}(t))\) are chosen in such a way that \(\underline{\tilde{x}}(t)\) converges to \(\mathcal{R}^{n}_{\delta}\) in finite time. Convergence is achieved by fulfilling the following conditions for all \(s\in\mathcal{S}\) and all \(t\geq 0\)  
\[h_{s}(\underline{\tilde{x}}(t),\underline{\tilde{x}}_{\text{ref}}(t))>0 \forall\quad\underline{\tilde{x}}(t),\underline{x}_{\text{eq},s} \in\left\{\mathbb{R}^{n}\Big{|}\underline{\tilde{x}}(t)\
eq\underline{x}_{ \text{eq},s}\right\} \tag{11}\] \[h_{s}(\underline{\tilde{x}}(t),\underline{\tilde{x}}_{\text{ref}} (t))=0 \forall\quad\underline{\tilde{x}}(t)=\underline{x}_{\text{eq},s}\] (12) \[h_{s}(\underline{\tilde{x}}(t),\underline{\tilde{x}}_{\text{ref} }(t))=\hbar \forall\quad\underline{\tilde{x}}(t)=\underline{x}_{\text{ref}}(t) \tag{13}\]  
where \(\hbar\) is some constant, and  
\[h_{s}(\underline{\tilde{x}}(t),\underline{\tilde{x}}_{\text{ref}}(t))<0\quad \forall\quad\underline{\tilde{x}}(t),\underline{x}_{\text{eq},s}\in\left\{ \mathbb{R}^{n}\Big{|}\underline{\tilde{x}}(t)\
eq\underline{x}_{\text{eq},s} \right\}\quad\text{if subsystem $s$ is active.} \tag{14}\]
# Training from Zero: Forecasting of Radio Frequency Machine Learning Data Quantity
### RF Characteristics
#### 1.2.2 Understanding RF Data Origin  
There are three common sources of data within ML dataset generation [8]. The first is the _captured_ or _collected_ data acquired by using a sensor and recording the data. Under the most intuitive conditions, data collection performed using this approach in the application space provides the highest quality of data for the problem to learn from because all unknown characteristics and sensor degradations will be present in the data [41]. However, when performing the capture of rare events or while in search of other infrequent and uncontrollable events, performing collection events can prove to be difficult to properlylabel, let alone find. These problems, along with having to procure and sustain the equipment and personnel to perform the collection, often make collection in large quantities impractical and expensive.  
_Synthetic_ datasets are therefore the most common and typically orders of magnitude cheaper to procure due to not being bound to waiting on real-world limitations. For example, synthetic generation can occur in parallel for vastly different conditions, with the limitation being computation resources, rather than the sensors and personnel in collection events. The trade-off with synthesis is that significantly more information on the data is necessary in order to properly simulate, which, without the appropriate knowledge, can render models in the field useless [41].  
The process of creating an _augmented_ dataset tries to bridge the strengths of captured and the synthetic dataset creation, while covering their weaknesses [8]. By taking captured data and adding synthetic permutations of SNR, FO, and SRM, the augmented dataset can smooth out missing observations from a limited collection event by having a better understanding of the detection characteristics of the sensors in use, while preserving all other real-world degradations native to the application space.  
Here, the different origins of data are kept separate from each other to achieve a better understanding of the characteristics of each approach, but the fusion of such datasets should be carried out either by directly combining the datasets or by performing staged learning in practice.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 2 Materials and Methods
### Dilatation Height IoU (DHIoU)  
Data association is a crucial task in MOT, typically involving the generation of an affinity matrix and using an assignment algorithm to match objects. We observed that while the IoU distance between bounding boxes can implicitly address occlusion to some extent, allowing for the correction of continuous object movements through frame-by-frame associations, it falls short when dealing with rapid deformations of bounding boxes caused by complex object motion. Our analysis revealed that changes in an object's height are generally smaller compared to its width, suggesting that height possesses a certain degree of stability and compensatory capability. Based on this observation, we proposed the combination of height Intersection over Union with IoU distance to calculate the affinity matrix, aiming to achieve a more comprehensive representation of information and improve the accuracy and robustness of data association.  
However, both height Intersection over Union and area IoU range from (0,1), and the result of their multiplication tends to be 0, which limits the utility of the affinity matrix and affects the model's accuracy and robustness. To address this, we introduced a dilatation coefficient to counteract this effect, thereby enhancing model performance.  
Specifically, we defined two boxes, \(b^{1}(x_{1}^{1},y_{1}^{1},x_{2}^{1},y_{2}^{1})\) and \(b^{2}(x_{1}^{2},y_{1}^{2},x_{2}^{2},y_{2}^{2})\), where \(h^{1}(y_{1}^{1},y_{2}^{1})\) and \(h^{2}(y_{1}^{2},y_{2}^{2})\) represent the heights of the two boxes and \(B^{1}\) and \(B^{2}\) denote their respective areas, with \(d\) being the dilatation coefficient. By multiplying the dilatation coefficient, height IoU, and area IoU, we obtained the Dilatation Height IoU (DHIoU), as shown in Equation (19). Figure 2b shows that the dilatation coefficient \(d=1.2\) is the optimal parameter.  
\[\text{DHIoU}=d\cdot\frac{Inter\sec tion(h^{1},h^{2})}{Union(h^{1},h^{2})}\cdot \frac{Inter\sec tion(B^{1},B^{2})}{Union(B^{1},B^{2})} \tag{19}\]  
We calculated the DHIoU between each pair of detection boxes filtered by CEF and prediction boxes predicted by AMN-KF, which results in an affinity matrix. This affinity matrix was then used for data association via the Hungarian algorithm, yielding the tracking results.
## 4 Conclusions  
In the present study, the authors proposed a simple method for on-site dosimetry using a radiochromic film (Gafchromic EBT-XD) coupled with a portable colorimeter (Nix) for reading and investigated its performance and practicality for up to 40 Gy of 160 kV X-rays. It was confirmed that the color intensities of red and green components simply measured with Nix had favorable dosimetric properties such as a monotonical increase with dose, good reproducibility and low variability (CV \(<\) 3%) in addition to excellent portability. While it was found that the readings with Nix show some angle dependence such as the orientation effects reported in previous studies using flatbed scanners, this issue could be well averted by careful handling of a radiochromic film.  
These findings indicate the good potential of the proposed on-site dosimetry method for application to various occasions of real or potential high-dose radiation exposure. It is expected that the feature of simple and rapid dose quantification would be desirably used for QAs of radiation sources used in industry and research as well as medicine. The radiation-induced color change that can be detected by the naked eye would enable us to take immediately proper medical actions for a patient who received a high-dose radiation exposure in radiological emergencies. Moreover, such a simple dosimetry technique is expected to be utilized as a supplementary tool in QA of the external radiotherapy beams that are delivered repeatedly to many cancer patients with short-time intervals. Further studies with different exposure situations and different quality radiations are needed to clarify the effectiveness and limitations of this method in those possible applications.  
Conceptualization, H.Y. ([PERSON]); material preparation and maintenance, H.Y. ([PERSON]) and H.Y. ([PERSON]); irradiations and measurements, H.Y. ([PERSON]) and H.Y. ([PERSON]); data analysis, H.Y. ([PERSON]) and H.Y. ([PERSON]); writing--original draft preparation, H.Y. ([PERSON]); writing--review and editing, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by JSPS KAKENHI Grant Number 18 KK0147 and the Program of the Network-type joint Usage/Research Center for Radiation Disaster Medical Science funded by the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan and Hiroshima University.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Methodology  
### Frameworks  
In this research, we propose to employ a framework using pre-trained word embedding and training net LSTM. Figure 1 shows the framework for Arabic text classification. Moreover, this research aims to understand the effect of word embedding algorithms. fastText [14] and GloVe [15] are compared in both frameworks. Glove [15] and FasText [14] are advanced developments of Word2 vec [13]. Glove was developed to capture global context; however, most embedding techniques, including Glove, to fail to capture incomplete words or subwords with slightly different meanings. According to [15], fastText was  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Research Papers** & **Dataset** & **Composition** & **Language** & **Algorithm** & **Accuracy** \\ \hline \multirow{4}{*}{[41]} & Corpus on Arabic & 40,000:20,000 Positive, & Arabic (Modern & LSTM + & \multirow{2}{*}{88.05} \\  & Egyptian tweets & 20,000 Negative & Standard Arabic & & \\  & & & and Egyptian Dialect & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \hline \multirow{4}{*}{[44]} & LABR (Large Scale Arabic & 63,257:5 classes & \multirow{4}{*}{Arabic} & FastText Skip & \multirow{2}{*}{90.75} \\  & Book Review) & 2.5258, 3:12,201, & & & \\  & & 4:19,054, 5: 25,778 & & & \\ \hline \multirow{4}{*}{[36]} & LABR (Large Scale Arabic & 63,257:5 classes & \multirow{4}{*}{Arabic} & FastText Skip & \multirow{2}{*}{90.75} \\  & Book Review) & 2.5258, 3:12,201, & & & \\  & & 4:19,054, 5: 25,778 & & & \\ \hline \multirow{4}{*}{[42]} & LABR (Large Scale Arabic & 63,257:5 classes & \multirow{4}{*}{Arabic} & \multirow{4}{*}{Logistic Regression} & \multirow{4}{*}{84.97} \\  & Book Review) & 2.5285, 3:12,201, & & & \\ \cline{3-5}  & & 4:19,054, 5:25,778 & & & \\ \hline \multirow{4}{*}{[42]} & Arabic SemEval-2016 dataset & \multirow{4}{*}{24,028} & \multirow{4}{*}{Arabic} & CNN & \multirow{2}{*}{82.7} \\  & for the Hotels domain & & LSTM & & \\ \hline \multirow{4}{*}{[43]} & Arabic SemEval-2016 dataset & \multirow{4}{*}{24,028} & \multirow{4}{*}{Arabic} & Bi-LSTM & \multirow{2}{*}{87.31} \\  & for the Hotels domain & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Arabic Sentiment Classification Literature review.  
developed to capture the internal structure of the words in morphologicaly rich languages, including Arabic. FastText, on the other hand, was trained not only using words, but also with subwords using n-grams. For example, the word \"Eating\" would be considered as \"eating\", \"eat\" and \"ing\" during training. This process leads to higher computing load during the training process; however, it successfully deals with incomplete words and chanes in word form. The Arabic language has some morphological operations that slightly affect meaning, such as by changing the word \"kataba\" (write), into \"kitaabun\" (book), and \"maktabun\" (place to write). Therefore, this research observes the impact of implementing a morphologically aware embedding technique (fastText) compared to Glove.
## 4 Validation and Comparison
### Comparative Testing of Baseline Methods
#### 4.4.3 Path Loss Prediction Method Based on RNN  
Recurrent neural networks are well suited for processing sequential data and have applications in some scenarios related to path loss prediction [17; 22]. We implemented a recurrent neural network model using PyTorch version 2.3.0 with hyperparameters that include the input feature dimensions, the number of hidden layer units, the type of activation function, and the setting of the fully connected layers. Table 7 shows the optimal combination of hyperparameters for the recurrent neural network determined through the hyperparameter optimization process.