esa-sceva/satcom-chunk-collection huggingface dataset:
# I3 Mote: An Open Development Platform for the Intelligent Industrial Internet
## 4 Platform Hardware
### Data Fusion  
The platform is equipped with a high performance processor with high-computing capabilities. This enables the I3 Mote not only to collect data from multiple sensors, but to manage the data received beyond the simple preprocessing (e.g., aggregation, compression, mining, etc.) The board includes an expansion socket that allows the connection of external sensing systems (e.g., industrial probes). The module is complemented by several on-board sensors with multiple functions, as well as all the required connectivity. In more detail, the data-fusion unit (Figure 5) includes the following modules:  
* MSP432: The MSP432 is the data fusion core. This advanced MCU platform is built around the high performance ARM Cortex-M4F core, featuring DSP extensions and an integrated FPU (Floating-Point Unit). The MSP432 acts as the data hub of the board. This involves having full access to the different sources of data, mainly on-board and external sensors, with direct access to the SSI. The MSP342 is connected to the communications processor (CC2650) through a dedicated high-speed Serial Peripheral Interface (SPI).
* Non-Volatile Memory: The board integrates a 32 K\(\times\)8 (256 Kbit) Serial Electrically Erasable PROM (EEPROM). This device is capable of both random and sequential reads up to the 256 K boundary. The 24 LC256 has a 2-wire inter-integrated circuit (I2C) compatible serial interface accessible from both the MSP432 and the CC2650. Therefore, it can be used as a mail-box to exchange messages between the two processors, as an off-line alternative to the dedicated SPI channel.
* Smart Sensor Interface: The SSI is a \(2\times 10\) pin, 1.27 mm expansion header designed to connect to an application-specific module. The SSI includes a standard SPI port enhanced with an interrupt line, I2C, 2 pulse with modulation (PWM) pins, 2 single analog to digital converter (ADC) lines or 1 differential pair, 4-20 mA bus lines and 3/5 V power sources.
* On Board Sensors: The I3 Mote incorporates multiple on-board sensors. The specific ICs belong to the new generation of high-performance low-cost sensors including: an ambient light sensor (OPT3001), a temperature/humidity sensor (HDC1080), a MEMS thermopile sensor (TMP007) for contactless temperature measurements, an absolute barometric pressure MEMS sensor (BMP280) and, finally, a three-axis linear accelerometer (LIS2 HH12). All these devices have been selected among those with the lowest power consumption on the market.
## 5 Discussion and Analysis
### Why ConvProtoNet Works: Some Comparisons
#### 5.2.2 With Prototype Network  
Prototypical Network (ProtoNet) [9] is one of the classic few-shot models which makes some assumptions that mean of embedded support set can represent the class distribution during meta-testing. However, this assumption only holds when each class has a unimodal distribution in the embedding space according to [11]. On the contrary, embedded data in the same class often satisfies multimodal distribution and mean of embedded support set will become an improper prototype in this case, as shown in Figure 8.  
ConvProtoNet is designed to alleviate the negative effect of multimodal distribution. To demonstrate ConvProtoNet can produce more representative class prototypes than ProtoNet, we project the samples in the same class to embedding space and compute prototypes with ProtoNet and ConvProtoNet respectively. Distributions of prototypes from two models are estimated by gaussian kernel density estimation (KDE) where embedded data distribution is estimated in the same way. Finally, the fitting degree of prototype is measured by KL divergence (also called relative entropy) and results in 9 show that prototypes produced by ConvProtoNet have much lower KL divergence to the real data distribution than these produced by ProtoNet. This indicates ConvProtoNet can better handle the data satisfying multimodal distribution. Besides, Figure 9 shows that ConvProtoNet can properly process the noisy dataset whereas ProtoNet is heavily influenced by the noise. For ProtoNet, the difference between fitted distribution and real data distribution is even getting much larger when more samples (\(K=10\)) are provided.  
Another shortcoming of ProtoNet is that the last ReLU in the embedding module truncates the negative values which shrinks the valid embedding space. As shown in Figure 10, because ReLU only allows positive values in the embeddings, the valid embedding space only occupies \(1/2^{D}\) of the real vector space when the embedding space has \(D\) dimensions and this seriously limits the optimization of the model. Besides, maximum angle \(\theta\) between two vectors can only be \(90^{\circ}\) in the shrinking embedding space which becomes an obstacle for cosine similarity.  
In ConvProtoNet, we solve this problem by removing the last ReLU in induction module to allow negative values in prototypes described in Section 3.
# Video Games in Rehabilitation Programs for People with Parkinson's Disease: A Systematic Review
## 1 Introduction
In semi-immersive VR, users interact through screens or panoramas, creating a partial immersion. In non-immersive VR, users participate through interfaces such as a computer without losing contact with the real world [19]. Finally, augmented reality integrates digital and physical environments by superimposing computer-generated holograms onto real-world surroundings [20]. VR has shown great relevance in the world of rehabilitation in recent years due to its low costs. Many current VR systems are versatile, thus being able to extrapolate different rehabilitation guidelines to the home environment [18; 21]. Digital technologies play a key role in rehabilitation and health promotion, contributing to improved daily functioning and extending independent living among older adults, including those with PD. However, age-related declines in motor and cognitive abilities, whether stemming from natural aging or conditions like PD, may create challenges that hinder the independent adoption and effective use of these technologies [22; 23]. Preliminary studies have suggested that these technologies may offer benefits in terms of improving balance, coordination, gait, and other aspects of motor function in PD patients. Despite the growing interest and emerging research in this field, there is still no systematic and comprehensive synthesis of the available evidence on the effectiveness of VR and video games in the rehabilitation of people with PD. The aim of this paper is to conduct a systematic review of the published information on the application of VR and video games in rehabilitation programs for people with PD and analyze their impact on rehabilitation outcomes.
# Numerical Simulation of Anchor Pullout and Shear Tests Using a Regularized Damage Model
## 1 Introduction
Additionally, the size of the concrete sample affects strength. To avoid the edge effects, the support distance from the anchor should be at least 2 h\({}_{\text{ef}}\). The strength of an anchor, in cases where concrete failure occurs, is proportional to h\({}_{\text{ef}}\)\({}^{1.5}\)[14] when concrete failure occurs. Finally, tests conducted by [PERSON] [15] have demonstrated the impact of concrete slab thickness on the tensile strength of the stud. Concerning reinforcement, its impact during anchor pullout tests is a topic of debate. On one hand, reinforcement appears to be less beneficial for relatively strong concretes (C40/50) and standard studs (f\({}_{\text{yk}}\) = 365 MPa) [3]. Moreover, it has been observed that for embedded head studs, the resistance of the anchorage with and without reinforcement is roughly the same [13]. On the other hand, for weaker concretes, fiber reinforcement or rebar reinforcement can enhance the anchor strength [15]. Additionally, adding reinforcement close to the anchor can be beneficial [16]. It is crucial to consider the anchor embedment depth in such cases. The anchor must be deep enough for the reinforcement to effectively transfer the forces transmitted by the stud. Tests with large-sized anchors and high embedment lengths are relatively rare. However, a few studies [2; 3; 17] have highlighted the impact of load eccentricity and, consequently, the stiffness of the plate. Moreover, the group effect has been discussed with an estimated loss of approximately 30%, as well as the limited impact of reinforcements [3; 18]. Concerning simulations, finite elements [19] or discrete elements [20; 21] are generally chosen. In terms of computational cost, finite elements are better suited for large structures and will be chosen in this contribution. Within the finite element framework, the constitutive laws are key components of the simulation; for example, microplane modeling [22; 23; 24; 25; 26], the Rankine criterion [27] for crack initiation, or the Menetrey-Willam model [28] have been used to simulate the nonlinear behavior of concrete [16; 29]. The CDP (Concrete Damaged Plasticity) model, including plasticity and damage in tension and compression, is also often used [30; 31]. In this contribution, a modified isotropic damage model based on [PERSON] formulation [32] and regularized in tension [33; 34] and compression [35] is used. The aim is to keep the model as simple as possible, with sufficient representativeness to assess the influence of the various parameters, which requires a large number of simulations. The objective of this work is to first explain the numerical strategy for modeling concrete and then use this modeling to replicate tensile and shear tests using the finite element code Cast3M [36]. The goal is to predict the resistance of studs and anchorage systems under various loading conditions.
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 5 Deep Learning Techniques in Agriculture
### Datasets Used in Papers  
It can be seen that the majority of the research used a dataset containing images to train the models, as such datasets include thousands of photos. Some studies used a public dataset, PlantVillage [56], consisting of 54,303 healthy and unhealthy leaf images divided into 38 categories by species and disease. The majority of them collected and created their own datasets through several methods according to the needs of the research. Papers were concerned with coved land, crop type classification, yield estimation, and weed detection, and utilized a smaller number of images taken by UAV or satellite-based remote-sensing deep learning models. Figure 5 shows the percentage of research studies that used an existing available dataset vs. created and collected their own dataset
# Low-Rank Approximation of Frequency Response Analysis of Perforated Cylinders under Uncertainty
## 2 Preliminaries
### Frequency Response Analysis under Uncertainty
#### 2.2.2 Frequency Response Analysis  
In frequency response analysis, the idea is to study the effects of excitation or applied force in the frequency domain. It is clear that the uncertainty in the eigenproblem is strongly connected to the frequency response.  
The starting point is the equation of motion for the system [28]:  
\[\mathbf{M}\dot{\mathbf{v}}(\xi)+\mathbf{C}\dot{\mathbf{v}}(\xi)+\mathbf{S}(\xi )\mathbf{v}(\xi)=\mathbf{f}, \tag{8}\]  
where, given \(\xi\in\Gamma\), \(\mathbf{M}\) is the mass matrix, \(\mathbf{C}\) the viscous damping matrix, \(\mathbf{S}(\xi)\) the stochastic stiffness matrix, \(\mathbf{f}\) the (deterministic) force vector, and \(\mathbf{v}(\xi)\) the displacement vector. The matrices \(\mathbf{M}\) and \(\mathbf{S}(\xi)\) are defined as above. The viscous damping matrix \(\mathbf{C}\) is a linear combination of \(\mathbf{M}\) and \(\mathbf{S}(\xi)\). This choice is often referred to as proportional Rayleigh damping, and is central to the low-rank approximation scheme considered below.  
In the case of harmonic excitation, a steady-state solution is sought. The angular frequency is \(\omega=2\pi f\), where \(f\) is the ordinary frequency. The force and the corresponding response have harmonic function representations.  
\[\mathbf{f} =\hat{\mathbf{f}}(\omega)e^{\mathrm{i}\omega t}, \tag{9}\] \[\mathbf{v}(\xi) =\hat{\mathbf{v}}(\xi,\omega)e^{\mathrm{i}\omega t}.\]  
Taking the first and second derivatives of Equation (8) and substituting using (9) leads to  
\[-\omega^{2}\mathbf{M}\dot{\mathbf{v}}(\xi,\omega)e^{\mathrm{i}\omega t}+ \mathrm{i}\,\omega\mathbf{C}\dot{\mathbf{v}}(\xi,\omega)e^{\mathrm{i}\omega t} +\mathbf{S}(\xi)\dot{\mathbf{v}}(\xi,\omega)e^{\mathrm{i}\omega t}=\hat{ \mathbf{f}}(\omega)e^{\mathrm{i}\omega t}, \tag{10}\]  
finally reducing to a linear system of equations:  
\[(-\omega^{2}\mathbf{M}+\mathrm{i}\,\omega\mathbf{C}+\mathbf{S}(\xi))\dot{ \mathbf{v}}(\xi,\omega)=\hat{\mathbf{f}}(\omega). \tag{11}\]  
In principle, any quantity of interest can be derived from the solution \(\dot{\mathbf{v}}(\xi,\omega)\). Often, in engineering practice, maximal deflections at specific locations are used. For shells, the presence of boundary layers makes it difficult to guarantee satisfactory convergence. A mathematically reasonable choice is to take the mechanical energy defined by the natural energy norm as the quantity of interest, even though, in practice, it may be difficult to measure.
## 4 Measurements and Experimental Results
### Evaluation of Model Performance
* If a reduction of the intervertebral disc height occurred, the patient's blood pressure difference was greater than 60 mm/Hg, the patient was younger than 77 years, the patient was male, and the patient had leg pain, the patient ultimately underwent the operation. * If a reduction of the intervertebral disc height occurred, the patient's blood pressure difference was greater than 60 mm/Hg, and the patient was older than 77 years, the patient ultimately underwent the operation. In addition, three rules could be used to evaluate whether an operation was not ultimately performed:  
* If no reduction of the intervertebral disc height occurred and the patient was younger than 77 years, the patient ultimately did not undergo the operation. * If a reduction of the intervertebral disc height occurred, the patient's blood pressure difference was greater than 60 mm/Hg, the patient was younger than 77 years, the patient was female, and the patient had leg pain, the patient ultimately did not undergo the operation. * If a reduction of the intervertebral disc height occurred, the patient's blood pressure difference was greater than 60 mm/Hg, the patient was younger than 77 years, and patient did not have leg pain, the patient ultimately did not undergo the operation. The study results indicated that several key features were obtained using the model combining LGR and C5.0 DT for feature selection, and a C5.0 DT: reduction of the intervertebral disc height, age, blood pressure difference, leg pain, and gender. The study was limited by inherent problems such as a small sample size and imbalanced categorical data. Features were selected after categorical data were balanced. Despite the limitations, the prediction accuracy rate remained up to 90%.
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 4 Our Approach
### Preprocessing  
Bug reports and source code files primarily consist of textual data, necessitating preprocessing to enhance their suitability for analysis [39]. In this study, preprocessing was carried out in two stages. First, for bug reports, word tokenization and stop-word removal were applied to eliminate irrelevant terms and refine the textual content. Second, CamelCase formatting was implemented for source code files to improve readability and optimize their structure for further analysis.  
Figure 4 shows an example of a bug report preprocessing step that highlights how these tokens are extracted for similarity analysis. Using these preprocessed data, a similarity calculation was conducted between the bug report and the source code file based on the extracted tokens. For instance, in the processed bug report, words such as \"found,\" \"check,\" \"editor,\" and \"maximized\" were identified. Similarly, the source code file included terms such as \"private,\" \"static,\" and \"maximized.\" Notably, the term \"maximized\" appeared in both the bug report and the source code file, indicating a strong textual correlation between the two.  
Figure 4: An example of a bug report preprocessing step.  
This preprocessing and token extraction process establishes the foundation for quantifying the relationship between bug reports and source code files. By identifying shared terms and contextual relevance, this approach facilitates the accurate calculation of correlations, enabling more precise bug localization.
## 2 System Reliability Evaluation Method of a Single Failure Criterion
### System Reliability Estimation Based on Multivariate Copulas
#### 2.2.1 Failure Criterion I: Failure of Any One Component  
Failure criterion I represents a series system, which assumes that failure of any one component will cause the whole structure to fail, as shown in Figure 2.  
The bridge system is composed of multiple components, and the performance function of each component is established using Equation (6):  
\[Z_{i}(\mathbf{X})=Z_{i}(X_{1},X_{2},\ldots,X_{n}),i=1,2,\ldots,n, \tag{6}\]  
where \(Z_{i}\) and \(X_{i}\) represent the performance function and random variables of the \(i\)th component, respectively.  
According to Equation (1), the joint failure probability of all components can be denoted using Equation (7):  
\[\begin{array}{ll}P_{f}&=P\{Z_{1}(\mathbf{X})\leq 0,Z_{2}(\mathbf{X})\leq 0, \ldots,Z_{n}(\mathbf{X})\leq 0\}\\ &=C\Big{(}F_{Z_{1}}(0),F_{Z_{2}}(0),\ldots,F_{Z_{n}}(0)\Big{)}\\ &=C\Big{(}P_{f_{Z_{1}}},P_{f_{Z_{2}}},\ldots,P_{f_{Z_{n}}}\Big{)}.\end{array} \tag{7}\]  
where \(F_{Z_{i}}(\cdot)\) and \(P_{f_{Z_{i}}}\) represent the joint distribution function and the failure probability of the \(i\)th component, respectively; \(C(\cdot)\) is the multivariate copula function.  
We consider the failure probability of each component as a probability event. According to the series system shown in Figure 2, the system failure probability of failure criterion I is the sum event of multiple events. Then, based on Equations (1) and (7), the system reliability of failure criterion I (failure of any one component) can be obtained as follow:  
\[\begin{array}{ll}P_{f_{i}}&=P\{Z_{1}(\mathbf{X})\leq 0\cup Z_{2}(\mathbf{X}) \leq 0\cup\cdots\cup Z_{n}(\mathbf{X})\leq 0\}=\sum\limits_{i=1}^{n}P\{Z_{i}( \mathbf{X})\leq 0\}-\sum\limits_{1\leq i<j\leq n}^{n}P\{Z_{i}(\mathbf{X})\leq 0,Z_{ j}(\mathbf{X})\leq 0\}\\ &+\sum\limits_{1\leq i<j\leq n}^{n}P\{Z_{i}(\mathbf{X})\leq 0,Z_{j}(\mathbf{X}) \leq 0,Z_{k}(\mathbf{X})\leq 0\}+\cdots+(-1)^{n-1}P\{Z_{1}(\mathbf{X})\leq 0,Z_{ 2}(\mathbf{X})\leq 0,\ldots,Z_{n}(\mathbf{X})\leq 0\}\\ &\approx\sum\limits_{i=1}^{n}P\{Z_{i}(\mathbf{X})\leq 0\}-\sum\limits_{1\leq i <j\leq n}^{n}P\{Z_{i}(\mathbf{X})\leq 0,Z_{j}(\mathbf{X})\leq 0\}=\sum\limits_{i=1}^{n}P_{f _{Z_{i}}}-\sum\limits_{1\leq i<j\leq n}^{n}C\Big{(}P_{f_{Z_{i}}},P_{f_{Z_{j}}} \Big{)}.\end{array} \tag{8}\]  
where \(C(\cdot)\) represents the binary copula function and \(P_{f_{1}}\) represents the system failure probability of failure criterion I.
## 2 Materials and Methods
### Fish Tagging  
Juvenile Chinook salmon were reared in vertical raceways at LPW as described by [PERSON] and [PERSON] [36]. In October 2015, 100 fish were transferred to two round ponds with free-flowing freshwater (3000 L capacity per pond). Saltwater was introduced into the round ponds in April 2016 to induce solidification. In May, individual fish were randomly selected, sedated with an anesthetizing solution of tricaine methanesulfonate (MS-222) at a concentration of 60 mg/L of water, weighed, and measured (snout to fork of tail). The fish were surgically tagged with acoustic transmitters as described by [PERSON] et al. [37]. Briefly, a small incision was made slightly offset and parallel to the ventral midline, and the transmitter gently inserted into the abdominal cavity. The incision was closed with simple,  
Figure 1: Map of Port Walter and Big Port Walter, Alaska, showing the survey routes used during autonomous underwater vehicle (AUV) missions and the sites where acoustically tagged juvenile Chinook salmon were released. AUV operations were based at the Little Port Walter (LPW) research station.  
interrupted sutures. Handling methods were IACUC-approved under protocol 04-020 issued to [PERSON] by Rutgers University.  
The transmitters (Model MM-M-8-S0, Lotek Wireless, Newmarket, ON, Canada) were cylindrical (8.5 mm in diameter, 43 mm in length), weighed 8.5 g, and emitted a unique code division multiple access (CDMA) signal burst [38] every 4 s with a power output of 149 dB (referenced to 1 \(\upmu\)Pa at 1 m), transmitting frequency of 76 kHz, and 12-h on/off duty cycle with signal transmissions from 0900 to 2100 h. The CDMA signal uses an encoding scheme that relies on correlation to known patterns from a noise carrier wave and thus is highly robust against code collisions [5; 38]. An equal number of fish were tagged with dummy tags that had the same size and weight characteristics as the transmitters. The transmitters and dummy tags were placed in separate storage pouches, sterilized with low temperature ethylene oxide gas, and sealed prior to use. All of the fish were tagged with a PIT tag (Model AVID 2028: Avid Identification Systems, Inc., Norco, CA, USA) to enable subsequent identification.  
Each fish was returned to the round pond after tagging and observed until its swimming behavior could not be distinguished from the untagged fish. The fish were held and observed for over 15 days after tagging. Water temperature during this period averaged 8.3 \({}^{\circ}\)C (\(\pm\)0.6 \({}^{\circ}\)C). Fish tagged with acoustic transmitters were transferred to a container filled with aerated saltwater, and transported to Port Walter or Big Port Walter for release into the marine environment. Ten untagged fish were released with each group of tagged individuals to bolster the size of the release since schooling has been reported for juvenile salmonids [39; 40; 41] and social behavior may influence the movement patterns exhibited by the fish. The fish with dummy tags were sacrificed and examined as described by [PERSON] and [PERSON] [42].