esa-sceva/satcom-chunk-collection huggingface dataset:
# Sparse Indoor Camera Positioning with Fiducial Markers
## 3 Proposed Method
### Mathematical Definitions  
Let us represent a three-dimensional point within a given reference system \(a\) as \(\mathbf{p}_{a}\in\mathbb{R}^{3}\). To convert this point to a different reference system, labeled as \(\mathbf{p}_{b}\), rotation and translation are required. Let us denote \(\varphi_{ba}\) as the SE(3) homogeneous transformation matrix that transforms points from \(a\) to \(b\) as follows:  
\[\left[\begin{array}{c}\mathbf{p}_{b}^{\top}\\ 1\end{array}\right]=\varphi_{ba}\left[\begin{array}{c}\mathbf{p}_{a}^{\top} \\ 1\end{array}\right]. \tag{1}\]To ease the notation, we employ the operator (\(\cdot\)) as follows:  
\[\mathbf{p}_{b}=\varphi_{ba}\cdot\mathbf{p}_{a}. \tag{2}\]  
It should also be noted that the transformation from system \(c\) to system \(b\) (\(\varphi_{bc}\)), followed by the transformation from \(b\) to \(c\) (\(\varphi_{cb}\)), can be combined through matrix multiplication into a single transformation \(\varphi_{ac}\) as shown below:  
\[\varphi_{ac}=\varphi_{ab}\varphi_{bc}. \tag{3}\]  
\(\mathbf{u}\in\mathbb{R}^{2}\) uses the pinhole camera model. Assuming that the camera parameters are known, a projection can be obtained as a function \(\Psi\): a point in three-dimensional space, \(\mathbf{p}\in\mathbb{R}^{3}\), is projected onto a camera's pixel \(\mathbf{u}\in\mathbb{R}^{2}\) according to the pinhole camera model. Given known camera parameters, this projection can be described by the function \(\Psi\):  
\[\mathbf{u}=\Psi(\delta,\varphi,\mathbf{p}), \tag{4}\]  
where \(\delta\) denotes the intrinsic parameters of the camera, and \(\varphi\) represents the camera pose at the time the image was captured, i.e., the transformation that relocates a point from any reference system to the camera's system.
# Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference
## 4 Experiments
### Experiments on Imagenet Categories
#### 4.2.2 Visual/Semantic Embeddings and Knowledge Graphs  
For the visual inference in this experiment, we use the entire model of Inception-V1 [57; 58] on the ConSE baseline model and 1024-dimensional outputs from the top-layer pooling units of Inception-V1 on the other baselines to extract visual features of image instances, which is the same technique used in other recent ZSL researches [49; 59]. We consider Inception-V1 to be reasonably appropriate for our experimental setting because it is pre-trained on the ImageNet 2012 1K dataset, so that all of our seen class labels are involved but our unseen class labels are not. Z-score normalization on the dimensions is applied to extracted image features, and the mean and standard deviation values of the training visual features are used on testing visual features for normalization.  
Our approach uses a semantic embedding space to measure the CS between the class labels of a surrounding object and a target object. Recall that word vectors are considered to be a semantic embedding space \(E\) in this evaluation. In this experiment, we use a skip-gram model [26] trained on Wikipedia English in February 2015 with a window size of 10. The dimension of the word vectors is set to 1000, which is a column dimension of embedding matrices for baselines. All the word vectors are L2 unit-normed, and all class labels are anchored to their own semantic embedding. Specifically, each class label that consists of multiple words has a representative semantic embedding that is the averaged word vector for all the words.  
For the SM measurement, a sub-graph of WordNet [32] is used in the proposed model. WordNet is a large-scale hierarchical database with more than 100K English words. The concepts in WordNet are represented as synset IDs, which is the same as in the ImageNet dataset and allows us to integrate the namespace of the class labels into the form of synset IDs. We adopt three SM metrics provided by the NLTK library in WordNet, _path_, _lch_, and _wup_, from the work in [48]. In particular, the distance relationships for _path_ are scaled to the similarity measurement in NLTK to range the value from 0 to 1. We conduct a performance evaluation separately for each metric, as presented in the next subsection.
# Effect of Plasma Treatment of Titanium Surface on Biocompatibility
## 2 Materials and Methods  
### Sample Preparation  
Titanium disks (diameter: 15 mm; thickness: 1 mm) of commercially pure grade 2 titanium were prepared by machining (Daido Steel, Osaka, Japan). The disks were polished with abrasive SiC paper (Nos. 1000 and 1500), ultrasonically rinsed in acetone, ethanol, and distilled water for 10 min each, and air-dried. A Piezobrush(r) PZ2 (Relyon Plazma GmbH, Regensburg, Germany) was used to coat the disks by using active gas in atmospheric-pressure, low-temperature plasma treatment under irradiation at 0.2 MPa for 30 s at 10 mm. The disks of the test group were subjected to plasma treatment whereas the control disks were untreated.
## 4 Results and Discussion
### Performance of System Delay in Different Aggregation Algorithms  
This experiment demonstrated the performance of delay in different aggregation algorithms and the performance of the proposed approach achieved by employing a dynamic adaptive aggregation selection scheme for the minimum delay. Due to the effects of heterogenous traffic demand and channel conditions in the actual network, it was difficult to achieve the maximum throughput while minimizing delay by employing a specific frame-aggregation algorithm. In addressing these challenges, our proposed approach attempted to achieve an optimal trade-off between maximizing throughput and minimizing delay by employing an adaptive aggregation selection scheme. The results displayed in Figure 2 show the performance of the delay in different traffic models when the average offered traffic increased. As the results showed, when the traffic load increased, the system delay time increased similarly in all traffic scenarios. However, due to the adaptive aggregation policy selection scheme employed in the proposed approach, it always attempted to achieve the minimum delay.  
According to the results shown in Figure 2, the Equal Frame Size FA contributed the minimum delay of 9 s when the traffic load increased in the Weibull traffic model; however, the Equal Num MPDUs FA and Avg Num MPDUs FA achieved the maximum delay of 10 s. Therefore, the proposed approach achieved the minimum delay because the proposed approach adopted a dynamic aggregation selection strategy with the minimum delay. In the Pareto traffic model, the FIFO FA (baseline approach) contributed a minimum delay of 2.7 s as compared to the maximum delay of 3.3 s achieved by the Avg Num MPDUs FA and the Equal Num MPDUs FA. Moreover, the proposed approach achieved a better performance in the Weibull and fBM traffic models than in the Pareto traffic model because the traffic rate among streams was highly bursty in the Pareto traffic model. Therefore, under such a type of traffic pattern, the FIFO FA (baseline approach) outperformed because  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline Max. number of MPDU frames & & 64 \\ aggregated & & \\ \hline Max. A-MPDU length & & 1.0 Mbyte \\ \hline SNR & & 5, 8, 20 dB \\ \hline Max. number of retransmissions & N\({}_{\text{Ret}}\) & 3 \\ \hline Buffer size at the AP & ButAP & 50 MB \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._the number of frames waiting in the buffer was higher than when using the Equal Frame size FA, Avg Num MPDUs FA, and Equal Num MPDUs FA. On the contrary, the results showed that the fBM traffic model had the shortest system delay of 1.7 s contributed by the Equal MPDUs Agg FA and Avg Num MPDUs FA when the average traffic load increased, whereas the FIFO FA (baseline approach) achieved the maximum 2 s delay. In general, the results illustrated that a minimum delay could be achieved by employing an adaptive aggregation selection strategy.
## 4 Results and Discussion
### Sensitivity Analysis
#### 4.2.1 Pressure  
From Figures 15 and 16, with the increase in the pressure of the injected drilling fluid, the reservoir was more and more sufficient and had larger and larger ranges impacted. Therefore, the hydrate decomposition became more thorough, and the front of the hydrate decomposition enlarged. Meanwhile, the accelerated invasion of drilling fluid made the elastic modulus and cohesion of the stratum drop sharply, and the range of the elastic modulus and cohesion drop in the reservoir expanded, which together made the mechanical properties of the hydrate reservoir with weak cementation intrinsically decline further.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pressure of Injected** & \begin{tabular}{c} **Temperature of** \\ **Injected Drilling** \\ **Fluid/\({}^{\circ}\)C** \\ \end{tabular} & **Absolute Porosity** & \
\begin{tabular}{c} **Absolute** \\ **Permeability/m2** \\ \end{tabular} &
\begin{tabular}{c} **Initial Hydrate** \\ **Saturation** \\ \end{tabular} \\ \hline
14.6 & 15 & 0.3 & 2.961 \(\times\) 10\({}^{-14}\) & 0.33 \\
14.882 & 16.32 & 0.4 & 4.935 \(\times\) 10\({}^{-14}\) & 0.4 \\
15 & 17 & 0.5 & 6.909 \(\times\) 10\({}^{-14}\) & 0.5 \\
15.2 & 18 & 0.6 & 8.883 \(\times\) 10\({}^{-14}\) & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of key parameters for horizontal wellbore stability analysis.  
Figure 14: The distribution patterns of effective porosity and effective permeability. (**a**) Distributions of effective porosity in the near-wellbore zone, (**b**) Distributions of effective permeability in the near-wellbore zone.  
Therefore, for the formation of a hydrate reservoir with poor mud cake, the density of drilling fluid should be lowered as much as possible.
###### Abstract
Flash memory is a non-volatile memory that stores the program's instructions and constant data values, whereas RAM is a volatile memory that is used as data storage for the program while it is running. Nodes usually have a simple real-time operating system that executes all functionalities, also known as tasks, concurrently. The sensor subsystem collects data from natural phenomena in the form of analog signals via sensor readings and transforms them into digital signals using an Analog-to-Digital Converter (ADC). Finally, the communication subsystem is made up of a transceiver and normally a co-processor device that is responsible for transmitting and receiving data. Figure 1: An IoT mesh network with a DOA-based positioning system. While Bluetooth specifies a protocol for transmitting a constant tone for DOA purposes, the development of direction of arrival algorithms is left to implementation. However, the implementation of DOA methods in IoT devices presents a significant challenge as these devices are often battery-powered and resource-constrained embedded systems with limited computational resources as previously mentioned. In contrast, DOA methods consist of complex numerical algorithms that are resource-intensive and time-consuming, potentially leading to rapid battery depletion, unacceptable execution times, and resource starvation. Moreover, IoT devices typically run a simple real-time operating system that executes small tasks concurrently, such as data acquisition from sensors and communication with other devices. The execution of DOA methods in such a multi-threaded environment is even more challenging, and computational resource management needs to be carefully thought out.Therefore, developing DOA algorithms for IoT devices demands an innovative approach that considers the balance between resource constraints and DOA accuracy, without compromising battery life or real-time system performance.
Sensors_**2014**, _14_, 2663-2682; doi:10.3390/s140202663  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Standardized Low-Power Wireless Communication Technologies for Distributed Sensing Applications**  
**[PERSON] 1,2,*, [PERSON] 1, [PERSON] 3, [PERSON] 3 and [PERSON] 4**  
\({}^{1}\) Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC) C/Roc Boronat 117, Barcelona 08018, Spain; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Berkeley Sensors and Actuators Center, University of California Berkeley 403 Cory Hall #1774, Berkeley, CA 94720, USA  
\({}^{3}\) M2M Department, Centre Tecnologic de Telecomunicacions de Catalunya (CTTC) Av. [PERSON] 7, Castelldefels 08860, Spain; E-Mails: [EMAIL_ADDRESS] (F.V.-G.); [EMAIL_ADDRESS] (J.A.-Z.)  
\({}^{4}\) Signal Theory and Communications Group, Universitat Politecnica de Catalunya (UPC) Av. [PERSON] 7, C4-204, Castelldefels 08860, Spain; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-630-30-34-40 (ext. 5413).  
_Received: 2 January 2014; in revised form: 26 January 2014 / Accepted: 29 January 2014 / Published: 10 February 2014_  
**Abstract:** Recent standardization efforts on low-power wireless communication technologies, including time-slotted channel hopping (TSCH) and DASH7 Alliance Mode (D7 AM), are starting to change industrial sensing applications, enabling networks to scale up to thousands of nodes whilst achieving high reliability. Past technologies, such as ZigBee, rooted in IEEE 802.15.4, and ISO 18000-7, rooted in frame-slotted ALOHA (FSA), are based on contention medium access control (MAC) layers and have very poor performance in dense networks, thus preventing the Internet of Things (IoT) paradigm from really taking off. Industrial sensing applications, such as those being deployed in oil refineries, have stringent requirements on data reliability and are being built using new standards. Despite the benefits of these new technologies, industrial shifts are not happening due to the enormous technology development and adoption costs and the fact that new standards are not well-known and completely understood. In this article, we provide a deep analysis of TSCH and D7 AM, outlining operational and implementation details with the aim of facilitating the adoption of these technologies to sensor application developers.  
keywords:low-power wireless; wireless sensor networks; radio frequency identification; medium access control; time-slotted channel hopping; DASH7 alliance mode +
Footnote †: journal: Journal of the Acoustical Society
# Microbial and Enzymatic Biodegradation of Plastic Waste for a Circular Economy
## 3 Factors Affecting Plastic Biodegradation
### Extrinsic Factors  
Extrinsic factors refer to the external conditions which influence the growth of microorganisms. Microbes require specific conditions to grow and thrive. The temperature and pH level are prominent influences. Extremes of temperature can hinder microbial growth and activity to slow the biodegradation process [155]. For instance, extremely high temperatures may denature microbial enzymes and disrupt cellular structures, while extremely low temperatures can reduce metabolic rates and enzyme activity. However, some cold-adapted microbes can effectively degrade polymers at low temperatures due to specialised enzymes which remain active in cold environments. Despite this, the overall rate of degradation is typically slower at low temperatures. For example, _Penicillium_ sp. has shown a degradation efficiency of just 3.53% for LDPE at low temperatures [58] compared with _P. simplicissimum_ from the _Penicillium_ genus which demonstrated a significantly higher degradation efficiency of 38% for PE [156]. These examples underscore the importance of considering environmental factors when evaluating the potential for microbial biodegradation. However, while certain specialised microorganisms can operate in more extreme conditions, most microbes and purified enzymes prefer neutral to slightly alkaline conditions [49; 157] The primary consideration for effective biodegradation must be the environment in which it will occur.
## 3 Results and Analysis
### Infrared Spectrum Analysis  
Fourier transform infrared spectroscopy (FTIR) of \"environmentally friendly\" wood-based nano-metal composite materials is shown in Figure 6. Since the copper film itself has a strong absorption effect on infrared ray, the deposition of copper film on the wood surface will result in a significant reduction in the infrared absorption peak intensity of wood veneer [31]. In the FTIR spectrum of the original wood, the absorption peak around 3330 cm\({}^{-1}\) is caused by the vibration of the surface hydroxyl (-OH) of the wood surface [32]. Compared with the FTIR spectrum of the original wood, depositing copper film on the surface of the wood veneer will result in a significant decrease in the absorption peak intensity of the hydroxyl groups of the wood veneer. Deposition of copper film on the surface of wood veneer resulted in a significant decrease in the hydroxyl absorption peak strength of the wood veneer, which is attributed to the deposition of copper film covering hydroxyl (-OH) functional groups on the surface of the wood veneer. It can be seen that, as the sputtering time increases, the magnetron sputtering method deposits copper film on the surface of the wood to completely cover the surface of the wood veneer, resulting in a decrease in the absorption peak intensity.
# Minimum Distance Optimization with Chord Edge Growth for High Girth Non-Binary LDPC Codes
## 3 Minimum Distance Optimization Using Progressive Chord Edge Growth
### Proposed Progressive Chord Edge Growth (PCEG) Method
_Step 1:_ add the chord edge \(e_{j}\) to the Hamiltonian cycle, and obtain the subgraph \(G_{j}\) composed of the Hamiltonian cycle and the added \((j-m)\) chord edges. _Step 2:_ according to the remaining optional non-zero elements of the vertices connected to the chord edge \(e_{j}\), select the non-zero elements corresponding to the chord edge \(e_{j}\), in order that the cycles with the length not greater than \(l_{max}\) in the subgraph \(G_{j}\) related to the chord edge \(e_{j}\) all satisfy the FRC. _Step 3:_ if there is no non-zero element that meets the condition, go to the initialization process; otherwise, judge whether the chord edge index \(j\) is equal to \(n\), and if \(j=n\), go to Step 4; otherwise, \(j=j+1\), and go to Step 1;  
_Step 4:_ from all of the non-zero element configurations that meet the conditions, select the configuration that violates less FRC criterion, and finally obtain the optimized parity-check matrix of the NB-LDPC code. In the configuration of the non-zero entries for each chord edge, there are some subtle points that need further explanation. First, if there is no configuration that meets \(N_{j}^{l\leq l_{max}}=0\) after traversing all optional non-zero elements of edge \(e_{j}\), the non-zero elements that correspond to \(E_{C}\) are re-allocated and the non-zero elements are re-configured from the first chord edge. Second, if there are multiple choices for configuring the non-zero entries corresponding to the last chord edge, that is, multiple parity-check matrices satisfying \(N_{n}^{l\leq l_{max}}=0\) are obtained, then the FRC check will be performed on the cycles with a cycle length that is greater than \(l_{max}\), and the matrix that has the minimum number of cycles not satisfying the FRC is selected. Third, if there are a large number of candidate configurations of non-zero entries for a chord edge, then one candidate configuration is randomly reserved, which is, the non-zero elements that are related to the added chord edges are fixed. This effectively prevents excessive memory usage due to the rapid increase in the number of optional configurations. Figure 4: The scheme of configuring non-zero entries based on the progressive chord edge growth.
# Optically Pumped Magnetometer Measuring Fatigue-Induced Damage in Steel
## 4 Test Results and Discussion
The positions of the Villari reversals contain information about the materials' history and the irreversible interactions between domain walls and defects in the material. Therefore, monitoring the behavior of the Villari reversals is of particular interest as a potentially good indicator for early detection of defects generated or evolving with fatigue. The aim for the detection of early defect formation is to find a magnetic signature before macroscopic crack initiation at cycle 51. Figure 4 shows features that can be extracted from the data generated with this setup. We present the temporal evolution of 2 potential signatures derived from the hysteresis curves in Figure 3. On the top, the areas of the mechanical and the piezomagnetic curves are drawn. The dots and squares mark the values measured by integration of each cycle and the lines are fit with a seventh degree polynomial. As strain is a unitless quantity, the units of the areas are those of force \(F\) in case of the mechanical and of magnetic field \(B\) in case of the piezomagnetic curve. Therefore, they are normalized to their common maximum at cycle 12 (first dashed line) with values 1.46 N in the mechanical and 0.14 nT in the magnetic curve. Both curves rise in parallel in the first six cycles, which might be caused by some initial hardening. Alternatively, this might be due to the manual change in the frequency after cycle 5 since this comes along with an increase in the force amplitude. The area of the mechanical curve remains almost constant until cycle 84 (dashed line on the right), whereas the piezomagnetic curve drops between cycles 12 and 40. Afterwards, it exhibits a plateau and drops after cycle 84, i.e., in the range of rapid crack growth. Thus, it exhibits a much earlier reaction to fatigue-induced damage than the area of the mechanical curve. The second potential signature of early damage detection is the magnetic field strength \(B_{Villari}\) of the Villari reversals drawn on the bottom of Figure 4. The blue triangles pointing upwards are those on the rising slope (blue arrow on the left of the \(B-\epsilon\) curves in Figure 3). The green triangles pointing downwards represent the corresponding reversals from the falling slopes (i.e., the compression half cycles) at the green arrow in Figure 3 (tail-reversal). Up to macroscopic crack initiation near cycle 51 (dashed line in the center), the value of \(B_{Villari}\) increases for both slopes, potentially due to an increase in the defect density. Remarkably, they feature a different behavior after macroscopic crack initiation. The values from the rising slope exhibit a maximum whereas those from the falling slope increase throughout the experiment but manifest an inflection point at the macroscopic crack initiation. Thus, Villari reversals could be promising candidates for damage indicators before macroscopic crack initiation.