esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensory Panel Performance Evaluation--Comprehensive Review of Practical Approaches
## 4 Methods, Models and Frameworks in Sensory Panel Evaluation
### Structure and Models of Sensory Data
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Panelist or Panel** & **Panel Performance Parameter** & **Statistical Method/Plot** & **Software** & **References** \\ \hline \multirow{4}{*}{Panelist and panel} & Discrimination & _ANOVA_1 (Fisher’s _F_) & \\ \cline{2-5}  & Agreement & _CPCA_2 (Tucker-1), _Kendall’s_\(\tau\) ([PERSON]) & \\ \cline{2-5}  & Consensus & _PCA_3 (Manhattan) & PanelCheck * & [57; 58] \\ \cline{2-5}  & Repeatability & _MSE_4 & \\ \cline{2-5}  & Repeatability and Discrimination & _p_*_MSE_ & \\ \hline \multirow{4}{*}{Panelist and panel} & Discrimination & _ANOVA_1 & \\ \cline{2-5}  & Agreement & _ANOVA_1 & \\ \cline{2-5}  & Repeatability & _RMSE_5 & \\ \cline{2-5}  & Scaling & _ANOVA1_ & TimeSens & \\ \cline{2-5}  & Repeatability & \(h\)6  & S-Plus 2000 & [59] \\ \hline \multirow{4}{*}{Panelist and panel} & Discrimination & \multirow{4}{*}{_ANOVA_1  & SensoMineR * and & \\ \cline{2-5}  & Agreement & _ANOVA_1  & FactoMineR * & [60; 61] \\ \cline{2-5}  & Repeatability & _MFA_8  & FactoMineR * & [60; 61] \\ \hline \multirow{4}{*}{Panelist and panel} & Discrimination & \multirow{4}{*}{_ANOVA_1  & SAS macro & [62] \\ \cline{2-5}  & Repeatability & & & \\ \hline \multirow{4}{*}{Panelist} & Discrimination & _ANOVA_1  & SenPAQ & [63] \\ \cline{2-5}  & Agreement & _ANOVA_1  & XLSTAT & [64] \\ \cline{2-5}  & Repeatability & _RMSE_5  & Fizz & [65] \\ \hline \multirow{4}{*}{Panelist} & Response accuracy & frequency counts & \\ \cline{2-5}  & DT 9, ADT  & \\ \hline \multirow{4}{*}{Panelist and panel} & Accuracy & \multirow{4}{*}{_ICC_11 (Cronbach’s \alpha\)_} & \multirow{4}{*}{R-code *  } & [67] \\ \cline{2-5}  & Validity & \multirow{4}{*}{_Cronbach’s \(\alpha\)_} & & [68] \\ \cline{2-5}  & Reliability & & & [69] \\ \hline \multirow{2}{*}{Panelist and panel} & Agreement & _CSM_12 (Consensus) & _Poincare_ plot & R-code *  & [51] \\ \cline{2-5}  & (Consistency) & \multirow{2}{*}{_Poincare_ plot} & & [70] \\ \cline{2-5}  & Reliability & _GCAP_13 (Carpack \(\!\)) & Excel macro & [71] \\ \cline{2-5}  & Panel & Discrimination & _CVA_14 (CVApack *  & [72] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Method and software developments in assessor and sensory panel performance evaluation.
## 2 Data Preparation and Methodology  
### Data Preparation  
The microseismic data in Micang Mountain and Grand Canyon tunnels are recorded by the same microseismic monitoring system. Therefore, the data types are the same. The microseismic monitoring system includes a data acquisition station with a sampling frequency of 20 kHz, a uniaxial accelerometer with a response frequency ranging from 50 Hz to 5 kHz, and a data processing station. The amplitude of the microseismic record is in voltages, and each record has the same sampling window of 1.5 s, which means that it has the same size of 30,000 sampling points. A total of 8000 micro-fracture waveforms with a high signal-to-noise ratio (SNR) and 16,000 noise waveforms are selected to form a dataset of 28,000 semisynthetic noisy waveform samples, which is randomly divided into two parts: training (90%) and test (10%) datasets. The test datasets are used to obtain the model with the best performance and record the network performance, respectively.  
It can be found that the feature information of non-micro-fracture waveforms (blasting, mechanical, and noise waveforms) in the time domain is significantly different from that of micro-fracture waveforms (Figure 1). The high amplitude of the blasting waveform exceeds 4000 MV and lasts for a long duration, which is associated with high energy and high frequency. A mechanical waveform typically appears as repeated and regular vibrations along the time axis, whereas the noise waveform appears as cyclic or irregular vibrations. The micro-fracture waveform can show different amplitude values from high to low, and because of which, it exhibits different energies and frequencies. Moreover, the waveform attenuation is faster.  
In this study, the onset time of the micro-fracture waveform is first labeled by experienced experts. Subsequently, the noisy waveforms with different SNRs are generated by the superimposition of high-SNR micro-fracture waveforms and randomly selected real noise waveforms. Finally, a noisy waveform with accurate onset time picking is generated for model training (Figure 2). Note that the onset time of the non-micro-fracture waveforms need not be labeled.  
Figure 1: Microseismic waveforms: (**a–d**) blasting, mechanical, noise, and micro-fracture waveforms, respectively.
## 3 Network, Assumptions and Adversary Model
### Assumptions  
For reason of scalability, it is assumed that no sensor node needs to know the global network topology, except that it must know the geographical location of its own, its neighboring nodes and the base station. In order to find out the location information, any proposed mechanism could be used, such as [12; 13].  
It is assumed that each sensor node in the network can share a unique secret key with the base station [14; 15]. These keys are periodically updated. The public key of the base station is also assumed known to all the nodes in the network. Sensor nodes do not require their own public and private keys; because computation cost of public and private keys is generally consider being high. However, many researchers [16; 17] have shown the feasibility of using public key cryptography in wireless sensor networks. It is also assumed that sensor nodes are capable of performing encryption and decryption of the data by using any cipher algorithm such as DES, AES _etc_. This provides an additional layer of security.  
This paper only focuses on the development of a prevention strategy against network level privacy disclosure attacks, such as eavesdropping, traffic analysis and hop-by-hop trace back attacks. Other general attacks, such as flooding attacks, could be detected and prevented by using any IDS scheme proposed for WSNS.
## 2 Materials and Methods
### Improved CenterNet
#### 2.3.2 High-Resolution Feature Maps  
As an important part of the tracking algorithm, pedestrian detection technology is often applied in dense pedestrian scenes, such as shopping malls, traffic hubs, road intersections, etc. Due to the high similarity in appearance of pedestrian targets and serious mutual occlusion, the visual pixels of occluded pedestrians in the image are limited, and the foreground target occupies part of the area of occluded pedestrians. In Figure 3b, the red box indicates the foreground pedestrian and the green box the obscured pedestrian; their centroids are indicated by red and green dots, respectively. According to the matching mechanism of CenterNet, centroids falling into the same sub-feature are sent to the training process as belonging to the same pedestrian, resulting in obscured pedestrians being directly ignored by the detection network and missed detection of pedestrians. Moreover, due to the effect of occlusion, most pixels are shared between occluding and occluded pedestrians, resulting in low differentiation between the two in the feature extraction process. As seen in Figure 3, compared with the one occluding, the occluded pedestrian occupies a very limited number of pixels, which results in the extracted features being mixed with a large  
Figure 1: Three branches of CenterNet detection network: (**a**) heatmap; (**b**) center point offset; (**c**) size of box.  
amount of information about the foreground target, which in turn affects the detection of the obscured pedestrian and easily causes missed detection, which leads to tracking trajectory interruptions and IDs.  
Therefore, this paper introduces a high-resolution feature map to address the problem of missed detection of obscured pedestrians in the detection network; that is, up-sampling is performed on the feature map after the FPN, fusing the shallow pedestrian information extracted from the backbone network and the multi-layer pedestrian information. Then, as shown in Figure 3, we use up-sampling to increase the resolution of the FPN and make the centroids of highly overlapping pedestrians fall in different sub-feature regions as much as possible. The features of obscured pedestrians originate from their limited visible pixels, and the shallow features contain more details than the deep features due to their small perceptual field. Therefore, the introduction of FPN and high-resolution  
Figure 3: Difficulty in detecting obscured pedestrians: (**a**) high overlap of two pedestrians; (**b**) centroids falls into the same grid; (**c**) centroids falls into different grids.  
Figure 2: Multi-Layer feature aggregation network.  
feature maps not only enhances the ability of the network to extract information about the obscured pedestrians but also improves the accuracy of their localization in the subsequent tracking process.
## 4 The Recent and Active Deformation and Stresses of the Granada Basin and Surrounding Regions
### Seismological Data
#### 4.2.2 Earthquake Focal Mechanism Data and Current Stress Tensors  
Earthquake focal mechanisms are highly varied (Figure 4). For small earthquakes, moment tensors with normal, reverse, and strike-slip kinematics can be found [144; 145; 43; 113]. This variety has been associated with a complex crustal structure and local permutation of stresses [43]. The Granada Basin is characterized by NW-SE normal faulting earthquakes, as evidenced by the 1984 earthquake and the 2021 seismic series (Figure 4) [124; 43; 127]. This sector is also characterized by focal mechanism solutions with NW-SE to N-S low-angle nodal planes [104]. The Campo de Dalias and the adjacent Alboran Sea, on the contrary, are characterized by strike-slip faulting (Figure 4) [125; 110; 126].  
The reduced stress tensor (Figure 4) of shallow seismicity reveals a vertical \(\sigma_{1}\) and a NE-SW horizontal \(\sigma_{3}\) in the eastern Granada Basin. In the central sector of the Granada Basin, the right dihedra diagrams of the 1988 Agron series indicate two deformation phases [43]. One is related to a vertical P-axis and radial extension, while the other is related to a NW-SE horizontal P-axis and a NE-SW horizontal T-axis. In the western Granada Basin, the right dihedra diagrams show a P-axis that is almost vertical [44]. The diagram obtained from [PERSON] [144] displays a high dipping NW-SE P-axis and a predominantly NE-SW horizontal T-axis. The other diagram [146] shows an almost vertical P-axis, but it slightly dips to the ENE and mainly shows radial extension. In the Campo de Dalias and northern Alboran Sea, the P-axis is oriented horizontally NW-SE, while the T-axis is horizontal in the NE-SW direction [38].  
The intermediate seismicity is mainly related to strike-slip and reverse faulting [142]. [PERSON] et al. [88] calculated two reduced stress tensors and right dihedra diagrams for earthquakes of the external arc of the slab and for earthquakes of the internal arc. The first group is related to a NW-inclined \(\sigma_{1}\) and P-axis and SE-inclined \(\sigma_{3}\) and T-axes. In the internal arc, both the reduced stress tensor and right dihedra diagram indicate the opposite.
# Design of A Parallel Decoding Method for LDPC Code Generated via Primitive Polynomial
###### Abstract  
An effective way of improving decoding performance of an LDPC code is to extend the single-decoder decoding method to a parallel decoding method with multiple sub-decoders. To this end, this paper proposes a parallel decoding method for the LDPC codes constructed by m-sequence. In this method, the sub-decoders have two types. The first one contains only one decoding module using the original parity-check constraints to implement a belief propagation (BP) algorithm. The second one consists of a pre-decode module and a decoding module. The parity-check matrices for pre-decode modules are generated by the parity-check constraints of the sub-sequences sampled from an m-sequence. Then, the number of iterations of the BP process in each pre-decode module is set as half of the girth of the parity-check matrix, resulting in the elimination of the impact of short cycles. Using maximum a posterior (MAP), the least metric selector (LMS) finally picks out a codeword from the outputs of sub-decoders. Our simulation results show that the performance gain of the proposed parallel decoding method with five sub-decoders is about 0.4 dB, compared to the single-decoder decoding method at the bit error rate (BER) of \(10^{-5}\).  
LDPC; parallel decoding; belief propagation; short cycles +
Footnote †: journal: Electronics  
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Footnote 1: [[https://doi.org/10.3390/electronics1004025](https://doi.org/10.3390/electronics1004025)]([https://doi.org/10.3390/electronics1004025](https://doi.org/10.3390/electronics1004025))
## 2 Materials and Methods  
GLADE inherits most of the mission profile of PROBA3, in particular the occulter structure and the relative spacecraft geometry management throughout their orbits. We recall that the two satellites will fly together in a highly elliptic 19.6 h orbit and implement an artificial 6 h eclipse throughout the low-disturbances apogee phase, implementing a giant externally occulted coronagraph. ASPIICS will perform regular white-light observations of the solar corona in the radial range 1.1 \(R_{\sun}\leq\rho\leq 3\,R_{\sun}\). The Occulter spacecraft has the basic function of keeping the occulting disk (diameter \(\sim\)1.4 m) at the desired \(\sim\)150 m distance from the Coronagraph spacecraft during observations, ensuring an unvignetted view of the corona above 1.17 \(R_{\sun}\). The observing instrument is a refractive telescope equipped with an internal occulter and Lyot stop to mitigate the residual diffracted light from the external occulter; its resolution is limited by the entrance aperture diameter of 50 mm to a pixel angular scale of 2''.81 on its \(2k\times 2k\) CMOS detector.  
GLADE relies on the same overall observing setup, modified in order to achieve higher angular resolution on a circular region around the Sun, thus optimizing the astrometric performance on the field stars. Better imaging resolution, projecting a smaller sky region on each pixel, generally improves astrometric precision ([PERSON] et al., 2016; [PERSON] et al., 2016) because a narrower photon distribution has a lower dispersion on a photo-center estimate, i.e., it is better localized. However, in our case, it also helps in reducing the background (from the solar corona and residual diffracted light from the Sun disk) collected by the smaller pixels.  
Hereafter, we outline the instrument concept in Section 2.1, its operation in Section 2.2, and some of the key aspects of performance analysis in Section 2.3.
# Emerging Trends in Pharmacotoxicological and Forensic Sample Treatment Procedures
## 3 Main Solid Matrices for Forensic Purposes
### Muscle Tissue  
In some cases of brutal death or when the cadaver is burned or decomposed, conventional fluids are not available. A good alternative is represented by muscle tissue. [PERSON] et al. have validated a method for simultaneously quantifying cocaine and its metabolites after extracting the analytes from rectus femoris. After preliminary tests on muscle samples obtained from Sika Deer, subsequent confirmation was made on samples obtained from the autopsy. The muscle was sectioned and chopped into fine pieces, then homogenized at 18,000 rpm. An accurately weighed quantity was diluted with 8 mLphosphate buffer (0.1 M; pH 6), centrifugated, and extracted by SPE with mixed-mode sorbent. After derivatization, analytes were quantified by GC-ion trap-MS/MS [54; 55].  
[PERSON] et al. have used muscle samples to simultaneously determine codeine, morphine, and their metabolite. After homogenization, the tissue was mixed with ammonium carbonate and extracted using an Agilent Bond Elut-C18 column. The column was conditioned using 1 mL of methanol, 1 mL of water, and 2 mL of ammonium carbonate pH 9. After washing and vacuum drying, the column was eluted with 0.5 mL methanol:0.5 M acetic acid (9:1, _v:v_). The eluent was evaporated, and the residue was reconstituted with 50 mL 50 mM ammonium acetate pH 7.0 and injected into the LC-MS [56]. [PERSON] et al., after validating the method, used muscle tissue and other solid samples to investigate postmortem redistribution of fentanyl and its metabolite. Extraction of tissue was performed after homogenization using two LLE; for the first extraction was added 250 mL of 0.2 M phosphate buffer and 0.8 mL of butyl acetate:ethyl acetate (1:1, _v:v_). After agitation and centrifugation, the supernatant obtained was transferred into a vial, and the remaining sample was treated with the second LLE. The supernatant obtained was added to the previous extract. After the addition of 25 mL of formic acid, the sample was evaporated to dryness under a stream of nitrogen and successively reconstituted with the mobile phase [57; 58].  
The use of complex and effective extraction techniques like those listed above (SPE and LLE) is important when dealing with solid matrices because a simple purification by protein precipitation often leads to an alteration of the results due to the high matrix effect.
# An On-Demand Scheduling-Based MAC Protocol for UW-WiFi Networks
###### Abstract  
Underwater Internet-of-Things (UIoT) is an extension of Internet-of-Things technology in underwater. The underwater acoustic network with WiFi architecture (UW-WiFi), as a specific deployment of UIoT, has been proved to be a promising technique for wide-ranging marine applications. However, due to the unique features of underwater acoustic channel, such as long and variable propagation delay, low available bandwidth and high bit error rate, conventional medium access control (MAC) protocols designed for terrestrial WiFi networks need an overhaul to work efficiently for UW-WiFi networks. In consideration of the aforementioned channel features, different demands of nodes to occupy channel resources and the reliability of data transmission, a time sequence-based dynamic on-demand assignment (SDDA) MAC protocol for UW-WiFi networks is proposed in this paper. In SDDA, the collision-free scheduling is integrated with the reservation mechanism, aiming to address the issue of various access requirements of diverse task nodes on channel resources in the UW-WiFi network system. The designed protocol employs propagation delays and the amount of data to be sent by terminal nodes to achieve non-conflict transmissions of control packets and on-demand scheduling of data packets. Additionally, the scheme does not require extra overhead for time synchronization. Comparison simulations demonstrate that SDDA provides considerable benefits in terms of channel utilization, end-to-end delay and packet delivery ratio. At last, the SDDA protocol is implemented and a UW-WiFi network system is set up in the marine environment. The ocean field experiment results agree well with the simulated ones and also verify that the proposed protocol achieve conflict-free on-demand scheduling, fairness among terminal nodes at different ranges and the practicability in actual environments.  
underwater acoustic network; UW-WiFi network; MAC protocol; on-demand scheduling 2023  
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[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] On-Demand Scheduling-Based MAC Protocol for UW-WiFi Networks 1
## 4 Research Methodology
### Data Collection
#### 4.1.1 Literature Search Parameters  
The purpose of the literature review was to identify barriers with various databases, such as Scopus, Web of Science, Google Scholar, and other relevant publications such as Elsevier, American Society of Civil Engineers (ASCE), Emerald and Taylor and Francis. Keywords such as \"building information modeling\", \"construction industry\", \"construction projects\", and \"barriers\" were used. Subsequently, the listed publications were screened, primarily by concentrating on the title, abstract, and conclusions, as well as the figures and  
Figure 1: Research design flowchart.  
tables. Table 1 elaborates the BIM implementation barriers that were used in this research study based on the existing literature.  
After rigorous review of current literature relating to BIM implementation barriers, various factors were identified. Table 1 elaborates a list of 20 factors that are well documented and hence more applicable. Prior to identification of BIM implementation barriers, it is kept in mind that the selection of well-known factors is more reliable and also easy for the respondents to understand the theme and feedback easily.  
Previous studies were used to identify the aforementioned BIM implementation barriers. Following a thorough evaluation of these studies, this study identified 20 potential barriers to BIM implementation, which are presented in Table 1. For example, cost, lack of expertise, and lack of promotion are commonly acknowledged in the literature as crucial barriers to BIM implementation.
## 5 Experimental Investigations
### Experiments on CADS  
Through experimental investigation, this paper compares the performance of three typical joint models for multi-intent detection tasks (AGIF, GL-GIN, and Co-Guiding Net) and Auto-HPIF on the CADS dataset. In this paper, accuracy, recall, F1 value, and precision are used as evaluation metrics, and the performance of each model on each metric is compared. Experimental results on the CADS dataset are given in Table 4.  
* **Experimental Findings:** The table observations highlight the following key points: (1) The accuracy of AGIF is 83.27% for overall performance, 90.31% for intent F1 score, and 94.58% for slot-filling F1 score; (2) The improvement in overall accuracy to 84.69% is attributed to the GL-GIN model's explicit modeling of slot dependencies. This enhancement is achieved through the implementation of a local slot-aware graph interaction layer, facilitating effective interconnection among the hidden states of each slot; (3) Co-Guiding Net introduces a novel co-guiding network based on a two-stage framework. For the overall accuracy and slot-filling F1 score metrics, it showed improvements of approximately 1.07% and 0.42%, respectively, compared to GL-GIN. In terms of the F1 score, Co-Guiding Net demonstrated an increase of around 0.09% relative to GL-GIN.  
* **Analysis of Experimental Results:** Compared to most baselines, Auto-HPIF achieved the best results in terms of both slot filling and overall performance. The following is an analysis of the experimental results: (1) In the intent detection task, the overall intent accuracy of Auto-HPIF with Chinese BERT removed outperforms the best baseline model, Co-guiding Net, on the CADS dataset, which indicates that the proposed joint multi-intent detection framework is better adapted to the intent detection task in the in-automotive domain; (2) Auto-HPIF improves the overall accuracy by 2.07% over the best baseline model Co-Guiding Net. This is because Auto-HPIF combines Chinese BERT and Gaussian prior attention mechanism in the shared encoder layer stage, which not only can better learn the semantic information of the input sequence but also, by introducing the Gaussian prior attention mechanism, the model can make full use of the historical information and thus can capture the contextual information of the input sequence more accurately when dealing with the joint multi-intent detection task; (3) The heterogeneous network interaction mechanism introduced by Auto-HPIF allows the slot information and intention information to interact, which also helps to obtain rich intention information and slot semantic representation for the joint multi-intent detection task.