esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Seafarer Fatigue, Sleep and Ship Noise
### Seafarers' Fatigue and Ship Noise
#### 2.3.3 Sleep Disturbances and Fragmentation  
[PERSON] et al. found that noise exposure in the maritime environment results in an increased number of awakenings during sleep among seafarers. The papers reveals an average of 30% increase in the frequency of awakenings due to noise. Furthermore, the data highlight a significant association between noise intensity and the number of awakenings, with noise levels above 60 dB showing a 40% rise in awakenings compared to lower noise levels [46].  
According to [PERSON]'s research, seafarers exposed to noise experience shorter sleep durations compared to those in quieter environments. The meta-analysis provides numerical data indicating a significant reduction in total sleep time among seafarers exposed to noise, with an average decrease of 1 to 2 h of sleep per night. These disruptions impact the overall sleep duration, potentially leading to sleep deprivation and its associated negative consequences on the well-being and performance of seafarers in the maritime industry [47].  
[PERSON] et al. found that noise in the maritime environment causes fragmented sleep patterns among seafarers. The study provides numerical data indicating a significant increase in the number of sleep interruptions due to noise, with an average of 20 interruptions per night. These disruptions lead to fragmented sleep patterns characterized by reduced periods of continuous, restorative sleep. The data also reveal a 30% decrease in the duration of uninterrupted sleep periods, further emphasizing the impact of noise on sleep fragmentation [48].
## 4 Experiment Results and Analysis
### Localization Performance of the Proposed Method  
The performance of positioning algorithm based on fingerprints is directly proportional to the quality of fingerprinting database. Refs. [13; 15] use multiple base stations or subcarriers to enhance the robustness of fingerprint database in Wi-Fi or Bluetooth system, which has high requirements for hardware deployments. Refs. [13; 23] use the CSI fingerprints at subcarrier level to obtain richer information than RSS fingerprints. CSI reporting is not supported in existing 5G base stations. The construction of fingerprinting database  
Figure 10: The positioning error of hybrid positioning model and CNN model in room A.  
Figure 9: Path-loss model curve of room A.  
is also related to the environmental complexity of the positioning area. We compare the proposed method with some references using 5G system, which is shown in Table 3.  
As shown in Table 3, although Refs. [11; 18] achieve a little higher positioning accuracy than the proposed method, their positioning area is smaller and has lower complexity.  
In this paper, we use single base station and multiple RSS related measurements to construct fingerprinting database, which reduces the requirements for equipment. The proposed method achieves similar positioning accuracy in a more complex environment.
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
## 2 Materials and Methods
### Inflatable Solar Dryer (ISD)
#### 2.1.3 Instrumentation for the Field Experiments and Velocity Measurements  
Solar radiation was measured using a pyranometer (CMP6, Kipp and Zonen, Delftechpark, Netherlands). Temperature and relative humidity capacity sensors (OM-EL-USB-2, Omega, Stamford, CT, USA) were placed inside the dryer at 1.5, 4, 14, 24 m from the inlet (Figure 2a). A further sensor was set to record the ambient temperature and relative humidity. The sensors were connected to a data logger (34970A, Agilent Technologies Inc., Loveland, CO, USA). The frequency of measurements was at five minutes intervals. A hot-wire anemometer (HHF42, Omega, Taipei, Taiwan) was used to measure the drying air velocity at the outlet position at one-hour intervals during the daytime.  
To monitor the air velocity profile over the paddy rice, additional grid measurements were taken with the hot wire anemometer at nine positions along the dryer length and the six positions over the dryer width. The anemometer was fixed at 0.08 m above the paddy layer, facing the main airflow direction (Figure 2b). Contour plots were generated by kriging (Surfer(r) 19.1.189, Golden software, Golden, CO, USA).  
Figure 1: Inflatable solar dryer: (**a**) perspective view and (**b**) inside of the dryer.
## 4 Results and Discussion
### Durability against sulphuric Acid
In this study, there were more pores and cavities in the mortar with 50% cement replacement than in the control mix, so acid penetration into this mortar mix was higher, and consequently, its durability against acid attack, in terms of compressive strength loss, was lower. Previous studies [81; 82] on OPC-based binders showed that sulphuric acid has a deleterious effect on mass loss, which can be explained by the Ca(OH)\({}_{2}\) decomposition and gypsum formation that deteriorates the matrix by scaling and softening [79; 81; 82]. The weight reduction results of a sulphuric acid attack test are shown in Figure 19. Contrary to the results obtained for compressive strength loss, incorporating waste powders in mortar mixes improved their performance in terms of mass loss when exposed to acid attack. With 0, 5, 15, 20, 30, 40, and 50% replacement ratios, average mass reduction was equal to 7.30, 6.12, 5.53, 5.27, 4.72, 3.32, 2.13, and 2.80%, respectively. Figure 18: Compressive strength of concrete cubes after 7 days’ immersion in 25% sulphuric acid solution relative to normal curing specimens.
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 5 VLSI Implementation of Dual-Mode LDPC Decoder for 5G NR Systems  
The proposed dual-mode LDPC decoder is designed to support two different 5G NR matrices with the same extension factor of 384. To support two completely different matrix designs, we adopted two controllers to manage the data flow and achieve shared memory. In the calculation unit, multiplexers are used to support different matrix operations. The details of the VLSI implementation are introduced in this section. The proposed LDPC decoder uses a \(4\times 26\) base matrix in BG 1 index 1, with a code rate = 11/13, and \(w_{r}\) = 19. The \(4\times 14\) base matrix in BG 2 index 1 has a code rate = 5/7 and \(w_{r}\) = 10. Both are QC-LDPC codes and the expending factor is equal to 384.  
The total bits that contain one sign bit, integer bits, and fraction bits for the posterior and extrinsic messages of the proposed decoder are extracted from fixed-point simulations for the 5G NR BGs 1 and 2. First, we use six to nine bits for the posterior and extrinsic messages in 5G NR BG 1. The simulation shows that eight bits are sufficient for BG 1. In BG 2, the simulation shows that seven or eight bits are sufficient. Thus, we decided to use eight total bits for both the posterior and extrinsic messages. According to Equations (3) and (4), the posterior message is summed by an extrinsic message and a prior message. Hence, we can reduce the unnecessary integer bits of the extrinsic message to relieve the hardware burden based on the integer bits of the posterior message. In addition, we can add fraction bits of extrinsic messages to enhance the accuracy and improve the decoding capability. Four to seven integer bits, and one fraction bit of extrinsic messages, can be performed very close to the floating-point simulation when the SNR = 4.5 dB. Eventually, we chose eight total bits for posterior messages and five total bits for extrinsic messages.
## 3 System Model
### Cluster Head Arrangement Phase  
In this phase cluster construction occurs. For every transmission round each node \(s_{i}\) calculates the probability threshold \(T(s_{i})\) and chooses a random number between 0 and 1. If the number is less than threshold \(T(s_{i})\), the node \(s_{i}\) becomes a CH during the current round. The CHs then broadcast the message to the network and declare themselves as CHs. After this message, each regular node chooses its closest CH with the largest received signal strength and then informs the CH by sending a join cluster message. The CH sets up a TDMA schedule and transmits it to the nodes in the cluster then the set up phase is completed and the next phase begins.
## 5 Module I: Anonymity
### Communication Phase
#### 5.3.4 Transmission as a Broadcaster  
Typically, the BS is required to broadcast a message for control and management purposes. Likewise, a sensor might need to broadcast a message to the BS or to the neighbors for network setup, maintenance and other management issues. The framework requires keeping all the messages indistinguishable throughout the network, so all the messages need to have the same size. Each SN is preloaded with a broadcast key (kbi) and assigned broadcast pseudonym (BPIDi). The broadcast message sent by SNi is formatted as in Equation (17):  
\[\mathrm{M_{b}}=\mathrm{Padding}\parallel\mathrm{BPIDi}\parallel\mathrm{E_{ kbi}(D_{b})} \tag{17}\]  
All the neighbors will receive the broadcast message from a source SNi. SNi and the recipients will update BPIDi according to Equation (18).  
\[\mathrm{BPIDi}=\mathrm{H_{1}(BPIDi}\oplus\mathrm{b_{i})} \tag{18}\]  
Upon receiving the broadcast message (\(\mathrm{M_{b}}\)), SNj decrypts the message using (kbi) stored in the table (Tj). It then encrypts it again using (kbi) and broadcasts (\(\mathrm{M_{b}}\)) to its one-hop neighbors set (\(\mathrm{N_{j}}\)) as in Equation (19):  
\[\mathrm{M_{b}}=\mathrm{BPIDi}\parallel\mathrm{BPIDj}\parallel\mathrm{E_{ kbi}(D_{b})} \tag{19}\]  
When the BS receives a broadcast message, it is ultimately the destination, so intuitively it does not need to broadcast the message again. Our proposed framework assumes that the BS behaves similar to a normal sensor. To maintain this pre-course, we require the BS to broadcast the message again for acknowledgement purpose. Thus, we introduce the limited broadcast where the BS will be able to broadcast to only one hop (TTL = 1).  
Figure 7: Sliding window for received PIDs [27].
## 2 Materials and Methods
### ROI Hunter Procedure  
In our particular application, accurate tumor borders were not fundamental, so we used a simple detection/segmentation method based on the application of thresholds followed by region classification.  
Prior to be processed and in view to minimize false positive (FP) ROIs, the mammary area, containing the breast, was semi-automatically selected in all slices by a bounding box (working volume) and the tissue outside the box removed. Figure 2 shows an example of breast area selection.  
Then the candidate tumors inside the working volume were detected. Since the tumor mass normally appears as a bright area, an iterative 2D ROI Hunter procedure, based on a dynamically changing threshold, was implemented. The number of ROIs detected from each slice was not set a priori, rather it was related to the intensity properties of the image.  
First, the images were converted to pixel values in the range 0 to 1, where the 99.9 th percentile of the gray values of the whole image was used for normalization, in order to exclude outliers. The following iterative procedure was then performed on a per-slice basis, giving a small number of 2D ROIs, per image section. An initial threshold (T) was set to 0.9 and only pixels with value \(\geq\)T were extracted, considering the found objects as tumor candidates. If no objects were detected, the threshold  
Figure 1: A flowchart of the software. CONF1 and CONF2 denote the two configurations in which the tumor classification (Step 2) was tested, i.e., respectively with manual ROIs as input, or fed up with the automatically detected ROIs (coming from Step 1).  
was iteratively lowered by 5% from the current value, until at least one object was identified in the current slice. Tumor lesions were normally fairly round, thus, elongated and threadlike objects were excluded by thresholding on their geometrical features. In particular, for each ROI, the length of the major and minor axes of the ellipse that had the same second moments as the ROI were calculated (as the eigenvalues of the covariance matrix of the ROI point coordinates), and their ratio R was derived. In all examined cases, R was lower than about 1.5 or just slightly larger, so a conservative threshold was set at R = 2, discarding ROIs with larger R, which were always artefacts.  
Each object gray-value median was calculated and the ROIs were labeled from 1 onwards in gray-value median descending order (the highest median value possibly selecting the most plausible tumor). Borders of the ROIs were also extracted.
###### Abstract  
A 2048-element dual-polarized receive (RX) phased array for _Ku_-band (10.7-12.7 GHz) satellite communication (SATCOM) is presented in this paper. The design of the multilayer printed circuit board (PCB) it uses adopts a novel copper paste sintering interconnection technology that allows for more flexibility in the design of vias and can reduce the PCB's lamination number. This technology is more suitable for manufacturing multilayer and complex PCBs than traditional processes. The array is designed to consist of sixteen \(8\times 16\) element subarrays, each based on the silicon RX beamformer and multilayer PCB. Dual-polarized antenna elements are arranged in a regular rectangle with a spacing of 0.5 for a wavelength of 12.7 GHz, thus achieving a scanning range of \(\pm\)70\({}^{\circ}\) in all planes. By adjusting the amplitude and phase of two line polarizations with cross-polarization levels better than \(-\)25 dB at boresight, the array can generate linear or circular polarization. Moreover, the antenna gain-to-noise temperature is above 12 dB/K (\(T_{\text{ant}}\) = 20 K) at boresight. The aperture of the 2048-element RX phased array is 768 \(\times\) 450 mm. With its low profile, the array is appropriate for usage in _Ku_-band SATCOM terminals.  
Keywords:phased array; copper paste sintering; multilayer PCB; SATCOM; dual-polarized _Article_
# Analysis of the Impact of Small Group Behavior on Cruise Ship Emergency Evacuation
## 3 Cruise Ship Personnel Evacuation Model Establishment
### Crowd Evacuation Speed Determination  
In many emergency situations, the movement speed of a crowd is crucial for evacuation. The crowd is composed of individual passengers and various sized small groups. This study aims to analyze the speed of individual passengers, adjust the movement speed of members in small groups, and ultimately determine the overall movement speed of the evacuating crowd in order to construct an evacuation model for cruise ships.  
(1) The calculation of individual evacuation speed includes self-propulsion, personnel force, repulsive forces between pedestrians and obstacles, and disturbance force [33; 34]. Therefore, the kinematic equation for n personnel at moment \(t\) is represented by Equation (9).  
\[\mathrm{m_{n}}\frac{\mathrm{dv_{m}(t)}}{\mathrm{d(t)}}=f_{m}(t)+\sum_{\mathrm{ n,m\
eq n}}\mathrm{f_{nm}}(t)+\sum_{\mathrm{w}}\mathrm{f_{mw}}(t)+\varepsilon_{ \mathrm{m}}(\mathrm{t}) \tag{9}\]  
where \(\varepsilon_{\mathrm{m}}(\mathrm{t})\) is the disturbance force received by the nth passenger at moment \(t\), \(\mathrm{m_{n}}\frac{\mathrm{dv_{n}(t)}}{\mathrm{d(t)}}\) is the individual evacuation speed, and \(\mathrm{v_{n}(t)}\) indicates the actual moving speed of person nth at time t.  
(2) Speed correction for small groups: During the evacuation process, group members will spontaneously adjust their own movement speed in order to stay together with other group members and avoid getting separated. As a result, the expected speeds of group members remain relatively consistent, leading to similar arrival times at the assembly point.  
The expected speed of person n during the evacuation process of small group members is represented by Equation (10).  
\[v_{ngroup}^{\ 0}(t)=\frac{1}{k-1}\sum_{j=1,j\
eq n}^{k}v_{j_{group}}^{\ 0}(t) \tag{10}\]  
where \(v_{ngroup}^{\ 0}(t)\) indicates the expected speed of the \(n\)th person at time \(t\), \(k\) and \(j\), respectively, indicate the number of people that belong to group \(i\) and other members of the small group, and \(v_{j_{group}}^{\ 0}(t)\) indicates the expected speed of the other member \(j\) at time \(t\).  
(3) Determination of crowd evacuation speed: During the cruise ship evacuation, the crowd is composed of numerous individual passengers and different numbers of small groups. The expected speed of passenger \(n\) is represented by \(v_{i}^{0}(t)\).  
\[v_{i}^{0}(t)=\left\{\begin{array}{ll}v_{jgroup}^{\ 0}(t)&\ \ \ \ \left(i\in U_{ group}\right)\\ v_{i}^{0}(t)&\ \ \ \ \left(i\
otin U_{group}\right)\end{array}\right. \tag{11}\]  
where \(U_{group}\) denotes small groups.
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
## 2 Methodology
### Model Bases Selection  
Many standard estimators can be improved by shrinkage methods, such as ridge regression [23] and Lasso regression. This study adopts Lasso regression to obtain sparse solutions from the model bases selection.  
In a Lasso regression, the value of the parameter controls both the size and the number of coefficients. Cross-validation is a resampling technique which can find a parameter value that ensures a proper balance between bias and variance. In this case, cross-validation considers the best tuning parameter value to be the one that minimizes the estimated test error rate of the forecasting results. More details about the Lasso estimate and adaptive Lasso can be found in [17; 18].
# A Review of the Applications of OCT for Analysing Pharmaceutical Film Coatings
## 2 Overview of OCT Techniques
### Time-Domain OCT  
Figure 1 shows a schematic diagram of a typical TD-OCT system. Here the optical beam from a broadband light source is split into a reference and sample arm, respectively, using a beam-splitter, which also recombines the respective back-reflected beams into a photodetector, such as a photodiode, for heterodyne detection [12; 13; 14]. By systematically displacing a reference mirror by means of a motorized stage, scanning along the depth/axial direction of the sample can be achieved. A single depth scan is performed by measuring the interference signals as a function of time while the reference arm is moved. Thus, such a measurement requires time and the mechanical movement of a mirror to acquire axial information of the sample.
# An Analysis of the Short Utterance Problem for Speaker Characterization +
## 5 Experiments & Results
### Enrollment-Test Distance versus Performance (_Eer and Mindcf_)  
Previously we have studied the effect of relative phoneme distances on the score, individually analyzing each trial. Now we study the whole set of trials at once, providing the evaluation metrics, the Equal Error Rate (EER) and the minimum Decision Cost Function (minDCF).  
In Figure 7 we analyze the impact of the relative phonetic distance for the two evaluation metrics. For this purpose, we have conducted the SRE10 \"coreext-coreext det5 female\" experiment, selecting the scores for each trial from the previously described pool of scores. The results show the performance in terms of the average KL2 distance between enrollment and test trials. More than 10,000 different score sets were studied.  
Figure 6: Trial score in terms of KL2 distance for the whole data pool. Represented the mean and the mean plus/minus the standard deviation.  
Figure 7: Evaluation metrics, EER (**a**) and minDCF (**b**) in terms of the KL2 distance. Represented the mean and mean plus/minus the standard deviation of the metric per bin.  
Figure 7 also confirms our previous conclusions. Previously we inferred a non-linear behaviour of the EER and minDCF with respect to the relative phoneme distance. We realized that low values of phoneme distance should generate low degradations, getting more relevant as long as the KL2 distance increases. Figure 7 shows an elbow shaped relationship with two different behaviours. Below a certain value, in this case approximately 3 nats, both evaluation metrics experiment low degradations (0.5% EER and 0.03 minDCF). However, once the relative phoneme distance exceeds this value, degradation increases rapidly. This elbow shape has great implications. By working within the lowest range of relative phoneme distance (in our case below 3 nats) we can assume a certain reliability in our results.
## 4 ATT-BiLSTM Anomaly Data Processing
### ATT-BiLSTM Anomaly Data Repair  
In order to verify the practicality of the model, the trained model was used to predict the anomalous data at measurement point 2 (the mid-span section, which better reflects the structural response state of the bridge) and analyze the fit with the actual data.  
The data at measurement points 1 and 3, corresponding to the anomalous location of measurement point 2, are utilized as the input set for the ATT-BiLSTM data repair model that was trained in the previous section. The prediction input set is delineated in Table 3. Following the precise predictions made by the ATT-BiLSTM data repair model, it was determined that the anomalies had been successfully rectified, thereby enhancing the continuity and smoothness of the data series. This observation is substantiated by examination of the model-repaired data Table 3.  
Figure 7: Loss curve of the ATT-BiLSTM model.  
In order to demonstrate the impact of model repair in a more intuitive manner, the abnormal data repair results are represented in Figure 8. A comparison of the original anomalous data with the model repair data reveals that the repaired data and the original data remain consistent in trend, yet there is a significant improvement in outliers. The abnormal fluctuation points in the original data are clearly discernible, as illustrated in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Point** & \begin{tabular}{c} **Measurement** \\ **Point 1 Input Data** \\ \end{tabular} & \begin{tabular}{c} **Actual Anomaly Data for** \\ **Measurement Point 2** \\ \end{tabular} & \
\begin{tabular}{c} **Measurement** \\ **Point 3 Input Data** \\ \end{tabular} &
\begin{tabular}{c} **Measurement Point 2** \\ **ATT-BiLSTM Repair Data** \\ \end{tabular} \\ \hline
6 & 12.2500 & 11.8750 & 11.6875 & 12.450481 \\
311 & 3.8750 & 3.9375 & 2.3125 & 4.201364 \\
312 & 3.5000 & 3.5000 & 2.0625  
As illustrated in Figures 8 and 9, the data post-repair demonstrates a tendency of amplitude diminution at the anomaly, thereby effectively mitigating the likelihood of false alarms. Concurrently, at the deflection jump point, the rectified data did not fully eliminate the prominence of the jump point, thereby ensuring the thoroughness of the warning information and averting the occurrence of underreporting. This finding suggests that the model not only learns the internal correlation of the data during the repair process, but also pays particular attention to the associated deflection of the main girder mid-span section and its neighboring parts. Consequently, this data repair method can provide more reliable data support for deflection monitoring of steel truss bridges.