esa-sceva/satcom-chunk-collection huggingface dataset:
# Forced Convection Heat Transfer for Stratospheric Airship Involved Flight State
###### Abstract  
Forced convection heat transfer is a significant factor for the thermal control of a stratospheric airship. However, most of researches were conducted without considering the influence of flight state causing serious errors. In order to accurately predict the forced convection heat transfer of the stratospheric airship at an angle of attack, firstly, an empirical correlation of Nusselt number (\(Nu\)) as function of Reynolds number (\(Re\)) andlength to diameter ratio (\(\epsilon\)) is developedunder horizontal state based on a validated computational fluid dynamic (CFD) method. Then, a correction factor \(K\), considering its angle of attack (\(\alpha\)), is proposed to modify this correlation. The results show that: (1) Nusselt number increases with the increase of Reynolds number, decreases as the length to diameter ratio changes from \(2\sim 6\), and increases as the angle of attack changes from \(0^{\circ}\sim 20^{\circ}\). (2) At higher Reynolds number, the calculated results are 30 percent higher than those of previous studies with \(\alpha\) = \(20^{\circ}\). (3) Compared with \(\alpha\) and \(\epsilon\), the effect of \(Re\) on correction factor \(K\) can be ignored, and \(K\) is a strong equation of \(\alpha\) and \(\epsilon\). The efficiency of heat transfer is increased by 6 percent with \(\alpha=20^{\circ}\). The findings of this paper provide a technical reference for the thermal control of a stratospheric airship.  
stratospheric airship; angle of attack; forced convection heat transfer; flight state; thermal control +
Footnote †: journal: Applied Sciences
## 3 Illustrative Example  
### The Study Area  
To illustrate some capabilities of the Multisilva simulator, a case study of a forest area near Flaxweiler in eastern Luxembourg (Figure 5) is presented in this section. The area used in this example is a subset of a larger set, visible in Figure 3. The subset covers 27.6 ha of forest and is made up of 12 individual management parcels (stands). Six of the tree species listed in the previous section are present, along with the three \"other\" species group classifications (Table 4). This section of the forest contains the sources of the Donwerbach river, a tributary of the Moselle. The majority of the study area (24.1 ha) is part of the Widdebierg-Hierden Nature Reserve, with several footpaths and a mountain bike trail providing opportunities for recreation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Species** & **Latin Name** & **\# Trees** \\ \hline European beech & _Fagus sylvatica_ & 1776 \\ Sessile oak & _Quercus petraea_ & 2070 \\ Douglas fir & _Pseudoltsuga menziesii_ & 262 \\ European spruce & _Picea abies_ & 1160 \\ European ash & _Fraxinus excelsior_ & 89 \\ European larch & _Larix decidua_ & 43 \\ Noble broadleaves & & 24 \\ Other broadleaves & & 455 \\ Other conifers & & 47 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tree species summary in the study area.
## 2 Literature Review  
### Development of Bridge Inspection  
In earlier years, bridge inspection mainly contained three major parts: deck, superstructure and substructure [15]. Inspectors needed to perform component-level inspections to obtain general condition ratings of the three parts. However, this level of detail is insufficient to help make maintenance decisions. To improve the precision and reasonability in maintenance decision making, ninety-eight Commonly Recognized elements and eight Smart Flags were recommended [16], based on which bridge inspection moved to element-level bridge inspection. Afterwards, bridge elements are further subdivided into three major classes: National Bridge Elements (NBEs, including 89 element types), Bridge Management Elements (BMEs, including 13 element types), and Agency Developed Elements (ADEs) in the AASHTO Guide Manual for Bridge Element Inspection [17]. ADEs may be sub-elements of NBEs or BMEs, or agency-defined elements without ties to the elements defined in this manual. In this context, more and more elements need to be inspected. The same development trend on bridge inspection can also be observed in China. Bridges are now classified into four primary categories, with each category broken into at least 16 components [18].  
To improve the efficiency of bridge inspection, the method of inspection documentation has changed from record-keeping by pencil and paper to electronic data collection using a BMS [19; 20]. The inspection time is significantly reduced, and the errors are avoided since the inspection data does not need to be re-entered into a database management system [21]. During a bridge inspection using a BMS, inspectors need to input a keyword to search for the target element and record the inspection results of the element. This procedure is quite time-consuming especially for bridges with many elements. Actually,this search process is not strictly necessary because the target element can be predicted according to spatial relationships among bridge elements. However, the widely used keyword-based IR method in BMSs cannot take account of semantic relationships (e.g., spatial relationships) among inspection-relevant objects [22]. Therefore, the IR method in the BMS should be improved to further enhance the efficiency of bridge inspection.
# A 60 GHz CMOS I/Q Receiver for High-Speed Wireless Communication System
### Design of 60 GHz Passive Mixer and Base Band Amplifier (BBA) Circuit  
The passive switching quad is implemented for the double-balanced mixer for the high linearity and low DC power consumption as shown in Figure 6. The passive mixer is realized with a transistor size of 40 \(\upmu\)m total width is implemented to achieve high conversion gain at 60 GHz. The conversion gain is dependent on the gate bias and the vgs of 0.45 V is chosen for the high conversion gain; it can be digitally controllable. The capacitive loads are used to filter out the unwanted RF and LO signals. To achieve the wide bandwidth, the capacitors, including the loading capacitance of the baseband amplifier, are chosen.  
The block diagram of BBA is shown in Figure 7a. It consists of six stages of an amplifier cascaded in series, and a DC block capacitor is placed after every two stages to prevent the DC offset. It is a 4-bit digital controlled wideband programmable gain control amplifier.  
Figure 5: Schematic of 60 GHz passive I/Q generator.  
Figure 6: Schematic of 60 GHz double-balanced passive mixer.  
The structure is differential, and the gain depends on the load resistance \(\rm{R_{L}}\) and the value of source degeneration resistance at each gain stage as shown in Figure 7b. The buffer circuit shown in Figure 7c drives the external 50 \(\rm{\Omega}\) load of the baseband signal. The simulated gain control range is around 60 dB, and the 3 dB baseband bandwidth is around 1 GHz. The parameter values of the components of the baseband amplifier circuit are shown in Figure 7.
# Research on the Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS
## 2 Methodology  
This paper mainly selects the PCG and ECG signals collected synchronously as the research objects. PhysioNet published a cardiovascular disease dataset containing both PCG and ECG signals in 2016 [9]. Based on this dataset, this paper proposes the classification method based on BiLSTM-GoogLeNet-DS to predict and classify the signals in the database. Firstly, the ECG and PCG signals are preprocessed, respectively, mainly for filtering. Then the processed signals are sent to the BiLSTM network to perform network fusion and classify the signals. After that, the filtered PCG and ECG signals are, respectively, subjected to wavelet transform to obtain the time-frequency diagram of each signal. Each time-frequency diagram is resized and converted into the image size suitable for GoogLeNet network training, and then classified through the improved GoogLeNet network to obtain the classification results of PCG and ECG signals, respectively. The three-channel classification results, combined with the classification results of the above BiLSTM network, are obtained. The classification decision is made by the fusion strategy of the improved D-S theory. Calculating the probability values of ECG and PCG signals belonging to different categories can lead to the basic probability assignment (BPA) function. The local recognition credibility of each feature is estimated by using the confusion matrix. The weighted correction is carried out when constructing the BPA of the classifier. Finally, the classification results are obtained. The following is the specific implementation process, as shown in Figure 1.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals
### Results  
In Study 1, we collected data on user decisions, decision time, and behavioral metrics, including head rotation angles and movement distances to evaluate performance. As the data were not normally distributed, we employed generalized estimating equations (GEEs) [89; 90; 91] to analyze our within-subjects experimental design with multiple factors (feedback modality, frequency, and size). GEE models are capable of handling non-normally distributed dependent variables while accounting for the correlated nature of repeated-measures data [90; 91]. For binary user decisions (0 or 1), we specified binomial distribution with a Probit link function. For decision time and behavioral metrics, we used gamma distribution with a log link function. We also assessed users' subjective perceptions through questionnaires. For urgency perception (which contained zero values), we applied negative binomial distribution with an identity link function, while, for decision confidence (range: 0-100; no zero values), we utilized gamma distribution with a log link function. All models employed an exchangeable correlation structure to account for within-subject correlations and included feedback modality, frequency, and size as main effects, along with their interactions. This comprehensive analysis provided a thorough understanding of how different feedback modalities influence user decision-making and behavior.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 6 Unlocking the Potential: Advanced Applications of Kaolin in Industry
### Medicine Industry  
Related to medicine, kaolin is valued in the pharmaceutical industry for its multifunctional roles due to its inherent properties of high purity and stability [111]. Primarily, kaolin serves as an excipient in various formulations, in which it functions as a binding agent and a filler [112; 113]. Its use in tablet formulations is particularly notable, as it contributes to the structural integrity of the tablets, ensuring that they remain intact during storage and handling. The adsorption properties of kaolin are also exploited in the preparation of suspensions and gels, in which it helps to stabilize the formulation by preventing the settling of active ingredients.  
Moreover, kaolin's high surface area and particle size distribution make it effective in adsorbing impurities and controlling the release of active pharmaceutical ingredients (APIs) [114]. Its inert nature ensures minimal interaction with other components of the pharmaceutical formulation, making it suitable for sensitive applications. Recent developments have also explored the use of kaolin in controlled-release drug delivery systems, in which its role in modifying the release kinetics of drugs is under investigation [115].  
The annual usage of kaolin in pharmaceutical applications underscores its importance as a reliable and versatile component in the production of various medicinal products. Companies like _Merck_ utilize kaolin in the formulation of specific medications, benefiting from its inertness and safety.
# A Novel 2D Micro-Displacement Measurement Method Based on the Elliptical Paraboloid
## 3 Experimental Results  
To verify the feasibility and accuracy of the measurement method, a series of experiments were carried out under laboratory conditions. Figure 5 shows the experimental configuration used for the micro-displacement measurement system in the \(x\) direction.
# A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection
## 4 Experiments
### Qualitative Results  
In this section, we present the qualitative results of our RTFM+SP-MIL and three baseline models (DeepMIL, RTFM, UR-DMU) on the UCF-Crime, ShanghaiTech, and XD-Violence datasets. Figures 5-7 show that our framework can locate abnormal events more effectively than the baseline models. For the UCF-Crime dataset, Figure 5a-c demonstrates that the positioning curve of our framework closely aligns with the ground truth. Even when the abnormal events occur intermittently in Figure 5a, our detecting results are only slightly delayed or advanced. This highlights the robustness and accuracy of our framework in detecting abnormal events, such as arson and robbery. Additionally, Figure 5d illustrates that our framework has a low false positive rate. Similar conclusions can be drawn from the positioning curves of the ShanghaiTech and XD-Violence datasets. In addition, Figure 6b illustrates that the classifiers in RTFM and DeepMIL exhibit a bias in localizing anomalies, resulting in delayed anomaly detection, while the classifiers in UR-DMU cause significant false alarms. In contrast, our method effectively identifies the location of anomalies. This highlights the effectiveness of our SP-MIL strategy in providing unbiased predictions. Although our framework exhibits a few false positives in Figures 6a and 7c, overall, our detecting results accurately promptly reflect the real anomalies in the videos.  
\begin{table}
\begin{tabular}{l l l c} \hline \hline
**Method** & **Source** & **Feature** & **AP (\%)** \\ \hline DeepMIL * [7] & CVPR 2018 & I3D RGB & 73.24 \\ DeepMIL * [7] & CVPR 2018 & CLIP & 74.03 \\ [PERSON] et al. [27] & ECCV 2020 & C3D RGB & 67.19 \\ MSL [6] & AAAI 2022 & C3D RGB & 75.53 \\ [PERSON] et al. [16] & TIP 2021 & I3D RGB & 75.90 \\ RTFM [5] & ICCV 2021 & I3D RGB & 77.81 \\ RTFM * [5] & ICCV 2021 & I3D RGB & 78.07 \\ RTFM * [5] & ICCV 2021 & CLIP & 79.11 \\ MSL [6] & AAAI 2022 & I3D RGB & 78.28 \\ [PERSON] et al. [35] & ICME 2023 & I3D RGB & 77.92 \\ MGFN [40] & AAAI 2023 & I3D RGB & 79.19 \\ [PERSON] et al. [34] & PR 2023 & I3D RGB & 79.89 \\ DMU [24] & AAAI 2023 & I3D RGB & 81.66 \\ [PERSON] et al. [13] & TNNLS 2023 & I3D RGB & 79.00 \\ [PERSON] et al. [11] & CVPR 2023 & I3D RGB & 78.74 \\ [PERSON] et al. [11] & CVPR 2023 & I3D +VGGish & 81.43 \\ [PERSON] et al. [36] & CVPR 2023 & I3D RGB & 81.30 \\ [PERSON] et al. [28] & PR 2024 & I3D RGB & 80.07 \\ [PERSON] et al. [39] & WACV 2024 & I3D RGB & 82.10 \\ [PERSON] et al. [29] & CVPR 2024 & CLIP & 83.68 \\ \hline DeepMIL+SP-MIL & & I3D+CLIP & 76.63 \\ RTFM+SP-MIL & & I3D+CLIP & 84.42 \\ UR-DMU+SP-MIL & & I3D+CLIP & 84.32 \\ \hline \hline \end{tabular}  
* means that we re-implemented this method.  
\end{table}
Table 8: Performance comparison of state-of-the-art methods on the XD-Violence dataset.
# Assessing Abrasion Resistance in Concrete Pavements: A Review
## 6 Discussion and Analysis
### Current Limitations in Understanding Abrasion Resistance  
Despite significant advancements, there are still several limitations to fully understanding the abrasion resistance of materials, particularly concrete pavements. Variability in material qualities, intricate tire-road surface interaction mechanisms, and a dearth of standardized testing procedures that accurately replicate real-world conditions are some of the main obstacles. Addressing these shortcomings and creating more accurate models to forecast pavement durability in the long term require more research.
# Multi-Connectivity for Multicast Video Streaming in Cellular Networks
## 2 Multi-Connectivity in MBMS
### Enabling Multi-Connectivity in Multicast Transmissions  
The MBMS user plane protocol architecture defines a synchronization (SYNC) protocol layer on the transport network layer for content synchronization [37]. This layer carries the information needed for identifying transmission times and detecting packet loss. The SYNC protocol is terminated in the BM-SC and the base stations. As a result, the MBMS content sent to the base stations associated with the same BM-SC are synchronized. Consequently, UEs can receive and combine multiple copies of the same content received from these base stations without the need to exchange any additional control signaling. The proposed multi-connectivity multicast scheme leverages this inherent synchronization in MBMS systems, enabling UEs to obtain multicast content from multiple sources without requiring additional synchronization. Furthermore, since MBMS operates as an idle-mode procedure, UEs can use MC multicasting without establishing a Radio Resource Control (RRC) connection to a base station. The signaling procedures for enabling MC in MBMS were proposed in [10].  
For enabling multi-connectivity in MBMS, we redefine the dynamic between the primary and secondary base stations of a multi-connected UE compared to what is traditionally defined for unicast transmissions [38]. Specifically, we propose the following:  
* _Connectivity:_ Firstly, depending on its capability, a UE can connect to any number of base stations and receive multicast content from all of them. A UE can also remain in the RRC idle mode if it is not connected to any base station and still receive content from any number of base stations [10].
* _Primary and Secondary Base Stations:_ For a UE using MC multicasting in RRC idle mode, the _primary_ base station refers to the base station that it is capped on. For a UE in RRC connected mode, the _primary_ base station refers to the one it is connected to. All other base stations from which the UEs may receive content are called _secondary_ base stations. Furthermore, the primary and secondary base stations of a UE do not operate in a traditional master-slave configuration in MC multicasting. Secondary base stations are not dictated by the primary base station in their interaction with the UE [10]. A multicast UE can receive relevant control information and multicast data from multiple base stations independently. Thus, there is no real distinction between _primary_ and _secondary_ base stations for a UE. Each base station that serves the UE under MC multicasting is equivalent from the perspective of the MC multicast transmissions.  
Figure 1: MBMS architecture.
## 5 Results
### Temporal and Channel Variability Analysis  
Although the experiments were conducted in a controlled indoor environment, the collected data exhibited natural fluctuations in throughput and flow characteristics that provide insight into the performance of wireless communication channels, even under nominally stable conditions. Figure 9 shows the average throughput observed during all capture sessions, computed over 15-min intervals. This representation reveals spontaneous throughput variation, highlighting the system's sensitivity to dynamic conditions, even in controlled scenarios.  
To further quantify the flow-level performance, Figure 10 presents the distribution of TCP/UDP flow durations grouped by hour of the day.  
Despite stable configuration parameters, temporal variability emerged due to the environmental and network stack behavior. This highlights the importance of accounting for temporal factors when evaluating the stability and robustness of IoT communications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Session** & **CV (Unitless)** & **Flows** \\ \hline
1 & 1.59 & 58 \\
2 & 1.17 & 131 \\
3 & 1.26 & 102 \\
4 & 1.25 & 52 \\
5 & 1.25 & 192 \\
6 & 1.29 & 154 \\
7 & 1.18 & 193 \\
8 & 1.38 & 126 \\
9 & 1.49 & 33 \\
10 & 1.17 & 185 \\
11 & 1.22 & 167 \\
12 & 1.22 & 142 \\
13 & 1.16 & 197 \\
14 & 1.12 & 67 \\
15 & 1.18 & 140 \\
16 & 1.29 & 371 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Daily coefficient of variation (CV) of flow inter-arrival times
# IoT: A Long-Reach Fully Passive Low-Rate Upstream PHY for IoT over Fiber
## 2 Materials and Methods
### IoToF Prototype Implementation
#### 2.5.3 Optical-to-Electrical Module and Demodulation Strategy  
There are several factors which improve the O/E conversion performance: (i) in the optical domain, the sensitivity is function of the edge filter slope inclination, and the dynamic range is function of the visibility and bandwidth of the filter; (ii) in the electrical domain, the OP-AMP bandwidth as well as the maximum ADC sample rate defines the end-to-end system bandwidth. The sensitivity is related with the ADC quantization, and for ADC with high resolution (16 bits or higher), this parameter is considerably improved. Our O/E module follows [12]. After passing the modulated signal through the transmission channel, a delayed and noisy version of the transmitted signal will be received at the demodulator (E/D module). In order to perform the demodulation (E/D) and decision processes (D/I), two different methods can be implemented. One is the phase-coherent demodulation and detection, where it is necessary to estimate the M carrier phases. This process is extremely complex and impractical for a large number of signals. The other solution neglects the carrier phase, simplifying the demodulation and detection processes. In this case, per each signal waveform, two basic correlation functions (quadrature carriers) are used, requiring 2\(M\) correlators [36].
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 2 Materials and Methods
### Measurement of Dielectric Properties  
The system for the measurement of dielectric properties comprised an impedance analyzer (model: IM3570, manufacturer: HIOKI (Shanghai) Measurement Co., Ltd., Shanghai, China), a self-made acrylic capacitor, an LCR digital bridge test line (model: THZ26029B, manufacturer: Changzhou Tonghui Electronic Technology Co., Ltd., Changzhou, Jiangsu Province, China), and a computer (Figure 2).  
The impedance analyzer was preheated for 30 min, and the LCR digital bridge test line was calibrated for short-circuit and breakage as well as for 50 \(\Omega\) load. For the measurement, the excitation voltage was first set to 1 V. The impedance analyzer was used to measure the empty cylinder capacitance \(C_{0}\) at 113 logarithmic frequencies ranging from 1 to 100 kHz, and then, the prepared samples were placed into a homemade capacitor. The empty capacitance value of the capacitor (\(C_{0}\)), the parallel equivalent capacitance of the sample (\(C_{x}\)), and the dielectric loss angle tangent (tan\(\delta\)) were measured. Each sample was measured five times, and the average value was used as the measurement result. Dielectric constant \(\epsilon^{\prime}\) and loss factor \(\epsilon^{\prime\prime}\) were calculated as follows:  
\[\epsilon^{\prime}=\frac{C_{x}}{C_{0}} \tag{2}\]  
\[\epsilon^{\prime\prime}=\epsilon^{\prime}\tan\delta \tag{3}\]
## 3 Materials and Methods
### Features
#### 3.3.1 Throughput  
**Feature feasibility:** Stability of throughput is typical for ICS networks. Changes in throughput can be used to detect high traffic events (scanning, DoS, malfunction). Attacks with low traffic requirements are not detectable with monitoring throughput.  
**Our data:** The average packet rates and sizes in both recording locations are depicted in Table 2. From these it appears that there is some fluctuation in both locations within the total recording interval.  
Longer period of traffic recordings could provide us with further insight, but from this data it is difficult to assess whether the average packet rate would be usable.