esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Monitoring Neurodegenerative Diseases
Recent developments in this sector include: wearable technology advancements (wearables with high-precision sensors and clothing-integrated sensors for continuous, discreet monitoring), enhanced data analytics (complex datasets are analyzed by machine learning--ML algorithms to develop predictive models for disease progression), Big Data integration (combining genetic data, wearable data, and medical records to improve the accuracy of predictive models), personalized healthcare (customizing interventions and treatment based on data insights), remote monitoring (integrating monitoring information with telemedicine systems to enable ongoing care and remote consultations), and home-based diagnostics (development of portable diagnostic tools that can be used at home for regular health assessments). The high cost of cutting-edge wearables and AI-powered systems, clinical validation of the efficacy and safety of new technologies, privacy and security concerns regarding personal health data, interoperability issues in integrating data from diverse sources into a unified system, ensuring the accuracy and reliability of IoMT devices and AI models, and patient acceptability of wearable devices are some of the limiting factors.
## 5 Conclusions  
In this paper, we propose a multiple-access fiber and free-space time and frequency transfer technique. This technique uses a two-way time-frequency comparison between the femtosecond OFC and CW lasers to achieve high-precision, real-time time and frequency transfer between the master site and multiple slave sites. Based on this transfer technique, we have built a multiple-access time and frequency fiber and free-space transfer experiment, which includes a master and two slave sites. The transmission distance between the master and each slave site is about 3.9 km in optical fiber plus 100 m in free space. The experimental results show that the RMS timing fluctuation of the time and frequency transfer between master site A and slave site B is 119 ps, with a frequency stability of 3.3 \(\times\) 10\({}^{-11}\) at 1 s and 2.8 \(\times\) 10\({}^{-14}\) at 2000 s. The RMS timing fluctuation of the time and frequency transfer between master site A and slave site C is 59.5 ps, with a frequency stability of 3.0 \(\times\) 10\({}^{-11}\) at 1 s and 2.6 \(\times\) 10\({}^{-14}\) at 2000 s. This technique uses the characteristics of the frequencies of the slave sites being the same as the harmonic frequencies of the OFC and adopts a single light source and a single receiving detector design in the master site, which improves the system reliability and greatly reduces the system complexity and cost. It can be applied to the construction of large-scale multiple-access time and frequency synchronization networks in the future.  
Conceptualization, D.H.; methodology, W.H. and P.Z.; software, P.Z.; hardware, P.Z.; investigation, W.H.; data curation, W.H.; writing--original draft preparation, W.H. and D.H.; writing--review and editing, W.H., P.Z., and D.H.; supervision, D.H.; project administration, D.H.; funding acquisition, W.H. and D.H. [PERSON] is the first author of the article. [PERSON] is the corresponding author of the article. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Stability results of the multiple-access time and frequency transfer.  
**Funding:** This study was funded by the National Natural Science Foundation of China, grant number 62271109, the Natural Science Foundation of Sichuan Province of China (No. 2023 NSFSC0445), and the State Key Laboratory of Advanced Optical Communication Systems and Networks (2023 GZKF09), China.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## Materials and Methods
Thus, a capacitor model that should be optimized through a simulation can be designed, because a capacitive effect exists between the metal layers and the ground. Figure 1: Proposed bandpass filter (BPF): (**a**) layout (\(u=20\)\(\upmu\)m, \(s=14\)\(\upmu\)m); (**b**) equivalent circuit. The simulations and optimization were performed on the Agilent Advanced Design System Momentum version 2016.01 (Keysight Technologies, Inc., Santa Rosa, CA, USA) to confirm and support the results and theoretical analysis in this study. The simulated Y parameter of the inner spiral line is depicted in Figure 2a. From this curve, a value of 0.405 pF at a resonant frequency of 1.35 GHz was calculated as in [18]:  
\[C=\frac{imag(Y_{11})}{2\pi f} \tag{8}\]  
As shown in Figure 2b, in the L band, where most of the satellite communication systems operate, the capacitance value is stable as only minor differences can be observed. The inductor and capacitor are connected in series, and the center frequency related to the total inductance and capacitance can be expressed as in [18]:  
\[f_{c}=\frac{1}{2\pi\sqrt{LC_{\mathrm{O}}}} \tag{9}\]  
the error between the calculated and designed resonant frequencies does not exceed 2.3%. A representative cross-section of the technology used for the proposed design is illustrated in Figure 3a, where the dimensions are not to scale. The structure is mainly composed of a GaAs substrate and metal layers. Only three metal surfaces, which are the Leads layer, Text layer, and Bond layer from top to bottom, are presented to make the design and processing more concise. The relatively inexpensive copper/gold metal is preferred for slowing down oxidation. The seed metal is selectively added to increase adhesion. A thin SiN\({}_{\mathrm{x}}\) layer is deposited on the 200 \(\upmu\)m thick GaAs substrate, to form a flat surface, and top metal for long-term protection, with a thickness of 100 nm [19; 20; 21]. The dielectric constant (\(\epsilon_{\mathrm{r}}\)) of GaAs is 12.85, with a loss tangent of 0.006. The finest line width can be processed to 10 \(\upmu\)m, with a thickness of 1.8 \(\upmu\)m as a middle metal layer. Small gaps between the two metal layers can be used to build an air bridge. More details on the processing are detailed in Figure 3b. After cleaning, plasma-enhanced chemical vapor deposition is used to deposit the SiN\({}_{\mathrm{x}}\) layer on the substrate. The thickness of Cu/Au is 4.5/0.5 \(\upmu\)m, as the bottom metal and the top metal. A plasma etcher (SF\({}_{6}\):O\({}_{2}\) = 45:5 sccm) is adopted after depositing a dielectric layer to remove the unnecessary parts of SiN\({}_{\mathrm{x}}\). A 100 nm thick Ti/Au seed metal deposition is followed by etching to avoid shorting, and another mask can be selectively used on the protective film of the top metal to form the specific area of the final SiN\({}_{\mathrm{x}}\) layer. \[C_{\text{S}}=\frac{1}{2}lwC_{\text{sub}} \tag{2}\]0 Simulation results of capacitance for optimization. (**a**) Y parameter of the inner spiral line; (**b**) Calculated capacitance value.
# An Improved CNN Model for Within-Project Software Defect Prediction
## 5 Results and Discussion
### Performance under Different Hyperparameter Settings  
**RQ2: How does the improved CNN model perform under different hyperparameter settings?**  
As a CNN-based deep model, our model featured various hyperparameters that required tuning. We used the SPSC dataset from the PROMISE repository to tune the hyperparameters. After initial exploration, we fixed some of the hyperparameters as follows: We set batch size to 256, which fit our RAM; the number of epochs to 50 using early stopping strategy; the learning rate to 0.0015 after initial selections; the convolutional layers and pooling layers to 3; the regularization alpha to 0.0001 to reduce generalization error; the dropout to 0.5 as suggested in [36]; the filter length to 2; the numbers of filters to 10; and the embedding output to 32. These hyperparameters applied to our experiment on the PSC and SPSC dataset. The remaining hyperparameters included dense layers and dense nodes as well as kernel size and stride.  
Figure 9 shows the F-measure of our improved CNN model under different numbers of dense layers and dense nodes. We observed that our model worked best under dense layers = 4 and dense nodes = 50. Adding more dense layers had a significant impact on F1. Dense node numbers were less significant for F-measure as compared with dense layer numbers. When the number of dense layers increased, the number of dense nodes had to decrease accordingly to avoid increasing the model capacity.  
Figure 10 shows the F-measure of our improved CNN model under different numbers of kernel sizes and strides. The epochs were set to 10 for speed acceleration. We observed that our model worked best under kernel size = 2 and stride = 2, which was slightly better than kernel size = 3 and stride = 3. There was a minor difference between choosing kernel size and stride of 2 or 3. However, kernel size and stride had to be matched in size, or there would have been a shape degradation in performance.  
The tuned hyperparameters were specified for the exact dataset, (i.e., SPSC dataset). However, when using this model for other projects, we can fine tune these hyperparameters based on the characteristic of the projects. For example, if we were to utilize the model to predict projects with larger source files, the length of the input vector to the embedding layer would be expanded. Thus, some hyperparameters such as embedding output, kernel size, and stride should be adjusted to fit the model capacity.  
Figure 9: Dense layers and dense nodes. Here dl means dense layers.
## 3 Experimental Results
### Impulse Transfer and Permanent Displacement  
A graph of impulse transfer versus charge mass is shown in Figure 11. Similar impulses were transferred by the three types of V-structure during the 10-15 g charge mass detonations. At 20 g, the 105\({}^{\circ}\) V-structures outperformed the 120\({}^{\circ}\) specimens, through lower impulse transfer. It also appears that the 32 mm tip radius is more effective at reducing impulse transfer than the 62 mm radius at 20 g. These were both expected results. The lower internal angle is known to be better at laterally deflecting the blast pressure [9; 10; 11; 12; 13; 14; 17], and previous modelling predicted that larger bend radii would be detrimental [4]. Unfortunately, the complexity and cost of the experiments limited the number of tests that could be performed; however, the consistent trends observed gave confidence in the test's execution and provided useful information for validation of the simulations.  
The influence of structural geometry on the permanent displacement is more clearly visible in the blast data. Figure 12 shows a graph of permanent mid-point versus charge mass. The 62 mm tip radius exhibited the largest displacement, despite the smaller internal  
Figure 11: Graph of impulse transfer versus charge mass obtained from the blast test data.  
Figure 10: Permanently deformed profiles of blast tested V-structures (120\({}^{\circ}\) internal angle, 32 mm tip radius) of the (**a**) ridge plane, (**b**) cross-cut plane.  
angle, even when the overall impulse transfer was lower. The larger bend radius seems to produce a more flexible central region in the V-structure, whereas the tighter bend radius may be more work-hardened due to the cold bending process and may result in a more structurally rigid shape. Interestingly, the permanent displacement of the V-structure seemed more strongly influenced by the bend radius than the internal angle, and the impulse transfer seemed more affected by the internal angle.
## 3 Analysis of the Onsite Monitoring Data  
Figure 4 shows the daily progress monitoring of the sinking process of the 17\(\#\) Shaft. Trial sinking began on 14 January 2022, and the design elevation was reached on February 11, with an actual sinking depth of 40.14 m. The actual average sinking rate of the shaft was 2.23 m per day, with a maximum sinking rate of 5.05 m per day.  
Figure 3: Stratigraphic profile of the shaft.  
Figure 2: Location and measurement point layout of 17\(\#\) shaft: (**a**) geographic location of the shaft and (**b**) Schematic of the shaft measurement point layout (unit: m).  
The shaft sinking process can be divided into two main stages. In Stage 1 (from January 14 to 29), the shaft sank to a depth of 36.14 m, with an overall sinking rate of approximately 2.12 m per day. Owing to the construction pause during the Chinese New Year holiday (from January 30 to February 8), the consolidation of the soil at the pit bottom occurred, resulting in a reduced sinking rate during Stage 2 (from February 9 to 11), with the sinking rate slowing to approximately 1.67 m per day.
# Controlled Drainage Effectiveness in Reducing Nutrient Outflow in Light of Climate Changes
## 4 Discussion
For normal years, the GFDL and UKESM models for all modeling scenarios indicate the same value of 25 mm of surface runoff, except for the 1 March date. In dry years, much lower values of surface runoff were obtained. CD starting on 1 April and 15 April, for all scenarios, for each model, had identical results to FD. According to [PERSON] et al. [34], surface runoff for current climate conditions is approximately 4.4 mm,1.0 mm, and 0.4 mm for wet, normal, and dry years, respectively. Comparing these values to the obtained results for future climate conditions, a significant increase was noted in all models. The largest increase was observed for wet years using CD on 1 March, with values ranging between 31-42 mm, 16-26 mm, and 16-18 mm for the GFDL, MPI, and UKESM models, respectively. However, the smallest increase was observed for dry years, with values ranging from 8 to 14 mm for the adopted models. When CD is applied on 15 March for wet years, there is an increase of 30 mm, 15-19 mm, and 16 mm for the GFDL, MPI, and UKESM models, respectively. [PERSON] et al. [30] have shown that, in the near future, outflows measure 9.5 mm on average and that, in the far future, they measure 9.0 mm on average. These values are almost five times higher than at the present time, due to the increase in rainfall intensity in the near and far future. [PERSON] et al. [51] indicate that the potential growth of surface runoff should be taken into account in the future. This is important because more nutrient losses have been predicted due to extreme precipitation events with surface outflow. However, [PERSON] et al. [52] have shown that surface runoff will be reduced by 25.3% and 23.6% under RCP 4.5 and 8.5 climate scenarios, respectively. Also, [PERSON] et al. [53] indicated that, according to the same trend, a roughly 10-21% reduction is observed in the average annual surface runoff under future climatic scenarios. The results obtained represent CD efficiency depending on the selected time of the simulations carried out. According to the adopted SSP370 path, an increase in average rainfall for the analyzed time period is indicated. In addition, the results presented depend on the basic parameters characterizing the drainage object, including geological conditions, soil conditions, topography, drainage network parameters, as well as hydrological and meteorological conditions. The results of this simulation show the potential hydrological impact of CD on an area in central Wielkopolska under varying meteorological forces using three models from a range of possible future climate scenarios. Thus, the results should not be interpreted as a strict forecast for the future. Moreover, in the future, vegetation can start earlier due to higher temperatures.
# High Spatial and Temporal Resolution Bistatic Wind Lidar
## 3 Validation and Characterisation
### PTB Bistatic Lidar/Ultrasonic Anemometer (Vaisala WMT700)  
In 2014, the first comparative measurements between the PTB bistatic lidar and a Vaisala WMT700 ultrasonic anemometer mounted at a height of 10 m were conducted [19]. Being representative of complex terrain, a common measurement site amidst several buildings at PTB was chosen.  
Considering the disturbed flow conditions and the distance of about 50 cm between the ultrasonic anemometer and the PTB bistatic lidar's measurement volume, the comparative measurements indicated a very good agreement for both wind speed and wind direction. Orthogonal linear regressions for the 1 min averages of wind speed and wind direction yielded Pearson correlation coefficients of 0.982 and 0.905, respectively.
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 4 Challenges and Design Requirements
### Power Consumption and Battery lifetime  
One of the most critical parameters to be considered when adopting WHNCS is energy efficiency. Almost all wireless motes support battery operation capabilities with a low power consumption [116]. In order to conserve the energy of the motes, clustering algorithms such as energy-efficient sleep awake aware (EESAA), sleep-wake energy-efficient distributed (SEED), and hybrid energy-efficient distributed (HEED) can be adopted to minimize redundant transmission to achieve energy efficiency and improve data collection [117; 118]. Adaptive free-shape clustering (AFC) is an emerging technique that greatly minimizes power consumption and increases the network lifetime [119]. Other non-conventional power generation methods such as photovoltaics, wind power, and thermoelectric conversion can be combined with WirelessHART field devices to achieve long-term operation without the need of any human intervention [82; 120]. A detailed survey of energy harvesting in IWSN is discussed in [121]. Thus, power consumption and battery lifetime are a significant bottleneck while using the sensor nodes for their extended features at the field level.
# OTFS-IM Modulation Based on Four-Dimensional Spherical Code in Air-to-Ground Communication
## 3 Performance Analysis  
In this section, we analyze the performance of the OTFS-IM4 system by deriving the approximation value of the BER and the upper bound of the PAPR.
## 3 Results
### X-ray Fluorescence Spectroscopy (XRF)  
Preliminary analysis performed with the portable XRF showed that the dominant elements of the black varnish (samples AV1, AV2, AV3, EZ1, EZ2, EZ3) are Fe, Mn and Si, with lower counts of K, Ca and Ti, thus in agreement with the composition described in the literature, i.e., clay minerals mixed with iron and manganese oxides. In sample EZ4, which does not have the black crust but presents instead a thicker orange crust, Mn shows much lower counts or, depending on the spot of analysis, is not present at all. Fe presents high counts, while Ti, K, Ca and Si are detected with low counts. The bedrock is mainly composed of Ca and Si.  
To better understand the composition of the different layers (white bedrock, black coating and orange layer beneath it) the powders obtained from each layer of sample AV1 were analyzed with WDXRF, and the results are reported in Table 2.  
Figure 5: Pictures of the petrographic thin sections acquired with the Keyence digital microscope in transmitted light: (**a**) detail of the black varnish and of the reddish layer underneath it of sample AV1; (**b–d**) examples of the weathered area occurring beneath the black coating in samples EZ1, EZ3 and AV2; (**e**) weathered area colonized by cyanobacteria aggregates in sample AV1; (**f**) detail of the orange crust of sample EZ4.  
The results confirmed that the black varnish is rich in Fe and Mn with a high content of Si and lower of Ca and highlighted the presence of light elements (not detectable with the portable XRF) such as Mg, Al, P and S in the sample. Mg is present in higher amounts in the bedrock, while Al, S and P showed the highest percentages in the black varnish, followed by the reddish layer. Low amounts of Ti and Ba were also detected in the black and reddish layers; the latter being present only in the black varnish. While Mn is mostly present only in the black coating, Fe is present in higher amounts also in the reddish layer, which seems to be composed mainly of Si and lower percentages of Ca, Al, K and Mg. In the bedrock, in addition to the presence of Ca and Si, low percentages of Mg, Al and Fe were also detected.  
Because of the difficulty in accurately separating the layers, some elements might show a higher percentage due to contamination coming from the adjacent layer.  
The distribution of the elements in the samples is further discussed in the following section.
# SybilHP: Sybil Detection in Directed Social Networks with Adaptive Homophily Prediction
## 3 Problem Formulation
### LBP-Based Sybil Detection
#### 3.3.2 Existing Potential Function Designs  
The LBP-based methods are differentiated mainly according to the edge-potential function \(\varphi_{uv}(x_{u},x_{v})\), which partially reflects the distribution of a neighboring pair \(x_{u}\) and \(x_{v}\).  
SybilBelief [16] designs a potential function that encodes the coupling strength between nodes \(u\) and \(v\) as follows:  
\[\varphi_{uv}(x_{u},x_{v})=:\left\{\begin{array}{ll}w,&\text{if }x_{u}x_{v}=1\\ 1-w,&\text{if }x_{u}x_{v}=-1.\end{array}\right. \tag{5}\]  
Specifically, when \(x_{u}x_{v}=1\) (i.e., \(x_{u}\) and \(x_{v}\) share the same state), \(\varphi_{uv}(x_{u},x_{v})\) takes a presumed homophily strength \(w\) ranging from 0.5 to 1, which implies the possibility that \(x_{u}\) coincides with \(x_{v}\). Similarly, when \(x_{u}x_{v}=-1\), \(\varphi_{uv,v}(x_{u},x_{v})\) should take the heterogeneity strength between \(x_{u}\) and \(x_{v}\), that is, \(1-w\).  
SybilSCAR [17] follows a similar method to encode the interaction between nodes by neighbor influence. GANG [20], on the other hand, aims to capture the asymmetric relationship by the following design:  
\[\varphi_{uv}(x_{u},x_{v})=:\left\{\begin{array}{ll}w,&\text{if }x_{u}=1\text{ or }x_{v}=-1\\ 1-w,&\text{if otherwise,}\end{array}\right. \tag{6}\]  
which is based on the intuition that, in a unidirectional edge, only if the tail is benign or the head is Sybil, then this pair tends to share the same label with homophily strength \(w\). Otherwise, this edge tends to link two nodes with different labels. However, the use of a global homophily strength \(w\) still limits its modeling fidelity because different nodes may have different behavior patterns and, therefore, different local homophily with their neighbors.
###### Abstract  
Effective fire prevention in mine workings and tunnels requires a thorough theoretical analysis of the heat and mass transfer processes within these structures. This involves using established models to calculate non-isothermal air flow dynamics in long tunnels and mine workings. While the ventilation of tunnels has been extensively studied, significant challenges persist regarding mine ventilation systems, particularly due to their complex and branched topology. This study aimed to address these challenges and gaps in mine ventilation. We designed a laboratory bench to simulate an inclined mine working with a heat source (fire) and validated a mathematical model of heat and mass transfer in such settings. Using experimental measurements, we verified the model's accuracy. It is important to note that our experimental and theoretical analyses focused solely on the thermal effects of a fire, without considering the release of harmful impurities. Using the validated model, we conducted multiparameter simulations to identify the conditions leading to the formation of a thermal slug in an inclined mine working and the subsequent reversal of air flow. The simulation data enabled us to determine the dependency of the critical heat release rate on the aerodynamic parameters of the mine working. Additionally, we evaluated the changes in average air density within a mine working at the critical heat release rate. These findings are crucial for the further development of a network-based method to analyze air flow stability in mine ventilation networks during fires.  
m line ventilation; underground fire; laboratory experiment; inclined channel; heat release rate Article