esa-sceva/satcom-chunk-collection huggingface dataset:
# A Traffic Equilibrium Model for Multi-Modal Networks with Uncertain Demands
## 3 Generalized Travel Cost Function
### Transfer Links  
Travel time in transfer links includes transfer waiting time and transfer walking time. For simplicity, in this study, the frequency of the bus and subway modes running on fixed routes and the average walking time transfer node are fixed. Then, the travel time for transfer links can be written as follows:  
\[t_{l}^{h}=t_{l}^{hi}+t_{l}^{di} \tag{18}\]  
where \(t_{l}^{hi}\) (min) is the average walking time for transfer to transport mode \(i\); \(t_{l}^{di}\) (min) is the average waiting time for transfer to transport mode \(i\); \(t_{l}^{bi}\) (min) is a nonzero constant if \(i=3\), and otherwise \(t_{l}^{bi}\) (min) is 0; \(t_{l}^{di}\) (min) is a nonzero constant if \(i=2,3\), that is, the time of frequency of modes 2 and 3. Otherwise, \(t_{l}^{di}\) (min) is 0.  
To maintain consistency with (4), crowd discomfort due to transferring can be expressed as follows:  
\[u_{l}^{h}=\gamma u^{hi^{0}}t_{l}^{h} \tag{19}\]  
where \(u^{hi^{0}}(\min^{-1})\) is the average crowd discomfort per time unit due to transferring.  
While the travel fares of the transport modes (apart from mode 2) have been considered on physical links, only the travel fares of mode 2 are considered on transfer links. They are considered to be fixed, while the fares for the other modes are set to zero.  
\[m_{l}^{h}=\pi m^{hi} \tag{20}\]  
where \(m^{hi}\) (km\({}^{-1}\)) is a nonzero constant if \(i=2\), and otherwise \(m^{hi}\) is 0.  
Consequently, the travel cost function of a transfer link is represented as follows:  
\[c_{l}^{h}=\left(\lambda_{l}+\lambda_{u}\times\gamma\times u^{h^{0}}\right) \times\left(t_{l}^{hi}+t_{l}^{di}\right)+\lambda_{m}m^{hi} \tag{21}\]
# Hydrakon, a Framework for Measuring Indicators of Deception in Emulated Monitoring Systems
## 5 Threat Evasion Methodology  
### EMS Threat Evasion Model  
We construct an EMS threat evasion model as depicted in Figure 4 that comprises the following four layers: EMS ecosystem; security tools; indicators of deception (IoDs); and evasive vectors.  
The threat evasion model begins with the foundation that is the EMS ecosystem layer. The layer above the EMS ecosystem encompasses the four main types of EMS security tools that include debuggers, honeypots, virtual machines and sandboxes. Their role is to analyze potential malware and threat actor access. The security tools exhibit IoDs, which are artifacts that reside on their system and give away their true nature as analysis tools. The IoDs are divided into five main categories:  
* Application IoD: anomalies related to system applications.
* Environmental IoD: anomalies targeting native features and reverse Turing test.
* Hardware IoD: timing, hard drive, memory, CPU and register anomalies.
* Network IoD: network protocol anomalies.
* Operating System IoD: processes, files, services and system call anomalies.  
Figure 4: EMS threat evasion model encapsulating the distinction of evasive vectors applied to indicators of deception to a given EMS security tool.  
The top layer includes evasive vectors that are constructed and facilitated to test if the system targeted contains IoD to expose it as an emulated monitoring system. Some of the IoD to test the true nature of the target system are the MAC address or the number of processes running.
## 4 Cost and Energy Demand  
The goal of achieving maximum solids reduction and biogas production from AD by applying pretreatment methods that have been reviewed in this paper with the previous section summarizing technical performance. For these various pretreatment processes to be economically viable, their application must provide enough economic benefit to offset installation costs, operation and maintenance costs, and the energy requirements. Technical performance provides the entrance toward considering the use of a pretreatment system, but it is the additional cost of installing and operating the system that must provide economic benefits if their maturation to full commercialization is to be realized. Table 4 provides a brief comparison of capital cost, reduction in disposal cost, and overall saving using various pretreatment methods. The operation and maintenance cost had a higher impact on the net savings (which is dependent on the characteristics of the WAS). Therefore, it is challenging to present the exact savings when scaling up from the laboratory scale to actual commercial application. These relative assessments reflect the opinion of the author team toward the relative reported costs of each technology. However, a technically high performer sonication, ozonation, and the direct thermal systems will be expensive to implement--again, costs needed versus savings with the installation must be assessed via a solid techno-economic analysis (TEA). Phased heating, microwave, enzymatic, and mechanical-chemical all showed the best economic performance. However, it must be realized that these economics are often based on laboratory performances with minimal full-scale performance data to support these cost assessments. Clearly, more R&D is needed along with some level of demonstration histories at real WWTPs. Caution is given in that it is best to not be the first field application of a developing/new process unless a partnership between the vendor and user are developed along with shared costs during the demonstration between the WWTP, vendor, and potentially, government entities.
## 6 Experimental Results and Analysis
### Experimental Results of Detection and Recognition  
The parameter settings of the algorithm used for detection and recognition training are summarized as follows. The training, validation, and test dataset are 60%, 20%, and 20%, respectively. The number of epochs is 30, and the batch size is 16. The weight decay rate was set to 0.0005, the momentum to 0.937, and the initial and final learning rates to 0.1  
Figure 8: Positional relationship between the mobile aircraft landing stairs and parked aircraft.  
and 0.01, respectively. The following widely used metrics are adopted for the quantitative assessment of the performance of the algorithm of detection and recognition [36].  
(1) \(Precision_{C}\)  
\[Precision_{C}=\frac{TP_{C}}{TP_{C}+FP_{C}}, \tag{13}\]  
(2) \(Recall_{C}\)  
\[Recall_{C}=\frac{TP_{C}}{TP_{C}+FN_{C}} \tag{14}\]  
(3) _Mean Average Precision (mAP)_  
The precision values corresponding to all recall points under the precision-recall curve are averaged to calculate the average precision (_AP_). Furthermore, the average of _AP_ across all classes is _mAP_.  
Here, a true positive (_TP_) refers to the scenario that the actual class is positive, and the class predicted by the algorithm is also positive. A false positive (_FP_) and false negative (_FN_) refer to falsely predicted positive and negative values, respectively. \(C\) denotes the different categories.  
Table 3 gives the evaluation results on our dataset for five classes of target. From this table, we can observe that _mAP_ values are above 90% for both two target categories. Compared with aircraft detection, the detection performance of movable aircraft landing stairs do not perform as well as the former. In our opinion, this result is due to the shape diversity of mobile aircraft landing stairs (as shown in Figure 9). Table 4 lists the results of comparative experiments using different _loU_ losses, namely, IoU-loss, GloU-loss, DIoU-loss, and CIoU-loss, in our sub-module of detection and recognition. In Table 4, although the _Recall_ value for CIoU-loss is slightly worse than that for GIoU-loss, CIoU-loss outperforms the other two losses in terms of precision and _mAP_. Therefore, the detection module of the proposed framework uses CIoU-loss as the regression loss.
## 5 Experimental Results
### Comparison between the Genetic Algorithm and the Two Heuristics  
Table 10 provides average waiting time (AWT) results for different scenarios using genetic algorithms (GAs) and two heuristic approaches for different number of elevators (two to six cars) within 5 min. The results indicate that GA consistently outperforms both heuristic 1 (HR1) and heuristic 2 (HR2) in most cases, reducing average waiting time. Specifically, as the number of elevators increases, GA consistently shows a decrease in AWT compared to the heuristic approach for almost all floor levels. For example, at Level 10 with 6 elevator cars, GA recorded 29.1 s, while HR1 and HR2 recorded 37.6 s and 34.3 s, respectively. This trend continues at various floor levels and car configurations and illustrates the superior efficiency of genetic algorithms in reducing average waiting times in elevator group control systems. Overall, these results show that genetic algorithms are effective in optimizing elevator dispatch strategies, resulting in shorter wait times than the evaluated heuristic approaches in different scenarios and configurations.
## Case Study
### Climatic Data  
According to the Koppen climate classification, the subtype climate of Villahermosa, Tabasco, Mexico, is \"Af\" (tropical rainforest). Monthly average temperature (T) and relative humidity (RH) data have been calculated on the basis of hourly records of the past five years (2013-2017) (Table 1), referring to measurements taken at the International Airport of Carlos Rovirosa Perez, which is located 13 km from Villahermosa and has an altitude above sea level of 16 m. Thus, the atmospheric pressure taken into account for all the following psychometric calculations is 101,325 Pa.  
Analysis of hourly values showed that moderated daily variations of temperature and relative humidity characterize Villahermosa climate. In particular, data for the average day of January (the coldest month, Figures 1 and 2) and of August (the warmest month, Figures 3 and 4) show that temperature and relative humidity do not have significant differences. Enthalpy, dew point, and moisture content are also almost equal in the two months. The maximum differences between the hourly values and the average in August are 5.9% for enthalpy and 3.1% for moisture content, while during January the differences are, respectively, 6.3% and 3.7%.  
Figure 4: Thermo-hygrometrical data of the average August day (Enthalpy, moisture content).  
Figure 3: Thermo-hygrometrical data of the average August day (T, R.H.).  
Figure 2: Thermo-hygrometrical data of the average January day (Enthalpy, moisture content).
# Effect of the Metro Train on the Smoke Back-Layering Length under Different Tunnel Cross-Sections
## 6 Conclusions  
This paper investigates the effect of the metro train on the smoke back-layering length in tunnel fires with different tunnel cross-sections. A series of large eddy simulations are performed correspondingly using the fire dynamic simulator. Based on the result of the FDS simulation and dimensionless analysis, we compared the smoke back-layering length of the metro train located in different cross-sections of the tunnels. The following can be concluded:  
1. The impact of metro trains on the length of smoke back-layering is significant at all different tunnel cross-sections. The blockage ratio alone cannot well characterize the blockage effect of metro trains on the smoke back-layering length in tunnels with different cross-sections.  
Figure 14: Comparison of dimensionless back-layering length between results from FDS simulation and previous model: smoke back-layering is shorter than the metro train length.  
Figure 15: Comparison of dimensionless back-layering length between results from FDS simulation and those from the previous study: smoke back-layering if longer than the metro train length.  
2. Based on the blockage ratio, the headroom ratio \(\varepsilon\) was proposed to more accurately characterize the effect of metro trains on the length of the smoke back-layering in tunnels with different cross-sections.
3. A new prediction model was proposed to take into account the effect of metro trains stopped in a tunnel with different tunnel cross-sections to predict the length of back-layering in metro tunnels. The superiority of the proposed model for predicting the length of smoke back-layering from metro trains in tunnels with different cross-sections was demonstrated by comparing it with previous prediction models and experimental results.  
Funding acquisition, M.W. ([PERSON]); investigation, M.W. ([PERSON]), H.L. and L.S.; methodology, M.W. ([PERSON]) and H.L.; project administration, M.W. ([PERSON]); resources, M.W. ([PERSON]); software, H.L., F.W. and L.S.; supervision, M.W. ([PERSON]); writing--original draft, H.L.; writing--review and editing, F.W. and M.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 52178064), the Chongqing Science and Technology Commission (Grant No. cstc2021 jcyj-msxmX0677, cstc2019 jscx-msxmX0243), and the Chongqing Construction Science and Technology Planning Project (Grant No. 2019-1-5-5).  
Not applicable.  
Data availability Statement: Data are contained within this article.  
The authors thank the reviewers for their great help with the article during the review process.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 6 Possible Applications and Improvements
### Insights on Modeling the Over-the-Sea Wireless Channels
Sea wave height, average sea wave length, and sea wave period are important parameters for characterizing sea wave movement. For highly rough sea wave movement, the incoming wave fronts confront more scattering and reflections. The two-ray model in this type of scenario is not suitable. Change in the sea surface height while calculating the amplitude of the main reflection path, and the components scattered in the irregular sea surface, should be considered. [PERSON]'s model [52] can be highly suitable because it accounts for these parameters. Unfortunately, none of the channel models in the literature exploited this model to develop channel models for over-the-sea communication. **Three-ray model reduced to Rician fading:** A three-ray model consists of one LoS path and two other reflection paths [7]. The relative amplitude of these two reflection paths can get affected by the unstable maritime propagation environment. For a calm sea surface, a stronger reflection path is dominant. For a rough sea surface, it is usual that the strength of all the reflection paths will decrease. A single reflection path is denoted as the superposition of the multiple weak reflections. If the number of weak reflection paths is large, the three-ray model will reduce to Rician fading for narrowband transmission. **Incoherent super position of the diffuse scattered radio waves:** It is possible to determine the path length between the specular and direct paths. Phase relationships among the reflections across the sea surface are tough to predict due to the roughness of the sea surface. It is necessary to address the time dispersion of the diffused paths in the statistical sense because of the uncertainly of the diffuse points in a glistening surface. The channel model presented in [5] takes this issue under consideration. Not considering this in a multipath channel model will lead to an incoherent super position of diffused scattered radio waves.
## 3 Performance Analysis of iPyroxy Block
### S11 Reflection Coefficient with iPyroxy Block  
The composite material has remarkable reflective performance, especially regarding electromagnetic waves, as shown by a constant low reflection coefficient over the whole operating frequency range. When an iPyroxy block is included in the composite, the S11 reflection coefficient is tested, and the results are consistently well below \(-10\) dB (Figure 5). This indicates a low amount of energy lost due to reflection and an effective decrease in the size of reflected signals. By absorbing incoming electromagnetic waves and matching their impedance, the iPyroxy block, likely a mixture of iron pyrite and epoxy resin, helps to reduce the reflection coefficient. Because of its low reflection coefficient, this composite material is well-suited for various communication systems, radar technologies, and other electromagnetic devices that require optimal performance over a wide frequency spectrum. These applications place a high value on signal integrity and minimal signal loss.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 5 Novel Implementation Methodology  
### Firmware Development and Optimization  
The ESP32 firmware was developed using the Arduino framework, providing access to a rich ecosystem of libraries while maintaining low-level control of the hardware peripherals. Several key optimizations were implemented to enhance performance and reliability:  
1. Direct register manipulation for time-critical I2S operations, bypassing the abstraction layers of standard libraries to minimize latency and jitter, following techniques described by [PERSON] and [PERSON] [40].
2. Memory-aligned buffer allocation to optimize DMA transfers for audio data, building on buffer management strategies proposed by [PERSON] et al. [41].
3. Effective exploitation of the ESP32 dual cores with communication and motor control handled by the Core 0 and audio proof, and task partitioning techniques as for example those tested by [PERSON] [15] applied to audio processing on the Core 1.
4. Custom interrupt handlers and minimal execution time in order to achieve real-time sampling along with time optimization steps as described by [PERSON] et al. [38].  
Energy-efficient power management techniques, by activating peripherals only when necessary to minimize power consumption and heat generation, or applying the energy conservation method proposed by [PERSON] and [PERSON] [8]  
The audio pipeline was both minified to run on the ESP32 and optimized for sound quality. The dynamic range compression process was refined by several iterations that were performed, finding a good trade-off between the running time of the process and the perceptual quality of the audio by tuning the parameters accurately, according to studies carried out by [PERSON] et al. [25] in the field of noisy speech recognition.  
The dual-thread architecture was implemented using the techniques of real-time scheduling that have been developed for constrained-resource systems, including mechanisms for preventing priority inversion by means of priority inheritance and for recovering from possible deadlocks by means of watchdog timers. This style of concurrent programming is consistent with the design pattern proposed by [PERSON] et al. [30] to control autonomous robots.
# Dynamic Characteristics of Sandstone under Coupled Static-Dynamic Loads after Freeze-Thaw Cycles
## 3 Strength Characteristics of Sandstone under Freeze-Thaw Cycles and Axial Pre-Compression Stresses
### Strength Degradation of Sandstone under Freeze-Thaw Cycles  
The relationships between the average dynamic peak strength and the number of freeze-thaw cycles are shown in Figure 10.  
As clearly shown in Figure 10, the dynamic peak strength of the sandstone in general decreases with an increase in freeze-thaw cycles. This is mainly due to the damage caused by the freeze-thaw processes as discussed above. A linear and nonlinear correlation was found between peak stress and freeze-thaw cycles when the axial pre-compression stress was 0 MPa and 15 MPa respectively. There were two exceptions for the pre-compression stresses of 35 MPa and 55 MPa, where the peak strength increased when the freeze-th  
Figure 10: Relationship between the average peak strength and number of freeze-thaw cycles.  
Figure 9: Relationship between peak strain and pre-compression stress under different freeze-thaw cycles.  
Figure 11 presents the relationships between the dynamic peak strain of sandstone and freeze-thaw cycles under different pre-compression stresses.  
As can be seen in Figure 11, the peak strains have a general increasing trend with the number of freeze-thaw cycles. The highest increment rate is found to be 0 MPa of the pre-compression stress. This was explained above. The increment rate for the 55 MPa axial pre-compression stress is also found to be higher than 15 MPa and 35 MPa. In this case, the rock can be easily damaged by the additional dynamic load, as discussed above.  
In summary, the dynamic peak strength of the rock decreases linearly with the increased number of freeze-thaw cycles, while the dynamic peak strain increases almost linearly. The decreasing rate of peak strength for different pre-compression stresses is almost constant while the increasing rate of peak strain is the highest at 0 MPa pre-compression stress, followed by 15, 35, and 55 MPa, which is related to the densification issue caused by the pre-compression of the specimen. Based on the analysis, 15 MPa can be regarded as the pre-compression threshold stress, where the microstructures are fully compacted and thus no densification stage under dynamic impact can be observed, for the sandstone in this study. In other words, the densification stage is completed already by the pre-compression stress at this threshold value.
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 6 Proposed Link Cost Estimation and Modified Route Selection Procedure
### Implementation Considerations  
To implement the U-RR procedure, the following must be implemented in ZigBee nodes:  
* Nodes need to track the number of packets transmitted and the number of packets acknowledged with each neighbor node separately. This information should be stored within the network layer to enable access by the route selection function. The network layer already has provisions for a neighbor list [13], which could be expanded to store this additional information. Ideally, the time of each transmission would be stored in order to determine when transmission records become older than the averaging window. Results that follow assume this ability. Alternatively, the tracking of the number of packets successfully transmitted could be implemented with a cyclic buffer, where the result of the latest transmission would overwrite the result of the oldest transmission in the buffer.
* The IEEE 802.15.4 MAC layer must be augmented so that it provides the network layer with the number of retransmissions needed to transmit a packet. This would probably be done in the service application point and the MAC would provide this information in additional fields of the MCPS-Data.Confirm message. Although provisioning of such information is not forecasted by the IEEE 802.15.4 MAC specification, it is possible for manufacturers to offer additional information in their service access points while still complying with the IEEE 802.15.4 specification.
* The treatment of the RREQ at the network layer would have to be augmented to follow the procedure of Figure 2(b).  
It is important to mention that the U-RR procedure does not require any changes to the ZigBee protocol; i.e., there is no need for new protocol messages nor changes to protocol frame formats.