esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Using GSM/SMS signals, which can more easily cover multiple monitoring areas, we developed an anti-blind spot gateway, which we deployed in monitoring systems for military warehouses in remote locations. We gathered data on each ZigBee-controlled device. Figure 1: Signal coverage and reception quality of 3G/4G mobile phone networks. When wireless networks are operating as expected, this gateway uses the Ethernet model to communicate with the remote monitoring server. In the event of network failure, a back-up mechanism is immediately activated, and the system automatically switches to communicating via GSM/SMS. This offers a solution to the current problem of being unable to implement monitoring systems in military warehouses located in communication blind spots. The Taiwan National Army has many warehouses in remote areas and lacks the infrastructure for network data communication. This type of warehouse requires a communication backup mechanism to ensure that monitoring data can be transmitted to the monitoring center. Therefore, the design of the gateway in the WSN system is very important. How the gateway communicates with the wireless sensor network and how to automatically switch backup communication is the main problem in this study.
# Estimating Specific Patient Organ Dose for Chest CT Examinations with Monte Carlo Method
###### Abstract
In the past few years, several researchers have made many attempts to establish patient-specific models through retrospective research and to estimate patient organ doses based on patient size metrics for CT scan with ATCM [13; 14; 15; 16; 17; 18; 19]. [PERSON] et al. [13] directly measured the circumference of patients at the skin-air boundary as a size metric, and compared a linear relationship between organ dose and patient size under ATCM and FTC. [PERSON] et al. [14] used an exponential model to discuss the relationship between normalized organ dose and effective diameter under ATCM conditions. Since the publication of AAPM Report No. 220 [20], some researchers have begun to pay attention to the relationship between _WED_ and organ dose, and have proposed an exponential model to provide the possibility of individualized organ dose estimations. [PERSON] et al. [16; 17] and [PERSON] et al. [18] respectively used patient CT images and computational human phantoms to study the influence of _WED_ to organ doses. However, to the best of our knowledge, no relevant studies have been reported to estimate patient-specific organ doses with Chinese populations. In fact, the Chinese population has a relatively smaller size compared with that of Europe and America [21; 22; 23], and there is currently no relevant research showing whether the difference in patient size affects the accuracy of the existing models. As chest regions, with a relatively greater tube current reduction for ATCM [24], represent one of the most frequently scanned body regions in clinical application [25; 26], and as lung and breast are the most radiosensitive organs fully exposed in the imaged region, we further investigated chest CT imaging for more accurate organ dose estimations in this study. A set of exponential models were preliminarily established to estimate the organ doses of Chinese adults undergoing thorax CT scans with an ATCM system. The established model was based on the Monte Carlo calculation of the 18 investigated Chinese adults with a variety of sizes, and the model could be improved with more patient samples. We compared the organ doses in this study with developed dose evaluation parameters and models, and discussed their applicability to the Chinese population.
## 4 Design of Proposed Scheme: FAPR
### Algorithms and Formulation
#### 4.5.6 Latency Estimation Method  
Even though the estimation of latency is easy to realize for end hosts, it is challenging for switches in SDN networks. This is because SDN switches can only purely forward packets with almost no intelligence, and all actions such as requests are directed by the controller. In the FAPR scheme, we adopt a theoretical method to measure the latency between any two switches in the network.  
Assume that at time \(t_{A}\), the controller sends a packet to switch \(A\). The data segment of this packet carries the timestamp when the message is issued, and its action instructs the switch to flood it. After a while, switch \(B\) receives the packet flooded by switch \(A\), while the packet cannot match the any flow table entry, and thus is uploaded to the controller. After receiving this packet, the controller records the current time, \(t_{B}\), and calculates the time difference, \(t_{B}-t_{A}\). It can be inferred that this time difference approximates the propagation delay from the controller to switch \(A\), from switch \(A\) to switch \(B\), and finally from switch \(B\) back to the controller. Next, assume that at time \(t^{\prime}_{B}\), the controller sends a similar packet to switch \(B\). Similarly, when this packet is uploaded via switch \(A\), the controller records the current time, \(t^{\prime}_{A}\), and calculates the second time difference, \(t^{\prime}_{A}-t^{\prime}_{B}\). By combining the two metrics together, we can obtain twice the sum of the round-trip time (RTT) from the controller to switch \(A\), the RTT from the controller to switch \(B\), and the RTT from switch \(A\) to switch \(B\). We will denote this sum as \(RTT_{2 total}\), as shown in Formula (5).  
\[RTT_{2 total}=t^{\prime}_{A}-t^{\prime}_{B}+t_{B}-t_{A} \tag{5}\]  
In order to deduce the delay between two switches, we need to subtract the delay from each switch to the controller from the sum mentioned above. As a result, the delay between the control plane and data plane should be calculated. Assume that at time \(t_{r}\), the controller sends a simple request-response message to switches \(A\) and \(B\) simultaneously, and the switches will respond immediately upon receiving it. Suppose the controller receives the response messages from the two switches at times \(t_{r}A\) and \(t_{r}B\), respectively, then the total RTT from the controller to switches \(A\) and \(B\), denoted as \(RTT_{2 cs}\), can be shown as follows:  
\[RTT_{2 cs}=t_{rA}-t_{r}+t_{rB}-t_{r} \tag{6}\]  
Therefore, the latency from switch \(A\) to switch \(B\), which we will denote as \(L_{ss}\), is shown as follows:  
\[L_{ss} =\frac{RTT_{2 total}-RTT_{2 cs}}{2} \tag{7}\] \[=\frac{t^{\prime}_{A}-t^{\prime}_{B}+t_{B}-t_{A}-t_{rA}-t_{rB}}{2 }+t_{r}\]
# The Contributions of Climate and Human Activities to Water Use Efficiency in China's Drylands
## 3 Result
### Factors Affecting Spatial Differentiation of WUE in China's Drylands  
#### 3.4.1 Dominant Factors of Spatial Differences in WUE  
This study selected five periods (2001, 2005, 2010, 2015, and 2020) to determine the driving force behind the spatial differentiation of WUE and calculated the explanatory power (\(q\) value) of each factor on China's drylands' WUE spatial differentiation. The results of the factor detector are shown in Figure 7. Elev, Geomt, and Soilt all exhibited high explanatory power during the five periods, thereby providing a comprehensive explanation for the primary cause of the spatial differentiation of WUE in China's drylands. Among the climatic factors, Tem, Solr, Relah, and VPD also showed high explanatory power for the spatial distribution of WUE in China's drylands. The explanatory power of Relah increased over time, while that of VPD decreased. The explanatory power of Prec as a single factor for spatial drivers of WUE was relatively low (less than 0.2). Simultaneously, GPP had a significant impact on the spatial distribution of WUE. However, the explanatory power of ET was smaller. Among the human activity factors (GDP, POP, and Lanct), Lanct exerted the most significant influence on the spatial variation of WUE, demonstrating a rising trend from 2001 to 2020.  
Figure 6: The effects of climate change and human activities on WUE are separated. (Notes: (**a**) being the climate change contribution, (**b**) being the human activity contribution, and the table below showing the area ratios of the WUE drivers.).
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 3 Problem Definition  
In recent years, multiple manufacturers have developed BLE beacons focusing on finding lost items. While the introduction of Bluetooth 5.1 specifications has allowed us to detect the AoA and angle of departure (AoD) of the Bluetooth signal, which helps to accurately track a beacon, it requires the scanner device to have an antenna array. Several design considerations must be made in the development of these antenna arrays, like antenna spacing, grounding, and antenna efficiency, to improve their effectiveness.  
Implementations of BLE beacons have suffered from lower accuracy as Bluetooth signals can be blocked and bounced off of objects, making it difficult to approximate the exact location of the beacon without any prior knowledge of the channel information over which the beacon is broadcasting. The task of estimating the AoA using a single receiver without an antenna array by monitoring the RSS, which is defined as the focus of this research, is complex, and the implementation can help users with hearing ailments track and locate an item. This can also be extended into robotics applications, such as finding the home base of a robot or communicating with different robots in a larger area like a warehouse without the need to upgrade the existing infrastructure to UWB or add antenna arrays to BLE receivers.
###### Abstract  
Having come of age, gas film bearings enable high-speed oil-free (micro) rotating machinery with gains in efficiency and reliability, longer maintenance intervals, and a reduction in contaminants released to the atmosphere. Among gas bearing types, porous surface gas bearings (PGBs) have proven successful for 50+ years and presently are off-the-shelf mechanical elements. This paper reviews the literature on PGBs since the 1970s and reproduces an exact solution for the performance of cylindrical PGBs. Both the analytical model and an accompanying finite-element (FE) model predict the performance for two PGBs, a commercially available 76 mm diameter bearing and a smaller 25 mm diameter laboratory unit whose experimental performance is available. As expected, the FE model results reproduce the analytical predictions obtained in a minuscule computing time. For a set external supply pressure, as the radial clearance increases, the flow rate through the bearing grows until reaching a peak magnitude. The PGB load capacity is a fraction of the product of the set pressure difference (\(p_{S}-p_{a}\)) and the bearing projected area with a significantly large centering static stiffness evolving over a narrow region of clearances. Operation with shaft speed enhances the bearing load capacity; however, at sufficiently high speeds, significant magnitude cross-coupled forces limit the stable operation of a PGB. At constant operating shaft speed, as the whirl frequency grows, the bearing effective stiffness (\(K_{\mathit{eff}}\)) increases, while the effective damping (\(C_{\mathit{eff}}\)) becomes positive for whirl frequencies greater than 50% shaft speed. Similar to a plain hydrodynamic journal bearing, the PGB is prone to a half-frequency whirl, albeit the system natural frequency can be high, mainly depending on the external supply pressure. In essence, for the cases considered, PGBs are linear mechanical elements whose load capacity is proportional to the journal eccentricity.  
gas bearings; porous materials; rotordynamics; force coefficients +
Footnote †: journal: Applied Sciences
# Advances in the Metabolic Engineering of _Escherichia coli_ for the Manufacture of Monoterpenes
## 5 Controlling the Flux Distribution of Essential Intermediates  
### Fusion of Key Enzymes  
Bidirectional fusion enzyme works as a multifunctional protein derived from a single nucleotide sequence that may contain different enzyme genes. This construction renders the active site of one enzyme face to another, which channels the intermediates directly though successive catalytic bioreactions. The spatial distance between enzymes lowers the substrate transmission loss and accelerates the reaction rate [64]. Meanwhile, the direction of internal enzyme components as well as the length and the amino acid composition of the linkers involved in the fusion enzymes are considered the optimizable parameters for the improvement of their catalytic efficiency [65; 66]. [PERSON] et al. programed an engineered _E. coli_ strain for pinene synthesis by introducing the exogenous MEV pathway and a series of GPPS-PS fusion enzymes with the components derived from three species. The highest-flux synthase combination elevated the concentration of pinene in host cells to about 28 mg/L, whereas the subsequent expression of assembled GPPS-PS fusion protein achieved a higher production of 32 mg/L, approximately 6-fold than that previously reported [34]. The high local accumulation and exchange of substances though the fusion enzyme system could theoretically overcome the metabolic burden of the essential intermediate leakage, such as GPP, and relieve or even eliminate its feedback inhibition effect and toxicity to the host. However, the critical drawback of this strategy is its high dependence to the accurate architecture of different enzyme components, which are restricted for expanding to higher-order enzyme fusion.
# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 5 Results and Discussion
### Limitations of Application  
Although the present study provided evidence indicating that ML models can be used to predict the self-healing of ECC, there are some challenges that need to be considered. For example, the prolonged time required to optimize and tune the parameters and the high dependence on engineering datasets are the major concerns. The former can be addressed by using an optimization algorithm or developing a hybrid model to automatically adjust the parameters. In addition, high-performance computing (HPC) [82] can also be used to achieve parallel data processing so as to improve the computing performance and save time. In terms of the engineering dataset, it can be improved by experimental design and experimental process control [75].  
Besides, it is worth noting that the ML models in this study mainly focused on the internal factors (features) such as materials and mix composition; other external environmental factors such as W/D cycles and healing time should also be considered. More research is required to explore the potential benefits and challenges of using ML models to predict the self-healing of cement-based composites.
## Appendix A State Space Equations for FG-GPLRC Arch with Different End Conditions
### C-C Arch  
The end conditions can be given as:  
\[\begin{array}{ll}U_{1}=W_{1}=0,&\text{at }\theta=0\\ U_{N}=W_{N}=0,,&\text{at }\theta=\theta_{0}\end{array} \tag{10}\]  
The state equations are:  
\[\begin{array}{ll}\frac{\partial U_{i}}{\partial\zeta}=\frac{ \lambda}{\eta}U_{i}-\frac{\lambda}{\eta}\sum\limits_{k=2}^{N-1}g_{ik}^{(1)}W_ {k}+\frac{c_{0}}{c_{55}}\Gamma_{i}&(i=2,3,\cdots,N-1)\\ \frac{\partial Y_{i}}{\partial\zeta}=&\frac{\lambda^{2}}{\eta^{2}}\frac{k_{11} }{c_{0}}\sum\limits_{k=2}^{N-1}g_{ik}^{(1)}U_{k}+\frac{\lambda}{\eta}\bigg{(} \frac{c_{13}}{c_{33}}-1\bigg{)}Y_{i}+\bigg{(}\frac{\lambda^{2}}{\eta^{2}}\frac{ k_{11}}{c_{0}}-\frac{\rho}{\rho_{0}}\Omega^{2}\bigg{)}W_{i}&(i=1,2,\cdots,N)\\ &-\frac{\lambda}{\eta}\sum\limits_{k=1}^{N}g_{ik}^{(1)}\Gamma_{k}&(i=2,3, \cdots,N-1)\\ \frac{\partial\Gamma_{i}}{\partial\zeta}=&-\frac{\rho}{\rho_{0}}\Omega^{2}U_{ i}-\frac{\lambda^{2}}{\eta^{2}}\frac{k_{11}}{c_{0}}\sum\limits_{k=2}^{N-1}g_{ik}^{(2)}U_ {k}-\frac{c_{13}}{c_{33}}\frac{\lambda}{\eta}\sum\limits_{k=1}^{N}g_{ik}^{(1)} Y_{k}&(i=1,2,\cdots,N)\\ &-\frac{\lambda^{2}}{\eta^{2}}\frac{k_{11}}{c_{0}}\sum\limits_{k=2}^{N-1}g_{ ik}^{(1)}W_{k}-\frac{2\lambda}{\eta}\Gamma_{i}&(i=2,3,\cdots,N-1)\end{array} \tag{11}\]
# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
## 2 Related Work
### Econometric Approach  
These models are based on the underlying economic conditions to predict the exchange rate. In comparison with technical analysis, this econometric model ignores the financier's psychosomatic biases and also assumes that economic fundamentals determine prolonged trends. This econometric model can be further categorized into two different categories. Under the first category of the econometric model, the Mundell-Fleming Model [14], [PERSON] [15] proposed an asset market technique for the exchange rate. The second category of the econometric model emphasizes the effect of the current rate of exchange on the probable future path of the currency supplies and of influences that affect money demands [16]. These econometric models are extensively utilized by numerous central bankers all over the world. Though, empirical research specifies that their effectiveness is still dubious when forecasting the short-term exchange rate [17]. The research by [PERSON] and [PERSON] [2] is still a standard in contradiction of all econometric rate-of-exchange predicting frameworks. [PERSON] and [PERSON] demonstrated that structural frameworks fail to outperform the random walk in out-of-sample forecast. For the short-term exchange rate prediction, they concluded that the inefficiency of econometric models is still extensively acknowledged.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 4 System Architecture
### Communication Protocol  
The communication protocol offers bidirectional data exchange between the rover and a control device, using Bluetooth Classic's Serial Port Profile (SPP). The selection of this relatively simple protocol was motivated by the need to have wide compatibility and easy implementation on resource-constrained platforms.  
Control commands, which the Android device sends to the rover as single ASCII characters, consume minimal bandwidth but unambiguously instruct the rover to move. Operations are sent as required, up to a maximum of 100 Hz continuously (for example, with a directional button pressed).  
MCU sends audio data to the Android as a stream of eight-bit samples at a sampling rate of 16 kHz, which is compressed microphone output. This one-way stream collides with the two-way control channel, where audio packets are given higher preference within the available bandwidth to not interrupt the stream.  
The lack of complexity in this protocol also helps make it robust, because there is little state to maintain and recover between connection loss and recovery. Both the rover and Android application can return to known states when the connection is lost, allowing for fast reconnects without the need for complex handshaking or synchronization requirements.
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 4 Results
### DTMC-\"Content Purchase\" Screens  
In order to incorporate purchase flow through to the payment screen into the Markov Model, an extra \"Payment\" state has been included to model the trajectories of the BT Player customers' fixations, to better understand which AOIs they are fixating on before they go through to payment and which they are not. The \"Payment\" state will be both the last row and column in transition matrices.  
Once the BT Player customer goes through to the payment screen, they are classified as choosing to pay for the TV Show/Film, and therefore if they choose to go back from payment to the previous screen, this is regarded as a failure to go through with payment. This means the payment state is accessible from any of the states on screen A or B, but the converse is not true, and the other states are not accessible from the payment state. Therefore, these transition matrices are no longer irreducible, and the steady state cannot be calculated.  
Furthermore, the newly introduced \"Payment\" state is an absorbing state as once the Markov chain enters the state, it will remain there with a probability of one by definition of the state. Therefore, the expected number of steps to absorption and therefore expected time to absorption can be calculated for each state. In our case, it is possible when including all AOIs states and the non-AOI (Z) state as it is representative of the whole model. In a word, the Payment screen will simply be defined as an additional state in the Markov chain, \"Payment\". The inclusion of this state allows the expected time taken by the user to reach payment, based on the Markov Model.
## 2 Materials and Methods
### Network Model Construction
#### 2.2.2 Structure of the BFFDC-YOLOv8-Seg Network  
To enhance the accuracy of weed segmentation in complex, cluttered agricultural fields with overlapping plants, while ensuring model simplicity and real-time segmentation, this paper introduces the new BFFDC-YOLOv8-seg instance segmentation network. As shown in Figure 3, the network reconstructs the Concat module in the original Neck structure, replacing the existing FPN and PAN with BiFPN for innovative multiscale feature fusion, effectively enhancing the network's ability to detect small targets. The introduction of DSConv to replace some of the \(3\times 3\) convolutions in the original Backbone, integrating features extracted by \(3\times 3\) convolutions with DSConv, increases the flexibility of the convolution kernels, thus improving the accuracy of segmentation for irregular edges of plant stems and leaves. The BFFDC-YOLOv8-seg network achieves precise segmentation of highly similar, cluttered, and overlapping weeds in complex field backgrounds, with features that ensure simplicity and real-time performance, making it feasible for operation on standalone devices.  
* **Appropriate weight documents and scales** Ultralytics officially provides five different scales of networks (N/S/M/L/X) and corresponding initial weight files on Github to cater to various application scenarios. These training weight files, essential for assisting training, enhance the accuracy and speed of training, containing model parameters such as weights and biases for each layer. However, the official weight files, trained on the COCO2017 dataset, lack the capability to perceive vegetable field environments. To better acclimate the model to vegetable fields and achieve improved training outcomes, this paper utilizes a public plant instance segmentation dataset with over 5000 images. By iterating 200 times on the original YOLOv8(N/S/M/L/X)-seg  
Figure 3: The BFFDC-YOLOv8-seg network structure diagram.  
networks, this study obtains training weights adapted to vegetable field environments, serving as optimized training weights for the model.  
Table 2 clearly shows that under the same training batches, the S model has significantly lower inference speed compared to the N model, with only a 1.3% improvement in accuracy and a substantial increase in model size. The M/L/X models, compared to the N model, show a notable increase in size and a significant decrease in inference speed, with a maximum of only 1.9% improvement in accuracy. With no significant gains in detection accuracy, the larger models require substantial storage resources and higher processing power, making them unsuitable for resource-limited laser weeding devices. Therefore, this paper chooses to optimize the N-scale model.