esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Intravenous Infusion Dosing System
## 2 State of the Art
### Wireless Communication Systems
A real-time IV infusion monitoring system using a weight sensor to detect fluid levels in the IV bag is shown in [30]. The processor receives signals from the sensor and sends them to the cloud server via a communication module that compares the current and initial liquid level in the IV bag, on the basis of which it generates the remaining time until the IV bag is emptied, and after discharging, the alarm is activated. A wireless patient-nurse communication system is shown in [31]. When an alarm sounds, on the basis of the data collected from hardware and RFID devices, the software connects the patient to the nurse via the voice over internet protocol (VoIP). A system for monitoring a patient's blood glucose level using a sensor attached to the patient's body that measures glucose levels and wirelessly sends a signal to the doctor's phone application and monitoring devices in the nurse room is shown in [32]. If the blood glucose level is outside the range, the alarm in the application is activated. The system for monitoring energy consumption on a test machine using a sensor for measuring the change in current and a module for wireless communication is shown in [33]. The data from the sensor are sent to the cloud server and interpreted to calculate the total operating time of the machine. A robotic system for monitoring a patient with lower limb paralysis using exoskeletons and a cloud server is shown in [34]. The information from the sensor is sent to the cloud server where it is interpreted and, if necessary, the exoskeleton control is adjusted. The advantages of the described systems are wireless communication between sensor and controller [20, 23, 24], wireless communication between controller and computer at nurse room [21, 22, 23, 25, 26, 27, 28], the use of IoT technologies [21, 22, 29, 30], mobile application for IV therapy monitoring [21, 22, 25, 26, 27, 29, 30], personal computer (PC) monitoring application [28], continuous detection of IV bottle/bag fluid level [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], alarm at the patient or in the nurse room [20, 23, 25, 26, 28, 29, 30], flow control valve or pump for infusion fluid [20, 25, 27], and monitoring system in patient's room [20]. The disadvantages of the described systems are that there is no backflow solution [21, 22, 23, 24, 26, 28, 29, 30], there is no IV therapy monitoring application [20, 23, 24], there is no wireless between communication controller and control panel [20, 24], there is no usage of IoT technology [20, 23, 24, 25, 26, 27, 28], and it has not been tested in hospitals [20, 21, 22, 23, 24, 25, 26, 27, 28].
# Theoretical Methane Emission Estimation from Volatile Fatty Acids in Bovine Rumen Fluid
###### Abstract  
Methane production from livestock farming is recognized as an important contributor to global GHGs. Volatile fatty acids (VFAs) found in bovine rumen may be utilized as a substrate for methanogens to form CH\({}_{4}\), and thus improvement of quantitative VFA measurements can help facilitate greater understanding and mitigation of CH\({}_{4}\) production. This study aims to contribute to the development of more accurate methods for the quantification and specification of VFAs in bovine rumen. The VFAs were analyzed using the conventional method and an alternative catalytic esterification reaction (CER) method. Substantial differences in the detected concentrations of the C3+ VFAs (chain length \(\geq\) 3) were observed between both methods, especially for butyric acid. Evaluation of the sensitivity of both methods to detecting the VFA concentrations in standard solutions confirmed that the values resulting from the CER method were closer to the known concentrations of the standard solution than those from the conventional method. The results of this study provide the first quantitative proof to show the improved accuracy of the measurements of C3+ VFAs when using the CER method compared with the conventional method. Therefore, the CER method can be recommended to analyze the VFAs found in rumen, especially butyric acid and other C3+ VFAs.  
rumen; odor; volatile fatty acids; methane; catalytic esterification
## 2 Methodology
### Simulation Steps and Related Software
#### 2.4.3 Governing Equation  
[PERSON] et al. [51] proposed the flow field setting equation in the calculation domain when using CFD for air flow simulation. The advantages and disadvantages between the Navier-Stokes (RANS) equation and large eddy simulation (LES) are discussed in detail. The paper proposes that the RANS simulation may be computationally more economical, time-saving, and more versatile, but RANS cannot resolve fluctuating flow variables, so turbulence components must be removed from the simulation. In addition, they cannot simulate vortex shedding, and they usually overestimate the turbulent kinetic energy near the stagnation point and the flow recirculation of the airflow. LES can solve the governing equations of eddy currents larger than the sub-grid scale and can minimize the above-mentioned shortcomings of RANS. Using LES to simulate turbulence can provide more realistic flow data, including flow intermittent and separation in and around buildings, but this requires a lot of additional calculations. In this study, it is necessary to view the flow data during the simulation at any time and calculate the time for the indoor temperature to reach a steady state. In order to make the simulation more accurate, LES was finally selected as the main simulation method.  
The flow field in the computational domain was modeled as an instantaneous, three-dimensional Navier-Stokes equations for a confined, incompressible viscous flow of a Newtonian fluid using Equations (3)-(5) [52]:  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{3}\]  
\[\frac{\partial u_{i}}{\partial t}+\frac{\partial}{\partial x_{i}}\big{(}u_{i} u_{j}\big{)}=-\frac{1}{\rho}\frac{\partial p}{\partial x_{i}}+\frac{\partial}{ \partial x_{j}}\big{(}2 vs_{ij}\big{)} \tag{4}\]  
\[\frac{\partial c}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(}cu_{j} \big{)}=\frac{\partial}{\partial x_{j}}\Bigg{(}D\frac{\partial c}{\partial x_ {j}}\Bigg{)} \tag{5}\]  
where \(u_{i}\) and \(x_{i}\) are the instantaneous velocity and position vectors; \(p\) is the instantaneous pressure; \(t\) denotes time; \(\rho\) denotes density; \(v\) is the kinematic molecular viscosity; \(c\) is the instantaneous concentration; \(D\) is the molecular diffusion coefficient or molecular diffusivity; and \(s_{ij}\) is the strain-rate tensor as defined in Equation (6):  
\[s_{ij}=\frac{1}{2}\Bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u _{i}}{\partial x_{j}}\Bigg{)} \tag{6}\]
## 5 Experimental Results and Analysis
### Ablation Experiments  
In order to verify the validity of the different modules in the proposed network model in this paper, without loss of generality, we conducted a series of ablation experiments on the CS (cross-subject) protocols and CV (cross-view) protocols of the NTU RGB+D 60 dataset. In this subsection, we will analyze whether the modules in the proposed model affect the performance of action recognition in order to verify the plausibility of the network model.
## 2 Sensors
### Respiration Sensors
#### 2.1.3 Design of Sensor Nodes
The fault detection rate of the former is more reliable, while the latter can help to validate the former. (3) Reduction of Sensor Nodes  
Reduction of sensor nodes mainly aims at inertial sensors for behavioral recognition. It not only improves the wearability of behavioral recognition systems, but also lowers the cost, saves energy, simplifies the recognition process, as well as reduces data redundancy. Principal methods to solve the problem are the optimization of node placement and the improvement of activity recognition algorithms. At present, some research projects regard node placement optimization as one of the main research problems in the field of behavioral recognition in BSNs. In Reference [22], the authors propose several schemes for how to place wearable sensors for different body actions and provide an optimal framework of node placement. The framework can also help to find the most relevant band from all the time-frequency features collected by wearable accelerometers. In this paper, the authors divide the analytical method process into three steps: feature extraction, feature selection, and classification. Then they classify the actions by different data features, and provide the best placement scheme for each type of action. Different from the method mentioned above, Reference [53] proposes a method to reduce the number of sensor nodes by improving the activity recognition algorithms. The authors have also proved that the problem is NP-hard. However, they propose a behavioral recognition model which uses a decision tree structure to minimize the number of nodes involved in classification of each action. The method reduces nodes by 72.4%, while still maintaining 93.3% classification accuracy.
# Semi-Supervised Ridge Regression with Adaptive Graph-Based Label Propagation
## 4 Experiment and Analysis  
To evaluate the performance of the proposed SSRR-AGLP for classification, we test it on five facial image databases (Yale [52], ORL [53], Extended YaleB [54], AR [55] and CMU PIE [56]). The detailed information of the five databases utilized in our experiments is shown in Table 1 and some sample images from them are displayed in Figure 2. In all experiments, 50% samples of each subject are selected randomly for training and the remaining samples are used for testing in each database. Specifically, a part of the samples is randomly selected as labeled data in each training set, and the numbers of selected training and testing sets for experiments are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database** & **Size** & **Samples** & **Classes** & **Per Class** \\ \hline Yale & 32 \(\times\) 32 & 165 & 15 & 11 \\ ORL & 32 \(\times\) 32 & 400 & 40 & 10 \\ Extended YaleB & 32 \(\times\) 32 & 2432 & 38 & 64 \\ AR & 32 \(\times\) 32 & 1400 & 100 & 14 \\ CMU PIE & 32 \(\times\) 32 & 1632 & 68 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information of databases.  
Figure 2: Some of the images from five databases.  
Moreover, to justify the superiority of the proposed SSRR-AGLP, we compare it with some well-known algorithms, such as traditional \(k\)-nearest neighbor (KNN) [57], least squares regression (LSR) [1], ridge regression (RR) [22], inter-class sparsity based discriminative least square regression (ICS_DLSR) [18] and the locality-constrained and label embedding dictionary learning (LCLE-DL) [35].
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 3 Understanding Large Language Models
### Evolution of Large Language Models
GPT-2 [82], released in 2019 with 1.5 billion parameters, demonstrated unprecedented language generation abilities, raising awareness of both the potential and the ethical considerations of such models. GPT-3 [83], introduced in 2020, expanded to 175 billion parameters, significantly enhancing its ability to perform tasks such as translation, question-answering, and creative writing with minimal or no fine-tuning [84]. Subsequent models like GPT-4 have continued this trajectory, incorporating more sophisticated training methods, larger datasets, and multimodal inputs that include not just text but also images and other data types. These models have benefited from reinforcement learning techniques, including reinforcement learning from human feedback  
Figure 5: Simplified evolution of LLMs. (RLHF) [85; 86; 87; 88], to align the generated outputs more closely with human expectations and values [89]. Improvements over time have included better contextual understanding, reduced incidence of nonsensical or irrelevant outputs, and enhanced ability to follow complex instructions. These advancements have expanded the applicability of LLMs across various domains, making them valuable tools in fields ranging from healthcare [90] and finance [91; 92] to education and, notably, maritime safety [93; 94; 95].
# Low Power Consumption Signal Detector Based on Adaptive DFSD in MIMO-OFDM Systems
## 2 System Model  
Figure 1 shows the MIMO-OFDM system model. At the transmitter, the data is divided into sub-streams by serial to parallel and each sub-stream is mapped to digital modulation symbols. Each symbol is transformed into the time-domain symbols by inverse fast Fourier transform (IFFT). Then, the cyclic prefix (CP) is added to reduce inter-symbol interference (ISI) and inter-carrier interference (ICI). These symbols pass through a Rayleigh fading channel. The baseband equivalent model for the MIMO-OFDM systems is as follows,  
\[\mathbf{Y}=\mathbf{H}\mathbf{X}+\mathbf{N}, \tag{1}\]  
where \(\mathbf{Y}~{}=~{}[y_{1},~{}y_{2},~{}\cdots,~{}y_{N_{t}}]^{T}\) is the \(N_{r}\times 1\) receive symbol vector. \(\mathbf{X}~{}=~{}[x_{1},~{}x_{2},~{}\cdots,~{}x_{N_{t}}]^{T}\) is the \(N_{t}\times 1\) transmit symbol vector. \(\mathbf{N}\) is the \(N_{r}\times 1\) complex zero-mean Gaussian noise vector. \([\cdot]^{T}\) denotes transpose matrix. A complex channel matrix \(\mathbf{H}\) is denoted as follows,  
\[\mathbf{H}~{}=~{}\left[\begin{array}{cccc}h_{11}&h_{12}&\ldots&h_{1N_{t}}\\ h_{21}&h_{22}&\ldots&h_{2N_{t}}\\ \vdots&\vdots&\ddots&\vdots\\ h_{N_{t}1}&h_{N_{t}2}&\cdots&h_{N_{t}N_{t}}\end{array}\right], \tag{2}\]  
where an element \(h_{ij}\) (\(i=1,~{}2,~{}\cdots,N_{t}\)), (\(j=1,~{}2,~{}\cdots,N_{t}\)) denotes channel coefficient from the \(j\)-th transmit antenna to the \(i\)-th receive antenna. Also, each element \(h_{ij}\) is independent and identically distributed (i.i.d) random variables. At the receiver, CP is removed to process the reverse transmitter. Each symbol is transformed into the frequency domain symbol by fast Fourier transform (FFT). Then, these symbols pass through the detector to demodulate the original data.  
Figure 1: Multiple input and multiple output based on orthogonal frequency division multiplexing (MIMO-OFDM) system.
## 3 Results and Discussion
### Experimental Results and Discussion
To approximate the lifespan improvements of our LED system, we utilized [PERSON] equation [31]:  
Figure 3:0  
where \(k\) is the rate of degradation, \(A\) is the frequency factor, \(E_{a}\) is the activation energy, \(R\) is the gas constant, and \(T\) is the saturation temperature of the filter. In our experiment, the polarizing filters are made of PVA, with an activation energy of 185 kJ/mol [32]. The gas constant (\(R\)) is 8.31446 J\(\cdot\)K\({}^{-1}\cdot\)mol\({}^{-1}\). Additionally, the saturation temperatures after 30 min in the two cases are \(T_{H}\) = 58 \({}^{\circ}\)C (331 K, halogen system) and \(T_{L}\) = 36 \({}^{\circ}\)C (309 K, LED system), as shown in Figure 3:1  
From the above equation, the degradation ratio of the halogen and LED headlight filters were calculated as follows:  
Figure 3:2  
where \(AF\) is the degradation ratio of the halogen and LED headlight systems, \(A\) is the frequency factor, \(E_{a}\) is the activation energy, \(R\) is the gas constant, and \(T_{H}\) and \(T_{L}\) are the saturation temperatures of the halogen and LED filters, respectively. The degradation ratio is calculated to be approximately 120, meaning that the polarizing filter attached to the LED headlight system will have a lifespan roughly 120 times higher than that of the halogen system.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis
### Network Latency
#### 4.2.3 Latency Measurements Analysis Comparison  
The comparison of the latency measurements of the cellular network and the G5 is illustrated in Figure 20. The G5 network, on average, has 63.6% lower latency than the cellular network. It achieves a shorter transmission time across all security approaches. This difference is justified by the transmission in G5 being direct (ad hoc), without needing a broker, thus being more efficient. The impact of the security protocols on network latency is, on average, 7.1% on the cellular network and 8% on the G5 network. Comparing both protocols, DLAPP is slightly more efficient. However, this difference is minimal as it represents tenths of milliseconds, which is justified by the fact that there is only a 4-byte difference in the payload.
# Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
## 2 System Model and Problem Formulation
### Optimization of \(\{Q_{k}\}\) and \(\{\rho_{k}\}\) with Fixed \(\{P_{k}\}\)
3:
4:\(I\gets I+1\);
5:Compute \(P_{\text{EH}}\) according to (17);
6:Compute \(P_{\text{ID}}=\sum_{k=1}^{K}P_{k}\):
7:a) compute \(\mathbf{\kappa}\) in Problem (31), based on lagrange Multiplier Method and Karush-Kuhn-Tucker Condition;
8:b) compute \(\{\Gamma_{k}\}\) by inversely plugging \(\mathbf{\kappa}\) into (28) as: \(\Gamma_{k}=\tilde{f}^{-1}(\frac{\alpha_{k}}{1-\alpha_{k}})\) ;
9:c) compute \(P_{k}\) by inversely plugging \(\mathbf{\kappa}\) and \(\{\Gamma_{k}\}\) into (26);
10:if\(P_{\text{EH}}\leq P_{\text{ID}}\), then
11:Output:\(\{P_{k}^{I}=P_{k}\}\); \(P_{\text{SN}}^{I}=P_{\text{ID}}\);
12:else
13:Output:\(\{P_{k}^{I}=\eta P_{k}\}\) based on (32); \(P_{\text{SN}}^{I}=P_{\text{EH}}\);
14:endif
15:if\(P_{\text{SN}}^{I}\leq P_{\text{SN}}^{I-1}\)then
16:a) Compute \(\{Q_{k}^{I}\}\) based on (14) with \(\{\Gamma_{k}\}\) and \(\{P_{k}^{I}\}\);
17:b) Compute \(\{\rho_{k}^{I}\}\) based on (33) with \(\{Q_{k}^{I}\}\) and \(\{P_{k}^{I}\}\);
18:c) Go back to Line 3 with \(\{Q_{k}^{I}\}\) and \(\{\rho_{k}^{I}\}\) for a new iteration;
19:else
20:Break;
21:Output:\(\{P_{k}^{*}=P_{k}^{I}\}\), \(\{Q_{k}^{*}=Q_{k}^{I}\}\) and \(\{\rho_{k}^{*}=\rho_{k}^{I}\}\). 22:endif
```  
**Algorithm 1** The proposed sub-optimal Algorithm
## 4 Materials and Methods
### Forecast Accuracy Measures  
In this section, we introduce the metrics used to evaluate the forecast accuracy of the models under study, enabling a comparison of their performance. We denote the observation at time \(t\) by \(y_{t}\) and the forecast of that observation by \(\hat{y_{t}}\). The forecast error is calculated as the difference between \(y_{t}\) and \(\hat{y_{t}}\). Let \(m\) denote the total forecasted period.  
The root mean square error (RMSE) is the most commonly used scale-dependent measure. We used the normalized RMSE (nRMSE), which measures the variability in errors and is often expressed as a percentage, where lower values indicate a lower residual variance. Although there are no consistent means of normalization in the literature, we considered the mean of the measurement \(\hat{y}\) to normalize the RMSE. nRMSE measures the difference between the predicted values and the actual values, and is a useful metric for evaluating the accuracy of the model.  
\[\begin{split}\text{RMSE}=\sqrt{\frac{\sum_{t=1}^{m}(y_{t}-\hat{ y_{t}})^{2}}{m}},\\ \text{nRMSE}=\frac{\text{RMSE}}{\hat{y}},\end{split} \tag{5}\]  
To comprehensively assess the relationship between total and pairwise errors, we also measured pairwise total errors. This was achieved by computing the correlation between the ranks of these errors. To provide a more robust indicator of extreme values, we consider the rank of the errors rather than the errors themselves. To calculate [PERSON]'s rank correlation coefficient, we employed the following method:  
\[\text{RankCorr}=1-\frac{6}{m(m^{2}-1)}\sum_{t=1}^{m}d_{t}^{2}, \tag{6}\]  
where \(d_{t}^{2}\) is the distance between the ranks of time series \(y_{t}\) and \(\hat{y_{t}}\). Similar to the other correlation coefficients, the values of the [PERSON]'s rank correlation varied between \(-1\) and \(1\). A strong positive correlation, that is, a distance rank score close to \(1\), suggests high performance in forecasting. The Spearman's rank correlation is a measure of the strength of the relationship between two variables, based on their rankings rather than their actual values. It is particularly useful in situations where the data may be skewed or contain outliers.  
Figure 3: Representation of the PVPF methods. Red, blue, and green colors are used to represent the forecasts generated by the AHW, LSTM, and HM models, respectively. (Our elaboration).  
The R\({}^{2}\) coefficient of determination measures how well the model predicts an outcome, or the proportion of variation in the observed dataset predicted by the forecasting method. The lowest possible value of R\({}^{2}\) was zero, whereas the highest possible value was one. The R\({}^{2}\) measures the proportion of variance in the actual values that is explained by the model, and can be used to evaluate the model's predictive performance. It is defined as follows.  
\[\text{R}^{2}=1-\frac{\sum_{t=1}^{m}(y_{t}-\hat{y_{t}})^{2}}{\sum_{t=1}^{m}(y_{t }-\bar{y})^{2}}. \tag{7}\]
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
## 5 Results
### Effect of Group Movement on Evacuation Time
#### 5.5.2 Effect of Different Age Compositions on Evacuation Times  
The effect of age composition on evacuation time, when the group size is the same, is shown in Figure 15. The evacuation results were categorized into two levels based on the evacuation time with t = 1000 s as the dividing line.  
There are six types of groups for two-person groups (Figure 15A). When the age of the group members is similar and the average age is large, the evacuation time belongs to the first level and the evacuation efficiency is better. When the group members contain juveniles, the evacuation efficiency is low and belongs to the second level.  
As the size of the group increased, there was a corresponding increase in the number of group types, with ten group types in the three-person group (Figure 15B). The percentage of group types belonging to the first level increased from 50% to 70%, indicating that a reasonable age composition can effectively increase the efficiency of group evacuation, even when the group size increases.  
In multi-age mixed groups, the improvement of evacuation efficiency needs to comprehensively consider the characteristics of different age groups. Middle-aged people have better physical condition and reaction ability and can provide necessary assistance and guidance to the vulnerable members of the group, which can effectively shorten the overall evacuation time and improve the overall evacuation efficiency.