esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Braking System Parameters--Braking Force Distribution  
Achieving a perfect distribution of braking forces between individual axles is practically impossible in real solutions. Therefore, for towing vehicles intended for the transport of goods and towed vehicles (excluding semitrailers), the regulation [29] specifies the permissible limits of deviation, the so-called utilized adhesion index \(f_{i}\) of individual axles (where \(f\) is the ratio of the braking force to the normal reaction of the ground) from the ideal distribution. In [29], each part of the vehicles set is treated as a single vehicle without considering the brake control of the towed vehicles. To ensure the compatibility of the braking forces in the set of vehicles, the permissible bands of changes, the so-called computational braking ratios of both vehicles \(\gamma_{HA}\) i \(\gamma_{HB}\) (expressing the ratio of generated vehicle braking forces to its weight). These bands are defined for the vehicles for their extreme load conditions (unloaded/unladen and fully loaded/laden) as a function of the control pressure \(p_{m}\) on the coupling head.  
The parameters of the braking system model of the tested vehicles set were selected to reflect the actual operation of the pneumatic braking system as realistically as possible and to meet the requirements mentioned above for stability (brake force distribution) and compatibility of braking systems according to ECE [29]. Figure 7 shows the selected characteristics of the indexes of utilized adhesion \(f_{A1}\) and \(f_{A2}\) for the tractor axles against the permissible bands determined by the conditions [29]:  
\[\text{solution }I:\ \left\{\begin{array}{l}f_{A1},\,f_{A2}\leq\frac{\gamma_{ HA}+0.07}{0.85}\,\text{ for }\gamma_{HA}\,\,=\,\,0.1\div 0.61\,\,\,\text{(line AB)}\\ f_{A1}>\gamma_{hA}>f_{A2}\,\,\text{for }\gamma_{HA}\,\,=\,\,0.15\div 0.30\,\,\, \text{(line CD)}\end{array}\right. \tag{27}\]  
or  
\[\text{solution }II:\ \left\{\begin{array}{l}f_{A1}\,\geq\gamma_{HA}-0.08\,\text{ for }\gamma_{hA}\,\,=\,\,0.15\div 0.3\,\,\,\text{(line MN)}\\ f_{A1},\,f_{A2}\leq\gamma_{hA}+0.08\,\text{ for }\gamma_{hA}\,\,=\,\,0.15\div 0.3\,\,\, \text{(line JK)}\\ f_{A2}\leq\frac{\gamma_{HA}-0.3}{0.74}+0.38\,\text{ for }\gamma_{hA}\geq 0.3\,\,\, \text{(line KB)}\end{array}\right. \tag{28}\]  
Figure 8 furthermore shows the characteristics illustrating the computational braking ratio of the tractor (a) and the semitrailer (b) as a function of the pressure in the coupling head \(p_{m}\). The required bands given in [29] are also plotted.  
Figure 7: Characteristics of indexes of utilized adhesion of tractor axles \(f_{A1}\) and \(f_{A2}\) against the background of the requirements specified in [29]: (**a**) A tractor with an equivalent coupling load simulating a laden semitrailer; (**b**) A tractor with an equivalent coupling load simulating a unladen semitrailer.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 3 Proposed SS-FGSM Scheme
### Underwater Acoustic Channel  
Assuming an underwater acoustic channel is a linear time-varying (LTV) multipath channel between transmitter ) and received antenna \(j\). The impulse response of such underwater channel can be written as [37; 38]:  
\[h_{j,j\mu}(t;\tau)=\ \sum_{\mu}A_{j,j\mu}(t)\delta\Big{(}\tau-\ \tau_{j,j\mu}(t) \Big{)}. \tag{8}\]  
where \(A_{j,j\mu}(t)^{\prime}s\) and \(\tau_{j,j\mu}(t)^{\prime}s\) denote time-varying path amplitudes and delays, respectively. In this channel model, the channel is composed in terms of significant multipath and each path is time-variant in amplitude and delay. Channel tap delay spread \(\tau_{max}\) is the difference between the minimum and maximum path delays. In this paper, we assume full knowledge of channel tap delay. As in [37; 38], we further assume the following:  
1-All channel path delays experience similar Doppler scaling with the data frame transmission time such as:  
\[\tau_{j,j\mu}(t)=\tau_{j,j\mu}-\beta_{j,j}t, \tag{9}\]  
where \(\beta_{j,j}\) is the doppler scaling factor. However, on the other hand when assuming every path has a different Doppler scaling factor, a part of useful signals is treated as additive noise where the overall noise variance can be increased considerably. Moreover, it's found that as long as the dominant Doppler shift is caused by the direct transmitter/receiver motion, as in the experiment cases. Additionally, the value of the Doppler scale factor is usually less than 0.01 when the relative speed between the transmitter and the receiver is below 10 m/s [37; 39]. Therefore this assumption is justified.  
2-Constant path amplitude \(A_{j,j\mu}(t)\) within the data frame transmission time.  
3-The underwater channel parameters \(A_{j,j\mu}(t)\), \(\tau_{j,j\mu}\), \(\beta_{j,j}\) are varied slowly during the whole transmission time.  
Under these assumptions the underwater acoustic channel can be written as:  
\[h_{j,j\mu}(t;\tau)=\ \sum_{\mu}A_{j,j\mu}(t)\delta\Big{(}\tau-\ \tau_{j,j\mu}- \beta_{j,j}t\Big{)}. \tag{10}\]
## 1 Introduction  
Waste heat recovery (WHR) technologies play an essential role in enhancing the efficiency of various processes by capturing and utilizing energy that would typically be lost to the environment [1, 2]. Unfortunately, conventional power generation methods fall short in converting low-quality waste heat into usable electric energy, leading to a substantial loss. By recovering this waste heat, not only can electric generation become more cost-effective, but it also contributes to reducing pollution.  
In the low-quality thermal recovery field, organic Rankine cycle (ORC) technology has emerged as a suitable solution for power production [3, 4, 5]. To this end, this paper aims to harness the waste heat generated from the equipment of data centers to support a Rankine-based energy cycle, contributing to clean production and effective energy transition.  
This work supports the objectives of the Energy Efficiency Directive (EED) [6], which mandates that data center operators monitor and submit key performance indicators (KPIs) to the European database every year to reduce their energy footprint. This directive highlights the growing significance of the information and communication technology (ICT) sector. In 2018, EU data centers consumed 76.8 TWh of energy, a figure anticipated to climb substantially by 2030. Predictions made in 2018 suggested a 28% increase, but with advancements in artificial intelligence (AI) technologies, some nations could experience a doubling or even tripling of this consumption. For instance, the International Energy Agency (IEA) [7] projects that by 2030, Ireland might witness a nearly 2-fold and Denmark a 6-fold surge in electricity demand by data centers.
## 3 Methodology
### Statistical Evaluation Indices  
With reference to the quantitative product quality evaluation indexes of meteorological satellites in the meteorological industry standards of the People's Republic of China, the statistical indexes used in this paper include the linear correlation coefficient (CORR), root mean squared error (RMSE), and relative bias (BIAS) (Table 1). The CORR is used to reflect the linear relationship between the satellite precipitation products and the station observations. The BIAS measures the systematic deviation of the satellite-based precipitation data. The RMSE measures the average magnitude of the error. The accuracy of the IMERG series products is evaluated using the above conventional statistical metrics. Meanwhile, it is also important to quantify the consistency between the satellite-based precipitation products and the observed rainfall when rainfall is present. Three categorical statistical indices, namely, probability of detection (POD), false alarm ratio (FAR), and Heidke skill score (HSS), were also employed to indicate the products' ability to capture rainfall events [30]. POD represents the proportion of observed rainfall events that are accurately predicted. FAR represents the proportion of observed rainfall events that do not occur. HSS characterizes the accuracy of the prediction resulting from random events.  
Figure 2: The main flowchart of this research.  
In addition, to evaluate the performances of hydrological models, [PERSON] proposed a new efficiency factor, the Nash-Sutcliffe efficiency (NSE), as a statistical criterion for hydrological utility assessments [31], and is widely used in hydrology and water quality modeling to evaluate simulation effects [32] (Table 1). The NSE coefficient ranges between \(-1.0\) and \(1.0\), and values closer to \(1.0\) correspond to better consistency between the model simulation value and measured value and higher model quality and reliability. NSE coefficients between \(0.0\) and \(1.0\) generally indicate acceptable levels of performance. More specifically, NSE values lie within the interval of \(0.5\) to \(0.65\) for satisfactory simulation results, \(0.65\) and \(0.75\) for good simulation results, and \(0.75\) to \(1\) for excellent results [33]. Moreover, the uncertainty is quantified by a measure referred to as the \(p\)-factor (the percentage of measured data bracketed by the \(95\%\) prediction uncertainty (\(95\)PPU)) and R-factor (the ratio of average thickness of the \(95\)PPU band to the standard deviation of the corresponding measured variable) [34]. For streamflow simulation, when the \(p\)-factor values are greater than \(0.6\) and the R-factor less than \(1\), the uncertainty of the hydrological model is acceptable [35].
## 2 Materials and Methods
### Statistical Analysis  
The statistical analysis was conducted using software packages including IBM SPSS, version 21.0 (IBM Corp., Armonk, NY, USA). Initially, descriptive analyses, such as histograms and box plots, were executed as part of the analysis process. Therefore, means, maximums, minimums, medians, etc., were calculated for all measures to show the difference before and after treatment in female and male voices. Next, we calculated the correlation coefficient and \(p\)-value by means of gender and effectiveness for all acoustic parameters. Subsequently, we employed the Pearson square correlation coefficient (r\({}^{2}\)), which quantifies the shared variance between two variables. A correlation analysis was conducted to ascertain the statistical significance of the correlation coefficients, with a significance threshold set at \(p<0.05\) for all findings in our study [24]. The normality of the data distribution was examined using the Kolmogorov-Smirnov test. When it came to comparing the distributions of the two groups in this study, the two-sample \(t\)-test was employed under the assumption of normality, considering means and standard deviations. Conversely, if the data did not meet the normality criteria, the Mann-Whitney U-test was utilized. The predetermined significance level was set at \(p<0.05\)[25].  
Binomial logistic regression analysis is a statistical method used when dealing with categorical data for the dependent variable [26]. It is used when the outcome variable is dichotomous, meaning it has only two possible outcomes [27]. Unlike linear regression, logistic regression uses the logit function to model the data, allowing the prediction of the probability of a certain class or event by fitting data to a logistic curve. The logistic regression model computes the log odds of the dependent variable, which represents the likelihood of an event occurring. It estimates the probability of the occurrence of a binary event based on one or more predictor variables. Logistic regression typically employs maximum likelihood estimation to determine the model parameters and evaluate the impact of independent variables on the dependent variable. It is commonly used to understand the influence of specific variables on an outcome and to make predictions for future observations [26; 27].
# IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring
## 2 Related Work
### The Fog Computing Layer
If the driver receives three warning messages, with five seconds between each two consecutive warnings, the vehicle will be considered in violation and the algorithm will request a fine from the traffic station. Furthermore, if no violations occur within a period of 30 s, data will be transferred to the cloud. In this way, the cloud software subsystem can check for violations again, utilizing the coordinates and the travel time between two coordinates. If the system identifies a violation case, the data will be recorded in the database in the cloud layer. The third algorithm is the seatbelt status check algorithm, which is responsible for checking whether seatbelts in the vehicle are fastened or not. The algorithm flow chart is shown in Figure 4. This algorithm is triggered by the change in seatbelt status. The algorithm essentially verifies that the seatbelt change from fastened to unfastened occurs when the vehicle is moving and when the pressure sensor is active (pressure = 1). We set the threshold speed at 20 Km/h to avoid requesting fines at very low speeds or in parking situations. The algorithm can identify the seatbelt locations based on the limit switch sensor ID. Thus, the algorithm warns the driver and the passenger three times using voice messages, with ten seconds between each two consecutive warnings. If the seatbelt is kept unfastened during the trip, the algorithm requests a fine. Figure 3: The flow chart of the speed monitoring algorithm in the fog layer. The algorithm estimates the speed using the Haversine function and warns the driver three times before sending the fine request to the cloud if the speed exceeds the permitted value. In this algorithm, we made an assumption that the driver is responsible for the violation of seatbelt laws. However, some countries place the responsibility on the passenger, and others on both the driver and the passenger.
# Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network
## 1 Introduction
Second, the input of hourly soil temperature can help the model to obtain high-resolution simulation results in related studies such as using terrestrial biogeochemical models to simulate the dynamic changes of N\({}_{2}\)O, CO, N\({}_{2}\), and CH\({}_{4}\) in soil [22; 23; 24], simulating soil respiration by land surface models [25], and quantifying hydrological and biological processes in hydrological models [26; 27]. However, most of the current studies have focused on daily or monthly ST predictions, and hourly ST predictions remain scarce. Third, as a branch of machine learning, deep learning technology performs well in nonlinear data processing, and therefore, has achieved success in predicting the solar radiation [28; 29; 30], wind speed [31; 32; 33], and soil moisture [34], whereas studies on ST prediction are relatively few in number. In this research, we treat the hourly ST prediction as the sum of the daily average ST prediction and ST amplitude prediction, and verify them at 30 sites located under different climates. The prediction model was built using a bidirectional long short-term memory network (BiLSTM), which is a deep learning technique for sequence learning, and although less commonly applied to ST predictions, is inherently suitable for this domain. The remainder of this article is organized as follows. Section 2 introduces the data applied in this research. The algorithm for predicting the hourly soil temperature is introduced in Section 3. The experimental results and their analysis are presented in Section 4, and finally, Section 5 provides some concluding remarks regarding this research.
# An Optimized Planning Tool for Microwave Terrestrial and Satellite Link Design
## 3 Tool Description
### Satellite Links
#### 3.5.6 Signal Attenuation  
For satellite links, only rain and atmospheric attenuations were considered. Although retaining the layout of the terrestrial, this step is divided into uplink and downlink subsections, as the frequencies used to perform the calculation may differ.  
To calculate rain attenuation, ITU-R Recommendation P.618-13 [29] was used. P.618-13 states that for links using a frequency less than 10 GHz, atmospheric attenuation can be neglected, and for links using higher frequencies, the method used for atmospheric attenuation calculations is the same used in terrestrial links, described in Recommendation P.676 [23].  
Figure 10: Possibilities for signal blockage due to Earth’s surface.  
Recommendation P.618-13 presents a step-by-step method to calculate the rain attenuation in satellite links, where the rain attenuation is obtained as follows:  
\[r_{0.01}=\frac{1}{1+0.78\times\sqrt{\frac{L_{G}\times\gamma_{R}}{f}}-0.38\times(1 -e^{-2\times L_{G}})} \tag{13}\]  
where \(L_{G}\) represents the horizontal projection of the slant path, \(\gamma_{R}\) is the product found using Equation (8), and \(f\) is the radio frequency used.
## 4 Results
### Dimensionality
#### 4.1.3 Impedance Invariant and Decomposition Model  
The geoelectric strike estimated from two different approaches (impedance invariant and decomposition model) are in the same range (Figure 4).  
Figure 3: Rose diagram of the orientation of the phase tensor strike for the stations along 2D profile (Figure 1) where each rose diagram corresponds to the mean strike estimated over all 14 MT stations along with the corresponding period range.  
Figure 4: **(A)** Strike angle from impedance invariants and (**B**) strike angle from decomposition model. The major and minor axes of ellipse, respectively, correspond to the \(\Phi_{\text{max}}\) and \(\Phi_{\text{min}}\) of phase tensor. Each ellipse is in map view, so up is North and to the right is East.  
The two methods provide consistent results and indicate how geoelectric strike is dependent on period and site location. As evidenced in Figure 4, the geoelectric strike at short-intermediate periods for most MT stations is relatively north-east with an average of 45\({}^{\circ}\) but some stations have a N120\({}^{\circ}\)E strike that can be described as an orthogonal strike direction of the north-east strike, which can result from 3D effects or local 2D heterogeneity. Further reinterpretation related to the position of those MT stations is given in the discussion section. For the long period, an approximate north-north-west strike direction with an average of N120\({}^{\circ}\)E can be seen (Figure 4). In a 2D Earth, the strike determined from impedance invariant is mathematically the same as the strike determined from decomposition model of Bahr [23], which corresponds to the observed results in Figure 4.
## 3 Experimental Results and Discussion
### Load-Displacement Behaviour
#### 3.2.1 Effect of Perforated Hole Arrangement  
The FP series specimens show a high mid-span deflection with values ranging from 18.39 mm to 18.77 mm. These values are approximately 2.5 times greater than the 7.27 mm deflection recorded by specimen CS. Figure 6a shows that the FP series specimens have less stiff load-displacement curves compared with that of specimen CS. This is attributed to the inability of the TMS plate without holes in the FP series specimens to achieve sufficient bonding with the concrete matrix. As a result, delamination of the side concrete cover from the concrete core was observed in the FP series specimens before the applied load reached the ultimate load capacity stage.  
Apart from the FP series specimens, all the HP series specimens yielded similar load-displacement curves as specimen CS. This shows that the presence of perforated holes in the TMS plates could prevent delamination of the side concrete cover, hence ensuring the integrity of the concrete beam.
# A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning +
## 4 Proposed Framework
### IoT Status  
As illustrated in Figure 5, the IoT module status is composed by intrinsic and extrinsic metrics. Intrinsic metrics could be the total free memory available in a given moment, or the CPU temperature. An example of extrinsic metric is related to communication, that is subject to external environmental conditions. In this example, we consider the ESP32 which has a built-in Wi-Fi communication, it is possible to obtain the Wi-Fi level of the network which the IoT module is connected to.  
The metrics free memory available, processor's temperature and Wi-Fi level may be collected with the ESP32. Figure 6 portrays the IoT status monitoring rationale to collect these three metrics. Wi-Fi and MQTT connection are monitored and reconnection procedures are applied if necessary. If both connections are available, then the metrics monitored are sent to the MQTT broker in JSON format each 2 s. Each module publishes in a specific topic \"chipID/status\", where \"chipID\" is an identifier available for the ESP32 module. As the free memory available is an instantaneous variable, it is monitored with the highest frequency possible, and its minimum value is stored and sent to the MQTT broker.  
Figure 5: Example of IoT module status collected metrics.  
Figure 6: IoT module flowchart for status publishing in a MQTT topic.  
We validate our approach with a Proof of Concept (PoC) with ESP32 and ESP8266 (its predecessor) Wemos IoT Development modules. The libraries used for the IoT module monitoring script are Wi-FiClient and Wi-Fi for Wi-Fi connection, and PubSubClient for MQTT connection.
# Recurrent Neural Networks and ARIMA Models for Euro/Dollar Exchange Rate Forecasting
## 3 Results and Discussion  
### Selection the Forecasting Window  
In this section, we compare the forecasting dataset calculated with the ARIMA (0, 1, 0) model, Elman, and LSTM using the validation dataset for windows of 5, 11, 22, 35, 44, and  
Figure 5: Elman network error with random behavior for the first 5000 iterations. The error stabilized after iteration 5000.  
Figure 6: LSTM network error.  
55 days. The purpose of this comparison is to select the window that best approximates the validation dataset in the short term.  
As shown in Figure 7, for the 5-day forecasting window, the Elman model provided predictions far from the validation dataset, while ARIMA and LSTM provided similar results to the validation dataset in observations 3 and 4. For the 11-day window, the forecasting dataset better approximated the validation dataset for the three models. LSTM matched in three observations, while ARIMA and ELMAN matched in only one observation. For the third prediction window of 22 days, the LSTM forecasting dataset closely approximated the validation dataset in ten observations, while Elman matched the validation dataset in just one observation. ARIMA remained constant, with predictions far from the validation dataset. The Elman network presented an upward trend in its forecasts, while for the LSTM network, the forecasting data showed random behavior.  
For the prediction windows of 35, 44, and 55 days, the forecasting dataset obtained with the Elman network best matched the validation dataset and improved further when considering long-term predictions. ARIMA and LSTM most poorly approximated the validation dataset for long-term forecasts with these types of time series behavior and conditions.  
In this analysis, the 22-day forecasting window best approximated the validation dataset in the short term. According to these results, we selected this window to evaluate and compare the prediction methods in detail.