esa-sceva/satcom-chunk-collection huggingface dataset:
# Line-of-Sight Initial Pointing Model of Space Dynamic Optical Network and Its Verification
## 4 Experimental Results and Analysis
The experiment was conducted in the atmospheric channel, and link establishment was completed using a coarse-fine composite axis structure. The tracking accuracy of the coarse tracking system was 50 \(\upmu\) rad. The impact of turbulence on the tracking accuracy was generally between 5-25 \(\upmu\) rad. Moreover, the position of the airship changed very little during the experiment, and the dynamic lag error was very small. Therefore, the recorded azimuth and elevation angle statistics basically obeyed Gaussian distribution. The data shown in Figure 6 were statistically processed according to Equation (10), and the pointing errors and FOU were obtained as follows. The variance in the azimuth angle and elevation angle of the master optical terminal pointing to slave optical terminal 1 was 2.82 mrad and 0.64 mrad, respectively. The variance in the azimuth and elevation angle of the master optical terminal pointing to slave optical terminal 2 was 2.68 mrad and 0.71 mrad, respectively. The pointing error \(\sigma_{1}\) was \(\sqrt{2.82^{2}+0.64^{2}}=2.89\) mrad when pointing to the slave optical terminal 1, and \(\sigma_{2}\) was \(\sqrt{2.68^{2}+0.71^{2}}=2.78\) mrad when pointing to the slave optical terminal 2. The sizes of FOU were 2.89 \(\times\) 3 = 8.67 mrad and 2.78 \(\times\) 3 = 8.34 mrad, respectively. The gaze + scan method was used for scanning and capturing. The scanning mode was spiral scanning, the scanning dwell time was 0.1 s, and the overlap factor was 0.3. After optical LOS scanning, an optical link was established. The 20 LOS pointing and capturing experiments were all successful, and the average scanning time was 18.3 s. The successful  
Figure 6: Statistical histogram of azimuth and elevation angle data. (**a**) Statistical histogram of azimuth angle data pointing to slave optical terminal 1; (**b**) Statistical histogram of elevation angle data pointing to slave optical terminal 1; (**c**) Statistical histogram of azimuth angle data pointing to slave optical terminal 2; (**d**) Statistical histogram of elevation angle data pointing to slave optical terminal 2.  
capture of the two links verified the correctness and feasibility of the initial pointing model of the spatial dynamic optical network's LOS.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Use of Separating Families for Construction of Authentication Tags
#### 4.6.1 Construction of the Tags  
The first step is to find a separating family \(U_{1},\ldots,U_{\ell}\) for \(\mathcal{R}\); the family size \(\ell\) should be minimal, that is, \(\ell=\Psi(r)\). For every \(U_{i}\), there is a corresponding key \(K_{i}\) known to nodes from \(U_{i}\) and to the sender \(S\) (these keys are established as described in Section 3). There is also a key \(K\) corresponding to the entire group \(\mathcal{R}\) and known to all its members and the sender.  
An authentication tag \(T\) generated by \(S\) for a message \(m\) is defined as a concatenation:  
\[T=T_{1}\mid T_{2} \tag{10}\]  
where \(T_{1}\) in turn consists of \(\ell\) distinct chunks, each being an output of a deterministic random number generator \(\mathsf{DRNG}\)  
\[T_{1}=t_{1}\mid t_{2}\mid\cdots\mid t_{\ell}\,. \tag{11}\]  
The chunks \(t_{i}\) are \(u_{i}\)-bit strings calculated according to the following formula:  
\[t_{i}=\mathsf{DRNG}^{u_{i}}(K_{i},K,m) \tag{12}\]  
where \(\mathsf{DRNG}^{u_{i}}\) denotes the output of \(\mathsf{DRNG}\) truncated to \(u_{i}\) bits. In practice, \(\mathsf{DRNG}^{u_{i}}(K_{i},K,m)\) may be instantiated by taking the leftmost \(u_{i}\) bits of the output of \(\mathsf{MAC}_{K_{i}}(K\mid m)\). Importantly, it should be infeasible to compute a new tag \(t^{\prime}_{i}=\mathsf{DRNG}^{u_{i}}(K_{i},K,m^{\prime})\) on a new message without knowledge of the secret keys, in particular the key \(K_{i}\), even if many pairs \((m,t_{i})\) have been observed.  
The value \(T_{2}\), on the other hand, is calculated as follows:  
\[T_{2}=\mathsf{MAC}^{v}_{K}(m) \tag{13}\]  
where the superscript \(v\) indicates truncation of \(\mathsf{MAC}_{K}(m)\) to the \(v\) leftmost bits. Whereas \(T_{1}\) ensures the authenticity of the sender and protects against internal adversaries (malicious receivers), \(T_{2}\) is a standard MAC (albeit truncated to save bandwidth) computed with the shared group key \(K\), which ensures the integrity of the messages and protects against external adversaries.  
Overall, the tag \(T\) defined by (10) has bit length  
\[L=\left(\sum_{i=1}^{\ell}u_{i}\right)+v\,. \tag{14}\]  
The choice of numbers \(u_{i}\) in (14) depends on the available bits for \(T_{1}\). If certain nodes are more likely to become malicious than others, it is possible to allocate more bits to the tags corresponding to sets from the separating family to which these malicious nodes do not belong.
# A Reconfigurable Radio-Frequency Converter IC in 0.18 \(\upmu\)m CMOS
###### Abstract  
This work presents a reconfigurable RF converter for DVB-T television applications using triple-play over GPON. The system takes the DVB-T input, a wavelength division multiplexing (WDM) signal with spectral inversion in the range from 47 MHz to 1000 MHz, up-converts its frequency to the band-pass of a highly selective surface-acoustic wave (SAW) filter centered at 1.3 GHz, and then down-converts it so that it is compatible with the antenna input of conventional television sets. The designed RF converter incorporates two pairs of frequency synthesizer and mixer, based, respectively, on an integer-N phase-locked loop (PLL) with two _LC_-tank VCOs with 128 coarse tuning bands in the range from 1.35 GHz to 2.7 GHz, and a double-balanced Gilbert cell, modified for better impedance matching and improved linearity. It is fed with regulated supplies compensated in temperature and programmed by an I\({}^{2}\)C interface operating on five 16-bit registers. This work presents the experimental characterization of the whole system plus selected cells for stand-alone testing, which have been fabricated in a 0.18 \(\upmu\)m CMOS process.  
RF converters; triple-play; TV applications Article
# Financial Optimization of a Solar-Driven Organic Rankine Cycle
## 2 Material and Methods
### The Examined System
#### 2.1.1 Solar Field Modeling  
The thermal efficiency of the PTC (\(\eta_{th,col}\)) is calculated as below. This formula regards the EuroTrough module, which is assumed to be used in this work [24; 25].  
\[\eta_{th,col}=0.7408\cdot K(\theta)-0.0432\cdot\frac{T_{col,in}-T_{am}}{G_{b}} -0.000503\cdot\frac{\left(T_{col,in}-T_{am}\right)^{2}}{G_{b}} \tag{1}\]  
The PTC incident angle modifier (\(K\)) for the EuroTrough module is calculated according to the next formula [26]:  
\[K(\theta)=\cos(\theta)-5.25091\cdot 10^{-4}\cdot\theta-2.859621\cdot 10^{-5} \cdot\theta^{2} \tag{2}\]  
The useful heat production of the PTC (\(Q_{u}\)) is calculated as:  
\[Q_{u}=m_{col}\cdot c_{p}\cdot\left(T_{col,out}-T_{col,in}\right) \tag{3}\]  
The solar direct beam irradiation on the PTC (\(Q_{sol}\)) is given as:  
\[Q_{sol}=G_{b}\cdot A_{c} \tag{4}\]  
The thermal efficiency of the PTC (\(\eta_{th,col}\)) is defined as:  
\[\eta_{th,col}=\frac{Q_{u}}{Q_{sol}} \tag{5}\]  
In this work, the inlet oil temperature in the PTC is equal to the mean storage tank temperature (\(T_{col,in}=T_{st}\)) and the heat source temperature in the inlet HRS inlet is equal to the mean storage tank temperature (\(T_{s,in}=T_{st}\)). About the storage tank, the general energy balance is given as:  
\[Q_{st}=Q_{u}-Q_{lns}-Q_{loss} \tag{6}\]  
Figure 1: The examined solar-driven organic Rankine cycle (Parabolic trough solar collector (PTC), tank, organic Rankine cycle (ORC)).  
Moreover, it has to be said that the storage sensible heat (\(Q_{st}\)) can be expressed as:  
\[Q_{st}=\rho\cdot c_{p}\cdot V\cdot\frac{dT_{st}}{dt} \tag{7}\]  
Practically, in this work, the storage system is modeled by using the energy balance of Equation (6). Equation (7) shows the way that the stored energy can be expressed in terms of a temperature increase in the tank. In this work, the tank is modeled by one thermal zone, which is an assumption of this study. The temperature is assumed to be uniform in the tank in every time step. It has to be said that the examined storage tanks have relatively high storage capacity and so there are not high fluctuations in the temperature during the day inside the tank, something that makes possible the modeling with a single thermal zone.  
The thermal losses of the storage tank (\(Q_{loss}\)) are calculated as below:  
\[Q_{loss}=A_{T}\cdot U_{T}\cdot(T_{st}-T_{am}) \tag{8}\]  
In this work, a cubic tank is assumed and its area is connected with the storage tank volume as below:  
\[A_{T}=6\cdot V^{\frac{2}{3}} \tag{9}\]  
The heat input in the heat recovery system (HRS) is calculated as:  
\[Q_{ltrs}=m_{s}\cdot c_{p}\cdot(T_{s,in}-T_{s,out}) \tag{10}\]
## 4 Results and Discussion
### Day-Ahead Hourly STLF
#### 4.1.2 Deep Learning Model's Training and Optimization
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layers** & **HL-1** & **HL-2** & **HL-3** & **HL-4** & **Output-L** \\ \hline
**\#Neurons** & 1024 & 512 & 256 & 128 & 1 \\
**Activation** & elu & elu & elu & elu & - \\
**Dropout-** & 0.1 & 0.1 & 0 & 0 & - \\
**Probability** & 0.18 & 0.18 & 0.18 & 0.18 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Final deep neural network architecture. Figure 13: December-2019 prediction results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **MAPE\%** & **RMSE** & **R2** \\ \hline
**Training** & 1.205\% & 31.17 & 0.9932 \\
**Validation** & 1.365\% & 38.39 & 0.9897 \\
**Testing** & 1.411\% & 43.18 & 0.9871 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Final deep learning model results on training, validation and testing data. the largest errors being mainly in the range of 10:00 to 13:00 around noon time. High prediction errors can occur for many reasons that cause an abnormal change in consumer behavior or affect the production of PV energy on distribution levels. It can be observed in the figure that rainy days such as the 6 th, 8 th and 9 th of December had very high errors during noon, this can be related to more people staying in the door as well as a reduction in distribution and consumer level PV production that the grid operator sees as a sudden increase in electrical demand as was observed in Figure 13. Cloudy days, depending on the area of cloud coverage in Jordan and the time of cloud coverage, can also disrupt PV generation, as can be seen in both the 2 nd and 10 th of December from 11:00 to 17:00. On the 25 th, the Christmas holidays could be a critical factor for the change in morning load between 8:00 and 10:00, causing higher errors in the period. Finally, regarding Sunday, the 22 nd of December, a sudden change in demand was traced for the 4 Sundays from the 1 st to the 22 nd, for example, at 11:00 going from 1876 MW, 2519 MW, 2354 MW to 2118 MW on the 22 nd. The first major increase in demand was due to both the rainy day observed and a large decrease in temperature, dropping from a 21 \({}^{\circ}\)C morning on the temperature on the 1 st to a 12 \({}^{\circ}\)C on the 8 th, then the temperature slowly rose to 13 \({}^{\circ}\)C then 18 \({}^{\circ}\)C at the 22 nd. Since the model relies on the demand from the same day at the previous week as one of its features, these sudden changes in weather conditions can have a significant impact on any specific day that has a large change in demand, especially at certain periods, and especially that Sunday depicts the starting day of the working day in Jordan.
## 1 Introduction
Hence, to augment knowledge on the use of FRAM in the healthcare sector by means of practical analyses that can support practitioners in systematically observing and analyzing events, this method was applied retrospectively and prospectively. The first case study concerned the analysis of an accident in an HBOT room that occurred in Italy in 1997 and caused 11 victims [54], while the second case is related to the analysis of the current management of a room where an MRI machine is used for patients' diagnosis. These case studies were selected as they present similar safety criticalities, which are due to the fact that in both cases, a \"special\" room has to be managed where both patients and medical staff interact. Accordingly, on the one hand, this study aims to augment knowledge on the practical application of a resilience approach for the risk assessment of medical device management, given that, compared to other sectors, the use of FRAM in the healthcare context appears scarce especially for what concerns accident/incident analysis. On the other hand, the practical contribution of the study consists in bringing to light discrepancies between clinical practice (WAD) and hospital protocols (WAI) to allow the safer management of the above-mentioned safety devices and thus enhance the performances of clinical personnel, in line with [PERSON] et al. [55]. As a matter of fact, although the occurrence of several accidents related to the use of complex medical devices such as the HBOT room [56] has led to strict safety regulations and standards, current safety management practices related to the use of these devices still present vulnerabilities when analyzed from a resilience engineering standpoint. The remainder of this manuscript is organized as follows. Section 2 concerns a brief explanation of the FRAM's main features and its application in the healthcare sector. In Sections 3 and 4 the two case studies are respectively described, while in Section 5, the discussion of the study outcomes is proposed. Finally, conclusive remarks are addressed in Section 6.
# Research on Nanoparticle-Enhanced Cooling Technology for Oil-Based Drilling Fluids
## 3 Results and Discussion
### Specific Heat Capacity of Nanoparticle-Enhanced OBDFs  
Specific heat capacity is a key index to evaluate the thermal management performance of drilling fluids in high-temperature environments, which determines the efficiency of drilling fluids in absorbing and storing heat in extreme downhole environments. Significant differences in the effect of different nanoparticles on the specific heat capacity of OBDFs are depicted in Figure 2. With the increase of CNTs, SiO\({}_{2}\), and Al\({}_{2}\)O\({}_{3}\) nanoparticle concentrations, the specific heat capacity of OBDFs showed different trends. The addition of CNTs significantly increased the specific heat capacity of OBDFs, especially when the addition of CNTs reached 3%. The specific heat capacity of OBDFs increased by 19.38%, which indicated that the ability of the drilling fluids to absorb and store heat under high temperature conditions was substantially improved. SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) nanoparticles also contributed to the improvement of the specific heat capacity, with maximum increases of 10.67% and 9.71%, respectively. This suggested that although all three nanoparticles enhanced the thermal storage capacity of OBDFs, the improvement of CNT was the most significant, followed by SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\).
# A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method
## 1 Introduction
The low order rule does not apply to, e.g., static predictors, in which the coefficients are determined for individual signal frames [14] (or entire sound files [15]), e.g., by means of minimising the mean square error (MMSE). The publication of the results of our research has been divided into three stages of research. In [16], the general concept of new predictive cascade coding was presented, and in [17], the advantages of adaptive Golomb coding [18] are described. A description of the last widest stage of research work is in this article. Among the published solutions of this type, the highest efficiency is characterised by a cascaded RLS-LMS coder (see Section 5) [2] (implementation of MP4-ALS-RM23 in the best mode). Our method is a significant improvement in this cascading concept. Our coder has higher compression efficiency with a noticeable shortening of the total encoding time relative to work [16]; the experiments shown in Section 9 prove this. In Section 2 the basic prediction methods was described. In Sections 3 and 4, we focus on simple and complex prediction methods with backward adaptation. In Section 4.2, the most effective OLS+ prediction block was described. Cascade connection of prediction block was described in Section 5. Details about removing context-dependent cumulate prediction error are presented in Section 6. In Section 7, a high efficiency version of the binary arithmetic coder using the Golomb code family was presented and analysis of practical aspects of prediction error encoder implementation is presented in Section 7.5. Schematic diagram of the proposed cascading audio data encoder method was presented in Section 8. The conclusion and summary are found in Section 10.
## 3 Problem Formulation  
This section studies the performance optimization and trade-offs of sensing, data transmission, and computation offloading. The overall system performance is optimized through joint beamforming, phase shifting design, and resource allocation.
## 4 Result and Discussion  
### Test System Description  
The IEEE 123 nodes test feeder is used to studying the feasibility of the proposed DSM for EVs charging load. The data of the feeder are extracted from [50]. Three different classical load profiles (residential, commercial, and industrial) are used as shown in Figure 7. Sample time of 1 min is chosen to run the QSTS. The QSTS is implemented using a sequential power-flow solver that is based on forward/backward sweep and programmed in C++ with a personal computer (Core I7, 2.66 GHz processor with 8 GB RAM). The nodes of the IEEE 123 test feeder are categorized into three types: residential, commercial, and industrial [46]. The residential demand is assumed to involve 40% of EV's power penetration level with respect to the total demand of IEEE 123 nodes test feeder, hence, 400 EVs are used. It is assumed that all EVs use GMEV1 battery type, and it is satisfactory for the battery to reach 90% SOC.  
The following different case studies are examined:  
Figure 6: Queuing management strategy for demand response at instant T for EVs aggregation N, at bus k.  
* Base case (without EV): this case represents the IEEE 123 with considering classical loads profile only.
* Case 1: (Uncontrolled EVs Charging) in this case, a statistical model [11] is used to present the impact of EV charging with the classical loads of the distribution system.
* Case 2: (Controlled EVs Charging) DSM based on queueing theory is used to manage the EVs charging demand with consideration of owner preferences.
* Case 3: Wind power generation is integrated to show its influence on the ETR and the total demand.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 2 Methodology and Implementation
### Cloud Integration  
The SFSS makes considerable use of cloud servers, reflecting the system's preference for cloud-based applications. At its heart, this integration is critical for numerous fundamental system tasks.  
* Data Storage and Retrieval: Cloud servers are critical as repositories for the continuous video feed acquired from agricultural regions. These servers store a large amount of video data effectively, offering an archive that can be accessed as required. This role is critical not just for record-keeping but also for any post-incident analysis and inquiry.
* Intrusion Detection in the Cloud: The video stream coming from the agricultural field is subjected to rigorous intrusion detection inside the cloud environment. To evaluate this stream in real time, the system employs AI models installed in the cloud. This method detects unwanted intrusions with greater precision and speed, ensuring that potential risks are discovered and treated as soon as possible.
* Real-time alarm Mechanisms: The cloud integration extends to the alarm system, which is important to the proactive defensive approach of the SFSS. When an intrusion is discovered, the cloud server sends out real-time signals. These signals fulfill two functions. First, they activate on-field measures such as the ESP32, which may involve warning intruders and inhibiting future unauthorized activities. Second, and maybe more importantly, SMS notifications are sent to the agricultural landowner. This real-time communication is a critical component of the system, allowing for rapid and educated reactions to recognized threats.  
The use of cloud servers is critical to the SFSS's high performance. The cloud enables data storage, real-time intrusion detection, and responsive alarm systems, resulting in a sophisticated and intelligent defensive system for defending agricultural fields against possible incursions, damage, and theft. Its extensive connection with the cloud emphasizes the system's dependence on cutting-edge technology to assure the safety and security of farming.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 4 Results
### Medium to Strong Earthquake Retrospective Tests  
The test set time window is from March 2011 to December 2018, allowing retrospective prediction of earthquake events from April 2011 to December 2019. During this period, 19 moderate to strong earthquakes of magnitude five or above occurred in the region, including \(M_{\text{L}}5.7\) earthquakes in Ninglang, Yunnan (24 June 2012), and Yiliang, Yunnan (7 September 2012). The LSTM model accurately forecast 13 of these events, achieving an accuracy rate of 68% (Figure 7). The largest earthquake event was the \(M_{\text{L}}6.8\) Ludian earthquake in Yunnan on 3 August 2014. Although not accurately predicted, the pre-seismic prediction curve shows an upward trend, indicating a higher risk of seismic activity in the region during that period.  
Figure 6: The comparison results of the predicted and true values of LSTM in the test set.  
Figure 7: Retrospective prediction results for medium to strong earthquakes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **5 \textless{} \(M_{\text{L}}\leq 6\)** & \(M_{\text{L}}>6\) \\ \hline Accuracy rate & 85\% & 37\% \\ False alarm rate & 15\% & 0 \\ Missed alarm rate & 0 & 63\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of LSTM model for medium and strong earthquake prediction.