esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Types and Challenges of NCS
### Challenges of NCS
#### 6.2.3 Network Security  
Security of NCSs is one in the various foremost challenges that is receiving much attention these days [123; 130; 132]. Any network is prone to interception, particularly the wireless networks. Hence, network security is usually involved. Attacks to the NCS (as shown in Figure 14) will be described in brief as: A1 and A3 symbolize deception attacks, where a person sends forged data from either the sensors or the controllers. The forged data consists of: faulty activity, like the incorrect time of activity or the incorrect sender id. The person will instigate these types of attacks by getting the key or by cooperating with some attacks on sensors (A1) or controllers (A3). Attacks A2 and A4 signifies Denial of Service (DoS) attacks, where the person is barred by the controller from attaining device measurements. To launch DoS attacks, the person jams the associated communication channels, negotiates the devices to stop, and attacks the routing protocols. Attack A5 symbolizes an on-the-spot attack against the actuators. So, attempts should be made to prevent the negotiation of the actuators and the other direct attacks against the physical system, for example, by securing the attacked-physical system, observation cameras, etc. In Reference [152], a new ETM is proposed, under which data packets could be actively dropped within consecutive steps, saving more communication resource than the existing ETM. Here, mainly the effect of DoS attacks obeying Bernoulli distribution is considered and analyzed. Figure 15 shows that the numbers of security events have increased, which is reported by the industrial control systems (ICSs) cyber emergency response team.  
Figure 14: Cyber-attacks on NCSs.  
\begin{table}
\begin{tabular}{c c} \hline
**Reference** & **Loss Rate/Loss Rate Range (in \%)** \\ \hline
[41] & \(10\) \\ \hline
[62] & \(0\) to \(80\) \\ \hline
[67] & \(26.39\) \\ \hline
[74] & \(25\) to \(31\) \\ \hline
[91] & \(25\) \\ \hline
[93] & \(10\) \\ \hline
[108] & \(30\) to \(70\) \\ \hline
[113] & \(20\) to \(40\) \\ \hline \end{tabular}
\end{table}
Table 8: Packet loss rate considered in different studies.  
In Figure 15, it can be seen that in the beginning, the security incidents were on the rise, but there is a drastic decrease in the security incidents from 2017 to 2020 because of the protective measures taken and the development of advanced networked security approaches in NCSs. Advanced NCSs security techniques are being developed so that the NCSs are safer to operate [153].
# The Current Development Status of Agricultural Machinery
## 5 Summary and Future Perspectives
### Cooperative Control Algorithm Optimization  
The optimization of cooperative control algorithms for agricultural machinery operating in hilly and mountainous regions--particularly through energy-driven cooperative control and the deep integration with powertrains to enhance energy efficiency--represents a critical technical pathway for addressing the challenges posed by complex terrain and improving overall performance. In such terrains, frequent variations in slope and fluctuations in load lead to low energy utilization efficiency within conventional powertrains. Traditional mechanical transmissions encounter issues such as gear-shifting impacts and power interruptions when navigating variable-gradient landscapes. Furthermore, the nonlinear disturbances inherent to hilly terrains necessitate control systems that exhibit high robustness. Consequently, there is an urgent need for comprehensive research into cooperative control algorithms to tackle: dynamic energy management under complex operating conditions; multi-physics-coupled powertrain optimization; and terrain- and load-adaptive global optimal control.
## 3 Results
Figure 1: Relaxation volume of a single neutral and charged (**a**) uranium vacancy in \(\text{UO}_{2}\), (**b**) Ga vacancy in GaAs, and (**c**) Si vacancy as function of the inverse of the supercell size (the corresponding number of atoms in the cell is provided on the upper \(x\)-axis). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Defect** & **Charge** & \(\mathbf{G_{11}}\) & \(\mathbf{G_{22}}\) & \(\mathbf{G_{22}}\) & \(\mathbf{G_{12}}\) & \(\mathbf{G_{13}}\) & \(\mathbf{G_{23}}\) \\ \hline \multirow{2}{*}{\(V_{\text{U}}\)} & \(4\)\(-\) & \(56.08\) & \(55.18\) & \(56.97\) & \(0.32\) & \(0.19\) & \(0.11\) \\  & \(0\) & \(-10.52\) & \(-16.86\) & \(-9.04\) & \(-0.16\) & \(3.06\) & \(-1.48\) \\ \hline \multirow{2}{*}{\(V_{\text{O}}\)} & \(2\)\(+\) & \(-22.12\) & \(-24.01\) & \(-22.69\) & \(-0.14\) & \(0.02\) & \(0.05\) \\  & \(0\) & \(0.24\) & \(1.02\) & \(1.06\) & \(0.02\) & \(-0.01\) & \(0.0\) \\ \hline \multirow{2}{*}{\(\text{O}_{i}\)} & \(2\)\(-\) & \(27.87\) & \(26.81\) & \(28.56\) & \(-0.08\) & \(-0.37\) & \(-0.33\) \\  & \(0\) & \(-4.83\) & \(-4.82\) & \(-4.29\) & \(-0.32\) & \(0.15\) & \(0.0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computed defect dipole tensors, for fully charged and neutral point defects calculated from DFT calculations. Figure 1 shows the dependence of the defect relaxation volume on the simulation cell size for charged and neutral vacancies. These results indicate that the large charged defect relaxation volumes are not a consequence of the finite system size limitation of DFT. In the next section, we investigate in detail the dependence of relaxation volumes on the various finite size charge corrections schemes to charge supercell calculations, which have been suspected [14] to have large volume dependence.
# Synchronous Homopolar Generator without Permanent Magnets for Railway Passenger Cars
## 1 Introduction
Article [27] presents the development of a control strategy for the traction SHM. In References [6; 17; 18; 20; 28; 29; 30], the analysis of the SHM characteristics for various generator applications is investigated. In Reference [17], the modeling and tuning of the control system of a high-speed synchronous homopolar generator (SHG) is considered. In [18], the analytical formulas for the design and the results of 3D FEA of a high-grade SHG with a power of 10 kW and a rotation speed of 24 krmp are described. In Reference [20], the simulation of a low-power SHG with a speed of 3000 rpm at idle is considered using a 2D FEA with virtual excitation windings. In Reference [28], a theoretical evaluation of the characteristics of an SHG with a power of 500 kW with a superconducting excitation winding is presented. In Reference [30], the manual optimization of SHG performance at no-load is considered, and an experimental verification is carried out. In References [6; 29], the analysis of SHG characteristics for undercar generators is considered. In Reference [6], the modeling of a 4.5 kW undercar generator is considered, taking into account the eccentric installation of the rotor. In Reference [29], the modeling of the design of an undercarriage generator based on SHG with hybrid excitation is considered. Based on the literature overview, it can be concluded that previous studies are limited to the manual optimization of the SHG no-load characteristics, as in [30], but do not carry out computer-aided optimization ((CAO), for example, using a genetic algorithm or the Nelder-Mead method) of the SHG on-load performance using FEA due to the fact that the 3D FEA of SHM takes too much time. At the same time, improving the performance of undercar SHGs through optimization is important to reduce their volume (and therefore cost) and power losses, which is required in practice [15]. The novelty of this article is to demonstrate that the proposed technique of CAO of the SHG on-load characteristics, based on the simplified model [9] and the Nelder-Mead method, makes it possible to reduce the volume (cost) and the power loss compared to a commercially available counterpart [31]. To overcome difficulties with the long 3D FEA time, it is proposed to use the simplified 2D SHM model presented in [9]. The optimization procedure was developed, which takes into account the operating cycle of the generator. The objectives of the optimization are to minimize losses in the generator, minimize the maximum current of the controlled rectifier, and minimize torque ripple. The optimization procedure requires the calculation of only two operating points of the generator, which ensures a short calculation time. The target characteristics of the SHG as a result of optimization are significantly improved. The article also compares the main characteristics of an optimized SHG with a mass-produced undercar generator.
## 2 Methodologies
### Long Short-Term Memory (LSTM)  
LSTM is a specialized type of Recurrent Neural Network (RNN) architecture that excels in time series analysis and forecasting. LSTM's ability to learn long-term dependencies enables it to outperform traditional RNNs in capturing such dependencies within sequential data. LSTM was originally designed to address the issues of gradient vanishing and explosion in traditional RNNs when processing long sequences. By introducing a special memory unit, LSTM can retain information from previous states.  
The LSTM cell incorporates three gating mechanisms: the forget gate, the input gate, and the output gate. The role of the forget gate is to determine whether information should be discarded or retained in the cell state, based on the hidden state from the previous time step and the current input. The input gate consists of a sigmoid layer and a tanh layer. The sigmoid layer decides which values to update, and the tanh layer generates a new vector of candidate values for updating the cell state. The output gate determines the value of the next hidden state. The interaction of these three gates enables LSTM to efficiently capture long-term dependencies in time series data while mitigating the effects of noise. The LSTM unit is depicted in Figure 3. The mathematical representation of the LSTM is presented below.  
\[\left\{\begin{array}{l}f_{t}=\sigma\left(W_{f}\cdot[h_{t-1},x_{t}]+b_{f} \right)\\ i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i})\\ C_{t}^{\prime}=tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C})\\ C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot C_{t}^{\prime}\\ O_{t}=\sigma(W_{O}\cdot[h_{t-1},x_{t}]+b_{O})\\ h_{t}=O_{t}\cdot tanh(C_{t})\end{array}\right. \tag{8}\]
# Modeling the Accuracy of Estimating a Neighbor's Evolving Position in VANET
## 2 Analytic Model for the Estimation Error of a Neighbor's Evolving Position  
In this section, we explain the system model that is used for establishing our mathematical model and then present the step-by-step mathematical derivation of an estimation error of a neighbor's evolving position. For the mathematical derivation, we first calculate the estimation error using a simple equation, which is associated with a probability of successful reception. Then, we describe the probability of successful reception in more detail. We summarize the notation used for mathematical mode in Table 1.
## 3 Results
### Rehabilitation Protocols and Discharge Settings  
Following the PMR assessment, a tailored rehabilitation protocol was established for 28 of the 29 examined patients (96.55%), aiming to address the functional concerns of each patient. In the majority of cases, the prevention of bed-related complications (22 patients, 75.86%) was prescribed. When necessary, interventions to maintain muscle tone, recover postural changes, and improve swallowing and respiratory function were  
\begin{table}
\begin{tabular}{c c} \hline
**Variable** & **Median** & **Min-Max** \\ \hline Total hospitalization days & 20 & 7–168 \\ \hline Days at the PMR evaluation and & \multirow{2}{*}{7} & \multirow{2}{*}{1–95} \\ onset of rehabilitation & & \\ Rehabilitation duration at the time & & \\ of discharge (or death) & & \\ \hline \end{tabular}
\end{table}
Table 2: Total hospitalization days, days at the Physical Medicine and Rehabilitation (PMR) evaluation and onset of rehabilitation, and rehabilitation duration at the time of discharge (or death) for the analyzed population.  
\begin{table}
\begin{tabular}{c c} \hline
**Comorbidity** & \multicolumn{2}{c}{**Number of Patients (Percentage of**} \\ \multicolumn{2}{c}{**Total Population)**} \\ \hline Previous non-cerebral thromboembolic event & 2 (6.90\%) \\ \hline Previous ischemic heart disease & 5 (17.24\%) \\ \hline Type 2 diabetes & 11 (37.93\%) \\ \hline Dyslipidemia & 5 (17.24\%) \\ \hline Chronic obstructive pulmonary disease & 3 (10.34\%) \\ \hline Active smoking & 1 (3.45\%) \\ \hline Previous smoking & 4 (13.79\%) \\ \hline Previous neoplastic disorder & 2 (6.90\%) \\ \hline Concomitant neoplastic disorder & 5 (17.24\%) \\ \hline Non-neoplastic hematologic disorder & 2 (6.90\%) \\ \hline Transplant & 2 (6.90\%) \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._prescribed (Table 3). Rehabilitation interventions were carried out daily, with each session lasting between one and two hours.  
The mean Barthel Index scores at admission and discharge were similar, 36.38 \(\pm\) 30.70 and 35.2 \(\pm\) 35.69, respectively. The comparison between admission and discharge values did not reveal statistically significant differences (\(p\) > 0.05).  
The analysis of the discharge diagnoses for the 25 patients who survived hospitalization revealed that the majority, 21 patients (84%), were diagnosed with meningitis or encephalitis, while 4 patients (16%) were diagnosed with AFP associated with encephalitis.  
Discharge settings could be defined for 24 out of 29 patients (due to 4 deaths and one case of missing data). Among these, 14 subjects (58.34%) were discharged home, 6 (25%) were transferred to other departments or community hospitals, with 2 awaiting potential transfer to a rehabilitation facility, and 4 (16.67%) were transferred to rehabilitation facilities.
# Experimental Study of LoRa Transmission over Seawater
## 2 LoRa Technology  
LoRa [6; 11] is proprietary modulation based on spread spectrum techniques developed by Semtech. In this type of modulation, the bandwidth spreading is done using \"chirps\", i.e., pulses of linearly increasing (or decreasing) frequency, spanning the whole bandwidth allocated for transmission. An example of such chirps is shown in Figure 1.  
Data to be transmitted is encoded in abrupt changes from up-chirp to down-chirp or vice-versa, maintaining a constant envelope of the modulated signal [12], which simplifies the design of the amplifiers since there is no need for the complex and expensive ones required by other types of modulation [13]. At the receiver side, the same \(F_{c}\) tone is used to down-convert the signal to baseband, where essentially the reverse of the operations performed in transmission is undertaken to recover the original data affected by the inevitable noise.  
Demodulation is accomplished in two steps: (1) multiplying the received signal by the conjugate of each of the two chirps used in transmission, which turns each modulated symbol into regions of constant frequency; and (2) performing a Fast Fourier Transform (FFT) in each region. The peak of the FFT represents the value of the received symbol, easily distinguishable even in the presence of considerable noise or interference [14].  
There are three LoRa parameters that enable the designer of the LoRa link to balance between bit rate and robustness of the transmission: Bandwidth (BW), Spreading Factor (SF), and Coding Rate (CR). The bandwidths used in LoRa links can take \(BW\in[125,250,500]\) kHz, while spreading factors can be \(SF\in[6,12]\). Coding rate offers the possibility to add up to four redundant bits for error handling after every 4-bit chunk of payload transmitted, making the transmission more resistant to interference bursts.  
Figure 1: LoRa physical layer. Signals used to convey information: (**a**) up-chirp and (**b**) down-chirp; (**c**) transmitted signal composed of two up-chirps and two down-chirps; (**d**) LoRa packet spectrogram captured by real-time spectrum analyzer.  
We can opt for higher sensitivity by choosing a higher spreading factor, or for a faster bit rate by selecting lower spreading factor. Higher spreading factors result in longer symbol periods and longer packet air times, whereas a lower spreading factor yields shorter transmission times. These trade-offs are illustrated in Figure 2.  
Finally, LoRa technology offers two advantages over other technologies used in LPWANs. Since the modulated signal is continuously changing in frequency, it is more tolerant to clock inaccuracies and also to Doppler shifts caused by movements between the transmitter and the receiver [15]. Additionally, the main modulation mechanism i.e., abrupt change of the frequency, facilitates reception of very weak LoRa signals, even below the noise floor of the transceiver.
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 6 Simulation Results and Analyses
### Influence of the Cost Factor  
From (5) in Section 3.2, we can find that the total loss in the utility function comes from the costs of both channel leasing and energy consumption. Thus, the factor \(\eta\) which combines the above two factors, \(\theta\) and \(\delta\), can affect the results of rate allocation. When the total channel bandwidth provided by AP is 3 Mbps, we change the value of \(\eta\) to obtain the corresponding allocation results. From Figure 7a, we can find, when \(\eta\) becomes larger, the rates of videos with complex scene and fast motion will decrease most obviously, such as Coastguard, Football and Mobile. When \(\eta>4\), Table's rate also begins to decrease. When the total channel bandwidth is set to 2 Mbps, 3 Mbps and 4 Mbps, respectively, we can also find this corresponding reduction in each total utility curves from Figure 7b. Therefore, from the aspect of channel leasing, the NSP can effectively control the bandwidth allocation by adjusting the price parameter \(\theta\). This plays an important role in differentiating the service level of multimedia users and maintaining the robustness of the whole network. On the other hand, energy consumption should also be considered in video transmission of UAVs. It is related not only to the actual video transmission rate, but also to the distance between the UAV and AP. The energy factor \(\delta\) in the utility function could to some extent describe the different energy consumption which is caused by different transmission distances. When the distance between the UAV and AP becomes obviously far, \(\delta\) will increase and the transmission will consume more energy, which will affect the results of the resource allocation.
# Twitter Data Mining to Map Pedestrian Experience of Open Spaces
## 4 Discussion
### Usability of Data in Relation to a Particular Open Public Space  
It should be considered here that the research of the potential of Twitter data for the purpose of evaluating the quality of pedestrian space was done in relation to one of the most famous and most visited open public urban spaces--Oxford Street in London. Therefore, the question arises to what extent the use of Twitter data would be possible for the needs of another pedestrian space, which is not so well known and popular, in relation to the sample size itself. There are also important cultural factors regarding the intensity of the use of social networks in a certain territory, as well as the language in which certain topics are tweeted, which can be considered a minor challenge. Also, the analysis cannot fully indicate the character of the users of the space, such as the age of the users, their gender, occupation, or place of residence, which is important for the needs of urban design.  
In addition, this work proposes a combined research approach, social network sentiment data analysis and survey of public opinions for mapping user experience and their opinions about environmental elements of the pedestrian or open public space. This Twitter data sentiment analysis demonstrates that certain patterns of user experience and opinions can be clearly mapped from the tweets' sentiments. Twitter data information is compared with research conducted using traditional methods (interviews and surveys). Research about public opinions of public space that was derived from traditional data collection was noticeably labour- and time-consuming, while being able to collect smaller amounts of data from specific demographics. On the other hand, collecting data from social networks is much faster, less labour-intensive and less time-consuming. Real-time availability of data-driven analysis is not slow and costly, as are traditional methods. In the case of the used case study of Oxford Street, it was noticed that certain events triggered a higher volume of data collection. Having this in mind, and other surveys conducted about the public space design of this location, it would be of interest to combine not only these sources but also to combine similar efforts at the same time, to aggregate a larger amount of usable and relevant data.
# Electrical Equalization Analysis of PAM-4 Transmission in Short-Reach Optical Systems
## 3 Methods--The Computer Simulation  
As mentioned in Section 1, our analytic tool is a computer simulation. This section gives a condensed review on the simulation, its structure and logic, and some defined properties and parameters.  
The code models an optical communication system as described in Figure 1. The model runs over four changing parameters' dimensions as described in Figure 2, while the first two dimensions--\(f_{c}\) and the optical input power (OIP), are manifested in the transmission as a 2-dim matrix, and the remaining dimensions are applied (with a \"for\" loop) on the camel and equalizer sections. All the parameters are defined in vectors that contained the desirable values for examination.  
In order to apply the channel as a filter, we had to define a finite response that suits the infinite response in Equations (8) and (9). According to [PERSON] [15], we can set the length of the response--\(N\), depending on the fiber length, the dispersion parameter, and the wavelength, as given in Equation (10).  
\[N=2\times\left|\frac{|D|\lambda^{2}L}{2 cT^{2}}\right|+1, \tag{10}\]  
\(L\)--the fiber length.  
At the end of running, all the bit error rate (BER) results are arranged in 4-dim array. This array is being re-arranged into 2-dim matrixes sets that correspond to optical input power (OIP)-defined values. Actually, each combination of the three other parameters has its own matrix. The results are visualized in BER curves as shown in Section 3, Figure 3b. Each set of equalizer taps, where \(N_{1}\) refer to the feedforward part, and \(N_{2}\) refer to the feedback part, owns a different curve pattern, and each LPF 3 dB cutoff (\(f_{c}\)) is represented in a separate figure.  
We defined the constant parameters according to [2] and used it to compare the results and for quality check. Table 1 lists those parameters.  
Figure 3: (**a**) histograms of the received signal, before the equalizer with different fiber length = 0.5, 2.5, 3.5 km. \(f_{c}\) = 0.75 \(R_{s}\) Hz, OIP = \(-\)10 dBm. (**b**) BER curve for three different fiber length = 0.5, 1.5, 2.5, 3.5 km, and two equalizers FFE with \(N_{1}=6\), DFE with \(N_{1}=6\), \(N_{2}=3\). RX \(f_{c}=0.7R_{s}\), TX \(f_{c}=0.55R_{s}\).  
Figure 2: The organization of the code running.