esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Vehicular VLC Methodologies
## 6 Vehicular VLC Channel Modeling and Propagation Characteristics
### Weather conditions  
Currently, there is a lack of research exploring the impact of changing weather conditions, such as rain, snow, and fog, on vehicular VLC systems. Limited studies exist on this topic [39; 76; 103; 104; 105]. Weather variations pose significant challenges for vehicular VLC, and it is challenging to conduct empirical trials to accurately characterize the performance of vehicular VLC under different weather conditions. In the existing literature focusing on weather studies and modeling of the communication channel in vehicular VLC, two well-established models are utilized. One is the Mi theory model [126], which is commonly employed for studying wireless communication in photovoltaic cells, particularly when lasers are involved. The other is the Kim theory model [127]. These models serve as frameworks for understanding the effects of weather on vehicular VLC and are adapted from related fields of wireless communication and photovoltaic cell research.  
These paradigms make it possible to calculate the coefficient factor of the atmospheric attenuation for fogy conditions. This coefficient has been generalized for rain and snow, since it is connected to the atmospheric visibility parameter [75; 104]. The most challenging aspect of utilizing this attenuation model is locating the appropriate physical parameters (such as the amount of liquid water present, the distribution of particle sizes, the average particle size, and the reduction in visibility), which accurately describe certain weather phenomena [128].  
Furthermore, when simulating the meteorological conditions, it is essential to consider the indirect effects of these factors on communication. Even though poor weather conditions almost always make it more difficult to communicate via LoS, there are situations in which they can increase the reflectivity of the ground (such as when it rains or snows, and the roads become wet), which in turn makes it easier to communicate via NLoS. This problem has been taken into consideration by some researchers [36; 39; 76], who have used a CAD tool to describe reflected aspects of the surroundings (for example, the road surface and automobiles). In addition to the physical properties necessary to photograph rain and fog influences (based on the theory of Mi scattering), atmospheric cases affect the kinds of sediment that accumulate on light equipment. This affects the amount of light emitted by that light unit. The attenuation of different wavelengths by the atmosphere under a range of other atmospheric conditions is given in Table 5[129].
## 4 Operation of the System Implemented  
Supply of the _Smart Modules_ is a key issue towards the adequate performance of the system. As well as output voltage stability, one of the most important features of the power supply implemented is energy storage. Since the system must keep sending information during the night, the supercapacitor is selected to provide the energy required. Figure 6 shows typical current of the _Smart Module_ during transmission of data, which represents an energy consumption that can be estimated to be some 185 mJ (the module is supplied at 3.3 V and an average current of 80 mA during 700 ms has been considered).  
During the night, the system is sending information only for safety sake. Thus, it has been established that these packages of data be sent only every 4 min. Taking this into account, the supercapacitor chosen must be capable of supplying the energy required to guarantee operation during the night: 185 mJ every 240 s during 24 h, which results in 67 J (note a 24-hour period has been considered although only night operation is to be guaranteed). Once the energy to be provided by the supercapacitor has been estimated, the value of this component can be obtained by applying the expression of the energy stored in a capacitor:  
\[\textit{W}=\frac{1}{2}.\textit{C}\cdot\textit{V}^{2} \tag{1}\]  
In this case, since the supercapacitor has a voltage of 2.5 V across its terminals, the value required to store 67 J is 21.4 F. A 25-F supercapacitor was selected for the preliminary prototype.  
This power supply can also be used with any other sensors attached to PV panels. Only the value of the supercapacitor might need to be changed depending on the total energy demanded by the sensors included in the design.  
Figure 6: Zoom-in of the transmission interval. The output of the power supply remains stable all throughout this period.  
The other key part in the system is the communication protocol. Two stages can be identified in this protocol: the system identification during start-up (or, as referred to in [18], synchronization of the WSN) and the steady-state performance during which the information is transferred through the network. This is described below.
# Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method
## 1 Introduction  
In recent years, fixed-wing UAVs have gained growing applications in the civilian field as well as military fields, such as search, mapping, rescue, surveillance, and patrol. These applications commonly require the UAV to autonomously track predefined waypoints or prescribed trajectories. In addition, the autonomous detection of potential threats and an online obstacle avoidance algorithm are necessary for UAVs to ensure adequate security [1]. Accordingly, the control problem of UAVs is a multi-objective issue, which indicates that it is extremely important to introduce an integrated approach, taking into account global navigation as well as the local potential obstacle avoidance simultaneously. However, fixed-wing UAVs are typical underactuated systems with nonholonomic constraints, and it is still a challenging problem to develop a guidance framework when facing such a system.  
Another important case is that UAVs need to perform multiple tasks, especially as there exist conflicts in sub-tasks, which motivates us to carry out our research. To this end, we introduce a hierarchical architecture [2; 3; 4] to cope with an arbitrary number of subtasks.
## 4 Measured Result Analysis of the Proposed FSS  
To check whether the FSS operates as a BPF or BSF when attached to an actual radar system, a 4 \(\times\) 4 array patch antenna with an operating frequency of 3.0 GHz was designed. The manufactured FSS was suspended in the air at a distance of 50 mm (0.5\(\lambda\)) above  
Figure 11: Fabricated coaxial cable with different lengths.  
Figure 12: Measured phase difference.  
the array antenna with a Teflon post. To measure the radiation pattern and gain of the array patch antenna with the FSS, measurements were conducted in an anechoic near-field chamber. The measurements were performed using the near-field measurement method, and the beam pattern was confirmed using a near-field-to-far-field conversion method. The measurement environment is shown in Figure 13a when the FSS does not exist and in Figure 13b when the FSS exists. To ensure the reliability of the experimental results, repeated and comparative experiments were conducted. Multiple measurements were performed under identical conditions to assess the consistency and reproducibility of the data. The results from these repeated experiments were compared to ensure that the variations were within acceptable limits. Comparative experiments were also carried out by measuring the performance of the antenna system with and without the FSS and by comparing the experimental results with theoretical predictions and simulations.  
Figure 14a shows the radiation pattern measured at 3.0 GHz. The maximum gain without the FSS was 17.25 dB, and with the FSS, the maximum gain was 17.12 dB. This shows that the FSS operated correctly using a band-pass filter. Figure 14b shows the maximum gain for each frequency near the operating frequency range. Measurement and simulation results showed similar trends not only at the operating frequency of 3.0 GHz but also in the remaining frequency bands. Figure 15a is the radiation pattern measured at 6.0 GHz. When the FSS is not present, the maximum gain is about 12.79 dB, and when present, the maximum gain is 2.69 dB, which is reduced by 10.1 dB due to the FSS. These results show that the FSS suppresses harmonics. Electronic beam steering was implemented using an array antenna to confirm the wide-angle beam steering characteristics of the FSS. To implement electronic beam steering, there must be a phase difference between each port. The phase at 3.0 GHz was adjusted by modifying the length of the coaxial cable connecting the power divider to the port. Figure 16a shows the radiation pattern during the 30\({}^{\circ}\) azimuth beam steering. The maximum gain during steering at an azimuth of 30\({}^{\circ}\) is 16.13 dB without the FSS and 16.68 dB with the FSS. Figure 16b shows the radiation pattern during the 30\({}^{\circ}\) elevation beam steering. The maximum gain when steering at 30\({}^{\circ}\) of elevation is 15.74 dB without the FSS and 15.90 dB with the FSS.  
Figure 13: Radiation pattern measurement setup: (**a**) without FSS, (**b**) with FSS.
# Experimental Investigation of Mechanical Properties of Clay-Cement Slurry Containing Graphene Oxide
## 3 Results
### Performance Analysis of Grouting Reinforcement
#### 3.1.3 Plastic Strength of Slurry  
The strength of the slurry is measured using a Vicker cone penetrometer, in which the clay slurry is placed in a 65 \(\times\) 75 \(\times\) 40 mm sized conical mold. The mold is kept in the SBY-32B constant temperature cement curing box produced by Yixuan Test Instrument Co., Ltd. in Cangzhou, China, and the plastic strength of different slurry ratios is measured at regular intervals. The specific formula is as follows:  
\[P=\cos^{2}\left(\frac{\alpha}{2}\right)G/\pi\sin\left(\frac{\alpha}{2}\right)h ^{2} \tag{1}\]  
where \(G\) is the weight of the entire cone, \(\alpha\) is the cone angle, and \(h\) is the distance that the cone penetrates the slurry.  
The plasticity feature is the late effect of the rheological characteristics of the slurry, and it plays a crucial role in resisting infiltration and water plugging. The results of the slurry's plastic strength are shown in Figure 6. The following can be observed: (1) The plastic strength of the slurry with different mix ratios exhibited the same trend. Before 12 h, the plastic strength remained at a relatively low level, gradually increasing moderately, and then explosively increasing exponentially after 14 h. (2) In comparison with the control group, there was a notable increase in the plastic strength of the slurry as the graphene oxide (GO) content increased. This indicates that GO plays an essential role in enhancing the early-stage water plugging performance of the slurry. This observation aligns with the principles of viscosity thixotropy mentioned earlier, demonstrating that GO exhibits optimal effects in this regard.  
The plasticity characteristics are the late effects of rheological characteristics of the slurry, which play a significant role in anti-seepage and water plugging. The clay-cement slurry needs to continuously overcome the resistance caused by soil mass and the slurry itself during the grouting reinforcement process. The time-varying nature of plastic strength reflects the change in the resistance caused by the slurry itself over time. Simultaneously, as the grouting progresses from the grouting hole to the front-end area of the slurry, the grouting pressure gradually decreases. This attenuation weakens the splitting and compaction effect on the soil mass that is located far away from the grouting pipe. Consequently, this reduction in pressure leads to a decrease in both compressive and shear strength. Additionally, it was found in the experiments that water and bubbles were discharged during and after the grouting process, indicating that a seepage channel had been formed within the medium from the front end of the slurry to the drainage hole. It played a certain pressure-relieving role near the drainage hole, reduced the extrusion effect of the slurry on the medium, and weakened the grouting reinforcement effect [38].
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
## 4 Modeling Channel Occupation Using SCHC
### Theoretical Model  
The theoretical model describing SCHC's fragmentation behavior is based on the following assumptions:  
* The transmission of a SCHC Packet comprises a transmission window and \(r\) retransmission windows of the lost Regular SCHC Fragments;
* For the transmission of all of the SCHC Fragments belonging to a single IPv6 packet, the LoRa spreading factor is the same for all Regular SCHC Fragments;
* For simplicity, all Regular SCHC Fragments have the same number of tiles;
* All messages in the downlink are received by the end device in the first LoRa reception window, that is, at the end of the \(RECEIVE\_DELAY\_1\) timer or \(RD_{1}\) timer [12]. In this way, the node works in Class A, which is the most restrictive mode of operation;
* The processing of an All-1 SCHC Fragment at the receiver takes longer than the maximum length of the receive windows on the end device. For this reason the node waits for the next message in the uplink to open the window again and thus receive the SCHC ACK message sent in response to an All-1 SCHC Fragment (see Figure 5);
* Our implementation uses a Regular SCHC Fragment to transport the last tile. When one of the Regular SCHC Fragments is lost, the receiver must send a SCHC ACK message to the sender indicating which tiles arrived correctly and which did not. The SCHC ACK message only reports the receipt of tiles belonging to a window. The standard recommends that the SCHC receiver sends a SCHC ACK after every window, even if there is no missing tile. The SCHC sender waits for the SCHC ACK from the SCHC receiver before sending tiles from the next window. If the SCHC ACK is not received, the transmitter sends a SCHC ACKREQ message that can be retransmitted up to seven times.  
The SCHC ACK message sent by the receiver requests the retransmission of the tiles that are missing and stores in the buffer the tiles that have been received correctly, with the aim to rebuild the message when missing tiles are retransmitted.  
To illustrate this, the message flow shown in Figure 5 shows that it is possible to separate the transmission with fragment losses in two message blocks.
## 4 Security Proofs
### Ticket Unforgeability
First, according to the above simulation, we can get \(q_{\mathcal{F}}\) signatures \((s_{\beta_{i}},\delta_{i},\sigma_{i},\gamma_{i})\)'s retrieved from \(L\) where \(\mathcal{F}\) computed \(s_{\beta_{i}}=(b_{i})^{-1}t_{\beta_{i}}\) mod \(n_{H}\) and \(s_{\beta_{i}}^{\epsilon_{H}}\,\equiv\,F_{2}(\delta_{i},\sigma_{i},\gamma_{i}) \pmod{n_{H}}\) with \(1\leq i\leq q_{\mathcal{F}}\) during the simulation. For the tickets \((s_{j},m_{j},\delta_{j},\sigma_{j},\gamma_{j})\)'s in \(S_{T}\), we can obtain \(q_{1}\) signatures \((s_{\alpha_{j}},F_{1}(m_{j}))\) by retrieving \(s_{\beta_{j}}\) from \(L\) via \((\delta_{j},\sigma_{j})\) and then computing \(s_{\alpha_{j}}=s_{j}(s_{\beta_{j}})^{-1}\) mod \(n_{H}\) where \(s_{\alpha_{j}}^{\epsilon_{H}}\equiv F_{1}^{2}(m_{j})\pmod{n_{H}}\) with \(1\leq j\leq q_{1}\). For the tickets \((s_{j^{\prime}},m_{j^{\prime}},\delta_{j^{\prime}},\sigma_{j^{\prime}},\gamma _{j^{\prime}})\)'s in \(S_{T^{\prime}}\), we can get \(q_{2}\) signatures \((\hat{s}_{\delta_{j^{\prime}}},\hat{s}_{\delta^{\prime}},\sigma_{j^{\prime}}, \gamma_{j^{\prime}})\)'s by the following procedure where \(1\leq j^{\prime}\leq q_{2}\). We first randomly select \(\hat{b}_{j^{\prime}}\in\{0,1\}^{l_{r}}\) and compute \(\hat{\beta}_{j^{\prime}}=\hat{b}_{j^{\prime}}^{\epsilon_{H}}F_{2}(\delta_{j^{ \prime}},\sigma_{j^{\prime}},\gamma_{j^{\prime}})\) mod \(n_{H}\). Then we send \(\hat{\beta}_{j^{\prime}}\) to \(S_{D}\) and obtains \(\hat{t}_{\beta_{j^{\prime}}}\). Finally, we compute \(\hat{s}_{\hat{\beta}_{j^{\prime}}}=\hat{t}_{\beta_{j^{\prime}}}(\hat{b}_{j^{ \prime}})^{-1}\) mod \(n_{H}\) and \(\hat{s}_{\alpha_{j^{\prime}}}=s_{j^{\prime}}(\hat{s}_{\beta_{j^{\prime}}})^{-1}\) mod \(n_{H}\) where \(\hat{s}_{\alpha_{j^{\prime}}}^{\epsilon_{H}}\,\equiv\,F_{1}^{2}(m_{j^{\prime}}) \pmod{n_{H}}\) and \(\hat{s}_{\beta_{j^{\prime}}}^{\epsilon_{H}}\,\equiv\,F_{2}(\delta_{j^{\prime}},\sigma_{j^{\prime}},\gamma_{j^{\prime}})\pmod{n_{H}}\) for each \(j^{\prime}\) with \(1\leq j^{\prime}\leq q_{2}\). Consequently, we query \(S_{D}\)\((2q_{\mathcal{F}}+q_{2})\) times and obtain \((q_{\mathcal{F}}+q_{1}+2q_{2})\) signatures. We succeed in one-more forgery to break the FDH-RSA blind signature scheme.
## 2 Theory
### Statistical Energy Analysis (SEA)  
In the SEA method, the power balance of a subsystem depends on the input power, transfer power between subsystems, and dissipated power through the subsystem. Equation (9) describes the power balance of subsystem \(i\).  
\[P_{i,input}=P_{i,dissipated}+P_{ij} \tag{9}\]  
where \(P_{i,input}\) is the input power of the subsystem, \(P_{i,dissipated}=\omega\eta_{i}E_{i}\) is the dissipated power within the subsystem, which depends on the internal damping loss factor \(\eta_{i}\) of the subsystem, and \(P_{ij}\) is the transfer of power between subsystems. This latter parameter depends on the modal energy of each subsystem as well as the coupling loss factors between subsystems \(i\) and \(j\). The power transfers from a subsystem with higher modal energy to one with lower modal energy [13]. This parameter for a system of \(N\) subsystems is defined as  
\[P_{ij}=\sum_{j=1}^{N}\omega\big{(}\eta_{ij}E_{i}-\eta_{ji}E_{j}\big{)} \tag{10}\]  
where \(\omega\) is the angular frequency, \(\eta_{ij}\) is the coupling loss factor, and \(E_{i}\) and \(E_{j}\) are the total subsystems' energy. In SEA, it is assumed that in the narrowband, the modal energies of all modes are the same. Based  
By substituting Equation (12) into Equation (9), the power balance of subsystem \(i\) can be written as  
\[P_{i,input}=\omega\eta_{i}E_{i}+\omega\sum_{j=1}^{N}\omega\eta_{ij}n_{i}(\frac{E_ {i}}{n_{i}}-\frac{E_{j}}{n_{j}}) \tag{13}\]  
This power balance equation is used in a matrix solution format to determine the energy vector.  
In this study, the virtual SEA method was utilized for analyzing the tiltrotor based on the VSEA module of ACTRAN. In Section 3.2 the methodology of using this method is explained in detail.
## 3 Experimental Section
### Sequence Assembly  
_De novo_ assembly was performed using the short reads assembling program Trinity [41], which combines reads with a certain length of overlap to form longer fragments called contigs. Then, the reads were mapped back to these contigs. Furthermore, the contigs were assembled using Trinity to generate sequences that could not be extended on either end, which are defined as unigenes. Using homologous transcription cluster analysis, the unigenes were classified into two groups: unigenes with homologies higher than 70% were designed as clusters (initialled with CL, numbered with the gene family), while sole unigenes were designed as singletons (initialled with Unigene).  
The assembled unique sequences were aligned to protein databases in the following order: Non-redundant (Nr) protein database, the Swiss-Prot protein database, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Cluster of Orthologous Groups of proteins (COG) database, by applying BLASTx (_E_ value threshold set at \(10^{-5}\)). Sequence hits in a former database would not advance to a search against the next database. The CDS of the unigenes were extracted and translated into peptide sequences. Based on the BLAST results, the sequence direction was determined. For unigenes with uncertain direction after BLAST analysis, a statistical Hidden Markov Model (HMM) program, ESTscan [42], was introduced to help determine the sequence direction. Sequences whose directions could not be predicted using the ESTscan program were assigned their initial assembled sequence direction.
###### Abstract
Decision support systems combined with various artificial intelligence (AI) methods have been used for supporting various medical decisions while analyzing complex biomedical signals and images such as echocardiograms [12], magnetic resonance images [13] and chest x-rays, and computed tomography images [14]. Usually, huge amounts and complicated medical data, reports, and images must be analyzed more quickly but with higher accuracy. It is challenging to implement algorithms to carry out such jobs in and of themselves, but it is even more difficult to improve algorithm accuracy while reducing execution time. Especially, hyperparameter optimization may require a much larger overhead, because multiple training rounds and evaluations of machine/deep learning algorithms for hyperparameter-optimized decision support are needed. However, most of these methods solely concentrate on feature selection while focusing specifically on the issues of underfitting and overfitting. The model can perform well on both datasets, i.e., training data and testing data, if overfitting and underfitting are prevented. Overfitting of the training data is frequently caused by irrelevant features and improper (i.e., suboptimal) model design [15]. Most models proposed in the literature are not generalizable, which raises the need for optimizing models that adapt well to new, previously unavailable medical data [16]. As hyperparameter optimization is often neglected in the validation of decision support systems, the significance of its effects remains unreported [17]. Moreover, there is a lack of systematic studies on the effect of hyperparameters on the accuracy and robustness of the AI models used for decision support [18]. Figure 1: Deep CNN Architecture [8]. This paper aims to propose a rigorous methodology for CNN transfer learning HPO for human posture image classification. For this, the random search approach was adopted to create recommended rankings for filter sizes for the convolutional layers and dense layers of the four models and the learning rate values for the optimizer used in the models. Additionally, the dataset used in the study includes the MPII human pose images obtained from the Kaggle repository. Unlike current approaches, the models and techniques utilized in this proposed system are more favorable since they enhance and optimize the classification of human posture using deep learning algorithms. The remaining part of the article is prearranged as follows: Section 2 presents the related works. Section 3 describes the two HPO models used in the study. This section also presented the materials and methods used in the study. Section 4 presents the experimental results and discussions. The discussion is presented in Section 5, and the article is concluded with future works suggested in Section 6.