esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Rigid Alignment of DWI and T2 Images  
In previous studies [6; 8; 13], it was demonstrated that, in prostate mp-MRI, different MRI sequences are deterministic for prostate cancer detection and classification results, but the sensitivity of detection results under single-modality images is limited, so there is a need to use multiple MRI sequences to make judgments and fully utilize the characteristics of cancerous tissues under different MRI contrasts. Among all mp-MRI sequences, T2 images are more favorable for prostate cancer detection and diagnosis based on previous studies, but the sensitivity of T2 images is low [4; 31]. DWI images show the extent of water diffusion in the prostate due to the tight accumulation of cancer cells, and any changes in the prostate cancer can be detected more easily in DWI images; thus, DWI is another type of image recommended for use in diagnosis. However, DWI does not completely represent prostate lesions [31; 32], so there are many studies combining DWI with T2 images to achieve better sensitivity and specificity [6; 8; 33]. As shown in Figure 2, in the present study, we use DWI and T2 sequences in mp-MRI for prostate cancer classification. One of the keys to accurately combining the DWI and T2 image features is to align the DWI and T2 sequences, which can effectively eliminate the small variations between different sequences caused by external factors during mp-MRI acquisition [11]. In this study, no drugs were used in the MR acquisition protocol to reduce and prevent motion. In order to ensure that the shape of the cancer lesion in the image is maintained without changing so that the model can acquire the actual features of the shape of the cancer lesion, we use a rigid 2D medical image alignment algorithm based on mutual information to maximize the mutual information between the reference image and the target image without changing the shape information of the cancerous region, and we use DWI as the target image and T2 as the reference image. In this study, we use DWI as the target image and T2 as the reference image. We use the best available medical image alignment algorithm, ANTs SyN [34], to align the images. The image alignment strategy generally starts with an initial globally aligned linear transformation, and the linear changes available in ANTs are optimized for the mean squared deviation and correlated similarity measures, each of which are optimized for translation and rotation. To ensure the accuracy of the alignment process, we checked each image after alignment.
## 4 Study and Evaluation
### Evaluation Metrics
Such complexity, as done by a fair number of authors, might be intuitively evaluated in terms of model size, reporting the results produced either in the form of the number of parameters [53; 54; 55; 60; 61; 62; 67; 60; 62; 67; 71; 74; 85; 90; 95; 99; 100; 109], their weight [48; 51; 54; 56; 60; 64; 67; 70; 86; 87; 98; 100; 102; 105; 107; 109], or their memory footprint [60]. The number of parameters responds to the number of model weights learned during the training process, essentially considering in that respect the parameters in convolution and fully connected layers. Weight, in turn, stands for the size of the parameters counted, while the memory footprint, although sometimes erroneously used as a synonym of the former, corresponds to the estimation of the space in main memory required by the model at runtime, thus constituting the measure that best characterizes the requirements of such nature. Model size, particularly the number of parameters and their weight, are straightforward metrics capable of providing a first intuition on its resource requirements at a glance. However, when discussing an algorithmic solution's computational complexity in a more rigorous fashion, it is necessary, especially within the on-device paradigm, to know the way computations are carried out and their cost in terms of demanded resources. In this regard, despite the scarcity shown in the AmI works reviewed, it is possible to observe additional representative metrics, commonly reported in DL studies to supplement the accuracy and inference information provided, denoting a model's computational complexity by the number of operations involved, either directly as MAdds (number of multiplication-addition operations) [61; 74], or indirectly as FLOPS (number of floating-point operations per second) [48; 49; 55; 56; 60; 64; 71; 85; 53; 55; 60; 64; 71; 85; 86; 90; 92; 105; 109].
# Radar Target Localization with Multipath Exploitation in Dense Clutter Environments
## 2 Model
### Multipath Propagation Model via Ray-Tracing
The round-trip path from the radar to the target and back to the radar is a combination of any two single-trip paths, so the number of round-trip paths is given as  
\[N_{p}=N_{1}^{2}. \tag{7}\]  
The TOA of the round-trip path is the sum of two single-trip paths, and the DOA of the round-trip path is equal to the DOA of the return path; thus, the multipath parameter set of round-trip propagation for the target located at \(\mathbf{x}(i,j)\) is given as  
\[\mathbf{H}_{ij}=\{\mathbf{h}_{i,j}^{k}\}_{k=1}^{N_{p}}, \tag{8}\]  
where each measurement vector is given by  
\[\mathbf{h}_{i,j}^{k}=[\tau_{ij}^{k_{1}}+\tau_{ij}^{k_{2}},\phi_{ij}^{k_{2}}], \quad 1\leq k_{1},k_{2}\leq N_{l}. \tag{9}\]
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 4 DVB-T2/C2 Demodulator Architecture  
In this section, a DVB-T2/C2 demodulator architecture, which combines DVB-T2 and DVB-C2 demodulators with low H/W complexity by sharing the functional blocks, is described.  
The proposed DVB-T2/C2 demodulator consists of a common OFDM demodulation unit, a DVB-T2/C2 specific unit, and a control unit as shown in Figure 9. The common OFDM demodulation unit includes a signal power detector, a GI detector, CFO and STO synchronization blocks, a variable FFT, a channel equalizer, and residual CFO and STO synchronization blocks. The DVB-T2/C2 specific unit is composed of a P1 symbol detector, a T2 pilot generator, a C2 sub-carrier and preamble detector, and a C2 pilot generator. The control unit controls for the DVB-T2/C2 demodulator operating in DVB-T2 or DVB-C2 mode.
## 2 Related work  
### Overview of IEEE 802.15.4g  
The IEEE 802.15.4 standard [5] was first released in May 2003 and defined a PHY (PHYsical) layer and a MAC (Medium Access Control) layer for LR-WPAN (Low Rate Wireless Personal Area Networks). At the PHY layer, the standard employs the DSSS-OQPSK (Direct Sequence Spread Spectrum-Offset Quadrature Phase Shift Keying) modulation and provides data rates of 20 kbps and 40 kbps in the sub-GHz bands (868 MHz in Europe, 915 MHz in America), and of 250 kbps in the 2.4 GHz band (available worldwide). The selected modulation provides a good trade-off between radio transceiver complexity, robustness, energy consumption, and communication range [10]. At the MAC layer, the standard defines slotted/synchronized and unslotted/unsynchronized operation based on the CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) channel access mechanism to trade-off the bandwidth, latency, and energy consumption of the devices.  
The adoption of the IEEE 802.15.4 standard by different low-power wireless technologies has promoted the revision of the standard three times (i.e., in 2006, 2011, and 2015) in order to clarify the operation and to add new features to both the PHY and MAC layers. For example, the 2015 standard revision adopted the MAC layer proposals defined in the IEEE 802.15.4e-2012 [11] amendment. Among others, this amendment defined the TSCH (Time Slotted Channel Hopping), a channel access mechanism that combines TDMA (Time Division Multiple Access) and FDMA (Frequency Division Multiple Access) to support industrial requirements, including reliable packet delivery (i.e., 99.999%) in adverse conditions, such as multi-path propagation and external interference. Similarly, the IEEE 802.15.4-2015 standard revision included three new physical layers targeted to SUN (Smart Utility Network) applications, as defined in the IEEE 802.15.4g-2012 [12] amendment. The SUN-FSK and SUN-OQPSK modulations focus on maintaining backwards compatibility with previous standards and commercially available transceivers, whereas the SUN-OFDM is focused on adding robustness and improving spectrum efficiency at the physical layer.  
In the following subsections, we present the operation of the SUN-FSK, SUN-OQPSK, and SUN-OFDM physical layers, as these are used in the deployment described in this article.
# Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1
## 2 Materials and Methods
### Dataset  
The TFBS dataset used is obtained from Kaggle.com. It contains 2400 records of SP1 transcription factor binding and non-binding sites. The protein encoded by SP1 TF is involved in immune responses, chromatin remodeling, cell growth and other cellular processes. Each record contains 28 features. Half of the records are of binding sites and the other half are of non-binding sites. The records are classified into two classes (i.e., 1 for a binding site and 0 for a non-binding site).  
Each record is a DNA sequence of 14 nucleotides. Each nucleotide is then transformed into a 2-bit binary number based on a binary coding rule, transforming the 14 nucleotides into a 28 binary sequence that acts as the features. The binary coding rule is shown in Table 1.  
For example, a record could be a sequence such as: \"ATCCTTCCGGGT, binding site\" which would be transformed into features such as: \"0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1\" (28 features and the resulting class).  
\begin{table}
\begin{tabular}{c c} \hline
**Nucleotide** & **Binary Equivalent** \\ \hline A & 00 \\ \hline T & 01 \\ \hline C & 10 \\ \hline G & 11 \\ \hline \end{tabular}
\end{table}
Table 1: Binary Coding rules for Nucleotides.
# Numerical Study of an Ultra-Broadband All-Silicon Terahertz Absorber
## 2 Structure and Design
The \(S\)-parameters and impedance \(Z\) can be expressed as [24; 34]  
\[S_{21}=\frac{1}{\cos(nkd)-\frac{i}{2}(Z+\frac{1}{Z})\sin(nkd)} \tag{5}\]  
\[S_{11}=\frac{i}{2}(\frac{1}{Z}-Z)\sin(nkd) \tag{6}\]  
where \(S_{21}\) is the coefficient of transmission, \(S_{11}\) is the coefficient of reflection, \(n\) is the effective refractive index of the absorber, \(k\) is the wave vector, and \(d\) is the thickness of the absorber. The impedance \(Z\) and absorption \(A\) are then obtained:  
\[Z=\pm\sqrt{\frac{\left(1+S_{11}\right)^{2}-S_{21}{}^{2}}{\left(1-S_{11}\right) ^{2}-S_{21}{}^{2}}} \tag{7}\]  
\[A=1-S_{11}{}^{2}-S_{21}{}^{2} \tag{8}\]  
The impedance \(Z_{0}\) of free space is equal to unity. When the impedance matches the free-space impedance (\(Z=Z_{0}=1\)) at the working frequency, a nearly perfect absorption can be obtained. Based on calculations, the impedance at the two resonant peaks is closer to unity.
# Domain Discrimination Expert Weighted Network for Multi-Source Carotid Artery Plaque Classification
## 3 Materials and Methods  
### Datasets  
The WUCF was used to evaluate the proposed method using three ultrasound datasets collected from the three collaborating centers: A, B, and C.  
Center A dataset: This dataset included 759 ultrasound images of 244 plaques from 156 patients. The plaque types were labeled as intima thickening, hypoechoic (30 images), mixed echogenicity (47 images), and hyperechoic (78 images). Hypoechoic and mixed echogenicity plaques (506 images) are classified as high-risk unstable plaques, while hyperechoic plaques (253 images) are classified as low-risk stable plaques.  
Center B dataset: This dataset included 310 carotid ultrasound images from 126 patients with carotid atherosclerosis, collected from cooperating medical center B between 2010 and 2017. The images were categorized into four types: intima thickening (53 images), hypoechoic plaques (132 images), hyperechoic plaques (78 images), and mixed echogenicity plaques (47 images). Hypoechoic and mixed echogenicity plaques (178 images) are classified as high-risk unstable plaques, while hyperechoic plaques (132 images) are classified as low-risk stable plaques. B_L is a subset of B, with all the transverse views removed from B, leaving only the longitudinal views.  
Center C dataset: The third dataset involved carotid ultrasound videos collected by cooperating institution C between 2020 and 2021, involving 216 patients. The dataset included 302 videos covering 119 hyperechoic plaques, 76 hypoechoic plaques, and 107 mixed echogenicity plaques. Each video contained 120 frames, with a resolution of 740 \(\times\) 540 pixels per frame, totaling 36,240 ultrasound images. For this study, we extracted static images from the videos, selecting two frames every 30 frames to balance the data volume across the centers. Finally, center C contained 248 stable plaques and 386 unstable plaques, for a total of 634 ultrasound plaque images.  
The dataset from all three datasets were annotated by radiologists with years of clinical experience. In this study, stable plaques are considered low risk, while unstable plaques are deemed high risk due to their risk of detachment [26]. All the datasets are summarized in Table 1. For the purposes of this study, the test set comprises the entire dataset of the target center, ensuring that our evaluation reflects the model's ability to generalize to new, unseen data.
## 3 Results and Discussion
### CO Emission  
Figure 11 depicts the CO emissions recorded in several engine configurations, namely, diesel (D100), DP20, DP20H5 (with 5 lpm hydrogen), DP20H10 (with 10 lpm hydrogen), and DP20H15 (with 15 lpm hydrogen). In a diesel engine, the creation of CO emissions is typically governed by parameters such as high cylinder temperature, oxygen concentration in the local burning zones, residence length, the latent heat of evaporation, and engine operating state [39; 40]. Alternative fuel blends emit less CO emissions that hydrogen blend fuels due to their superior combustion characteristics. As the fuel injection timing increases, this leads to decreases in CO emissions [41; 42].  
Figure 10: NOx emissions with injection timing and hydrogen blends.
## 4 Investigation on Orbital Decent  
### Parameters of Orbit Analysis  
We performed numerical simulations on the trajectory of ALE-1 with the SDOM film deployment. In general, the perturbed acceleration of a flying object in the upper atmosphere can be described by the following equation:  
\[a_{\rm drag}=-\frac{1}{2}\mathrm{C_{D}}\rho\frac{A}{m}v_{\rm rel}^{2} \tag{1}\]  
where \(\rho\) is the atmospheric density, \(C_{\rm D}\) is the drag coefficient, \(A\) is the cross-sectional area of the satellite, \(m\) is the mass of the satellite, and \(v_{\rm rel}\) is the relative velocity against the atmosphere. The mass of ALE-1 is 68.16 kg (65.29 kg after SDOM separation), as shown in Table 1, which is measured before launch. The \(C_{\rm D}\) is a dimensionless quantity; for a satellite, it is commonly set to be 2.2 [22]. Although \(A\) is 6.25 m\({}^{2}\) at maximum when SDOM is deployed, the cross-sectional area value cannot be measured in the situation because the film is in a drag flag state. In addition, because \(C_{\rm D}\) varies with the shape of the drag flag as well as the angles of attack, it is difficult to make a reliable assumption on the value of \(C_{\rm D}\). It is also difficult to calculate the \(C_{\rm D}\) due to the unknown shape of the drag flag for the long period of the orbit transfer. Thus, \(C_{\rm D}\) and \(A\) need to be handled as unknown parameters, which act as the limitation of the analysis.  
Figure 7: Disconnected SDOM film and its relative attitude as observed by DMC.  
According to this background, we decided to use the combination of these parameters \(C_{\rm D}\times A\) as an evaluation index. The range of \(C_{\rm D}A\) for the simulation analysis is determined based on the traditional fixed value of \(C_{\rm D}=2.2\) and the mean cross-sectional area of ALE-1. The mean cross-sectional area of a tumbling satellite can be approximated as the sum of the projected areas in the six orthogonal directions (plus and minus directions in each of the three orthogonal axes) divided by four. As this value is about 3.4 m\({}^{2}\) for ALE-1 with the fully deployed SDOM and about 0.4 m\({}^{2}\) without SDOM, we set \(C_{\rm D}A_{\rm ave}\) to be in the approximate range of 0.80-7.43.  
For the initial orbit conditions, the TLE of ALE-1 on 28 May 2020 was used. The reason for not using the TLE immediately after the SDOM deployment is to avoid the influence of the gas leak, which caused an instantaneous increase in orbit altitude. Orbit propagation was performed by using the fourth-order Runge-Kutta method, with the solar and lunar mass perturbations, atmospheric drag, solar radiation pressure, and Earth gravity field being based on a non-spherical central body model [23]. The environmental models applied to the simulations are listed in Table 2, and the parameters used are summarized in Table 3. The atmospheric density model was calculated based on the parameters available on 1 February 2023. The simulator used for the analysis is the MEVI\(\mu\)S system, which was developed by our research team [24; 25].
## 3 Results and Discussion
### Effect of Consolidation Pressure  
Figure 10a shows the effect of consolidation pressure, \(p\), on the skin friction of pile. As we can see, the slope of fully mobilized NSF and PSF versus depth were parallel in different consolidation pressures due to the same friction coefficient used. When \(p=50\) kPa, the PSF did not reach the ultimate value, which belongs to case one, as mentioned in Section 2.1. Although the consolidation pressure had a great influence on the skin friction distribution, it had little effect on the location of neutral point, which is consistent with the conclusion of [PERSON] et al. [14]. The normalized neutral point changed from 0.62 to 0.67 under the consolidation pressure increment from 25 kPa to 400 kPa in their studies. Similarly, it changed from 0.71 to 0.77 in this paper. This discrepancy is owing to the face that the piles in their studies were based on a much softer bearing layer. The results measured of the pile founded on a much stiffer bearing layer in the field was 0.83 to 0.95 [27]. Interestingly, at the depth of 0.775 \(L\), the PSF of pile under different consolidation pressure was very close.  
The axial load of pile versus normalized depth under different consolidation pressures are plotted in Figure 10b. Obviously, with the increase of consolidation pressure, the  
Figure 10: Effect of consolidation pressure on (**a**) skin friction and (**b**) axial load.  
Figure 9: Effect of drainage condition on (**a**) skin friction and (**b**) axial load.  
maximum axial load of pile increases. However, the increment of maximum axial force was not proportional to the increment of consolidation pressure. The maximum axial load increased by 43.8% when \(p\) increased from 50 kPa to 100 kPa, by 44% when \(p\) increased from 100 kPa to 200 kPa, and only by 19% when \(p\) increased from 300 kPa to 400 kPa. When the increment of consolidation pressure was constant, the pressure level was higher and the increase in the rate of maximum axial load was lower.
# Satellite Edge Computing for the Internet of Things in Aerospace
## 4 System Model
### Time and Energy Consumption  
We assume that the process by which the ground terminal generates a task is a Poisson process with a parameter of \(\lambda\). \(\lambda\) denotes the average speed of the task generation. Since a ground terminal cannot always communicate with a satellite, it is necessary to wait for the satellite to move into the window. We assume that the waiting time is \(T_{i,uavit\_upload}\). Next, it needs to be classified according to whether the task will be offloaded to the cloud computing platform.  
(1) Edge computing: According to Equation (2), it takes \(T_{SC}\) for the satellite to accomplish task \(i\). Currently, it is necessary to re-determine the visibility of the satellite \(s\) and the terminal \(t\). If visible, the results can be returned immediately. If it is not visible, regardless of results passing between satellites, the satellite needs to wait for the next communication window to send the result back. We assume the waiting time is \(T_{i,uavit\_return}\). Therefore, the total time for task execution in edge computing is:  
\[T_{i,edge}=T_{i,uavit\_upload}+T_{i,upload}+T_{SC}+T_{i,uavit\_return} \tag{11}\]  
Satellite energy consumption consists of three parts, the energy consumed by the satellite to maintain its operation, the energy consumed by the satellite to receive data, and the energy consumed by the satellite processing mission. Therefore, the total energy consumption for task execution in edge computing is:  
\[E_{i,edge}=E_{i,receive}+E_{i,computation}+E_{i,static} \tag{12}\]  
where \(E_{i,static}\) is the energy consumed by the satellite to maintain its operation, assumed to be a fixed value.  
(2) Cloud computing: Similarly, to offload tasks to the cloud computing platform, the satellites need to move into the ground station window. We assume the waiting time is \(T_{i,uavit\_offload}\). Considering that the computing power of the cloud computing platform is large enough, and the size of the outcome is quite small, the processing time of the cloud computing platform and the time that the cloud computing platform feeds back to the satellite are neglected. Therefore, the total time for task execution in cloud computing is:  
\[T_{i,cloud}=T_{i,uavit\_upload}+T_{i,upload}+T_{i,uavit\_offload}+T_{i,uavit\_ return} \tag{13}\]  
The total energy consumption for task execution in cloud computing is:  
\[E_{i,cloud}=E_{i,receive}+E_{i,transmit}+E_{i,static} \tag{14}\]  
According to Equations (11) and (13), the total time to complete all the tasks can be calculated as follows:  
\[T_{total}=\sum_{i}P_{i}T_{i,cloud}+(1-P_{i})T_{i,edge} \tag{15}\]  
Similarly, according to Equations (12) and (14), the total energy consumption to complete all the tasks can be calculated as follows:  
\[E_{total}=\sum_{i}P_{i}E_{i,cloud}+(1-P_{i})E_{i,edge} \tag{16}\]
# The Role of Surface Texture on the Photocatalytic H\({}_{2}\) Production on TiO\({}_{2}\)
## 3 Photocatalytic Hydrogen Production from Anatase TiO\({}_{2}\)
### Effect of Exposed Surfaces
[PERSON] and coworkers [98], in 2009, described a synthetic procedure to obtain decahedral anatase nanoparticles through a gas-phase reaction using TiCl\({}_{4}\) as precursor and with a rapid heating and quenching. The obtained material showed \(\approx\)40% of [001] surface, with a BET specific surface area of 9.4 m\({}^{2}\) g\({}^{-1}\). The activity of the material was evaluated on methanol photoreforming using UV light and Pt (2 wt %) as cocatalyst, deposited on the semiconductor from two different precursors: H\({}_{2}\)PtCl\({}_{6}\) or [Pt(NH\({}_{3}\))\({}_{4}\)]Cl\({}_{2}\). The authors compared the synthesized nanoparticles and the commercial TiO\({}_{2}\) P25 material. Due to the low density of crystalline defects, the decahedral nanoparticles were more reactive than TiO\({}_{2}\) P25 in the hydrogen evolution from methanol solution.