esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Geometric Model, Mesh Division, and Boundary Conditions
### Experimental Verification  
To verify the effectiveness of the numerical simulation, we use the numerical simulation method and model proposed in Section 2.2 to establish a numerical model, whose geometric structure and adiabatic boundary conditions are the same as those described in Reference [17]. The model material in Reference [18] is aluminum, the diameter of the main pipe is 0.015 m, the diameter of the branch pipe is 0.01 m, the thermal power consumption of the cold plate is 100 W, and the coolant is R134a. In the experiment, R134a was used as the cooling medium, and its physical properties are shown in Table 2.  
Figure 4: Mesh independence verification.  
The verification geometric model established according to the size of the experimental device in the literature is shown in Figure 5.  
The model is verified and analyzed. The 2 \(\times\) 7 model is divided into seven cold plate areas. We use multiple numerical simulations to verify the relationship between the temperature of the seven cold plates and the numerical simulation. The temperature difference between the numerical simulation and the experiment in the literature is compared. Figure 6 is the experimental verification of the physical model. Figure 7 compares the numerical simulation and experimental results of the seven cold plates, and the maximum error is 6%. Therefore, it can be concluded that the numerical simulation method proposed in this paper is effective.  
\begin{table}
\begin{tabular}{c c} \hline
**Materiality** & **Parameter Value** \\ \hline Density (kg/m\({}^{3}\)) & 1176 \\ Thermal conductivity (W/m\(\cdot\)K) & 0.078 \\ Specific heat capacity & 1.461 \\ (kJ/kg\(\cdot\)K) & \\ Kinematic coefficient & \\ of viscosity & \(1.8\times 10^{-4}\) \\ (kg/m\(\cdot\)s) & \\ Temperature (\({}^{\circ}\)C) & 20 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental cooling medium parameters.  
Figure 5: Numerical simulation to verify the model.  
Figure 6: Schematic of the 2 \(\times\) 7 \(Z\)-type parallel multi-branch pipe.
## 2 Related Work
### Geographic Routing Schemes  
Our proposed privacy solutions incorporate the basic design features from geographic routing schemes ([PERSON], 1999; [PERSON], 2000; [PERSON], 2001; [PERSON], 2001) that are discussed below.  
[PERSON] and [PERSON] and [PERSON] (2008); [PERSON], 2009) proposed Geographic Random Forwarding (GeRaF) scheme for ad hoc and sensor networks. This scheme is based on broadcast transmission and the sender only requires the position of its own and the destination. All active neighborhood nodes who receive the packet will go through the contention phase. Once the contention phase is complete, the winner (the node that is closest to the destination) will relay the packet using the same mechanism. This process will repeat until the destination becomes one-hop away. The authors assumed that all nodes in the neighborhood do not remain active all the time. Due to the dynamics of the sleep modes, different sets of potential relays will be available. However, mostly the potential route is close to the same or shortest route, which makes easier for an adversary to trace back to the sender.  
[PERSON] _et al_. [PERSON] _et al_. (2001) proposed Simple Forwarding over Trajectory (SiFT) scheme. This scheme is based on broadcast transmission and does not maintain neighborhood positions and states. Each node who receives the packet will make the decision of forwarding that packet based only on its own position, the position of a transmitter and the trajectory. The difference between the GeRaF ([PERSON] and [PERSON], 2008; [PERSON], 2009) and the SiFT scheme is that, the GeRaF does not use trajectories but the position of the destination. If nodes are static then similar to the GeRaF the potential route is close to the same or shortest route, which makes it easier for an adversary to trace back to the sender.  
[PERSON] _et al_. [PERSON] _et al_. (2007) have proposed a configurable secure routing protocol family called Secure Implicit Geographic Forwarding (SIGF) for WSNs. The SIGF is based on the Implicit Geographic Forwarding (IGF) protocol ([PERSON], 2008), in which a packet is forwarded to the node that lies within the region of \(60^{\circ}\) sextant, centered on the direct line from the sender to the destination. The SIGF protocol provides some aspects of networks privacy such as data, route and location privacy, but it does not provide identity privacy. Another limitation of the SIGF protocol is that, when there is no trusted node within a forwarding area (assuming \(60^{\circ}\) sextant), it will forward the packet to an un-trusted node. So, the reliability of the path is affected.  
Table 1 compares the proposed privacy preserving schemes. It clearly shows that none of the schemes currently provide full network level privacy.
## 4 Potential Applications, Discussions, and Perspectives
### Which Alternative Data to Better Constrain LDAS-Monde?
Consequently it better correlates with the analysis (median R values of 0.61) than with the model. If a strong statistical relationship between C-band VOD and LAI can be obtained through the use of, for example, machine learning techniques (like neural network techniques, [81], it could enable obtaining a surrogate of LAI based on C-band VOD that would have the advantage of having higher temporal frequency than the current LAI product (low frequency microwave observations are not affected much by clouds and are not affected by solar elevation). Such a product could then be assimilated into LDAS-Monde to better constrain the system. Looking at such a relationship for data assimilation purposes is currently under study at CNRM. Also, retrieved soil moisture is assimilated in LDAS-Monde, from active radar backscatter (\(\sigma_{\rm o}\)) observations. Retrieval methods usually make use of land surface parameters and auxiliary information (like vegetation, texture, and temperature), possibly being inconsistent with specific model simulations (which also include these parameters, but potentially from different sources). Also, if retrievals and model simulations rely on similar types of auxiliary information, their errors may be cross-correlated, potentially degrading the system performance [82]. This leads to an increasing tendency towards the direct assimilation of \(\sigma_{\rm o}\) observations (and of passive radiometer brightness temperature, \(T_{\rm b}\), as well) [83; 84; 85; 86; 87]. CNRM is also investigating the direct assimilation of \(\sigma_{\rm o}\). It requires the implementation of a forward model for \(\sigma_{\rm o}\) in the ISBA LSM. Ref. [85] used the Water Cloud Model [88] to relate surface soil moisture from the Global Land Evaporation Amsterdam Model (GLEAM, [45; 89]) to \(\sigma_{\rm o}\) for data assimilation purposes. Within LDAS-Monde, both surface soil moisture and leaf area index could be related to the radar backscatter.
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## 2 Waveform Modulation and Demodulation
### OFDM Waveform  
In OFDM systems, \(N\) data symbols \(X(k),k=0,1,...,N-1\) are mapped in the frequency domain, i.e., \(\mathbb{D}_{\tau}=0\). The pulse shaping filter \(g(t)\) has a rectangular pulse shape in the transmitted lattice. Then, by using (1), the transmitted OFDM discrete time-domain signal is given by [34]  
\[x[n]=\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}X(k)e^{j2\pi nk/N}. \tag{3}\]  
The discrete signal expression of the \(n\)-th received sample is given as  
\[y[n]=\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}H(k)X(k)e^{j2\pi n(k)/N}+w[n], \tag{4}\]  
where \(H(k)\) denotes the channel coefficients, and \(w(n)\) is the zero-mean the additive white Gaussian noise (AWGN) with \(\sigma^{2}\) variance \(\mathcal{N}\big{(}0,\sigma^{2}\big{)}\).
## 6 Conclusions  
This paper proposes two effective methods for integrating source-side syntactic information into neural machine translation models. The first method is based on CLIP pre-training technology, which utilizes advanced natural language processing techniques to extract both text and syntactic tree information and propagate them to a transformer model to complete machine translation tasks. In English-Chinese and Chinese-English translation tasks, this method results in BLEU score improvements of 3.37 and 3.18 percentage points, respectively. The second method is the adoption of cross-attention fusion, which can better capture the relationship between the vocabulary in the syntactic tree and improve the model's understanding of the sentence semantics. In English-Chinese and Chinese-English translation tasks, this method results in BLEU score improvements of 1.31 and 1.03 percentage points, respectively. The paper performed ablation experiments by integrating the two proposed models, but unfortunately, the results were unsatisfactory. Furthermore, this paper also investigates the impact of different sentence lengths on the translation results, providing important reference for researchers. These research results are of great significance in advancing machine translation technology and improving the quality of machine translation models.  
However, this study has certain limitations. Specifically, it is confined to English-to-Chinese and Chinese-to-English translation tasks, necessitating further research on translation tasks involving other languages. Additionally, the study solely focuses on integrating source-side syntactic information and does not address how to incorporate target-side syntactic information into the translation model. In translation tasks, target-side syntactic information may also have an impact on the translation results, requiring further investigation. In the future, our research direction will focus on incorporating rich syntactic information of the target language into neural machine translation models and further expanding the experimental scope to cover more languages. Additionally, we will explore how to integrate syntactic analysis of multimodal information such as visual and audio data to enhance the performance of our models in multilingual translation tasks. The goal of this research direction is to improve the translation quality and accuracy of machine translation models by leveraging the complementarity of syntactic information and multimodal data, in order to meet diverse language translation needs.  
Writing--original draft, Y.S.; writing--review and editing, Y.C. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Natural Science Foundation of China (grant no. U2004163).
# Effectiveness of Decision Support to Treat Complex Regional Pain Syndrome
## 4 Discussion  
Complex regional pain syndrome (CRPS) is a debilitating condition that usually develops subsequent to trauma or surgery and where the painful experience appears disproportionate, in time and intensity, to the level of injury [1; 2; 3; 43; 44; 45]. Despite numerous studies reported in current literature, the underlying pathophysiology of CRPS is poorly understood. Thus, CRPS still remains a contemporary medical challenge with a natural history characterized by chronicity and relapses which result in a significant disability over time [46].  
Although numerous treatment modalities have been claimed to be useful in the management of CRPS, at present, there is no clear consensus regarding the most effective  
Figure 8: Pain intensity as visual analog scale (VAS) score before (T0) and after (T1) the different rehabilitation paths. \({}^{*}p<0.05\) compared with T0.  
Figure 9: Amount of volumetric edema measured by a hand volunteer before (T0) and after (T1) the different rehabilitation paths. \({}^{*}p\leq 0.05\) compared with T0.  
treatment for this condition. These include pharmacologic therapies, physiotherapy, behavioral modification and psychotherapy, neuromodulation, surgical procedures, and miscellaneous complementary and alternative therapies [6; 8; 15; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. Most of these therapies are directed at managing the signs and symptoms of the disease and no single drug has proven to be efficacious for all patients with CRPS. In addition, ongoing reassessment of the adequacy of pain relief and careful attention to drug side effect profiles are needed to make meaningful decisions about drug initiation or continuation [8]. Only a few studies have evaluated mechanism-based treatment options [47; 48; 49; 50]. On the other hand, the available evidence is difficult to compare due to heterogeneous inclusion criteria, inappropriate or absence of adequate controls, lack of adequate power due to small sample sizes, and lack of blinding or randomization [51]. At the same time, long-term follow-up studies are scarce.  
Remarkably, from the present study, it clearly emerges that rehabilitation treatment is a key aspect of the therapeutic program that should be started as soon as possible. In fact, the timeliness of an adequate diagnostic and therapeutic approach avoids the unfavorable evolution of CRPS which is configured with functional limitation, pain, and stiffness. Therefore, initiating early diagnosis and early post-traumatic/surgical rehabilitation, where possible, is important for minimizing permanent loss of function. In addition, avoiding prolonged hand immobilization may be crucial for the return of normal limbs. Moreover, these CRPS patients will miss the possible benefits of early treatment, which may jeopardize the complete resolution of the syndrome. In fact, as evidenced in Table 1, the longer the time elapsed between the trauma and CRPS diagnosis and the longer the hand immobilization time, the longer the time to achieve patients' clinical and functional recovery.
# DRAM Retention Behavior with Accelerated Aging in Commercial Chips
## 2 Background and Motivations
### Wear-Out Mechanisms in DRAM  
As process technology scales down to smaller nodes aggressively, integrated circuits suffer from increasing performance variation and concerns about the reliability of operation, leading to a fabricated chip's failure. Failures can occur at various stages in the lifetime of an integrated circuit. ICs can fail during production due to disproportionately high process variations, weak design, or local defects. Electronic devices also suffer from a high failure rate known as infant mortality during the early life in operation [1]. Integrated circuits can also fail during their lifetime because of wear and tear, aging, harsh environments, overuse, etc. Figure 2 presents the typical bathtub curve, which is often used to illustrate electronic device failure characteristics [1].  
Recycled or aged electronic devices often exhibit weaker performance and a shorter time to failure. The fundamental wear-out mechanisms are essential to understand the factors influencing transistor and capacitor degradation with aging. Commonly found wear-out failure mechanisms, such as bias temperature instability (BTI), hot carrier injection (HCI), time-dependent dielectric breakdown (TDDB), etc. are discussed in this section.
## 2 Problem Model
### Environment Model  
#### 2.2.1 Mission Area Division  
The mission area, as shown in Figure 2, is defined as a square with side length \(L_{x}=L_{y}=12\) km. To accurately characterize situation information for the area search and guide UAV, the mission area is uniformly divided into square grid cells with \(\Delta L=500\) m side length. Each grid cell is uniquely identified by an ordered pair \(\mathbf{G}_{i,j}=(i,j)\) following a left-to-right and top-to-bottom numbering sequence, resulting in the grid cell set representation:  
\[\mathbf{G}=\bigg{\{}(i,j)\bigg{|}i\in\bigg{[}1,\frac{L_{x}}{\Delta L}\bigg{]}\cap \mathbb{Z},j\in\bigg{[}1,\frac{L_{y}}{\Delta L}\bigg{]}\cap\mathbb{Z}\bigg{\}}, \tag{2}\]  
where \(\mathbb{Z}\) is the set of integers.  
The ordered pair \(G_{i,j}=(i,j)\) is only used here for illustration and in the simulation model. For the convenience of subsequent description, the following discussion will only use one index to distinguish the grid, such as \(g_{i}\).
## 2 Methodology
### Description of the Case Studies  
The Grid Division of Tenaga National Berhad Transmission (TNB) is responsible for the design, management and operation of the transmission systems comprising three different line voltages, i.e., 132 kV, 275 kV and 500 kV. Here, a 500 kV double circuit line, denoted as Line A-B, was selected for a case study, taking into consideration that the line is located in a lightning-prone area and critical for the national grid. Furthermore, this particular line was subsequently chosen based on the high number of interruptions on this line compared with other 500 kV lines in Malaysia [25; 26; 27]. Within this area, 30% of the line is under forestation and at a high altitude with a higher tower footing resistance caused by the high altitude soil profile [25; 27]. Generally, 500 kV towers consist of a two earth wire design at the top with a height of around 46 to 67 m. For this analysis, the methodology of interpreting the soil profile characteristics was applied to a real case involving three towers on the 500 kV double-circuit line. Figure 1 shows the dimensions of a 500 kV transmission tower. In short, the study cases were as follows:  
* Case study A: Modelling of soil profile interpretation.
* Case A (1): Soil layer analysis
* Case A (2): Soil resistivity analysis
* Case study B: Simulation of tower footing resistance (TFR) with different earthing designs under steady state conditions, using CDEGS.
* Case B (1): Effect of design analysis (Towers T42, T48 and T50)
* Case B (2): Effect of soil profile analysis
* Case study C: Simulation of tower footing resistance (TFR) with earthing Design C and encasement with EEC under steady state conditions, using CDEGS.
# Just-in-Time Fluid Flow Simulation on Mobile Devices Using OpenVisFlow and OpenLB
###### Abstract  
The present state of research in computational fluid dynamics (CFD) is marked by an ongoing process of refining numerical methods and algorithms with the goal of achieving accurate modeling and analysis of fluid flow and heat transfer phenomena. Remarkable progress has been achieved in the domains of turbulence modeling, parallel computing, and mesh generation, resulting in heightened simulation precision when it comes to capturing complex flow behaviors. Nevertheless, CFD faces a significant challenge due to the time and expertise needed for a meticulous simulation setup and intricate numerical techniques. To surmount this challenge, we introduce _paint2 sim_--an innovative mobile application designed to enable on-the-fly 2D fluid simulations using a device's camera. Seamlessly integrated with _OpenLB_, a high-performance Lattice Boltzmann-based library, _paint2 sim_ offers accurate simulations. The application leverages the capabilities of the Lattice Boltzmann Method (LBM) to model fluid behaviors accurately. Through a symbiotic interaction with the open-source _OpenCV_ library, _paint2 sim_ can scan and extract hand-drawn simulation domains, affording the capability for instant simulation and visualization. Notably, _paint2 sim_ can also be regarded as a digital twin, facilitating _just-in-time_ representation and analysis of 2D fluid systems. The implications of this technology extend significantly to both fluid dynamics education and industrial applications, effectively lowering barriers and rendering fluid simulations more accessible. Encouragingly, the outcomes of simulations conducted with _paint2 sim_ showcase promising qualitative and quantitative results. Overall, _paint2 sim_ offers a groundbreaking approach to mobile 2D fluid simulations, providing users with _just-in-time_ visualization and accurate results, while simultaneously serving as a digital twin for fluid systems.  
CFD; LBM; mobile device; just in time; digital twin +
Footnote †: journal: Applied Sciences
# Hardware Prototyping and Validation of a W-\(\Delta\)DOR Digital Signal Processor
## 3 W-\(\Delta\)DOR Hardware Implementation  
This section provides a description of the W-\(\Delta\)DOR DSP core hardware implementation. The down-link (DL) signal is Equation (1).  
\[s_{DL}(t)=A\sin\left[2\pi f_{c}t+TC(t)+s_{W\Delta DOR}(t)\right] \tag{1}\]  
It is the modulation of a X/Ka-band carrier by means of pseudo-noise phase components. In Equation (1), \(A\) represents the signal amplitude, \(f_{c}\) is the X- or Ka-band carrier frequency, \(TC(t)\) represents telemetry and command signals and \(s_{W\Delta DOR}\) is the spread spectrum component of the W-\(\Delta\)DOR signal. This method is proposed in [1] with the purpose of compatibility with the BepiColombo X/Ka Deep-Space Transponder design [11]. The signal represented in W-\(\Delta\)DOR part is the summation of \(N\) sub-carriers of frequency \(f_{i}\).  
\[s_{W\Delta DOR}(t)=\sum_{i=1}^{N}\phi_{i}PN_{i}\sin(2\pi f_{i}t) \tag{2}\]  
They are modulated by pseudo-random noise sequences \(PN_{i}\) of amplitude \(\phi_{i}\).
# A Comparison of PCA-LDA and PLS-DA Techniques for Classification of Vibrational Spectra
## 2 Materials and Methods
### Measured Spectra  
Three different cellular models were chosen to confirm the results obtained for simulated spectra:  
* MCF10A cells, as a model of a normal breast cellular line. These cells were exposed to proton beam radiation with a dose of 4 Gy, which causes cellular damage involving nucleic acid and DNA/RNA components, as reported in [27]. Unexposed cells were considered as a control sample. Both control and exposed cells were measured by means of a Raman confocal micro-spectrometer apparatus ([PERSON] from Jobin-Yvon Horiba) and the Raman spectra were pre-processed as described in [27]. Briefly, Raman spectra were measured for about 30 randomly chosen single cells grown on coverslip slides; each cell was excited by the 514.5 nm line from an Ar ion laser with about 10 mW power, which was focused, by means of a 100x oil immersion objective, on the cell nucleoplasm region; three acquisitions of 10 s each was averaged to produce each Raman spectrum. The Raman scattered signal was analysed, in backscattering geometry, by a spectrometer equipped with a 600 grooves/mm grating and it was detected by a charged coupled device cooled at 223 K.
* MCF7 and MDA-MB-231, as a model of malignant non-metastatic (MCF7) and metastatic (MDA-MB-231) breast cancer cell lines. The FTIR spectra of such cells were measured in transfection mode by means of an FTIR Microscope HYPERION 2000 connected to a Vertex 70 Bruker interferometer (Bruker Optik GmbH), as described in [28]. Briefly, each FTIR spectrum was recorded, using a 15\(\times\) objective, in the 1000-4000 cm\({}^{-1}\) spectral range with the resolution of 4 cm\({}^{-1}\) and 64 scans. The sampling area was about 80 \(\times\) 80 \(\upmu\)m in size, including 3-4 cells of each type. The absorption signal was detected with an MCT (mercury cadmium telluride) detector (cooled to liquid N\({}_{2}\) temperature). For each experiment, about 30 cells were measured.