esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analytic Solution of State-Constrained Optimal Tracking Problems
### Quadratic Penalty Function  
Suppose that the penalty function \(p_{i}\) of the inequality constraint \(h_{i}\leq 0\) is  
\[p_{i}(h_{i})=\frac{1}{2}\alpha_{i}(h_{i})h_{i}{}^{2},\ (i=1,2,\cdots,2n), \tag{15}\]  
where  
\[\alpha_{i}(h_{i})=\left\{\begin{array}{c}0,\ h_{i}<0\\ q_{i},\ h_{i}\geq 0\end{array}\right.,\]  
and \(q_{i}>0\) is the weight for \(\alpha_{i}\)[23; 24]. As shown in Figure 1, the meaning of the penalty function is the violation costs of the inequality constraint.  
The quadratic penalty function is defined as the sum of the penalty functions:  
\[p(\mathbf{h})=\sum_{i=1}^{2n}p_{i}(h_{i})=\frac{1}{2}\mathbf{h}^{T}(\mathbf{x },\mathbf{u})\text{diag}\Big{(}\boldsymbol{\alpha}^{T}\Big{)}\,\mathbf{h}( \mathbf{x},\mathbf{u}), \tag{16}\]  
Figure 1: An example of the penalty function (15).  
where  
\[\mathbf{\alpha}=\left[\begin{array}{c}\mathbf{\alpha}_{x}\\ \mathbf{\alpha}_{\tau}\end{array}\right]\ \mathbf{\alpha}_{x}=\left[\begin{array}{c}\alpha_{1}\\ \vdots\\ \alpha_{n}\end{array}\right],\ \text{and}\ \mathbf{\alpha}_{\tau}=\left[\begin{array}{c} \alpha_{n+1}\\ \vdots\\ \alpha_{2n}\end{array}\right],\]  
and the diag function is defined as  
\[\text{diag}\Big{(}\Big{[}\begin{array}{ccc}\sigma_{1}&\sigma_{2}&\cdots& \sigma_{n}\end{array}\Big{]}\Big{)}=\left[\begin{array}{cccc}\sigma_{1}&0& \cdots&0\\ 0&\sigma_{2}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&\sigma_{n}\end{array}\right].\]
The authors underline that clamping forces for these given fixtures should be critically considered to balance stress concentrations at the tissue-clamp interface and material slippage. The complex and sometimes unknown stress patterns generated by grip compression, confined specimen deformation, and potential microtears from the grip jaws pose significant challenges [2; 4; 5]. These issues can promote failure outside the gage section, particularly near the grips, which is highly problematic [4]. Often, sand paper [8; 9; 10; 11] or cryo clamps [12; 13; 14; 15; 16] are used in an effort to reduce material slippage [10; 17; 18]. Sinusoidal-shaped clamps [19] or roughened surfaces [20] may also offer workarounds for this challenge. Other measures such as partial plasination [2] or 3D-printed clamps [5; 6] combined with 3D image correlation [1; 5] facilitate data acquisition without the significant bias introduced by material slippage. Our group has been amongst the first to introduce 3D printing technology for soft tissue clamping in a highly standardized setup [5], which was adapted soon thereafter by a number of groups and for various testing purposes [21]. Different clamping and mounting technologies hamper direct comparison between studies. Moreover, for testing certain soft tissues, the native tissue geometry should ideally remain unaltered, which is the case for most tendons and ligaments. As a consequence, stress concentrations in the clamping area are inevitable with certain sample shape geometries, resulting in tissue failure at undesired regions and therefore not in an accurate depiction of tissue load-deformation and failure properties [6]. In this work, the aim was to develop and trial a 3D-printed capstan-based clamping system to investigate the mechanical properties of tendon tissues from uniaxial tensile testing. Capstan technology has initially been described in the context of marine engineering. The capstan principle makes use of a drum-shaped geometry to distribute force and control friction for moving ropes or cables. It therefore enables efficient handling of loads by optimizing tension. The capstan technology is already employed in mechanical testing of surgical sutures but has not yet been used to test soft tissues. A key consideration in the design was to localize failure in the mid region of the samples of interest, not at the clamping site. It was hypothesized that by using the given capstan system provided herein, specimen failure would more likely occur in the mid sample region when compared to 3D-printed mounting techniques without load-reduction curvatures [6].
# Sharpness-Based Distance Detection
## 2 Target Identification and Location
### DeepSORT Algorithm  
The DeepSORT algorithm is optimised and improved on the basis of the SORT [38] algorithm but still performs detection tracking. The DeepSORT algorithm takes the target feature information extracted by deep learning as the matching standard. The first step is target detection. The DeepSORT algorithm relies on the target detector to determine the target position of each frame in the video. The output of the detector usually includes the bounding box and category of the target. After that, feature extraction of the object is carried out by using deep learning models to extract the appearance features of the object, which are essential for the re-recognition of the target object. Through matching and tracking, this process involves calculating the similarity between the detection and prediction boxes and using the Hungarian algorithm to find the best match. There is also a special mechanism of cascade matching. First, the cost matrix is composed of the cosine distance matrix calculated by the track feature and the detection box feature, and the Mahalanobis distance matrix calculated by the prediction box and the detection box. By matching the detection output results, the unmatched target is defined as a new target, and a new ID number is assigned to the target. This helps improve the accuracy of matching. After cascading matching processing, the blocked target can be recovered and the number of ID switching when the target is blocked again can be reduced. Finally, there is trajectory management, where DeepSORT maintains trajectories for each target and initializes new trajectories for newly detected targets. The flow process is shown in Figure 2.  
Figure 1: ELAN module.
# Frequency Response Analysis of Perforated Shells with Uncertain Materials and Damage
## 5 Numerical Experiments: Frequency Response  
In order to reduce combinatorial complexity and thus focus the message, let us fix some parameters over the whole set of experiments. As throughout this paper, the representative parabolic profile function is \(f_{P}(x_{1})=1\) and the corresponding hyperbolic one \(f_{H}(x_{1})=1+(x_{1}/\pi)^{2}\). In all cases, we let \(x_{1}\in[-\pi,\pi]\) so that the 2D computational domain is  
\[D=[-\pi,\pi]\times[0,2\pi]\backslash\{\text{perforations \& damage}\}.\]  
Material constants adopted for all simulations are: \(E=2.069\times 10^{11}\) MPa, \(\
u=1/3\), and \(\rho=7868\) kg m\({}^{-3}\), unless otherwise specified. All numerical experiments are solved with the \(hp\)-FEM using standard elements at \(p=3\) or \(4\). We select the \(20\times 20\) regular penetration pattern as the representative configuration for both geometries, that is, let \(\epsilon=\pi/10\) with ligament efficiency \(\eta=0.61\), and hole coverage of 12%. This corresponds to a hole diameter \(2r\) where the radius \(r=\sqrt{3\pi}/50\approx 0.061\), with \((20\times 20\times\pi r^{2})/(4\pi^{2})=0.12\) exactly. The corresponding Halton grid \(H(7,37)\) is used in one of the experiments. The dimensionless thickness is constant \(t=1/100\), which is a realistic value in engineering context. The loads or excitation functions act only on the transverse direction \(w\) and are simply Fourier modes in the angular direction and constant in the axial direction, i.e., \(\hat{f}(x,y)=C\cos(Ky)\) N, where \(C=1000\) is a scaling parameter, and \(K\) is the angular wave number (We used \(K_{y}\) above). The random Young's modulus depends only on the axial direction and has the form  
\[E(x,y,\xi)=E_{0}(x)+\sum_{m=1}^{M}E_{m}(x)\xi_{m},\quad(x,y)\in D,\quad\xi\in[ -1,1]^{M},\]  
where we choose \(E_{0}(x)=E\) (constant), and \(E_{m}(x)=\sqrt{\lambda_{m}}\;E\,\sin(mx)\) with \(\lambda_{m}=1/(m+1)^{4}\). Finally, in the damage model, the intensity \(\mu\) is constant over the whole domain.  
The experiments progress in a natural order: first, we only consider the effect of the excitation (or loading) in a deterministic setting, second, the material parameter, [PERSON]'s modulus is assumed to be random, and the responses are computed at the collocation points, third, the damage model isintroduced with deterministic materials, and finally the damage model is combined with material uncertainty to complete the sequence so that the set of experiments spans the different possibilities discussed above.  
On average, the meshes have 6000 triangles corresponding to 150,000 d.o.f. at \(p=3\) and 250,000 at \(p=4\). One full solve at \(p=3\) took roughly 2 min on Aalto cluster with Intel Xeon processors (varied models) (Santa Clara, CA, USA). In other words, one full experiment with uncertain material parameters required one hour, and the last experiment with 100 such instances 100 h.
## 4 Discussion
### Estimation Performances of the SIF-Based and LUE-Based Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ecosystem** & **Models** & & **R\({}^{2}\)** & **RMSE** \\ \hline \multirow{6}{*}{This study (maize–wheat rotation)} & \multirow{2}{*}{OCO-2 SIF model} & Mid-day & 0.59 & 9.23 \\  & & Daily & 0.77 & 2.50 \\  & & monthly & 0.94 & 0.59 \\ \cline{2-5}  & \multirow{2}{*}{SL-LUE model} & Mid-day & 0.42 & 11.32 \\  & & Daily & 0.71 & 2.82 \\  & & monthly & 0.84 & 1.10 \\ \cline{2-5}  & \multirow{2}{*}{TL-LUE model} & Mid-day & 0.68 & 9.03 \\  & & Daily & 0.83 & 2.51 \\  & & monthly & 0.98 & 0.65 \\ \hline \multirow{6}{*}{[PERSON] et al. [4]} & GPP–SIF\({}_{760}\) & Half-hour & 0.62 & 20.74 \\  & GPP–SIF\({}_{686}\) & Half-hour & 0.46 & 31.10 \\ \cline{1-1}  & MuSyQ-GPP & Half-hour & 0.70 & 21.60 \\ \cline{1-1}  & BEPS model & Half-hour & 0.87 & 12.96 \\ \hline \multirow{6}{*}{[PERSON] et al. [44]} & GOME-2 SIF model & 8-day & 0.87 & 2.72 \\ \cline{1-1}  & VPM model & 8-day & 0.91 & 2.27 \\ \cline{1-1} \cline{2-5}  & SCOPE model & 8-day & 0.95 & 1.58 \\ \cline{1-1}  & MOD17A2 & 8-day & 0.47 & 5.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The estimation performance among different GPP models in this study, from [PERSON] et al. [4] and [PERSON] et al. [44]. The unit of RMSE is gC m\({}^{-2}\) d\({}^{-1}\). In summary, there is a compelling amount of data that supports the existence of a linear relationship between GPP and SIF for cropland that (1) is uniform for different temporal aggregation levels and under different meteorological conditions, and (2) contains adequate information for modeling ecosystem-level GPP. Additionally, whether our assessments are applicable to areas other than the cropland (maize-wheat rotation) used in this study is a problem that needs to be addressed in future research, and further studies are also needed to extend this analysis to other croplands, such as soybean, for cross-site comparisons.
## 3 Channel Model
### FSO Channel
#### 3.1.1 Atmospheric Attenuation  
The propagation of an optical beam is subject to power loss due to the scattering and absorption of particles in the atmosphere. The atmospheric attenuation \(h_{j}^{a}\) can be described by the Beer-Lambert law [13].  
\[h_{j}^{a}=\exp(-\omega L_{j}),j\in\{SR_{i},R_{i}D\}, \tag{11}\]where \(L_{SR_{i}}=(H_{AWACS}-H_{UAV})/\cos\xi_{UAV}\) and \(L_{R,D}=H_{UAV}/\cos\xi_{GS}\) are the distances of \(SR_{i}\) and \(R_{i}D\) links, respectively. The attenuation coefficient \(\omega\) can be defined by [PERSON]'s model [14].  
\[\omega=3.91/V\Big{(}\lambda_{fso}/550\Big{)}^{-q}, \tag{12}\]  
where \(V=1.002/(N_{C}M_{CLWC})^{0.6473}\) is the atmospheric visibility and determined based on the cloud liquid water content value \(M_{CLWC}\) and cloud droplet number concentration \(N_{C}\). \(q\) represents the size distribution of the scattering particles and is closely associated with \(V\).
# Rockburst Hazard Evaluation Using an Extended COPRAS Method with Interval-Valued Fuzzy Information
## 4 Case Study
### Evaluation of Rockburst Hazards
#### 4.4.3 Evaluate the Hazard Level with Extended COPRAS Method
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \(B_{1}\) & \(B_{2}\) & \(B_{3}\) & \(B_{4}\) & \(B_{5}\) & \(B_{6}\) \\ \hline \(C_{1}^{*}\) & [0, 0] & [0, 0] & [0, 0] & [0, 0] & [0, 0] & [0.1445, 0.2066] \\ \(C_{\Pi}^{*}\) & [0.0144, 0.0147] & [0.0119, 0.0152] & [0.0080, 0.0099] & [0.0410, 0.0452] & [0.0288, 0.0308] & [0.0548, 0.0784] \\ \(C_{\Pi}^{*}\) & [0.0360, 0.0368] & [0.0208, 0.0266] & [0.0241, 0.0297] & [0.0573, 0.0632] & [0.0432, 0.0461] & [0.0249, 0.0356] \\ \(C_{\Gamma}^{*}\) & [0.0720, 0.0736] & [0.0298, 0.0379] & [0.0362, 0.0445] & [0.0737, 0.0813] & [0.0792, 0.0845] & [0, 0] \\ \(C_{1}\) & [0.0431, 0.0529] & [0.0179, 0.0734] & [0.0218, 0.0686] & [0.0324, 0.0507] & [0.0754, 0.0850] & [0.0425, 0.0641] \\ \(C_{2}\) & [0.0448, 0.0881] & [0.0513, 0.1439] & [0.0331, 0.1049] & [0.0415, 0.0902] & [0.0598, 0.0837] & [0.0428, 0.0802] \\ \(C_{3}\) & [0.0458, 0.0693] & [0.0353, 0.2179] & [0.0328, 0.1854] & [0.0321, 0.0413] & [0.0749, 0.0843] & [0.0422, 0.0637] \\ \(C_{4}\) & [0.0340, 0.0371] & [0.0164, 0.0426] & [0.0396, 0.0834] & [0.0510, 0.0685] & [0.0982, 0.1067] & [0.0338, 0.0492] \\Finally, the ranking results of the samples were determined as \(C_{5}>C_{7}>C_{3}>C_{\text{IV}}^{*}>C_{2}>C_{4}>C_{6}>C_{1}>C_{\text{III}}^{*}>C_{ \text{II}}^{*}>C_{1}^{*}\). Therefore, the rockburst hazards of \(C_{3}\), \(C_{5}\) and \(C_{7}\) belonged to level IV, and those of \(C_{1}\), \(C_{2}\), \(C_{4}\) and \(C_{6}\) belonged to level III. Actually, several rockburst phenomena have been reported from the \(-630\) m level of Sizhuang district, the \(-630\) m level of Jiaojia district and the \(-670\) m, \(-710\) m and \(-750\) m levels of Wangershan district. The on-site investigation revealed that, with the increase in mining depth, rockburst phenomena such as rock ejection, spalling of rock masses and deformation of tunnels, were more and more severe. In addition, since the rockburst hazard was greater at higher depths under the same conditions, the ranking results of \(C_{7}>C_{6}\), \(C_{5}>C_{4}\) and \(C_{3}>C_{2}>C_{1}\) were reliable. Therefore, it can be inferred that the hazard levels determined with the proposed method were consistent with the field status.
## 5 Towards Enhanced Use of High Resolution EO Data in Earth System Modelling
### Delivering EO-Driven Research to Services  
Buizza and colleagues at ECMWF [326; 327] have presented a typical Research to Operation (R2O) cycle for new model and assimilation versions. It includes internal updates, but it does not address yet the inter-agency planning. The latter is achieved via a variety of collaborations, externally funded projects, and internal projects. We present a sketch of the R2O in a given organization specialised in global atmospheric data assimilation and discuss ways in which cooperative work can extend to multiple organizations particularly to tackle the evolution towards a kilometer-scale representation of the Earth surface.  
Earth Observations have a dual role in Earth-system monitoring and prediction applications. Firstly, they are required to define the initial state of the Earth-system components, i.e., the state from where forecasts are generated by integrating numerically the Earth-system equations. Secondly, they provide an estimate of the 'truth' (the true state of the Earth), that is required to evaluate model accuracy and forecast performance. Good quality observations are essential in the testing and evaluation phases. Diagnostics that allow objective evaluation of improvements in the development and testing phases, and routine monitoring of forecast performance are essential. The latter can trigger investigations that feed into subsequent model upgrades.  
The establishment of focused multi-institute projects and working-groups e.g., around international field-campaigns, is a common practice to link remote-sensing and Earth system modelling.
## 3 Model Formulation
### User Cost (\(C_{u}\))
#### 3.1.1 Access Costs (\(C_{a}\))  
Feeder and train passengers who have access to stops and stations mainly incur the access cost. The access cost is generally experienced by local and train passengers accessing the transfer station.  
The access cost for feeder bus passengers is the product of local demand, \(q_{i}\), with average access time \(t_{aF}\) and the value of time \(\mu_{a}\), where \(t_{aF}\) can be estimated from the average distance divided by the average access speed. The average access time for train passengers (\(t_{aT}\)) can be formulated similarly. \(t_{aT}\) is dependent on the distance between the platforms of bus and train services and access speed. Assume that access speed and the value of time for feeder bus and train passengers are identical. Thus, the access cost for feeder route \(k\) can be formulated as follows:  
\[C_{a}=\mu_{a}\left(Q_{k}\times t_{aF}+Q_{k}\times t_{aT}\right) \tag{4}\]  
The users' value of time (\(\mu_{a}\)) is an important parameter in determining the user cost and is usually dependent on the economic situation (e.g., annual income).
# Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture
## 3 The Signal Model and Principle of Virtual Synthetic Aperture Radiation Source Location Method
### Phase Compensation
Figure 3: The unified time–frequency resource grid of virtual synthetic aperture radiation source location method. Then we extract the compensated signal at a particular frequency in the range domain, obtaining an LFM signal in the azimuth domain with a particular centroid frequency and chirp rate which contain the location information. Finally, we construct a series of matched filters at different distances and perform pulse compression. By reorganizing the results by distance, the range and azimuth location can be obtained by focusing and searching.
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
## 2 Laboratory Model of the Soil-Nailing Process
### Soil-Nailing Process  
The laboratory model of the soil-nailing process (total soil slope model with a length of 1760 mm and a height of 850 mm) was completed in nine sequential stages [36], as follows (Figure 1).  
Stage 1: First excavation stage to a depth of 140 mm below ground surface, construction of the first facing panel and installation of the first nail row at a depth of 70 mm from the ground surface.  
Stage 2: Second excavation stage to a depth of 280 mm below ground surface and construction of the second facing panel.  
Stage 3: Third excavation stage to a depth of 420 mm below ground surface, construction of the third facing panel and installation of the second nail row at a depth of 350 mm from the ground surface.  
Stage 4: Fourth excavation stage to a depth of 560 mm below ground surface and construction of the fourth facing panel.  
Stage 5: Final excavation stage to a depth of 700 mm below ground surface, construction of the last facing panel and installation of the third nail row at a depth of 630 mm from the ground surface.  
Stages 6-9: Footing is placed at the desired location, applying footing pressure to obtain strip stress (q) equal to 5 kPa (stage 6), 10 kPa (stage 7), 20 kPa (stage 8) and 30 kPa (stage 9) (including foundation weight).  
Figure 1: Development stages of the soil-nailing process [21].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multirow{2}{*}{\begin{tabular}{c} **Length** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Maximum** \\ **Tensile Force** \\ **(kN)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Strains at** \\ **Ultimate** \\ **Stress (10\({}^{-6}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Maximum** \\ **Tensile Strength** \\ **(kPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Young’s** \\ **Modulus** \\ **(kPa)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Flexural** \\ **Rigidity, EI** \\ **(kN\(\cdot\)mm\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Normal** \\ **Stiffness, EA** \\ **(kN/m)** \\ \end{tabular} } \\ \hline \hline Nails & 700.0 & 17.4 & 7625 & \(8.8\times 10^{5}\) & \(21.2\times 10^{7}\) & \(6.51\times 10^{-3}\) & 4165.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Steel Reinforcement Material Properties [33].
## 2 Materials and Methods  
Compound **1** was prepared according to our previously reported procedure [33].  
Two-photon steady-state and time-resolved photoluminescence measurements were performed by means of an Edinburgh Instruments' FLS 980 fluorometer using a pulsed Ti-sapphire laser (repetition rate = 80 MHz, pulse width = 140 fs, by Coherent Inc., Santa Clara, CA, USA) as excitation source. In order to synchronize the spectrofluorometer with the Ti:Sa pulses, a pulse picker was used to decrease the repetition rate from 80 MHz to 4 MHz.  
Two-photon absorption cross section (\(\sigma_{2}\)) at 800 nm was calculated by using a Rhodamine B methanol solution as reference [36], through the following equation:  
\[\sigma_{2\ (sample)}=\sigma_{2(reference)}.\frac{\Phi_{(reference)}}{\Phi_{( sample)}}.\frac{C_{(reference)}}{C_{(sample)}}.\frac{I_{(sample)}}{I_{(reference)}}.\frac{n_{(sample)}}{n_{( reference)}} \tag{1}\]  
where \(\Phi\) is the luminescence quantum yield, \(C\) is the concentration, \(I\) is the integrated luminescence intensity and \(n\) is the refractive index.  
Two-photon-induced singlet oxygen generation was monitored by chemical oxidation of 9,10-Anthracenediyl-bis(methylene)dimalonic acid (ABDA) (Sigma-Aldrich, St. Louis, Missouri, MO, USA) in aqueous solution [37]. A volume of 15 \(\upmu\)L of ABDA solution (2 mg/mL DMSO) was mixed with 300 \(\upmu\)L of **1** water solution (8\(\cdot\)10\({}^{-5}\) M) and placed in a quartz cuvette with an optical path length of 0.1 cm. The absorption spectra were recorded upon irradiation at 800 nm for 15 min (100 mW/cm\({}^{2}\)) at 3 min intervals. For comparative purposes, a similar ABDA solution was dispersed in water and irradiated under the same conditions. Then, monitoring the ABDA absorbance as a function of number of moles present in solution [35], it was calculated the amount of reacted ABDA molecules, and conversely of \({}^{1}\)O\({}_{2}\), after a total irradiation time of 15 min. Absorption spectra were recorded using a Cary 300 UV-Vis spectrophotometer. To exclude any possible ABDA spectral variation due to photoactivated redox processes triggered by **1**, absorption spectra of ABDA in presence of **1** in aerated and deaerated water solution were collected, detecting no significant spectral variation of the probe in absence of molecular oxygen.  
Calcein-AM staining assay was performed on U87-MG cells (human glioblastoma cells). The cells were cultured with DMEM containing 10% FBS. One day before the experiment, the cells were plated into LabTek Chamber I. For the experiment, cells were incubated with a medium containing 10 \(\upmu\)M of compound **1** for 1 h. Then, calcium-acetyloxymethyl (alcein-AM) was added at 2 \(\upmu\)M. The region of interest (ROI) was scanned 10 times using an 800 nm, two-photon laser, at 100% power intensity (39 mW). After photo exposure, the fluorescence was collected in the ROI and at distance from the ROI using different excitation/emission wavelengths: compound **1** was excited at two-photon 800 nm, emission 535-590 nm; calcein was excited at 488 nm, emission 500-550 nm.