esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Osteoarthritis (OA) is characterized by articular cartilage loss, intra-articular inflammation, and osteophyte formation [1]. Osteophytes are often formed at the joint margins, first as cartilage outgrowth and subsequently undergoing ossification [2]. While the exact purpose of osteophytes remains unknown, their presence and size in the knee are associated with joint space width (JSW) decrease, and they are an important radiographic feature used to define the severity of knee OA in classifications like the Altman score and Kellgren-Lawrence grade [3; 4; 5; 6; 7]. Osteophytes are frequently present in patients with end-stage knee OA receiving surgical treatment such as total knee arthroplasty (TKA) [8].  
TKA is widely used because of its clinical effectiveness, but in younger patients (<65 years) it has a significantly higher risk of failure and revision surgery later in life [9; 10]. Therefore, there is a demand for joint-preserving treatments for (severe) knee OA at a younger age. A joint-preserving alternative for patients with unicompartmental knee OA as a result of malalignment is high tibial osteotomy (HTO), which shows good long-term results and clinical improvement and a certain degree of cartilage repair [11; 12; 13]. Knee joint distraction (KJD) is a relatively new joint-preserving treatment for patients with unicompartmental or generalized severe knee OA, where the tibia and femur are temporarily separated using an external fixation frame [14]. An open prospective study (OPS) has shown good long-term treatment results, and two randomized controlled trials (RCTs), one comparing KJD with HTO and one with TKA, showed that clinical outcome after KJD is comparable to that after HTO or TKA [15; 16; 17; 18; 19; 20]. Furthermore, cartilage repair has been shown on radiographs and on MRI scans, and systemic biomarker analyses suggest beneficial cartilage and bone turnover after KJD treatment [18; 21; 22; 23].  
Cartilage repair activity as a result of treatment could be related to an increase in transforming growth factor-81 (TGF\(\beta\)-1), which is generally appreciated to stimulate cartilage repair [24]. During KJD treatment, an increase in synovial fluid TGF\(\beta\)-1 level was observed [25]. While TGF\(\beta\)-1 is associated with joint repair, it has also been shown to induce osteophyte formation, predominantly in experimental animal studies, but in ex vivo human studies as well [26; 27; 28; 29; 30; 31]. Interleukin-6 (IL-6) was also observed to increase intra-articularly as a result of KJD treatment and could be positively associated with osteophyte presence as well, showing increased mRNA expression and protein production in in vitro studies with human osteophyte tissue [25; 30; 32].  
As such, we studied osteophyte formation during KJD and compared this to HTO and natural OA progression, hypothesizing that joint-preserving regenerative treatments demonstrating cartilage repair activity lead to tissue (re)generation in general, including osteophyte formation.
# Advances in High-Speed, High-Power Photodiodes: From Fundamentals to Applications
## 3 Solutions of Bandwidth-Responsivity Trade-Off
### High-Reflected Mirrors for Photodiodes
(**a**) Conventional RCE photodiode model, i.e., photodiode with DBR mirror; (**b**) photodiode with dual mirrors, i.e., bottom mirrors (DBR or subwavelength grating (SWG) mirrors) and top mirrors (dielectric layers, or SWG or ‘metal’ mirrors); (**c**) photodiode with SWG–focused mirror; (**d**) photodiode with tapered holes and bottom mirror (DBR or SWG mirrors); (**e**) waveguide photodiode with DBR mirror; and (**f**) waveguide photodiode with SWG–focused mirror (strip SWG or concentric circular SWG mirrors). HCG) on the silicon substrate. Based on the 84.59% FR-efficiency, the integrated device achieves 36.5% improvement for the responsivity, without altering the 3-dB bandwidth. Figure 20d shows a symmetric-connected UTC-PD array (SC-PDA) integrated with a two-dimensional (2D) SWG beam-splitter [140], where the integrated device obtains an RF output power of 16 dBm at 12 GHz with a 87.9 mA saturation current and 0.438 A/W responsivity, showing stronger high-power handling capability than that of a single photodetector (-11 dBm@12 GHz and 46.7 mA, 0.179 A/W), without complex coupling, phase matching as well as any cooling techniques. Therefore, these kinds of mirrors perfectly realize the decoupling between the responsivity and bandwidth for photodiodes, while the improvement of responsivity and/or bandwidth can be further enhanced if using the well-optimized bonding process in the future.
# Estimation of Stability Parameters for Wide Body Aircraft Using Computational Techniques
## 2 US DATCOM
### US DATCOM Results
#### 2.3.1 Evaluation of Lift, Drag, and Moment Coefficients  
Variation of basic aerodynamic coefficients of lift, drag, and moment with the angle of attack is shown in Figure 5. The plots show similar trends with stable conventional aircraft. Figure 5a shows a variation of lift coefficient with an increasing angle of attack. The values of CL0, CL\(\alpha\), and CLmax came out to be 0.12, 4.76, and 1.78, respectively. For the range of angle of attack (\(-\)0.4 to 0.5 rad), the stall lift coefficient can also be seen in the plot. A variation of drag coefficient for a range of angles of attack is plotted in Figure 5b, which gives a similar trend as the original aircraft, thereby verifying the results of DATCOM in the determination of the stability of the 747-200. The value of CD0 is 0.020; it is in agreement with the performance data of Boeing aircraft found in the literature. The zero-drag coefficient is an important parameter in the design phase of the aircraft.  
The value of the drag coefficient increases with increases in the angle of attack, leading to an increase in the lift. For a longitudinally stable aircraft, the value of the y-intercept of the Cm\(\alpha\) curve should be positive and the moment coefficient against a changing angle of attack must have a negative slope. The plot depicts the expected result, establishing that the selected aircraft results satisfy this condition of stability. The result shows that the aircraft has an inherent ability to generate a longitudinal counter moment as soon as it experiences a disturbance while airborne. The sensitivities of the air vehicle towards a disturbance and the associated response (as shown in the Figure 5c) are basic stability measuring criteria.
###### Abstract  
This study aims to enhance the performance of an innovative steel slag-ceramsite foam concrete (SSCFC) to advance sustainable green building materials. An eco-friendly composite construction material was developed by integrating industrial by-product steel slag (SS) with lightweight ceramics. Employing a three-factor, three-level orthogonal experimental design at a fixed density of 800 kg/m\({}^{3}\), 12 mix proportions (including a control group) were investigated with the variables of water-to-cement (W/C) ratio, steel slag replacement ratio, and ceramicistic replacement ratio. The governing mechanisms of the W/C ratio, steel slag replacement level, and ceramics replacement proportion on the SSCFC's fluidity and compressive strength (CS) were elucidated. The synergistic application of range analysis and analysis of variance (ANOVA) quantified the significance of factors on target properties, and partial least squares regression (PLSR)-based prediction models were established. The test results indicated the following significance hierarchy: steel slag replacement > W/C ratio > ceramicistic replacement for fluidity. In contrast, W/C ratio > ceramicistic replacement > steel slag replacement governed the compressive strength. Verification showed R\({}^{2}\) values exceeding 65% for both fluidity and CS predictions versus experimental data, confirming model reliability. Multi-criteria optimization yielded optimal compressive performance and suitable fluidity at a W/C ratio of 0.4, 10% steel slag replacement, and 25% ceramics replacement.  
foam concrete; compressive strength; orthogonal experiment; range analysis; variance components method; partial least squares regression method
# A New SORM Method for Structural Reliability with Hybrid Uncertain Variables
## 1 Introduction
Then, the efficiency and practicability of the proposed method are demonstrated by two numerical applications. The results with a single linear expression show the effectiveness and accuracy of the proposed method. Furthermore, the proposed method is applied in a real structural system, a spacecraft docking lock, demonstrating that the proposed method is effective.
# Determination of Reliability of Selected Case
## 2 Materials and Methods
In practice, additional elements connected in a parallel system to the basic ones can be used for joining elements of case furniture, e.g., when the primary connector of the furniture joint is an eccentric connector, an additional connector can be added. To determine the reliability of case furniture, the next step was to establish the groups of statistical distributions. The following distributions were taken into account [32]:  
* Exponential distribution with the density function: \[f(x)=e^{-x},\] (3)
* Gamma distribution with the density function: \[f(x)=\frac{x^{a-1}e^{-x}}{\Gamma(a)},\] (4)
* Log-normal distribution with the density function: \[f(x)=\frac{1}{sx\sqrt{2\pi}}e^{-\frac{\log^{2}x}{2x^{a}}},\] (5)
* Weibull distribution with the density function: \[f(x)=cx^{c-1}e^{-x^{c}}.\] (6)  
In all of the above cases, the variable \(x\) was appropriately shifted and scaled using the parameters \(x_{0}\) and \(\lambda\), so in the above formulas everywhere, instead of \(x\), a more general expression was used:  
\[\frac{x-x_{0}}{\lambda}. \tag{7}\]  
Moreover, in the gamma distribution, there is a shape parameter \(a\), in the log-normal distribution, there is a standard deviation \(s\), and in the Weibull distribution, there is a shape parameter \(c\).
# Shock Vibration Control of SDOF Systems with Tubular Linear Eddy Current Dampers
## 4 Transient Response Analysis  
### Parameters of the Structure, Shock Excitation, and TLecd  
The effects of the impulse trait of shock excitation and mechanical parameters of the ECD on shock vibration control are studied using the SDOF system with the ECD, as shown in Figure 4. The response of the SDOF system with nonlinear FVD subjected to shock excitation has been analyzed by [PERSON] [15]. The same structural parameters in [15] are also adopted in this study as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(m\) & 25,000 kg & \(T_{n}\) & 0.05 s & \(\xi\) & 2\% \\ \hline \hline \end{tabular} Here, \(m\) is the lumped mass, \(T_{n}\) is the period of the structure, and \(\xi\) is the inherent damping ratio.  
\end{table}
Table 1: Structure parameters.  
The above example structure is certain and maybe not applicable in typical Civil Engineering structures. However, the later shock response spectrum (SRS) is dimensionless, and therefore, it is applicable for structures with a wide range of frequencies regardless of whether they are rigid or flexible. As long as the ratio of the shock load excitation duration to the structure's natural period is within a fine scope, the shock can still be considered an impulse. Moreover, the dimensionless mechanical parameters of the ECD are also adopted to ensure they are widely applicable. The dimensionless maximum damping force \(\beta\) and the dimensionless critical relative velocity \(\gamma\) can be given as:  
\[\beta=\frac{F_{max}}{ma_{0}} \tag{12}\]  
\[\gamma=\frac{v_{cr}\omega}{a_{0}} \tag{13}\]
## 3 Results & Discussion
### Case III  
In this case, the relationship between the KPI \(B(t)\) and the PIs \(A_{1}(t)\), \(A_{2}(t)\), and \(A_{3}(t)\) is \(B(t)=A_{2}(t)/A_{1}(t)+A_{3}(t)\). The evolution of the PIs trends is, as follows:  
1. \(A_{1}(t)\) decreases in time;
2. \(A_{2}(t)\) increases in time; and,
3. \(A_{3}(t)\) is constant in time.  
It is evident from the contribution diagram in Figure 11 that there is success in predicting that the contribution of the first signal should be higher than that of the second one. It is apparent that both of the first two PIs are causes of the variation, however, the denominator change is the one that affects more the result. Accordingly, the algorithm has pointed towards the correct correction.  
Figure 10: Case II. Top: Evolution of KPI, Middle: Evolution of PIs, Bottom, Contribution of each PI.
## 3 Results and Discussion
### Comparative Analysis of HS-GC-IMS and HS-SPME-GC-MS in Identifying VOCs
#### 3.4.2 PCA of All VOCs
Notable compounds include (Z)-jasmone (floral), theaspirane (tea herbal), delta-elemene (herbal), hexyl isobutyrate (green), linalool oxide IV (tea-like, woody, and floral), undecanal (floral), (Z)-2-hepten-1-ol (fatty), (Z)-hex-3-en-1-ol (green), and nonanoic acid (waxy). In contrast, linalool (floral), (E)-beta-farnesene (herbal), neral (citrus), 1-nonanol (floral), cyclohexanol (green and rummy), alpha-calacorene (woody), (E)-3-hexen-1-ol (green), hex-(3Z)-enyl butyrate (green), acetic acid hexyl ester (fruity), T-cadinol (balsamic), and (Z)-3-hexenyl hexanoate (green) show a strong association with FD-CK. On the other hand, VOCs significantly correlated with FD-D include cryophyllene oxide (woody), pinocarveol (herbal and woody), perilla alcohol (green, cumin, and spicy), dodecanal (soapy, waxy, and citrus), 1-tetradcanal (waxy), Z-5-decen-1-ol (waxy, fatty, and rose), 10-undecen-1-ol (citrus), dodecanoic acid (fatty and coconut), exo-Isocitral (lemongrass), tetradecanal (waxy, citrus, and fatty), (Z)-9-octadecen-1-ol (fatty), dehydrolinalool (floral), 1-undecanol (fresh, waxy, and rose), 6-hepten-1-ol (herbal), 2-undecenal (fruity), and tridecanal (citrus and grapre fruit peel). Conversely, VOCs closely linked to WNZ-D predominantly include linalool oxide I (woody and floral), linalool oxide II (woody and floral), linalool oxide III (woody, floral, and citrus), alpha-farnesene (woody, and citrus), oct-(2E)-enal (fatty), nona-(2E,4E)-dienal (fatty, melon, and waxy), (3Z)-beta-ocimene (floral), (E)-2-decenal (waxy, fatty, and earthly), methyl salicate (minty), alpha-guaiene (sweet and woody), gamma-muurolene (woody), beta-damascone (fruity and floral), dehydrolinalool (floral), hexadecanoic acid methyl ester (waxy), and 1-octen-3-one (earthy and mushroom). A holistic analysis of these findings indicates that the drought-treated groups (FD-D and WNZ-D) are more closely associated with woody, floral, rose, waxy, fatty, fruity, citrus, and earthy VOCs. Notably, the drought-treated groups exhibit a wider diversity of VOCs, leading to more complex and enriched aroma profiles. One particularly interesting observation is that VOCs correlated with the oxidation and dehydrogenation products of linalool are more prevalent in WNZ-D, primarily in the form of four distinct compounds: linalool oxide I, linalool oxide II, linalool oxide III, and dehydrolinalool. These compounds significantly enhance the floral and woody aroma characteristics in WNZ-D.
## 3 Methods
### Registration Network  
Figure 2 depicts the architecture of the registration network R, which is made up of U-net and the STN. U-net is utilized to capture the mapping between the input image pairs, while the STN transforms the source image into the warped image.  
The registration network takes \(X\) and \(Y\) as input image pairs and produces the deformation field \(\phi\) as output, in which \(\phi=R(X,Y)\) and \(\phi\) is a 2D vector. The STN resamples the source image \(X\) and the transformed image \(Y^{{}^{\prime}}\) into the warped images \(X^{{}^{\prime}}_{w}\) and \(Y^{{}^{\prime}}_{w}\), respectively. The STN is defined as  
\[\begin{split} X^{{}^{\prime}}_{w}[u]&=STN(X,\phi)[p ]=X[p+\phi(p)]\\ &=\sum_{q\in N(p+\phi(p))}X(q)\prod_{d\{i,j\}}(1-|p_{d}+\phi_{d}( p)-q_{d}|)\end{split} \tag{2}\]  
\[\begin{split} Y^{{}^{\prime}}_{w}[u^{{}^{\prime}}]& =STN(Y^{{}^{\prime}},\phi)[p^{{}^{\prime}}]=Y^{{}^{\prime}}[p^{{}^ {\prime}}+\phi(p^{{}^{\prime}})]\\ &=\sum_{q^{{}^{\prime}}\in N(p^{{}^{\prime}}+\phi(p^{{}^{\prime} }))}Y^{{}^{\prime}}(q^{{}^{\prime}})\prod_{d\{i,j\}}(1-\left|p^{{}^{\prime}}_{ d}+\phi_{d}(p^{{}^{\prime}})-q^{{}^{\prime}}_{d}\right|)\end{split} \tag{3}\]  
where \(q\) and \(q^{{}^{\prime}}\) are pixel neighbors of \(p+\phi(p)\) and \(p^{{}^{\prime}}+\phi(p^{{}^{\prime}})\), respectively, and \(d\{i,j\}\) represents the two dimensions of the image.
## 3 Background
### Propulsion System Technologies Overview
#### 3.3.3 Propellant-Less Propulsion Systems  
Systems designated Propellant-less do not carry propellant. They are ideal as this reduces mass and complexity. They are ideal long-distance missions to other solar systems and planets, as they rely on perpetual fuel supply such as solar radiation and the main limiting factor is component life span. A notable application of propellant-less technology is the LightSail-A mission. A solar sail was employed as the craft's propulsion system with a total area of 32 m\({}^{2}\). The craft orbited at 720 km and could perform altitude regulation and alteration (Figure 23) [8].  
Figure 21: Busek BHT 200 Thruster [36].  
Figure 22: University of Toronto SFL Hall Effect Thruster [23].  
Solar Sails are the most commonly employed form of propellant-less propulsion system on board CubeSats [23]. Incoming solar radiation packed with photons, is reflected by the sails highly reflective film material generating a pressure, propelling the craft forwards [23]. The incoming photons rate of change of momentum during reflection exerts an equal and opposite force upon the craft's sail. This momentum is of an extremely small magnitude; typically sails for small buses (2-6U) are around 32 m\({}^{2}\)[8]. Unlike chemical thrusters, the incoming radiation is constant allowing a gradual gain of momentum over time. This allows craft to reach high speeds, making this a viable option for interplanetary travel [8]. Figure 24 illustrates the dimensions of stowed versus deployed solar sail configuration [40].
## 3 The Experiments
### Preliminary Study  
A preliminary study has been undertaken to determine the extent to which users upload inappropriate and revealing information on social networking sites. A random sample of 90 Twitter users has shown that 65.5% revealed their real names and the majority of them (94.4%) had posted pictures of themselves, see Table 1. Of these users 43.3% had geo-location turned on in their posts, which means that it would be possible to track them to their home or to their work place. 43% had links to other social networking sites, which meant it would be much easier to track these users through cyber space. Three quarters of these users had over 500 followers and were also following over 500 others. Followers are people who tend to retweet tweets from those that they follow.  
There was not a huge difference when it came to gender within the sample, with 59% being female and 41% being male. We also investigated the age range of these users and found that the biggest group were in the 21 to 25 year age range, being 39% of the sample. Teenagers, aged 15 to 20 formed 27% of the sample, while the 26 to 30 year olds accounted for only 20%. The age group 31 to 35 was 7% of the sample and the 41 to 45 year olds were only 5%. There were 2% of the sample whose age could not be determined. Within our sample it would appear that the 21 to 25 year old group are the biggest users of Twitter and this is actually the age range that our three users fell within.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Vulnerabilities** & **Number of Twitter Users from Sample** \\ \hline Displayed real name & 59 users (65.5\%) \\ Real name as username & 33 users (36.7\%) \\ Real name and geotag posts & 47 users (43.3\%) \\ Shared self-images & 85 users (94.4\%) \\ Specified link to other site & 39 users (43.3\%) \\ Same username on multiple platforms & 30 users (33.3\%) \\ Above five hundred followers & 61 users (67.8\%) \\ Above five hundred following & 54 users (60\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data from Twitter (90 random users).  
A survey completed using Instagram is shown in Table 2. Over 7 million users had publically identified their locations. Other personal and private information easily visible included pictures of their credit/debit cards with the 16 digit number clearly visible and various types of tickets with names, addresses and dates showing. Over 39,000 users posted a picture of their hospital band with their details showing their name, DoB and hospital ID.
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 2 Determining the Location of the Thief Zone and the Classification of Fluid Loss
### Location of the Thief Zone  
The thief zone can be at the bottom or above the bottom of the hole depending on the observed changes in conditions during drilling (see Table 1).  
An accurate estimation of the loss zone location allows for the application of the treatment directly at the point of loss, resulting in high efficiency and shorter non-productive rig time. Various tools and methods are used to determine the location of the loss zone and avoid lost circulation such as (1) Real-time geomechanical analysis, (2) Pre-drill geological analysis, (3) Pressure transducer surveys, (4) Open hole logs, (5) Hot wire surveys, (5) Radioactive transducer surveys, (6) Temperature surveys, (7) Spinner surveys, (8) and Oxygen activation and water flow measurements tools [2; 5; 37; 38; 39; 40; 41]. However, some of these  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Thief Zone is “on Bottom” If:** & **Thief Zone is “off Bottom” If:** \\ \hline Fluid loss first occurs while drilling ahead. & Fluid loss first occurs while tripping, \\ Fluid loss is accompanied by a notable change in the rate of penetration (ROP), torque, or drilling vibration. & drilling fast or increasing mud weight. \\ Fluid loss is due to natural fractures, faults, & Fluid loss is the result of shutting the well \\ caverns, vugs or high permeability sands and & A annular loading is such as to increase the \\ gravels (well site geology data, drilling break with & return apparent mud weight higher than \\ increase in torque and Kelly free falls while drilling & the last casing shoe fracture gradient. \\ coupled with an instant loss in circulation). & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The probable location of the thief zone according to [36].  
methods are difficult to be apply due to financial issues and lack of technology, while other methods are not accurate in the prediction of thief zones. More recently, artificial intelligence techniques such as radial basis function (RBF) and a support vector machine (SVM) have been used to predict lost circulation zones [42].