esa-sceva/satcom-chunk-collection huggingface dataset:
# Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain
## 3 Simulation Results and Discussion
### Received Information (RI)  
Assuming that 16 PPM is the maximum scheme of achievable distance whereas I-16 PPM is the lowest, the amount of received information (RI) obtained at different data levels is determined using Equation (25), as shown in Figure 14. The amount of RI can be estimated for vehicles moving at a speed of 60 km/h from point x (at 300 m) to point y (at 18 m), which is the length of coverage area. This length is changeable according to the type of attenuation condition and the type of modulation used, as shown in Table 7.  
Figure 13: Bit error rate vs. distance under 16 PPM scheme for (**a**) very clear weather and 2 mm/h wet, dry snow, (**b**) 4, 8 mm/h wet, dry snow, (**c**) very clear weather and different rain models at 25 mm/h, (**d**) different rain models at 10 mm/h, and (**e**) different rain models at 2 mm/h.  
It is found that when the vehicle speed decreases, the amount of RI increases according to Equation (25). In addition, as the perception rate increases, attenuation thereby increases and the amount of RI decreases. According to Equation (25), the maximum achievable distance (LSA) is inversely proportional to the amount of RI. As a result, the LPPM scheme can receive more information than I-LPPM, whatever the type of attenuation. A vehicle on a road can receive data everywhere, even on the second lane, using the 16 PPM scheme.
## 5 Small Borehole Array Lateral Logging Forward Modeling
### Low-Resistive/High-Resistive Annulus Impact  
Existing research has confirmed the impact of mud invasion on logging resistivity, but the understanding of low-resistivity and high-resistivity annuli characteristics remains insufficient (in logging, annuli typically refer to a ring-shaped formation area around the borehole, which has different characteristics from the surrounding formation and is usually formed during the process of mud invasion). Particularly, how to effectively and accurately predict the influence of these annuli in forward modeling remains an unresolved issue. This study, through detailed numerical simulations and specific case analyses, further deepens the understanding of this phenomenon, providing new insights for improving logging data processing and interpretation [15; 16]. To further elucidate this phenomenon, this paper selected several representative special cases to reveal the mechanism by which different resistivity annuli affect the resistivity curve changes. In these special cases, we assumed  
Figure 5: Resistivity curves during invasion.  
Figure 6: Degree of separation in resistivity curves.  
that all scenarios were in a low-invasion environment, and the formation thickness for each model was uniformly set to 6 m.  
Figure 7 presents the resistivity curves of different formation annuli under mud invasion conditions. It can be observed that the resistivity of each mode showed a positive correlation. Figure 7a,b represent the cases of low-resistive annuli, while Figure 7c,d reflect the characteristics of high-resistive annuli. When the instrument's center gradually approaches the boundary area of the formation, the resistivity curve will drop sharply and form a peak. For low-resistive annuli, the curve showed a direct decline; for high-resistive annuli, the curve first increased before touching the formation boundary, and then dropped sharply. Notably, after a sharp decline in the resistivity curve, an increase of varying degrees was observed. When the instrument's center position reached the center of the formation, the observed resistivity value peaked, and the degree of separation in resistivity between the different modes also reached its maximum.  
Figure 8 shows cases where the resistivity curves exhibited a negative correlation. These curves are broadly similar in shape to the low-resistive annular and the latter two high-resistive annular curves in Figure 7. When the instrument's center was aligned with the formation boundary, the resistivity also dropped rapidly. However, when the instrument's center reached the center of the formation, both the resistivity value and the degree of separation peaked. Yet, unlike Figure 7, in Figure 8, we can observe the phenomenon of resistivity curve intersections under different investigation modes [17].  
Figure 7: Cases of positive correlation in resistivity impact due to annular rings.
# Collaborative Networks: A Pillar of Digital Transformation
## 1 Introduction
Nevertheless, on the one hand a collaboration perspective is present, either explicitly or implicitly, in most requirements of Industry 4.0; on the other hand, CNs embed an interdisciplinary and multi-disciplinary nature, which can be helpful for getting a holistic understanding of the problems involved in this transformation. Therefore, we claim that \"collaboration\" is a crucial challenge for the 4 th industrial revolution, and thus the \"Collaborative Networks\" area, among others, needs to be taken as a core enabler for this transformation. In support of this claim we can notice some relevant keywords associated to Industry 4.0 including \"networking\", \"vertical and horizontal integration\", \"value chains\", and \"co-engineering/through engineering\", that fit well the topics in the research agenda of the CN community [1; 9]. A relatively recent literature survey [10] further points out that \"interconnection\" and \"collaboration\" are among the main detected \"clusters of terms\". Considering this context, this work is guided by two main questions:  
* In which ways can collaborative networks contribute to Industry 4.0 and associated digital transformation? and
* Which further research challenges are induced by the vision of Industry 4.0 and digital transformation aimed at? As such, we performed an analysis of requirements of Industry 4.0, namely in terms of its main dimensions and focused on identifying the involved collaboration-related issues, while also mapping them to results that can be adopted from the CN area. Additionally, we suggest a list of \"open issues\" for further research and development towards a new \"Collaborative Industry 4.0\". In other words, our point is that the areas of Industry 4.0 and collaborative networks can mutually benefit from a co-evolution process. The remainder of this article is structured as follows: in Section 2 the adopted research method and used inputs are briefly described; Sections 3 and 4 briefly characterize the areas of Industry 4.0 and collaborative networks, providing a background for the following sections; Section 5 analyses the main dimensions of \"Industry 4.0\" and discusses their collaboration needs, while also identifying relevant results that can be \"borrowed\" from the CN area; Section 6 complements this analysis by looking at the issues of coping with disruptive events, a crucial issue that crosses all dimensions, and the potential role of CNs; Section 7 introduces and discusses further research challenges; finally Section 8 presents some conclusions.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### gMIMO
#### 3.1.2 System Requirements  
As the user load and system complexity in wireless communications increase, more diverse and stringent requirements in the design of gMIMOs emerge. A system of gMIMO antennas must be constructed with an extremely large number of antenna elements in order to cover the contemporary demands of high capacity and coverage (ref. [27; 29]). Accurate channel state information (CSI) is critical for beamforming, user scheduling, and signal detection. The large number of antenna elements also increases the demands on the power supply and, furthermore, introduces a phase and amplitude noise between the elements. These problems can be reduced to a certain extent by designing hardware to mitigate these impairments, like the use of low-power linear amplifiers and efficient beamforming techniques to minimize energy consumption (ref. [30]). Given that the gMIMO usually operates in the near field area, the channel losses cannot be ignored, while the channel measuring methods must adapt to take into consideration the new angular parameters. Therefore, channel modeling and estimation should be focused on the effects and challenges, such as near-field losses and propagation characteristics due to the new focus in near field communications (refs. [29; 31; 32]).
## An Appropriate Wind Model for Wind Integrated Power  
**Systems Reliability Evaluation Considering Wind Speed Correlations**  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Electrical Engineering, University of Saskatchewan, S7N4S4 Saskatoon, Canada;  
E-Mails: [EMAIL_ADDRESS] (D.D.); [EMAIL_ADDRESS] (R.B.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-306-966-5384; Fax: +1-306-966-5407.  
_Received: 31 December 2012; in revised form: 29 January 2013 / Accepted: 29 January 2013 / Published: 6 February 2013_  
**Abstract:** Adverse environmental impacts of carbon emissions are causing increasing concerns to the general public throughout the world. Electric energy generation from conventional energy sources is considered to be a major contributor to these harmful emissions. High emphasis is therefore being given to green alternatives of energy, such as wind and solar. Wind energy is being perceived as a promising alternative. This source of energy technology and its applications have undergone significant research and development over the past decade. As a result, many modern power systems include a significant portion of power generation from wind energy sources. The impact of wind generation on the overall system performance increases substantially as wind penetration in power systems continues to increase to relatively high levels. It becomes increasingly important to accurately model the wind behavior, the interaction with other wind sources and conventional sources, and incorporate the characteristics of the energy demand in order to carry out a realistic evaluation of system reliability. Power systems with high wind penetrations are often connected to multiple wind farms at different geographic locations. Wind speed correlations between the different wind farms largely affect the total wind power generation characteristics of such systems, and therefore should be an important parameter in the wind modeling process. This paper evaluates the effect of the correlation between multiple wind farms on the adequacy indices of wind-integrated systems. The paper also proposes a simple and appropriate probabilistic analytical model that incorporates wind correlations, and can be used for adequacy evaluation of multiple wind-integrated systems.  
keywords: reliability; wind power; wind penetration; correlation; probabilistic model; analytical technique +
Footnote †: journal: Journal of Computational Physics
# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 5 Results  
This section presents the estimated sea-level trends for locations within the Baltic Sea region. Given the variations in data sources and time spans considered, four main aspects are examined: (i) long-term (1995-2022) relative sea level (RSL) and absolute sea level (ASL) trends are calculated using only hourly TG data at each station (Section 5.1), (ii) a comparison of long-term ASL estimates by both TG and along-track SA (Section 5.2), (iii) a comparison of decadal ASL trend estimates by TG and SA (Section 5.3), (iv) and inter-comparisons of the estimated SA-based trend (Section 5.4), which includes an examination of ASL sea-level trend calculations using different distances from the coast (Section 5.4.1), an examination of ASL calculated using different SA missions (Section 5.4.2), and a comparison of ASL trend estimates using other available sea-level datasets (Section 5.4.3).
## 6 Computational Model of the Structure  
### Modelling Methodology  
In order to assess the load-bearing capacity of the elements of the present structure, a numerical model was created and static and dynamic analyses were performed using the finite element method. Dlubal software was used for the numerical calculations, including the modules RFEM 5, RF-TIMBER, and RF-DYNAM, which are dedicated to engineering analysis of building structures.  
The building structure was modeled as a bar structure, in which the members are straight and their cross-sections are constant along their length (prismatic rods). For the calculations, timber of grade C24, classified in the medium-quality category, with a bending strength of 24 MPa, was used. It was assumed that the bars in the model are rigidly connected to each other, and the supports are hinged. Permanent loads caused by the structure's own weight and the weight of boarding and insulation materials were taken into account. Climatic loads induced by snow and wind were also modeled and included in the load combinations in accordance with relevant standards [39; 40; 41]. In addition, it was assumed that the spatial bar system is braced with panels (diaphrags) in the planes of the walls, in the planes of the floors and ceilings, and in the planes of the roof slopes (Figure 20). In the wall planes of the actual structure, this stiffening is provided by masonry infills. In the floor planes, the aforementioned stiffening is provided by ceiling sheathing and attic floors.  
Once the load was defined, static and dynamic analyses of the structure were carried out. Extreme nodal displacements, internal forces, and stresses in the elements of the structure were determined. Displacement values determined at selected points and stress values in selected elements were used as comparative criteria for the analyzed models.  
Figure 19: Selected modes of natural vibration (1 st to 3 rd) determined by the Z21 test.
## 3 Results and Discussion
### Impact Test Results  
The obtained silicon-ceramic composites were also subjected to impact forces generated at much lower velocities (about 1-10 m/s) during falls that occurred during various types of physical activities (roller sports, cycling, horse riding). The obtained results are summarized and presented in Figure 7 and Table 6.  
Slightly higher values of the maximum force transmitted under the sample were obtained for hybrid silicone-ceramic composites compared to samples consisting only of silicone elastomers of the same thickness. It should be noted that HSC composites show a much higher mass per unit area (increase by about 50%) compared to samples based only on silicone elastomers (Table 6). In view of  
Figure 7: Dependence of the maximum force transferred to the sample on the surface mass of the tested composite systems.  
the above, it can be clearly indicated that the presence of ceramic elements does not improve the shock absorption capacity during impact with 5 J affecting the composite. However, in the case of impact tests, it is possible to indicate the relationship between the type of silicone elastomer used and the maximum value of energy transferred to the sample. The lowest values for both the composite and the elastomer sample were obtained for MM 228 silicone, whereas the highest for MM 922. Analyzing the obtained test results in terms of correlation between the ability to absorb impact energy and the physical and mechanical parameters, it can be indicated that only in the case of tear strength, the following relation was noted, i.e., the increase in tear strength was accompanied by a decrease in the value of maximum force transmitted to the sample. For the remaining parameters, i.e., hardness, density, extension at tear, and tensile strength, no dependence on the ability to absorb impact energy was determined.  
The results obtained for silicone samples and HSC composites were also compared with commercially available materials such as Poron(r)XRDMA, EVA copolymer, polyethylene, used in market products intended for the production of protectors reducing the risk of human body injury. The tests were performed for an impact energy of 5 J, for samples with thicknesses corresponding to the remaining composite samples (Table 6). In this case, it has been determined that both the silicone elastomers and hybrid silicone ceramic composites used have a poorer ability to absorb impact energy with a significantly higher mass per unit area, which excludes them from use on an industrial scale in the construction of protectors. However, the obtained results did not allow to indicate the reasons for poor ability to absorb impact energy generated at low speeds of 1-10 m/s for the developed composites, as they depend on many factors, such as: properties of the composite used, its physical-mechanical parameters, shape and geometry of the sample or the value of the applied load, as well as on the processes occurring during impact, i.e., deformations, plastic and/or elastic deformations, cracks, delamination, thermal effects, and others. This may be a challenge for further research work.
## 3 Methods and Data
### Field Data
#### 3.1.1 Remote-Controlled ADCP
In fact, the center position of the cell that the measured value is assigned to is never actually measured, since the sensor integrates measured return signals from the four beams that surround the center point, but do not actually cover the area directly below the sensor.
# Mesenchymal Stem Cell Applications in Spine Disorders: A Comprehensive Review
### Safety and Complications  
The analysis of safety and complications represents a key step when investigating a new therapeutical option. While all of the aforementioned studies reported on complications and adverse events, most authors did not observe any major adverse event correlated to the intervention [18; 19; 20; 21; 22; 23; 24; 25]. Three studies reported that a small number of patients experienced lower back pain after the treatment, which either resolved spontaneously or required pharmacological management [19; 21; 23]. [PERSON] et al. reported discontinuation of the study due to severe pain in 3 out 100 patients, along with one case of infection at the transplantation site [21]. [PERSON] et al. observed one case of disc herniation in their cohort of 33 patients [21]. [PERSON] et al. reported one case of osteomyelitis and discitis following intradiscal stem cell injection; however, the transplantation procedure had been performed at another institution, and no details were available [27]. Due to the multipotent nature of stem cells, safety concerns exist regarding the possibility that these cells may potentially undergo uncontrolled proliferation or differentiation and thus initiate osteophyte formation [28] or tumorous processes [29; 30]. While there are no reports concerning the tumorigenesis potential following the use of stem cells in for orthopedic conditions in the literature, one case of glial hyperplasia has been observed [31]. The patient noticed progressive weakness in the lower limbs accompanied by increasing pain two years after he underwent two intradiscal MSCs injections at two different institutions in Mexico and Russia. No data are available regarding the procedures. The subject underwent a laminectomy and the implantation of an expansile duroplasty without improvement of the symptoms. The patient was known to suffer from multiple autoimmune diseases prior to the MSC transplantation. As no data on the procedure or the kind of stem cells used are available, it is not possible to draw conclusions on the correlation between injection, autoimmune diseases, and the development of glial hyperplasia. It is also possible that the MSCs may have been inadvertently injected intrathecally, as this case resembles a similar complication reported by [PERSON] after intrathecal MSC administration [32]. This complication highlighted the fact that serious complications are possible, although rare, and particular consideration should be taken when considering MSC management for patients with systemic chronic conditions that may influence the behavior of the cells per se.
## 4 Deviations in the Fixture Positioning and the Resulting Part Deviations
### Fivxture Deviation
Decomposing the relative deviation into the vertical direction of the line \(P_{1}P_{2}\) gives:  
\[\frac{\Delta T_{1}{}^{\prime\prime}}{cos\theta}=\Delta z_{P_{2}}-\Delta z_{P_{ 1}},\ \frac{\Delta T_{2}{}^{\prime\prime}}{sin\theta}=\Delta y_{P_{2}}-\Delta y_{P_{ 1}} \tag{11}\]  
\[\Delta T^{\prime\prime}=\langle c\theta\rangle\cdot\Delta T_{1}{}^{\prime \prime}+\left\langle c\left(\theta+\frac{\pi}{2}\right)\right\rangle\cdot \Delta T_{2}{}^{\prime\prime} \tag{12}\]  
Since the parts studied in this paper are thin-walled parts and the positioning holes are all through-holes, it can be assumed that \(P_{1}P_{2}\) coincides with the y axis, i.e., \(\theta\) = 0. Therefore, \(\Delta T^{\prime\prime}\) has nothing to do with the flexible deformation of the part, and \(\Delta N^{\prime\prime}\) makes the flexible deformation at point \(P_{2}\) relative to point \(P_{1}\). Then the flexible bending deformation angle \(\Delta\beta^{\prime}\) at point \(P_{2}\) with respect to point \(P_{1}\) is:  
\[\Delta\beta^{\prime}=\frac{\Delta T^{\prime\prime}}{\left\|H_{P_{1},P_{2}}{}^ {\prime}\right\|_{2}} \tag{13}\]  
The above equations are assembled to obtain the relationship between the deviation vector \(\Delta X_{P_{1}}\) of the positioning point \(P_{1}\) on the part and the set of deviation vectors \(\{\Delta U_{P_{1}}(k),\Delta U_{P_{2}}(k)\}\) of the fixture elements:  
\[\Delta X_{P_{1}}(k)=G_{P_{1},P_{2}}\cdot \begin{bmatrix}\Delta U_{P_{1}}(k)\\ \Delta U_{P_{2}}(k)\end{bmatrix}  \tag{14}\]  
\[G_{P_{1},P_{2}}= \begin{bmatrix}1&0&0&0&0&0\\ 0&1&0&0&0&0\\ 0&0&1&0&0&0\\ \frac{-(c(\delta+\frac{\pi}{2}))sin\kappa}{\left\|H_{P_{1},P_{2}}{}^{\prime \prime}\right\|_{2}cos\gamma}&\frac{-(c\left(\delta+\frac{\pi}{2}\right))sin \kappa}{\left\|H_{P_{1},P_{2}}{}^{\prime\prime}\right\|_{2}cos\gamma}&0&\frac {(c\left(\delta+\frac{\pi}{2}\right))sin\kappa}{\left\|H_{P_{1},P_{2}}{}^{ \prime\prime}\right\|_{2}cos\gamma}&0\\ 0&\frac{-(c\left(\delta+\frac{\pi}{2}\right))sin\theta}{\left\|H_{P_{1},P_{2}}{}^ {\prime\prime}\right\|_{2}}&\frac{-(c\theta)cos\theta}{\left\|H_{P_{1},P_{2}}{ }^{\prime\prime}\right\|_{2}}&0&\frac{(c\left(\theta+\frac{\pi}{2}\right))sin \theta}{\left\|H_{P_{1},P_{2}}{}^{\prime\prime}\right\|_{2}}&\frac{(c\left( \theta\right))cos\theta}{\left\|H_{P_{1},P_{2}}{}^{\prime\prime}\right\|_{2}} \end{bmatrix}  \tag{15}\]  
In Equation (15), \(\gamma=\pi-\delta\). In order to make the coefficient matrix \(G_{P_{1},P_{2}}\) universal, a correction to \(\alpha\) is required: \(\alpha_{new}=\langle s\delta\rangle\cdot\alpha,s\delta=sin\delta\).