esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Sixth-Generation (6G) Testbeds and Platforms
### Testbeds for 6G Technologies
#### 6.2.3 RIS Testbeds  
Research has confirmed that reconfigurable intelligent surfaces (RISs) are promising for improving communication performance. In [23], a field trial validated an RIS prototype with 1100 controllable elements operating at 5.8 GHz, achieving a 26 dB power gain in indoor scenarios. Intelligent omni-surfaces (IOSs) [245] support full-dimensional communications, presenting a reflective and refractive solution. The University of Surrey introduced an RIS testbed in the sub-6 GHz band [246], demonstrating successful configuration under different incident angles. Various RIS prototypes were proposed, including a 1-bit RIS testbed [247] and an mmWave frequency prototype [248], confirming their effectiveness in improving antenna gain under different conditions.
# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 1 Introduction
* The proposed method ensures consistent results regardless of the classification of the mission or the experience level of the satellite operators. * Additionally, the methodology was validated by analyzing the accuracy of the data-driven network and degradation modeling results using actual on-orbit telemetry data from the KOMPSAT-5 satellite. This paper is structured as follows. It begins with an introduction to KOMPSAT-5, including the configuration of its power system and the data-driven algorithm used, followed by a detailed explanation of the proposed data-driven voltage modeling approach for LEO satellites and its application to satellite systems. Subsequently, it presents a quantitative analysis of the estimation results using in-orbit data in order to validate the applicability of the proposed technique. Finally, the paper concludes with a summary of the findings and a discussion on potential future work.
# Temporal-Guided Label Assignment for Video Object Detection
## 4 Experiment
### Detection Visualization  
Figure 5 showcases one hard example in video object detection. The domestic cat is presented in a blurred appearance during movement. The top two rows show the result of the baseline and our TGLA without FIM. Compared with the result of our TGLA in the third row, they have a lower confidence. This first shows that it is effective to add temporal information to label assignment during training because the result of the second row was better than that of the first row. The result in the third row outperformed that in the second row, which reflects the effectiveness of FIM. This shows that the method of building the relation model of labels also needs to be considered.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Temporal-Guided**} & \multirow{2}{*}{**FIM**} & \multirow{2}{*}{**mAP(\%)**} \\  & **Label Assignment** & & \\ \hline TROI-Var & - & - & 81.2 \\ Our TGLA & ✓ & - & 81.5 \\ Our TGLA & ✓ & ✓ & 82.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The effectiveness of the temporal-guided label assignment framework and feature instructing module. Temporal-guided label assignment is the framework without feature instructing, and FIM is the feature instructing module.  
Figure 5: Comparison of detection results of different models for hard samples.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Backbone** & **mAP(\%)** \\ \hline FGFA [2] & R101 & 78.4 \\ D\&T [32] & R101 & 80.0 \\ PLSA [5] & R101+DCN & 80.0 \\ SELSA [8] & R101 & 80.25 \\ Leveraging [33] & R101-FPN & 81.0 \\ RDN [9] & R101 & 81.8 \\ \hline TGLA & R101 & 82.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison with other state-of-the-art models on the ImageNet VID validation set. TGLA is our method with the framework and FIM. Here, R101 is ResNet-101 and DCN is a deformable convolutional network.
# Surface Forming Criteria of Ti-6 AL-4V Titanium Alloy under Laser Loading
## 3 Results and Discussion
### Influence of Spot Diameter on Forming Effect  
Different spot diameters of 5 mm and 7 mm were selected, and a 10 J single pulse laser was used to impact TC4 titanium alloy targets with the same thickness. The target thicknesses were 0.08 mm and 0.3 mm. Figure 3 shows the back morphology of the target under different spot diameters.  
When h = 0.08 mm, the maximum deformation of the target under 5 mm spot diameter was 1.7406 mm, and the maximum deformation of the target under 7 mm spot diameter was 2.6519 mm. When h = 0.3 mm, the maximum deformation of the target material was 0.7282 mm under the 5 mm spot diameter and 0.9675 mm under the 7 mm spot diameter. The results show that when other experimental conditions stay constant, the laser spot diameter affects the load applied by the laser pulse on the target surface, which in turn affects the plastic deformation of the target material, and the deformation of the target  
Figure 3: (**a**) The outline of targets with 0.08 mm thickness. (**b**) The outline of targets with 0.3 mm thickness.  
grows with the increase of the spot diameter. The area where the laser acts on the surface of the target will increase with the rise of spot diameter, and that means the spalling area of the target material will become larger. The cracks and holes formed and grown under laser loading, when combined, will merge to form one or more consecutive fracture surface which penetrates the material, resulting in failure damage. At this time, the increase of the spot diameter will accelerate the failure damage behavior of the material. It is necessary to choose the appropriate laser process according to material characteristics. From the above results, a larger laser spot diameter can be selected for TC4 titanium alloy with high yield strength, whereas a smaller laser spot diameter selected for materials with low yield strength [19; 20].  
When h = 0.08 mm, it was found that the surface of the target material rebounded. For the titanium alloy target material with a thickness of 0.08 mm, the laser energy of 10 J is slightly low, although the target material can be deformed under this energy. However, due to the large range of the laser-induced impact force and the short deformation time, the target material rebounds at the center of the impact, and the deformed back of the target material presents a \"concaving in the middle and protruding at both ends\" shape. When the thickness of the target material increases, because the laser energy is low, the range of impact force is small, and the forming depth is shallow, so there will be no rebound. The following chapter will elaborate on this phenomenon.
# A Wideband Eight-Port MIMO Antenna with Reduced Mutual Coupling for Future 5G mm-Wave Applications
## 3 Results and Discussions
### Radiation Patterns, Peak Gain and Radiation Efficiency  
To assess the performance of the antennae further, the radiation patterns, gain and radiation efficiency are measured and discussed in this section.  
The radiation patterns in E-plane and H-plane at 30 GHz and 40 GHz are studied in Figure 17. Figure 17 confirms that the intensity of the radiation at 30 GHz in the both E-plane and H-plane is found between 150\({}^{\circ}\) and 180\({}^{\circ}\). The intensity of radiation at 40 GHz in the E-plane is analyzed at 60\({}^{\circ}\) and 120\({}^{\circ}\), with the radiation intensity in the H-plane at 40 GHz occurring at 150\({}^{\circ}\) for exciting port 1 and terminating the remaining ports. For identical antennas, its results are the same but with 90\({}^{\circ}\) rotation. The simulated and measured results of the proposed antenna matched well; however, marginal mismatch occurred due to fabrication tolerance, connector loss and noise from the environment during testing.  
The variation in the radiation patterns of element 1 of single-, dual-, quad- and octa-port antennas is given in Figure 18.  
The antenna secured 6.8 to 8.5 dBi gain with 91 to 97% radiation efficiency in the entire operating range, as confirmed by Figure 19.  
Figure 16: S-Parameters of the proposed MIMO antenna. (**a**) Antenna 1 and antenna 2, (**b**) antenna 1 with antenna 3 and antenna 4, (**c**) antenna 1 with antenna 5 and antenna 6 and (**d**) antenna 1 with antenna 7 and antenna 8.
# Concentration Influence of Complexing Agent on Electrodeposited Zn-Ni Alloy
## 3 Results and Discussion  
### Appearance of Zn-Ni Alloy Coating  
The current density distribution over the cathodic area is usually a huge concern in electrodeposition. Thus, the Hull cell has been suggested to observe the variation in current density along the cathode. The Hull cell is defined as a miniature electroplating tank, in which a cathode is angled in respect of the anode; hence, the current density distribution is reversely proportional to the distance between the anode and the cathodic area. The appearance of Zn-Ni alloy coatings with different TEPA concentrations is shown in Figure 1a, in which the right direction is along with increasing current density, and the left direction is along with decreasing current density. TEPA concentration probably did not significantly affect the brightness of coatings in the high current area. However, increased TEPA concentration improved the coating brightness in the low current region. At TEPA 0 M, the dark area was dominant over half of the scanned area. When the TEPA concentration increased to 0.035 M, the dark area became smaller, and it disappeared when the TEPA concentration was over 0.07 M.  
To further support this, the variation in glossiness according to the change of current density and TEPA concentration is represented in Figure 1b. The quality of the plating layer can be reflected by the plated surface gloss, which is higher when the mean grain size is smaller than the wavelength of visible light, i.e., smaller than 0.4 \(\upmu\)m [31]. Additionally, the glossiness is significantly affected by the plating process. Thus, the change in TEPA concentration was expected to vary the surface gloss of the coating at different current densities. A similar trend to the appearance of coating could be observed according to the change in current density and TEPA concentration. Specifically, at low TEPA concentrationsof 0 M and 0.035 M, the gloss unit almost linearly increased with the decrease in current density before decreasing at a low current density of 2 A/dm\({}^{2}\) (ASD). The severe degradation of the gloss unit at over 2 ASD explained the appearance of the dark area on scanned images. When TEPA was higher than 0.07 M, the gloss unit of coatings was reversely proportional to the current density. These demonstrated that TEPA concentration could enhance the brightness of the coating in the low current area. Additionally, an increase in TEPA concentration up to 0.07 M led to an increase in glossiness regardless of current densities. For example, the glossiness obtained at 2 ASD was approximately 500, 800, and 900 with TEPA 0 M, 0.035 M, and 0.07 M, respectively. The difference in gloss unit between TEPA 0.07 and 0.1 M was insignificant, suggesting that the brightness of coatings approached a limitation at 0.07 M of TEPA.
## 3 Proposed Methods
### \(\ell_{0}\)-Smoothing Based on Ball-Type Gradient Constraint  
The image-smoothing problem with the proposed ball-type gradient constraint (14), which is the modified version of (16), is defined by  
\[\min_{\mathbf{s}}\|\mathbf{D}\mathbf{s}\|_{0,1}+\frac{\lambda}{2}\|\mathbf{ s}-\mathbf{s}_{\text{in}}\|_{2}^{2}\ \ \ \text{s.t.}\ \ \ \|(\mathbf{D}\mathbf{s})^{\mathcal{G}_{n}}\|_{2}\leq\|(\mathbf{D}\mathbf{s}_{ \text{ref}})^{\mathcal{G}_{n}}\|_{2},\ n=1,\ldots,N. \tag{26}\]  
Similar to the preceding section, we solve the minimization problem (26) by applying ADMM to it. Now, the convex set \(\mathcal{B}_{\emptyset,\varepsilon_{n}}\) is defined for \(n=1,\ldots,N\) by  
\[\mathcal{B}_{\emptyset,\varepsilon_{n}}:=\{\mathbf{x}\in\mathbb{R}^{6}\ \ |\ \ \| \mathbf{x}\|_{2}\leq\varepsilon_{n}\}, \tag{27}\]  
where \(\varepsilon_{n}:=\|(\mathbf{D}\mathbf{s}_{\text{ref}})^{\mathcal{G}_{n}}\|_{2}\). We reformulate (26) into the following unconstrained problem by introducing the indicator functions of \(\mathcal{B}_{\emptyset,\varepsilon_{n}}\)\((n=1,\ldots,N)\), and then, we also introduce auxiliary variables \(\mathbf{z}_{1}\) and \(\mathbf{z}_{2}\) to non-differential functions  
\[\min_{\mathbf{s},\mathbf{z}_{1},\mathbf{z}_{2}}\frac{\lambda}{2}\|\mathbf{s}- \mathbf{s}_{\text{in}}\|_{2}^{2}+\|\mathbf{z}_{1}\|_{0,1}+\sum_{n=1}^{N}\iota_{ \mathcal{B}_{\emptyset,\varepsilon_{n}}}(\mathbf{z}_{2}^{\mathcal{G}_{n}})\ \ \ \text{s.t.}\ \ \ \mathbf{z}_{1}=\mathbf{D}\mathbf{s},\mathbf{z}_{2}=\mathbf{D}\mathbf{s}, \tag{28}\]  
where \(\mathbf{z}_{2}^{\mathcal{G}_{n}}\) is the \(n\)-th sub-vector of \(\mathbf{z}_{2}\) specified by \(\mathcal{G}_{n}\).  
The algorithm for solving (28) is obtained by slightly modifying Algorithm 1. In particular, the update of \(\mathbf{s}\) in line 4 of Algorithm 1 is obtained by  
\[\mathbf{s}^{*}=\mathbf{F}^{*}\Big{\{}\lambda\mathbf{I}+(\gamma_{1}^ {-1}+\gamma_{2}^{-1})\mathbf{\Sigma}\Big{\}}^{-1}\mathbf{F}\mathbf{q}^{\prime}, \tag{29}\] \[\text{where }\mathbf{q}^{\prime}=\lambda\mathbf{s}_{\text{in}}+ \gamma_{1}^{-1}\mathbf{D}^{\top}(\mathbf{z}_{1}-\mathbf{b}_{1})+\gamma_{2}^{- 1}\mathbf{D}^{\top}(\mathbf{z}_{2}-\mathbf{b}_{2}).\]  
In the update of \(\mathbf{z}_{2}\), we calculate the following metric projection onto the \(\ell_{2}\) ball according to (11) instead of the 8 th and 9 th lines of Algorithm 1, i.e., for \(n=1,\ldots,N\), by  
\[\mathbf{z}_{2}^{\mathcal{G}_{n}}\leftarrow\mathcal{P}_{\mathbf{B}_{\mathbf{0},\mathbf{c}_{n}}}\Big{(}(\mathbf{D}\mathbf{s}-\mathbf{b}_{2})^{\mathcal{G}_{n }}\Big{)}=\left\{\begin{array}{ll}(\mathbf{D}\mathbf{s}-\mathbf{b}_{2})^{ \mathcal{G}_{n}},&\text{if }\|(\mathbf{D}\mathbf{s}-\mathbf{b}_{2})^{ \mathcal{G}_{n}}\|_{2}\leq\varepsilon_{n},\\ \varepsilon_{n}\frac{(\mathbf{D}\mathbf{s}-\mathbf{b}_{2})^{\mathcal{G}_{n}}}{ \|(\mathbf{D}\mathbf{s}-\mathbf{b}_{2})^{\mathcal{G}_{n}}\|_{2}},&\text{ otherwise.}\end{array}\right. \tag{30}\]
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 3 Related Work
### Classification of Available Systems
#### 3.1.3 Anonymous Direct Download  
A further group of proposed approaches directly download freely broadcast adverts through the use of specialized hardware and software. Advertisers store their ads at broadcasting stations that operate in publicly-accessible locations. As a user comes into proximity of these stations, his/herdevice downloads the available adverts. The user's device is then responsible for sorting through the collected ads and selecting the most relevant, while the rest are discarded.  
Anonymity is achieved through the use of protocols that enable mobile devices to connect to the broadcasting stations without disclosing of any information that exposes the user's identity, such as username, network address or physical address. Some of these systems also enable the users to connect and exchange adverts with each other. In these systems, a user downloads ads from a broadcasting station and then he/she propagates them to other users that he/she later comes into proximity with. This extends the reach of the broadcasting stations, but it also requires a certain level of trust among the users.
# Satellite-Assisted Disrupted Communications: IoT Case Study
## 3 Methodology
\[sin\alpha_{0}=\frac{R_{e}}{R_{e}+H}*\mathrm{cos}\epsilon_{0} \tag{5}\]  
The surface of the coverage area is calculated by Equation (6), where \(h\) is the height of the spherical cap:  
\[S_{Coverage}=2*\pi*R_{e}*h \tag{6}\]  
From the smallest triangle built including the \(\beta_{0}\) angle:  
\[cos\beta_{0}=\frac{R_{e}-h}{R_{e}}=1-\frac{h}{R_{e}}\Rightarrow\frac{h}{R_{e} }=1-\mathrm{cos}\beta_{0} \tag{7}\]  
Finally, from Equations (6) and (7) we can express the satellite's footprint as a percentage of the Earth's surface area:  
\[Coverage(\%)=\frac{S_{Coverage}}{S_{Earth}}=\frac{2*\pi*R_{e}*h}{4*\pi*R_{e}^{ 2}}=\frac{1}{2}*(1-\mathrm{cos}\beta_{0}) \tag{8}\]  
Given the elevation angle and the satellites' altitude, we generate Figure 1:0 From this table, we can calculate the satellite footprint i.e., 0.62% for a 25\({}^{\circ}\) elevation angle and 600 km  
Figure 1:1 Variables for the calculation of satellite footprint. constellation, which will serve as the communication range in our experiments. Therefore, if a ground station is identified as the next hop of a satellite transmission, as determined by the Dijkstra algorithm, and is located within the communication range of the satellite, the transmission is feasible and is subsequently executed.
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 4 Scene Processing
### Mesh Simplification
```
1:procedureMeshSimplify(\(M=(V,T),\delta\))
2:\(n\gets 0\)\(\triangleright\) New number of vertices
3:for\(i\) in range of \(|V|\)do
4:\(x,y,z\leftarrow\lfloor V[i,:]\cdot\delta^{-1}\rfloor\)\(\triangleright\) Quantized vertex coordinates
5:if no vertex quantized to \((x,y,z)\) yet then
6:\(V^{\prime}[n]\gets V[n]\)
7:\(previous[x,y,z]\gets n\)
8:\(old2 new[i]\gets n\)
9:\(n\gets n+1\)
10:else
11:\(old2 new[i]\gets previous[x,y,z]\)
12:endif
13:endfor
14:for\(j\) in range of \(|T|\)do\(\triangleright\) Make new triangles
15:\(T^{\prime}[j]\gets old2 new[T[j]]\)
16:endfor
17:return\(M^{\prime}=(V^{\prime},T^{\prime})\)\(\triangleright\) Simplified mesh
18:endprocedure
```  
**Algorithm 1** Mesh simplification algorithm. Requires: \(V\), array of 3-D vertices; \(T\), concatenation of triplets of vertex indices, denoting triangles; \(\delta\), spatial quantization step.
# Mdpi
## 2 System Model
### Powerline Channel Model  
Powerline channels can generally be modeled as multipath channels. [PERSON] [25] proposed using the following frequency response \(H(f)\) to model the multipath effects caused by the powerlines:  
\[H(f)=\sum_{i=1}^{N}\text{path}\alpha_{i}\cdot\exp\left\{-(a_{0}+a_{1}f^{ \beta})d_{i}\right\}\cdot\exp\left\{-j2\pi f\tau_{i}\right\}, \tag{2}\]  
where \(N_{\text{path}}\) denotes the number of paths, \(\alpha_{i}\) is the weighting factor, \(a_{0}\) and \(a_{1}\) are the attenuation parameters, \(d_{i}\) is the length of the channel, \(\beta\) is the exponent of the attenuation factor, and \(\tau_{i}\) is the delay associated with the \(i\)-th path.
## 2 Geometry and CFD-Thermal Modeling  
### Governing Equations  
CFD simulations were carried out by solving the incompressible Reynolds-averaged Navier-Stokes (RANS) equations with ANSYS Fluent(r) (version 2019R1) software [59]. Reynolds-averaged continuity, momentum, and energy equations for incompressible flow are expressed as  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho\overline{u}_{i})}{\partial x _{i}}=0, \tag{1}\]  
\[\rho\frac{\partial\overline{u}_{i}}{\partial t}+\rho\overline{u}_{j}\frac{ \partial\overline{u}_{i}}{\partial x_{j}}=-\frac{\partial\overline{P}}{ \partial x_{i}}+\frac{\partial}{\partial x_{j}}\left(2\mu\overline{S}_{ij}- \rho\overline{u^{\prime}_{i}u^{\prime}_{j}}\right), \tag{2}\]\[\rho\frac{\partial\overline{T}}{\partial t}+\rho\overline{u}_{j}\frac{\partial \overline{T}}{\partial x_{j}}=\frac{\partial}{\partial x_{j}}\left(\frac{ \lambda}{c_{p}}\frac{\partial\overline{T}}{\partial x_{j}}-\rho\overline{u^{ \prime}_{i}T^{\prime}}\right), \tag{3}\]  
where, \(\rho,\mu,\lambda\) and \(c_{p}\) are the density, dynamic viscosity, thermal conductivity, and specific heat at constant pressure of the fluid, respectively. The mean strain rate \(\overline{S}_{ij}\) is defined as  
\[\overline{S}_{ij}=\frac{1}{2}\left(\frac{\partial\overline{u}_{i}}{\partial x _{j}}+\frac{\partial\overline{u}_{j}}{\partial x_{i}}\right), \tag{4}\]  
Reynolds stress tensor \(-\rho\overline{u^{\prime}_{i}u^{\prime}_{j}}\) and the turbulent heat flux vector \(-\rho\overline{u^{\prime}_{i}T^{\prime}}\) are modeled to close the system of governing equations. Reynolds stress tensor is expressed by the Boussinesq hypothesis as  
\[-\rho\overline{u^{\prime}_{i}u^{\prime}_{j}}=2\mu_{t}\overline{S}_{ij}-\frac {2}{3}\rho k\delta_{ij}, \tag{5}\]  
where eddy viscosity \(\mu_{t}\) and the turbulent kinetic energy \(k\) are calculated by a turbulence model. The turbulent heat flux vector is modeled with the simple gradient diffusion hypothesis (SGDH) as  
\[-\rho\overline{u^{\prime}_{i}T^{\prime}}=\frac{\mu_{t}}{Pr_{t}}\frac{\partial \overline{T}}{\partial x_{i}}, \tag{6}\]  
The turbulent Prandtl number (\(Pr_{t}\)) in the SGDH equation is assumed to be constant as 0.85. To evaluate the cooling performance on the target plate, the \(Nu\) number is calculated as  
\[Nu=\frac{hD}{\lambda}, \tag{7}\]  
where \(D\) denotes the orifice diameter. The convective HTC (\(h\)) is calculated as  
\[h=\frac{\dot{Q}}{A\left(\overline{T}_{w}-\overline{T}_{f}\right)}, \tag{8}\]  
where \(\dot{Q}\) is the heat transfer rate from the cooled wall, \(A\) is the wall area, and \(\overline{T}_{w}\) and \(\overline{T}_{f}\) are the Reynolds-averaged cooled wall and fluid inlet temperatures, respectively.
# New Materials and Effects in Molecular Nanomagnets
## 1 Introduction  
When thinking about nanomagnets from a physicist's point of view, usually zero-dimensional (0D) or one-dimensional (1D) magnets will come to mind. Such nanomagnets may be formed, e.g., from the ferromagnets iron, nickel, cobalt, or permalloy, or from the ferrimagnets of magnetite or nickel ferrite, to name just a few [1; 2; 3; 4; 5]. Ferromagnetic materials contain elementary magnets for which a parallel orientation is energetically favored, while ferrimagnets can be imagined as containing two antiparallelly oriented ferromagnetic sublattices with different magnitudes of magnetization, in this way also resulting in a net magnetization. Diverse shapes can be thought of, from square or round nanodots [6; 7] to magnetic nanowires, e.g., those used in the so-called Racetrack memory [8; 9; 10], to more complicated shapes, including 3D particles [11; 12; 13]. Combining different magnetic materials, e.g., a ferromagnetic and an antiferromagnetic one, can result in additional effects as a result of the surface interactions, such as the exchange bias [14; 15; 16; 17].  
Molecular magnets, in principle, have a similar hierarchy. Starting from single-molecule magnets (SMM) [18; 19; 20] and single-chain magnets (SCM) [21; 22; 23], more complex structures are also possible [24; 25; 26]. In general, molecular nanomagnets can be built without the aforementioned magnetic elements [27]; however, highly interesting molecular nanomagnets can be created by adding Mn\({}_{12}\), Fe\({}_{8}\), Mn\({}_{4}\), or other metallic elements [28; 29; 30].  
Here, we give a brief overview of the principle of why even purely organic molecules can exhibit ferromagnetism, followed by sections reporting on recent advances in SMMs, SCMs, and other structures prepared from purely organic molecules or containing typical inorganic ions, such as Mn\({}_{12}\) and Fe\({}_{8}\), concluding with possible applications, e.g., in data storage. The review is organized as follows: Starting with a brief overview of the magneticanisotropy of metal centers in molecular complexes and magnetic interactions via ligands, we review the most recent findings dealing with SMMs, followed by SCMs. In both sections, the information is ordered according to materials or material classes, respectively. These mainly experimental sections are followed by an overview of the recent theoretical approaches and methods. Finally, the most recent reviews dealing with special fields of molecular magnets are provided for deeper reading.