esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Collection  
Under the auspices of the KRIPIS-THESPIA-II project - funded by the General Secretariat of Research and Technology of Greece, the Atmospheric Research Team of the National Observatory of Athens derived TMYs (Typical Meteorological Years) for 33 locations across Greece. A TMY is a complete year of data consisting of hourly or daily values of meteorological and/or radiometric  
Figure 2: Bar charts showing the frequency of occurrence (in %) of the sky conditions over a complete year in climatic zone A: first raw, Irakleio (left), Kalamata (right); climatic zone B: second raw, Agrinio (left), Lesvos (right); climatic zone C: third raw, Alexandroupoli (left), Tripoli (right); climatic zone D: fourth raw: Kastoria (left), Serres (right). For information about the four climatic zones in Greece, see Section 2.2.  
parameters; each TMM (Typical Meteorological Month) of the TMY is chosen via a specific statistical procedure for that month, which is closer to the long-term climatic characteristics of the site for that month. The generation of a TMY is implemented from a database containing the selected parameters in a span of years--usually 15-20 and most preferably 30 years. More details about the generation of the TMYs for Greece are given in [PERSON] et al. [57], where data of meteorological parameters were obtained from 33 HNMS (Hellenic National Meteorological Service) stations during the period 1985-2014 (30 years; see Table 2). For each of the 33 sites, five different TMYs were derived for equal applications; i.e., TMY-Meteorology-Climatology (TMY-MC), TMY-Bio-Meteorology (TMY-BM), TMY-Agro-Meteorology-Hydroology (TMY-AMH), TMY-Energy Design for Buildings (TMY-EDB), and TMY-Photovoltaics (TMY-PV). Each type of TMY contains the necessary meteorological and/or solar radiation parameters. The solar radiation (\(G_{\rm e}\), \(B_{\rm e}\), \(D_{\rm e}\)) values for the purpose of TMY generation at each of the 33 stations were derived from the available meteorological data (ambient temperature, relative humidity, atmospheric pressure, sunshine duration) via the MRM (Meteorological Radiation Model; see [58; 59; 60; 61]), because the HNMS stations do not measure solar radiation. Each of those TMYs consists of hourly values of the parameters considered. For the purpose of this work, the databases of the TMYs-PV at the 33 sites have been selected because the climatology of the atmospheric turbidity is more related to solar radiation/energy applications. Table 2 gives the geographical coordinates, climatic zones and altitudes of the 33 stations, while Figure 3 shows their location on the map of Greece. This map also shows the four climatic zones of the country for energy applications [62]. The delimitation of these zones has been based on three parameters: the heating degree days (HDD), the cooling degree hours (CDH) and the available SSR (surface solar radiation) in a region. Table 3 shows the range of those parameters for each climatic zone.
# Intensified Pozzolanic Reaction on Kaolinite Clay-Based Mortar
## 3 Results and Discussion  
### Heat of Hydration  
The heat flow evolution of kaolinite clay-based mortar is shown in Figure 3a. All samples have two main peaks regardless of SF addition. Abrupt increases of heat flow within 2 h were measured for all samples almost identically. This infers that experiments were reliably conducted, and the addition of SF does not alter the start of first hydration reaction. The second main peak initiated at 12 h and reached its maximum at 20 h in the Ref sample, whereas it starts quickly from 5 h in samples with SF  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample Name**} & \multirow{2}{*}{**Water/Binder**} & \multicolumn{2}{c}{**Total Binder (\%)**} & \multirow{2}{*}{**SF/Binder**} & \multirow{2}{*}{**Superplasticizer/Binder**} & \multirow{2}{*}{**Fine** Aggregate/Binder**} \\ \cline{3-3} \cline{5-5}  & & & **Kaolinite Clay/hydrated Lime** & & \\ \hline Ref & & & 0 & \\ SF\_7.5 & 0.4 & 70:30 & 0.075 & 0.06 & 3 \\ SF\_15 & & & 0.15 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mix proportions of kaolinite clay based mortar (by weight ratio of binder).  
This observation supports the acceleration of the start of the main hydration reaction and increases in the duration of its period due to the addition of SF. As a result, the main hydration reaction was significantly intensified. This eventually results in a larger amount of cumulative heat of hydration as shown in Figure 3b. As will be explained later, this intensified hydration reaction is the intended pozzolanic reaction between amorphous silica and hydrated lime.  
Although the samples with SF emitted more cumulative heats compared to Ref (Figure 3b), there is no noticeable difference in the heats between two samples with SF (the heats are 42.53 J/g and 44.67 J/g in SF_7.5 and SF_15, respectively). Thus, it can be concluded that an excessive addition of SF (e.g., more than 7.5 wt % of SF) scarcely contributes to the enhancement of hydration reaction during heat treatment period. However, it may improve the strength because of the physical filling effect by unreacted SF particles, which will be discussed later.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 2 Security AODV Routing Protocol
### The Process of Route Discovery  
#### 2.4.1 Generate RREQ Message  
When the route to the destination node needs to be obtained, the node broadcasts a RREQ message. The RREQ message has a signature for the invariant part and a hash chain for the variable part. If the intermediate node is allowed to reply, the RREQ message is generated in the form of a double signature extended message with additional RREP signature fields. Otherwise, the RREQ message is generated according to the RREQ signature extension message format.  
Figure 2: Route request (RREQ) message format of the security ad-hoc on demand distance vector (SAODV) protocol.
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 3 Sharing of Optical Access Networks
### Point to Point (PtP) Fiber  
The simplest optical network technology is the Point to Point (PtP) fiber, which provides a dedicated un-contended fiber path to each end-user towards the central office.  
PtP optical networks are most suitable when it comes to transferring high volumes of data for specific purposes such as enterprise access or FITE (fiber-to-the-telecom-enclosure). In many cases, the PtP optical link carries data for residential users up to a switching point, where traffic is then distributed across a number of other technologies (e.g., Ethernet and WiFi). However, in a small number of cases, it is also used as a residential access technology. PtP has the main disadvantage of requiring one fiber strand and one active port in the CO for each end-user, leading to increase in cost, energy consumption and footprint. These shortcomings have led to rethinking this architecture and considering other options in the last mile.  
PtP fiber provides a simple method for network sharing, as it can enable full physical unbundling. Different operators can simply patch their customers' network termination point into their own equipment.
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 2 Review of Related Works
### Comparison of Four Stochastic Algorithms  
Here, we compare the proposed _parameter optimization algorithm (paraOpt)_ with three stochastic algorithms, _random hill climbing (RHC)_, _simulated annealing (SA)_, and _genetic algorithm (GA)_.  
_RHC_ is a mathematical optimization technique that belongs to the family of local search algorithms. It starts with a random solution to the problem and continues to find a better solution by applying incremental change to the current solution until no improvement can be found. When _RHC_ is converged to a local minimum, it starts with a random initial parameter value. It uses very little memory and can find the optimal solution if the solution space of the problem is convex. Actually, our _paraOpt_ is one implementation of _RHC_ for parameter optimizations where the initial solution is given in a deterministic way to improve the solution quality, instead of a random one.  
_SA_ is a random local search algorithm with a non-deterministic search capability for the global optimum. _Annealing_ is the process of cooling and freezing a metal to produce the minimum-energy crystalline structure. _SA_ mimics this process by occasionally accepting a decreased fitness function to make a chance to escape from the local optimum. _SA_ can be regarded as one implementation of _RHC_.  
_GA_ is a metaheuristic method that is inspired by the process of natural selection. _GA_ can belong to the larger class of evolutionary algorithms. _GA_ usually generates a population of chromosomes or solutions randomly. Then, it iteratively improves them by repeating the _mutation_ of a selected chromosome and the _crossover_ of the two parent chromosomes among the population to place the new offspring into the new population. Finally, it returns the best solution from the population.  
Table 1 compares five features of them. For the _time complexity_, \(c\) represents the number of data to be evaluated, and \(n\) represents the number of parameters to be optimized in the problem, where the complexity for one iteration is analyzed. For the _number of parameters_, the number of the algorithm parameters to be well selected is shown for better performances. For the _deterministic initial solution_, if it generates an initial solution in a deterministic way, it is _yes_, and otherwise, _no_. For the _local search_, if it continues visiting neighbor solutions for the deep local search, it is _yes_, and otherwise, _no_. For the _hill climbing_, if it applies the hill-climbing procedure to escape from a local minimum, it is _yes_, and otherwise, _no_.  
This table indicates that _paraOpt_ has the same time complexity as _RHC_ and _SA_, while it satisfies all the features considered in this study.
## 6 Microbiome, Dysbiosis and Osteoporosis
### Gut Microbiome/Microbiota and OP  
Recently, [PERSON] et al. [13] identified and characterized a specific change in gut microbiota of OP patients, consisting of an increase of the total amount of intestinal bacteria, with a prevalence of Bacteroidetes over Firmicutes in the OP group, thus reporting a characteristic fingerprint of gut bacteria composition associated with OP (_Bacteroidetes_, _Prevotellaceae family_, _Ruminococcaceae family_, _Faecalibacterium genus_, and _Dialister genus_). Additionally, [PERSON] et al. found a greater abundance of _Actinomyces_, _Eggerthella_ and _Clostridium Cluster XIVa_ in OP subjects compared to a control group [23].  
The evidence of pathways through the gut microbiota can influence bone metabolism and its homeostasis lead to the hypothesis of a \"microbiota-skeletal\" axis [58; 59] (Figure 1). According to a series of studies, the gut microbiota is capable of:  
1. influencing the absorption of mineral (calcium, phosphate and magnesium) [60];
2. interfering with the immune system (through the production of proinflammatory and osteoclastigenic circulating cytokines that can guide bone resorption in inflammatory conditions [23; 60];
3. regulating the synthesis of serotonin and other neurotransmitters involved in bone metabolism [58; 59].  
Furthermore, in a state of dysbiosis, the intestinal mucosal barrier is more permeable and the abundance of the bacterial endotoxin lipopolysaccharideesacharide (LPS) easily increases in serum as well, favoring endotoxin and osteocl activity, thus negatively affecting BMD [58; 59].
## 3 Analysis of Safety Thickness
### Potential Function of Rock Systems  
Based on the model illustrated in Figure 3, the overall potential energy of the system corresponds to the variation in the beam's strain energy, encompassing bending strain energy, beam potential energy, and alterations in the external force's work performed.  
\[U=U_{1}+U_{2}-U_{3}-U_{4} \tag{4}\]  
where \(U_{1}\) and \(U_{2}\) are bending strain energy and potential energy increased by the system, \(U_{3}\) is the stemming energy from the combined effects of gravity and water pressure, and \(U_{4}\) results from the action of axial tectonic forces.  
According to mechanical analysis, the deflection function of the beam is denoted as \(y(x)\)  
\[y(x)=\frac{w}{2}\left(1-\cos\frac{2\pi x}{L}\right) \tag{5}\]  
where \(w\) denotes the deflection at the position \(x=L/2\). The deflection is zero at both ends of the beam (\(x=0\) and \(x=L\)).  
Based on the principles of bending theory derived from elasticity and material mechanics, the elastic strain energy function \(U_{1}\) for the beam can be expressed as:  
\[U_{1}=\frac{1}{2}\int_{0}^{L}M(x)\mathrm{d}\varphi \tag{6}\]  
where \(E\) represents the elastic modulus of the rock beam, while \(I\) denotes the moment of the rock beam; \(y^{\prime}\) and \(y^{\prime\prime}\) are the first and second derivatives of \(u(x)\).  
\[\left\{\begin{aligned} M(x)=EIy^{\prime\prime}\\ \mathrm{d}\varphi=\frac{\mathrm{d}s}{\rho}=\frac{M(x)}{EI}\mathrm{d}s\\ \mathrm{d}s=\sqrt{1+\left(y^{\prime}\right)^{2}}\mathrm{d}x\end{aligned}\right. \tag{7}\]  
By introducing variables and substituting Equation (7) into Equation (6), then making a Taylor extension for Equation (6), the function of elastic strain energy \(U_{1}\) is obtained  
\[U_{1}=\frac{1}{2}\int_{0}^{L}EI(y^{\prime\prime})^{2}\mathrm{d}s=\frac{EI\pi^{ 6}}{8L^{5}}w^{4}+\frac{EI\pi^{4}}{L^{3}}w^{2} \tag{8}\]  
Similarly, bending strain energy and potential energy increase, as expressed by \(U_{2}\), work undertaken by gravity and water pressure is expressed as \(U_{3}\), and work undertaken by axial tectonic force \(U_{4}\) is calculated.  
\[\left\{\begin{aligned} U_{2}&=\int_{0}^{L}qDy\sin \beta\mathrm{d}x\\ U_{3}&=\int_{0}^{L}\left(qD\sin\beta+p\right)(L-x)(y^{ \prime})^{2}\mathrm{d}x\\ U_{4}&=\int_{0}^{L}N\bigg{(}1-\sqrt{1-\left(y^{ \prime}\right)^{2}}\bigg{)}\mathrm{d}x\end{aligned}\right. \tag{9}\]  
Substituting Equations (8) and (9) into Equation (4), then the simplified function of total potential energy is  
\[\begin{array}{ll}U&=U_{1}+U_{2}-U_{3}-U_{4}\\ &=\frac{\pi^{2}}{4L}\Big{(}\frac{4 EI\pi^{2}}{L^{2}}-(qD\sin\beta+p)L-N \Big{)}w^{2}\\ &+\frac{EI\pi^{2}}{8L^{5}}w^{4}-\frac{1}{2}qDL\sin\beta w\end{array} \tag{10}\]
# Algorithmic Modified Denavit-Hartenberg Modeling for Robotic Manipulators Using Line Geometry
###### Abstract  
This paper proposes a kinematic modeling method for robotic manipulators by extracting the modified Denavit-Hartenberg (MDH) parameters using line geometry. For single-branched manipulators, various joint axes can be represented as lines using Plucker coordinates. The forward kinematics is derived by performing the product of matrices which are the exponential maps lifted from two kinds of exponential coordinates using the MDH parameters. For extracting MDH parameters, line geometry systematically analyzes the following: (1) the closest point between a point and line, (2) the closest distance and twist angle between two lines, (3) the common perpendicular line and its intersection points, and (4) classifies line relationships into collinear, distant parallel, intersected, and skewed cases. For each case, five parameters including twist angle, closest distance, common perpendicular direction vector, and both feet on a common perpendicular line are sequentially computed as results of the line geometry block. Finally, the aforementioned line geometry blocks are utilized to extract the four MDH parameters according to their definitions. The effectiveness of the proposed algorithm is verified by four examples including a typical Selective Compliance Assembly Robot Arm (SCARA) robot and three different commercial manipulators.  
Keywords:line geometry; modified Denavit-Hartenberg (MDH) parameters; screw axis; kinematic modeling
# Generative Image Steganography via Encoding Pose Keypoints
## 2 Relate Work
### Human Pose Estimation  
Human pose estimation (HPE) is a crucial computer vision technology that analyzes images or videos to automatically identify and localize key body parts, such as the head, shoulders, elbows, wrists, hips, knees, and ankles.  
[PERSON] et al. [29] introduced DeepPose, a deep learning model that directly regresses 2D coordinates of human keypoints from images using a convolutional neural network. Following this work, [PERSON] et al. [30] developed OpenPose with a dual-branch architecture that predicts heatmaps for joint points and their affinities while employing the Hungarian algorithm for accurate keypoint matching. However, OpenPose's real-time performance can be improved; thus, [PERSON] et al. [31] proposed ROpenPose by replacing VGG-19 with MobileNets in the framework and utilizing smaller convolutional kernels to reduce computational load and increase speed.  
YOLOv8-Pose [20] is an efficient multi-person pose estimation model designed for end-to-end keypoint prediction without the complex postprocessing typical of traditional heatmap-based methods. Its architecture comprises three main components: the backbone, neck, and head networks. The backbone uses the C2f module, replacing YOLOv5's C3 to enhance training speed and gradient flow by merging outputs from multiple bottleneck blocks for faster image processing and better feature extraction. The neck integrates Feature Pyramid Networks (FPN) and Path Aggregation Networks (PANet) to improve object detection across various scales by refining multiscale feature maps. The head processes these features with a decoupled design that separates classification from regression tasks, enhancing performance. The model's loss function includes classification loss, box loss, keypoint loss, and keypoint confidence loss; box loss is further improved using CIoU and Distribution Focal Loss (DFL). Finally, non-maximum suppression (NMS) eliminates overlapping predictions to enhance localization precision and efficiency.
## 3 Methodology
### C2f-D2 CN Module  
To enhance pedestrian detection, we designed the C2f_DPCN module using deep deformable convolution [32] and dynamic convolution [33] to improve the extraction of pedestrian features at different scales. By introducing deformable convolution and dynamic convolution into the C2f, the model can capture richer feature representations, enhancing its ability to analyze images and improve computational efficiency, which strengthens the extraction of pedestrian features at different scales.  
The proposed C2f_D2 CN module innovatively integrates deformable convolution and dynamic convolution into the C2f module and introduces a branch-stacking structure along with skip connections. This not only enhances multi-scale receptive field aggregation but also maintains computational efficiency, making it more suitable for dense pedestrian detection tasks.  
As illustrated in Figure 2, the C2f_D2 CN module initially employs a \(1\times 1\) convolution to adjust the number of channels of the feature maps. Subsequently, a split operation is utilized to partition the features, effectively replacing the conventional \(1\times 1\) convolution. By stacking multiple deformable convolution and dynamic convolution modules, the network significantly expands its receptive field. Furthermore, the incorporation of an increased number of skip connections enhances the richness of the gradient flow, thereby facilitating the extraction of more diverse and multi-scale pedestrian features.  
For a standard convolutional layer, the parameter count is \(\text{C}_{\text{in}}\times\text{C}_{\text{out}}\times\text{K}\times\text{K}\), whereas for dynamic convolution, the number of parameters is \(\text{C}_{\text{in}}^{2}+\text{C}_{\text{in}}\times\text{M}+\text{M}\times \text{C}_{\text{in}}\times\text{C}_{\text{out}}\times\text{K}\times\text{K}\). The calculation of FLOPs includes the computation of coefficient generation and the dynamic weight fusion. However, the coefficient generator employs dimensionality-reduced operations, its computational cost is quite low, and thus its overhead can be ignored compared to the entire convolution operation. The total FLOPs of dynamic convolution do not significantly increase compared to the standard convolution layer. This design enables the C2f-D2 CN module to retain the efficient feature processing capabilities of the C2f module while leveraging the adaptability of dynamic convolution to maintain stable model  
Figure 2: Structure of C2f-D2 CN, the symbol \({}^{*}\) represents multiplication operation.  
performance and reduce computational burden. Consequently, the model's performance in detecting pedestrians in dense environments is substantially improved.
## 4 Conclusions  
Our study results present the ability of SBP-encapsulated _R. zopfii_ and _P. putida_ to biodegrade EE2 within a relatively short period of time, with an emphasis on a better biodegradation rate in a secondary effluent medium. This study shows that the SBP encapsulation method provides a selective and highly effective biological treatment tool which can be easily implemented in the WWTP infrastructure. It overcomes three main obstacles that previously caused the failure of biological bioaugmentation treatments: It enables the achievement of long-term selective biomass implementation inside WWTPs, it controls the location of the culture (for example, in a net or perforated box) and it yields a relatively rapid biodegradation rate which can be synchronized with the HRT of WWTPs. To date, most studies exploring the biodegradation of low EE2 concentrations demonstrated a relatively low degradation rate in the range of hours to days using a suspended _R. zopfii_ culture. However, use of a suspended additive culture is impractical in domestic wastewater treatment plants. In this study it was shown that SBP-encapsulated _R. zopfii_ cultures exhibit a faster EE2 biodegradation rate compared to other studies, in particular in the secondary effluent medium. Future studies are required in order to elucidate the environmental conditions which impact the encapsulated cultures during the microppollutant biodegradation process and the mechanisms involved in this treatment approach.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/1/336/s1](http://www.mdpi.com/2076-3417/10/1/336/s1)]([http://www.mdpi.com/2076-3417/10/1/336/s1](http://www.mdpi.com/2076-3417/10/1/336/s1)). Light microscope images of encapsulated _R. zopfii_ culture morphologies in activated SBP microfiltration capsules over time.  
Conceptualization, O.M. and E.K.; methodology, O.M., E.K. and D.A.; formal analysis, A.K.; investigation, Y.R., V.C.-Y. and M.E.K.; resources, O.M. and E.K.; data curation, Y.R., V.C.-Y., O.M and E.K.; writing--original draft preparation, O.M. and E.K.; writing--review and editing, O.M., D.A., H.M. and E.K.; visualization, E.K.; supervision, O.M. and E.K.; project administration, O.M.; funding acquisition, O.M. and E.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Israeli Ministry of Science, Space and Technology (MOST) [grant number 3-12375]. Additionally, this research was also funded by Kinneret Research Fund.  
The authors declare no conflicts of interest.