esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Classical structures are modeled against the destructive effects of earthquakes by providing sufficient damping, rigidity, and strength parameters. However, it is difficult to predict with confidence the degree of damage earthquakes will cause to structures, since the time and magnitude of earthquakes are not exactly known. Traditional seismic designs aim to protect structures from earthquake movements by using some estimates and predictions. In earthquake-resistant structural design, the basic philosophy is to prevent the structure from collapsing completely, even if it is damaged beyond repair due to earthquake effects [1]. This methodology is not appropriate for structures like hospitals, data centers, fire stations, and bridges that must remain operational during and after an earthquake. In order for such structures to continue operating without interruption, it is necessary to implement innovative strategies that extend beyond the conventional tools and techniques typically employed. One of the most effective of these strategies is the implementation of supplementary damping systems [2].  
In conventional structures, natural damping is usually quite low, ranging from 0.1% to 5%; however, even a modest increase in modal damping through the use of external dampers, devices, or damping materials can provide significant improvements in structure response [3]. Supplemental damping systems can be categorized according to structural properties and characteristics. Among the additional damping systems classified as passive, active, and semi-active, the most common and most studied are passive damping systems. This method consists of installing mechanical devices at certain locations along the structure or adding damping materials that control the structure vibrations without any energy source. Passive damper systems are basically divided into three groups. These groups are seismic isolation and energy-absorbing and energy-transferring systems. In high-rise buildings, energy-absorbing and -transferring systems typically offer benefits against the effects of earthquakes. These dampers provide a supplemental viscous damping capacity to the structure. In this way, the hysteretic energy required to be consumed by the structural elements is reduced. Seismic isolation, one of the passive additional damping systems, is placed on the base floor of the buildings. Because of its low horizontal rigidity, the structure has a longer period, which reduces spectral acceleration (Figure 1) [4; 5].
# Sorbets as Functional Food Products, Unexplored Food Matrices, Their Challenges, and Advancements
## 2 Functional Food Vehicles
### Developmental Considerations for a Functional Sorbet
#### 2.2.4 Stabilizers  
Stabilizers are added to ice creams and sorbets to provide smoothness in body and texture, reduce ice crystal growth during storage, as well as increase the stability of proteins. A relatively recent example is the addition of cellulose nanofibrils, which are a fibrous banana extract that have reduced the melting rate as well as extended the shelf life of ice creams and improved their sensory properties [48]. A second example is the protective effects that sugars have over proteins exposed to heat treatments during manufacturing processes [49; 50]. Sugars stabilize proteins by inducing hydration via hydrogen bonding mechanisms to folded protein structures by acting as a substitute for water [51], as well as inhibiting heat coagulation that causes denaturation [52]. Reducing sugars, such as trehalose, are considered as exceptional protein stabilizers, as they are not affected by color and flavor changes that occur during denaturation processes such as the Maillard reaction [49]. This is due to trehalose's structural rigidity, which allows this reducing sugar to show high heat and pH stability, as well as retaining enzyme activity in a solution in its freeze-dried state. Trehalose, in turn, is an ideal ingredient for use in the industrial food and pharmaceutical industries [49]. Examples of how this potentially can benefit human consumption are during the development of functional foods that contain added proteins and amino acids, which may become exposed to high heat. This includes dairy products prior to freezing, as well as heat-treating fruit juices as a base for sorbed production to eliminate microbes.  
Similarly, foods that require below 0 \({}^{\circ}\)C storage conditions, such as ice creams and sorbets, benefit from the addition of stabilizers. Stabilizing sugars such as oligosaccharides can form more hydrogen bonds during freeze-drying processes, which decreases denaturation as well as improves packing density [53]. The addition of traditional stabilizers such as sums (guar, locust bean and cellulose gum) also influences the viscosity and rheological properties of ice creams, which reduce the degree of ice crystal growth and result in improved stability and texture [34]. Similarly, the addition of whey proteins into a sorbet acts as a stabilizer, as whey proteins have a strong water-binding capacity that results in less free water being available to crystallize during the freezing process [34].
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### Dedicated Applications
#### 2.7.3 Time-Driven  
In this category, applications aim to periodically evaluate the measurements of the given physical quantities. Note, for example, the application of WSNs in the monitoring of the atmospheric pressure of a large area or variations in ground temperature at a small volcanic site [47]. These applications react in a given period sink. The sensors take a sample of the environment and transmit it by means of an appropriate communication protocol to the final sink. For the smooth operation of these applications, with better quality of service, it is important: that the sampling frequency is sink-chosen, that the connectivity of the network is kept under control, and to have communication protocols adapted to that kind of data. The sensor nodes used in this type of application can sleep, i.e., turn off their radio during periods of inactivity in order to manage the energy consumption of the network.
## 4 Conclusions  
Ridge waveguides with propagation losses as low as 0.4 dB/cm have been fabricated in Pr\({}^{3+}\)-doped YLF substrates using carbon ion (C\({}^{3+}\)) implantation with doses in the range of 2 \(\times\) 10\({}^{14}\)-6 \(\times\) 10\({}^{15}\) ions/cm\({}^{2}\) and precision diamond blade dicing. These low loss values are comparable to those of Pr:YLF waveguides produced by other methods such as fs laser inscription [14, 15, 16]. However, the ridge geometry realized here allows for both a stronger optical mode confinement in the lateral direction and a higher overlap of pump and laser wavelengths. During spectroscopic investigations, ion implantation did not have a negative influence on the fluorescence spectra nor on the lifetime of upper levels. Small-signal amplification in ridge waveguides of 6.5 dB/cm at 607 nm and 5 dB/cm at 639 nm wavelength was achieved, i.e., values that are significantly higher than the measured propagation losses. Thus, the obtained results are promising for the realization of efficient low-threshold waveguide lasers operating in the visible spectral range. Our future work will be focused on further reductions in propagation losses by optimizing the cutting parameters and on the realization of ion-implanted Pr:YLF ridge waveguide lasers pumped by high-power blue InGaN laser diodes. Using ion-implanted Pr:YLF ridge waveguides, visible lasers at various wavelengths (i.e., green, orange, and red) with high power and high beam quality could be integrated into a single crystal, which is of high significance for applications in display technology, communication, metrology, biology, and medicine.  
Not applicable. Not applicable.  
The data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflicts of interest.
# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 2 Materials and Methods
### Eligibility Requirements and Study Selection  
The inclusion criteria encompassed studies addressing the ability of ML models to predict mortality among HD patients. Articles of any study design, written in English, and with full text available were included. No limitations concerning the publication date were applied. We omitted studies with inadequate data for extraction. Reviews, book chapters, theses, editorials, letters, conference papers, non-English studies, and animal or in vitro research were excluded.  
Two independent reviewers evaluated the articles obtained from the four electronic databases for eligibility by reviewing the title, abstract, and full text in an Excel spreadsheet. A third impartial reviewer resolved any disagreements.
## 2 Our Proposed Methods
### Message Extraction  
Since E-channel is intact and the target embedding rate \(ER_{tar}\) is considered as a secret key, the receiver can reconstruct the E-channel mapping table similar to Table 1. Then, every pixel, \(\mathbf{P}^{\prime}=\left(P_{R}^{\prime},\ P_{G}^{\prime},\ P_{B}^{\prime},\ P_{E}^ {\prime}\right)\), in the stego image can be processed pixel-by-pixel. For each pixel, the receiver refers to \(P_{E}^{\prime}\) to obtain the optimal base \((b_{R},\ b_{G},\ b_{B})\) from the E-channel mapping table. Afterwards, one can extract the secret messages, \((d_{1},\ d_{2},\ d_{3})\), using the modulus operator with the divisor \((b_{R},\ b_{G},\ b_{B})\) for every component, where \((d_{1},\ d_{2},\ d_{3})=\left(P_{R}^{\prime}\ mod\ b_{R},\ P_{G}^{\prime}\ mod\ b_{G},\ P_{B}^{\prime}\ mod\ b_{B}\right)\). Finally, the receiver can apply Equation (13) with the parameter, \(n=3\), using the available \((d_{1},\ d_{2},\ d_{3})\) to composite the \(M\)-ary secret message \(S_{M}\), where \(M=\prod_{i=1}^{n}b_{i}\), and \(\mathbf{B}=(b_{1},\ b_{2},\dots,\ b_{n})\) represents a general \(n\)-tuple optimal base:  
\[S_{M}=d_{1}+\sum_{i=2}^{n}\left[d_{i}\times\left(\prod_{j=1}^{i-1}b_{j}\right)\right] \tag{13}\]  
Following up on our previous example, we first read in the stego pixel \(\mathbf{P}_{1}^{\prime}=\left(P_{R}^{\prime},\ P_{G}^{\prime},\ P_{B}^{\prime},\ P_{E}^ {\prime}\right)=\left(164,\ 87,\ 176\right)\). Then, referring to the E-channel mapping table with \(P_{E}=126\), as shown in Table 1, we derive the optimal base, \((b_{R},\ b_{G},\ b_{B})=\left(5,\ 5,\ 7\right)\). Next, we extract the message digits \((d_{1},\ d_{2},\ d_{3})=\left(164\ mod\ 5,\ 87\ mod\ 5,\ 17\ mod\ 7\right)= \left(4,\ 2,\ 3\right)\). Finally, applying Equation (13), we composite the final 175-ary secret message, \(S_{M}=4+\left[2\times\left(5\right)+3\times\left(5\times 5\right)\right]=89_{175}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(j\) & \(k\) & Exchange & 1 st & 2 nd & 3 rd & 4 th & 5 th & 6 th & 7 th & 8 th \\ \hline \multicolumn{3}{c}{Input index \(\{\pi t\}\)} & 5 & 8 & 2 & 6 & 7 & 1 & 4 & 3 \\
1 & \(0_{2}\) & No change & 5 & 8 & 2 & 6 & 7 & 1 & 4 & 3 \\
2 & \(2_{3}\) & \((8,2)\) & 5 & 2 & 8 & 6 & 7 & 1 & 4 & 3 \\
3 & \(1_{4}\) & \((5,6)\) & 6 & 2 & 8 & 5 & 7 & 1 & 4 & 3 \\
4 & \(4_{5}\) &
## 3 Results  
### Spherical Object Detection  
The performance of the new detection algorithm was verified under two different lighting conditions: a spherical object in a dark space with directional lighting, as shown in Figure 7(a1), and the same spherical object in a bright space with excessive backlight, as shown in Figure 7(b1). Figure 7(a1,b1) show the HSV filter detection results, and Figure 7(a2,b2) show the results of the new algorithm. Figure 7(a3,b3) represent the estimated radii over time under these lighting conditions. While the HSV filter captures a small portion of the spherical object, the new algorithm accurately detects it. Moreover, the radius calculated by the HSV filter strongly fluctuates over time, which leads to unstable MAV control. However, the new algorithm provides stable results. The mean absolute error (MAE) of the spherical object detection is presented in Table 1. Additional experiments were conducted to consider the effects of flickering lights and other surrounding objects on the hybrid detection algorithm. Details on the additional experiments are provided in  
Figure 6: Flowchart of the new hybrid detection algorithm; \(x_{h}(t)\), \(y_{h}(t)\), and \(r_{h}(t)\) denote the position along the \(x\)-axis, the position along the \(y\)-axis, and radius in the image calculated by the HSV filter at time \(t\), respectively. \(x_{c}(t)\), \(y_{c}(t)\), and \(r_{c}(t)\) are the position along the \(  
Supplementary material (Figures S1-S3). These results suggest that the new algorithm can provide accurate and robust detection of spherical objects under harsh lighting conditions.
## 3 Evaluation of Design Shape Coefficients  
As introduced in the previous section, the roof snow load can be obtained by multiplying two random variables: the ground snow load, \(s_{g}\), commonly described in Europe by an extreme value type I distribution (Gumbel), and the shape coefficient or conversion factor (\(\mu_{i}\)) described by a log-normal distribution. Under the assumption of independence of the two variables, the probability distribution function for the annual maximum of the roof snow load can be derived as  
\[f_{S}(s)=f_{S_{g}}(s)f_{\mu_{i}}(\mu_{i}). \tag{4}\]  
Consequently, the probability that a given value of roof snow load \(s\) is not exceeded can be computed as  
\[F_{S}(s)=\int_{-\infty}^{s}\left[\int_{-\infty}^{\infty}f_{S}\left(\mu_{i}, \frac{s}{\mu_{i}}\right)\frac{1}{\mu_{i}}d\mu_{i}\right]ds. \tag{5}\]  
No closed forms of these joint _pdfs_ or cumulative distribution functions (CDFs) are available, but they can be obtained by suitable numerical approaches. The evaluation of the shape coefficient for flat roofs (\(\mu_{1}\)) in a relevant case study is illustrated in the following.  
The case study illustrates the evaluation of the shape coefficient \(\mu_{1}\) for flat roofs in different exposure conditions, considering the Lodi weather station in Italy where the snow load on ground is characterized by \(COV_{g}=1\).  
In the study, the typical European values already given in Table 2 have been assumed for mean values (\(\overline{X}\)) and standard deviations (\(\sigma\)) of the shape coefficients.  
In that case, Equation (5) is the volume under the joint _pdf_ delimited by the vertical sectional area described by constant \(s\) values [26], i.e., the black curves in Figure 3, which can be computed by using the Monte Carlo method.  
Once the characteristic value of roof snow load \(s_{k}\) is derived, with an annual probability of exceedance \(p=1-F_{S}(s_{k})\), the design shape coefficient \(\mu_{1,d}\) results in  
\[\mu_{1,d}=\frac{s_{k}}{s_{g,k}}, \tag{6}\]  
where \(s_{g,k}\) is the characteristic value of the ground snow load.  
In Figure 3, the evaluation of design shape coefficient is provided by the intersection between the red dashed line corresponding to the characteristic ground snow load \(s_{g,k}\) and the red curve corresponding to the characteristic value of snow load on roofs \(s=s_{k}\). In the  
Figure 3: Joint probability density function (_pdf_) of ground snow load (\(s_{g}\)) and shape coefficient (\(\mu_{1}\)) for flat roofs (Lodi weather station) under different exposure conditions (black curves represent a constant roof load (\(s\)), while the red curve corresponds to \(s=s_{k}\)).  
considered case study, assuming an annual probability of exceedance \(p=0.02\), the design values of the shape coefficients (\(\mu_{1,d}\)) are thus equal to 0.91, 0.86, and 0.65 for sheltered, normal, and exposed conditions, respectively.
## 2 Background
### Fine-Tuning and Instruction Tuning  
The pre-trained foundation model requires fine-tuning to be useful for a specific task. Fine-tuning (FT) the model on labeled data can improve its results and reduce the complexity of prompt engineering, serving as an alternative to retrieval-augmented generation (RAG) [17]. Other reasons to fine-tune include exposing the model to new or proprietary data not covered during pre-training. An important reason to fine-tune large language models is to align their responses with human expectations when provided with instructions through prompts. This process, known as instruction tuning, involves further training LLMs on a dataset of supervised pairs, thus bridging the gap between an LLM's next-word prediction objective and the user's goal of having the model adhere to human instructions [21; 26].  
Fine-tuning adjusts the model's parameters, \(\theta\), to better predict a target dataset, \(D=\{(x,y)\}\), where x is the input and y is the output. The objective is to minimize the negative log-likelihood of the data:  
\[\mathcal{L}_{fine-tune}(\theta)=-\frac{1}{|D|}\sum_{(x,y)\in D}logp_{\theta}(y |x), \tag{2}\]  
where \(\mathcal{L}_{fine-tune}\) is a loss function and \(p_{\theta}(y|x)\) is the model's conditional probability of output y given input x.  
Figure 3: Configuration task example.  
Instruction tuning (IT) involves further fine-tuning the model using a dataset, \(D_{inst}=\{(x_{inst},\,y_{inst})\}\), where \(x_{inst}\) includes the task instructions and \(y_{inst}\) is the expected response. The goal is to align the model's behavior with human instructions. The loss function is similar but adapted to instruction-focused tasks:  
\[\mathcal{L}_{fine-tune}(\theta)=-\frac{1}{|D|}\sum_{(x_{inst},y_{inst})\in D_{ inst}}logp_{\theta}(y_{inst}|x_{inst}), \tag{3}\]
# Holistic Utility Satisfaction in Cloud Data Centre Network Using Reinforcement Learning
## 3 Modelling of the CDCN
### The Utility Modelling for Cloud Data Centre Provider  
The utility function of the cloud data centre manager/provider has a close relationship with the resource usage and energy consumption costs. If we represent the cloud-assigned resource price for user \(i\) for each time frame \(t\) with \(\mathcal{I}^{(i)}(t)\) and energy consumption cost of CDC \(j\) at each time frame \(t\) by \(\xi_{j}(t)\), the utility function associated with cloud provider can be modelled as described in Equation (7):  
Figure 2: Flow diagram of calculating user utility functions.  
\[\mathcal{U}^{(CP)}(t)=\mathcal{U}^{(CP)}_{max}\bigg{(}1+k_{7}\mathbf{exp}\bigg{(}- \alpha_{7}\big{(}\sum_{i=1}^{M(t)}\mathcal{I}^{(i)}(t)-\sum_{j=1}^{N(t)}\xi_{j}( t)\big{)}\bigg{)}\bigg{)}^{-1} \tag{7}\]  
\(\mathcal{U}^{(CP)}_{max}\) is the maximal cloud-side utility. \(\mathcal{I}^{(i)}(t)\) is in fact a resource pricing metric for user \(i\). It is a weighted summation of allocated resources (RAM, CPU, connection bandwidth, and storage capacity) of the CDCN to a specific cloud customer \(i\) for time frame \(t\), as described in Equation (8):  
\[\mathcal{I}^{(i)}(t)=\tau\sum_{j=1}^{N(t)}\bigg{(}w_{1}(t)c^{(i)}_{j}(t)+w_{2 }(t)d^{(i)}_{j}(t)+w_{3}(t)g^{(i)}_{j}(t)+w_{4}(t)b^{(i)}_{j}(t)\bigg{)} \tag{8}\]  
For the description of the parameters \(w_{i}\), please refer to Table 2.  
Similar to [57], the energy dissipation cost for CDC \(j\) for time frame period \(\tau\) can be represented as \(\forall j=1,2,\cdots,N(t)\), according to Equation (9):  
\[\xi_{j}(t)=\tau\bigg{(}\beta_{1j}(t)\sum_{i=1}^{M(t)}c^{(i)}_{j}(t)+\beta_{2j} (t)\sum_{i=1}^{M(t)}d^{(i)}_{j}(t)+\beta_{3j}(t)\sum_{i=1}^{M(t)}g^{(i)}_{j}(t) \bigg{)} \tag{9}\]  
where \(\beta_{1j}(t)\), \(\beta_{2j}(t)\), and \(\beta_{3j}(t)\) represent the amount of the relative per-unit price of the CPU, RAM, and storage, which is allocated from CDC \(j\) to all active cloud users at time slot \(t\), respectively.  
The schematic diagram associated with the cloud provider utility calculation process is depicted in Figure 3. As can be seen, for calculating the cloud provider's utility function, the information regarding the cloud data centres' energy dissipation and resource prices associated with cloud users is needed.  
Figure 3: Schematic diagram of calculating cloud provider utility function.
# Optimization of Slotted Steel Plate Shear Walls Based on Adaptive Genetic Algorithm
[22] comprehensively analyzed the hysteretic curves, load-bearing capacity, lateral stiffness, and energy dissipation coefficients across unstiffened, cross-stiffened, perforated, and slitted SPSW configurations. The emerging design innovations include edge stiffeners [23], inclined slots [24], and rhombic/non-uniform slot patterns [25,26]. Recent advancements have focused on hybrid SSPSW-frame systems, exemplified by prefabricated self-centering steel frames [27], buckling-restrained SSPSWs [28], and cold-formed steel shear walls with slitted sheathing [17]. The critical parameters governing the lateral stiffness and load capacity include the slot quantity and the inter-slot column width-to-height ratios and width-to-thickness ratios. Additional influential factors encompass the edge stiffener arrangements, corner instability mitigation, and bolt slippage control. Ensuring the optimal energy dissipation while maintaining structural reliability necessitates a rigorous slotted configuration design, underscoring the paramount importance of parametric rationalization for SSPSW performance enhancement. Building upon this research framework, this study systematically investigates the critical parameters governing the mechanical behavior of SSPSWs. The methodology integrates an enhanced genetic algorithm (IGA) with ABAQUS FE simulations to conduct comprehensive performance evaluations. Specifically, the SSPSWs are optimized as seismic damping components, prioritizing ductility enhancement through slit pattern refinement. The research objectives encompass three key aspects: (1) the identification of high-performance slit geometries that balance stiffness and energy dissipation; (2) a comparative evaluation of the SSPSW variants with optimized slit configurations under cyclic loading; and (3) the development of design recommendations for practical SSPSW applications. This integrated approach enables a quantitative assessment of the ductility-stiffness tradeoffs while establishing reliable correlations between the slit parameters and structural performance metrics.
# Communication Network Standards for Smart Grid Infrastructures
## 2 Network Standards That Support Communication in Smart Grid Infrastructures  
Given the constantly evolving field of telecommunications, standardization organizations provide standards that clearly define the communication parameters between all the key components in a smart grid network [2; 7; 10]. The various smart grid applications and the specific features of the employed telecommunication technologies are presented in detail below.