esa-sceva/satcom-chunk-collection huggingface dataset:
# Interpretable Optimization: Why and How We Should Explain Optimization Models
## 4 Analysis of Near-Optimal Solutions
### A Degenerate Class of Problems  
Mathematical optimization is traditionally aimed at finding a single optimal solution that either minimizes or maximizes an objective function while adhering to a set of constraints. However, real-world optimization problems commonly exhibit multiple quasi-optimal solutions, meaning that different configurations can lead to nearly identical outcomes. This phenomenon is evident across numerous domains, aligning with findings in combinatorial optimization, where solution spaces frequently contain clusters of similarly effective solutions [23].  
Figure 5: Near-optimal solution analysis techniques.  
For example, different network expansion strategies in power systems may yield comparable cost and reliability metrics despite involving distinct infrastructure investments [76]. Likewise, in healthcare resource allocation, varying bed capacity expansion strategies can lead to similar patient outcomes, offering greater flexibility in decision-making [10]. This suggests that decision-makers should not be restricted to a single recommendation but instead consider a spectrum of viable alternatives--which we will refer to as quasi-optimal alternatives (QOAs)--that are near-optimal or quasi-optimal solutions, defined as a set of solutions that remain close to the optimal one within a specified tolerance [77].
# Building Information Modelling and Energy Simulation for Architecture Design
## 5 Modelling and Pre-Analysis of a Simplified Building
### First Results of the Analysis  
According to the aim of this paper, some advantages and disadvantages were detected already in this step. The main advantages are:  
* Automatic construction of the BEM starting from BIM: The model obtained contains in itself a lot of fundamental information for energy analysis, so a few other parameters must be integrated;
* constant updating: Any type of modification made on the BIM model is automatically reflected in the BEM and in the EcoDesigner STAR cards, without any loss of information;
* reliability: The climatic data of the Strusoft server comply with other types of data used by certified analysis programs (see EnergyPlus); furthermore, the calculations are consistent with the parameters set and the result, for example, in terms of consumption, and is plausible if compared with similar buildings;
* good definition of the parameters: The thermo-physical attributes on which one can act are many, allowing us to represent even the most complex elements;
* optimized workflow: Thanks to the use of a single software and a single type of file, it is possible to work quickly and accurately without the loss of data that may occur in the passage of the file from one program to another. Furthermore, all EcoDesigner STAR tools are best calibrated to operate without conflicts with those present in ArchiCAD;
* design support: This tool is not to be used a-posteriori, or after the project is finished, to know only its behavior from an energy point of view. It must be used during the design phase to ensure the maximum result in terms of performance; so, the final simulation refers to a building designed in a truly sustainable way.  
Even if they are not many and do not affect the use of the program, some constraints were found and listed following:  
* Poor definition of the parameters relating to the systems (especially heating);
* and therefore, it is not possible to insert the calculation of terminals of any kind or the performance of the heating systems. It makes the calculation of these aspects more limited. For this reason, further investigations with other programs should be carried if necessary.
* A separate reasoning must be made for thermal bridges. They are connected to the ArchiCAD Detail tool and they are configured as separate 2D drawings. If a material or a type of construction element changes, the individual modification must be made on the detail and the thermal bridge recalculated. Generally, this operation is not long and it is easy, but for large architectural complexes with numerous thermal bridges where changes need to be made, it is long and hard.
## 3 Problem Formulation  
Due to differences in the calculation and communication capabilities of UDs, we aim for minimizing FL delay under imperfect CSI by jointly optimizing the user selection vector \(\mathbf{\mu}=[\mu_{1},\mu_{2},\ldots,\mu_{K}]^{T}\), UDs' CPU frequency \(\mathbf{f}=[f_{1},f_{2},\ldots,f_{K}]^{T}\), transmit power \(\mathbf{p}=[p_{1},p_{2},\ldots,p_{K}]^{T}\), the IRS phase shifts vector \(\mathbf{\phi}=[\phi_{1},\phi_{2},\ldots,\phi_{N}]\), interrupt transmission rate \(\mathbf{\
u}=[\
u_{1},\
u_{2},\ldots,\
u_{K}]^{T}\). Assuming that each UD uploads its local model after finishing local model computation. Moreover, the BS starts aggregation until receiving the local model from all UDs. The total delay of FL under one communication round can be expressed as \(t^{lu}=\max\limits_{\mathcal{K}}\mu_{k}(\tau_{k}^{l}+\tau_{k}^{u})\). Thus, the delay minimization problem is formulated as \[P0: \min_{\mu,\
u,\mathbf{p},\mathbf{f},\mathbf{\phi}}\max_{k\in\mathcal{K}}\mu_{k}(L \frac{c_{k}D_{k}}{f_{k}}+\frac{d}{v_{k}}), \tag{15}\] \[s.t. L\frac{\alpha_{k}}{2}f_{k}^{2}c_{k}D_{k}+\frac{dp_{k}}{v_{k}}\leq e _{k\max},\ \ \forall k\in\mathcal{K},\] (16) \[\Pr[R_{k}<v_{k}]\leq\delta_{0},\forall k,\] (17) \[|\phi_{n}|^{2}=1,\ \ \forall n\in\mathcal{N},\] (18) \[f_{\min}\leq f_{k}\leq f_{\max},\ \ \forall k\in\mathcal{K},\] (19) \[0\leq p_{k}\leq p_{\max},\ \ \forall k\in\mathcal{K},\] (20) \[\sum_{k\in\mathcal{K}}\mu_{k}=\mu,\] (21) \[\mu_{k}\in\{0,1\},\ \forall k\in\mathcal{K}, \tag{22}\]  
where Equation (16) denotes the energy constraint and \(e_{k\max}\) is the maximum energy consumption of the \(k\)-th UD in FL; Equation (17) is the constraint of outage probability and \(\delta_{0}\) is limit of the outage probability; Equation (18) represents the range of the phase shift; Equations (19) and (20) denote the range of CPU frequency and transmit power, where \(f_{\max}\) and \(f_{\min}\), respectively, represent the upper and lower bounds of \(f_{k}\) range, and \(p_{\max}\) is the maximum transmit power; Finally, Equations (21) and (22) denote the constraints of user selection vector, where \(\mu\) represents the total number of UDs participating in this model training. \(\mu_{k}=\{0,1\},\forall k\in\mathcal{K}\), where \(\mu_{k}=1\) indicates that \(k\)-th UD participate in this model training, and \(\mu_{k}=0\), otherwise.
## 3 Methods Applied
### Analytic Hierarchy Process with Logarithmic Least Squares Method  
In the case of the analytic hierarchy process (AHP) method [8; 9], the starting point is a reciprocal symmetric matrix called a pairwise comparison (PC) matrix. Its elements express how much stronger a team is relative to another. Let the PC matrix be \(\mathbf{B}=(b_{i,j})\), \(i=1,\ldots,n\), \(j=1,\ldots,n\). In AHP, the evaluation \"better\" is expressed by the number 3, \"much better\" by 5, and so on. Applying the Thurstone method, we distinguished three options (win, tie, loss), and we did not differentiate between wins based on how many more goals were scored by the winning team. Therefore, we also used only three categories in the case of AHP. We constructed the PC matrix as follows: during the \(m\)th match between teams \(i\) and \(j\), \(b_{i,j}^{(m)}=3\) if team \(i\) wins over team \(j\); \(b_{i,j}^{(m)}=1\) if the result is a draw; and \(b_{i,j}^{(m)}=1/3\) if team \(i\) suffers a defeat to team \(j\). In the case of \(k\) matches between the teams \(i\) and \(j\), we took the geometric mean as follows [50]:  
\[b_{ij}=\sqrt[k]{\prod_{m=1}^{k}b_{i,j}^{(m)}}. \tag{8}\]If team \(i\) does not play any matches with team \(j\), then the element \(b_{i,j}\) is not defined, and its place remains empty in the matrix. If there is at least one match between the teams \(i\) and \(j\), it can be easily seen that \(b_{i,j}=\frac{1}{b_{j,i}}\), letting \(b_{i,i}=1\) for each \(i=1,2,\ldots,n\). If every team plays with every other team, i.e., the comparison is complete, then the most frequently applied evaluation method is the eigenvector method. Completeness holds for all national leagues but, unfortunately, is not satisfied when we concatenate these national leagues. That is, lots of teams did not qualify for international cups, but we intended to locate them in the international rankings all the same. In the case of an incomplete comparison, the most frequently applied method is the logarithmic least squares method (LLSM) [51]. That is, minimize the function  
\[H(\underline{w})=\sum_{(i,j)\in I}\left(\log(b_{i,j})-\left(\log(w_{i})-\log( w_{j})\right)\right)^{2} \tag{9}\]  
under the conditions  
\[0<w_{i},i=1,2,\ldots,n \tag{10}\]  
and  
\[\sum_{i=1}^{n}w_{i}=1. \tag{11}\]  
In [51], the authors proved the necessary and sufficient conditions for the existence and uniqueness of the optimization problem. Let \(I\) be the set that contains all the pairs of teams for which there is at least one comparison. Let us define the graph of comparisons \(G_{I}\) as follows: the vertices are the teams, and there is an edge between two vertices if the pair is an element of \(I\).  
Theorem 3.2 ([51]): _The optimization problem minimizing (9) under conditions (10) and (11) can be solved uniquely if and only if the graph \(G_{I}\) is connected._  
We note that \(G_{TH}\subseteq G_{I}\); therefore, if the Thurstone method with ties works, so does the analytic hierarchy process with logarithmic least squares method. One can easily see that the requirements of Theorem 3.2 are less strong than those of Theorem 3.1.
## 3 Results and Discussion
### Bioactivity of pDNA  
#### 3.3.1 Cytotoxicity Assays  
The cytotoxicity studies were performed by using a resazurin assay in order to evaluate if there was any toxicity related with the sc pDNA samples recovered from different supports (Figure 5).  
Figure 4: Zeta potential for pDNA purified by the three chromatographic strategies (histidine–agarose, arginine–macroporous, and histidine–monolith), using different buffers. A native pDNA sample (oc + sc) purified by a commercial kit was used for comparison.  
The results for human dermal fibroblasts did not revealed significant cytotoxic effects after transfection with the purified sc p53 encoding pDNA samples. In the case of HeLa cell line, a slightly cytotoxic behavior was observed in the cells transfected with the sc p53-pDNA samples recovered from the histidine-based columns, and among these samples, a more pronounced cytotoxic effect was verified when the sc pDNA was obtained from the histidine-monolithic support (Figure 5). These results could suggest that the buffer conditions used for the recovery of sc pDNA (high ammonium sulfate concentration in the histidine-agarose and acidic pH in the histidine-monolith) could cause some slight cytotoxicity. The samples purified with the arginine-macroporous support did not induce any cytotoxic effect which can be related to the milder conditions used during the purification process, namely the low NaCl concentration and pH 8.0. Overall, in this case, the cell viability can be affected by the pDNA sample (resulting from different preparation conditions), or some effect can also be related to the apoptosis induced by transfection. Actually, this phenomenon was previously observed and discussed by [PERSON] et al. (2018). In that research work, it was verified that the reduction of cell viability was not directly related to the toxicity of the formulation, but it was more related with a higher p53 protein expression, resulting in a higher apoptotic effect for HeLa cancer cells ([PERSON] et al., 2018). Therefore, in our work, and considering the small cytotoxicity observed, further tests were performed to assess the pDNA bioactivity, namely regarding the ability to induce the p53 protein expression as well as the apoptotic effect induced on the HeLa cancer cells.  
Figure 5: Evaluation of cell viability following transfection of HeLa and human dermal fibroblasts with Lipofectamine 2000 lipoplexes loaded with p53-pDNA recovered from (**A**) histidine–agarose, (**B**) arginine–macroporous, and (**C**) histidine–monolith after 48 h transfection. Negative (k-) and positive controls (K+) correspond, respectively, to non-transfected cells and treated with ethanol. Data is represented as mean \(\pm\) SD, n = 3.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AAP & Azimuth Antenna Pattern \\ ASE & Area Spectral Efficiency \\ CNR & Carrier-to-Noise Ratio \\ CINR & Carrier-to-Interference-plus-Noise Ratio \\ HAP & High Altitude Platform \\ ICI & Inter-cell Interference \\ LoS & Line-of-Sight \\ QoS & Quality of Service \\ RB & Resource Block \\ SA & Simulated Annealing \\ SE & Spectral Efficiency \\ SPP & Sub-platform Point \\ SPPC & Sub-platform Point Cell \\ \end{tabular}
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Learning-Based Methods
#### 2.3.1 Machine Learning-Based Methods  
Early learning-based sharpness evaluation methods [68; 69] for no-reference images are mainly supported vector machine (SVM) models based on machine learning, including support vector regression (SVR)/support vector clustering (SVC) methods.  
[PERSON] et al. [70] presented a sharpness evaluation method of no-reference images based on large-scale structures. The statistical scale of edges with different widths and the average value of the maximum gradient were taken as the image features, and the SVR is used to obtain the features to obtain the evaluation results. This method can avoid the interference of small textures and contrast around the edge to the evaluation results. [PERSON] et al. [71]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Advantages** & **Disadvantages** \\ \hline \hline \multicolumn{1}{c}{Grayscale gradient-based methods} & Simple and fast calculation & \multicolumn{1}{c}{Rely on image edge information} \\ \hline \hline \multicolumn{1}{c}{Edge detection-based methods} & High sensitivity & \multicolumn{1}{c}{Susceptible to noise} \\ \hline \hline \multicolumn{1}{c}{Fourier transform-based methods} & Extract edge features clearly & \multicolumn{1}{c}{High computational complexity} \\ \hline \hline \multicolumn{1}{c}{Wavelet transform-based methods} & High accuracy and robustness & \multicolumn{1}{c}{High computational complexity and poor real-time performance} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of different methods based on spatial/spectral domain.  
Figure 6: The general framework of learning-based methods.  
used the anisotropy of the orientation selectivity mechanism and the influence of gradient orientation effect on vision to extract structural information and then used Toggle operator to extract edge information as the weight of local patterns. Finally, support vector regression (SVR) was used to train prediction models with optimization characteristics and subjective scores. [PERSON] et al. [72] proposed a new two-step framework for no-reference image quality assessment based on natural scene statistics (NSS). SVM is used to classify the distortion types of the fitted parameter features, and then SVR is used to calculate the image quality evaluation results under different distortion types.  
Machine learning-based evaluation methods can achieve better results than other algorithms on small-sample training sets, but the extracted features determine the quality of the evaluation results.
## 2 Information Fusion Process Based on D-S Evidence Theory  
In an information fusion system with a multi-sensor, a fire alarm is raised by a combination of data detected from different sensors. The system collects information from sensors, which constitutes the evidence. Then, the system needs to make a judgment by the combination of the evidence, based on D-S evidence theory. The principle of D-S theory is to combine different basic probability assignments into a general basic probability assignment with Dempster-Shafer combination rules in a suitable framework. The general basic probability assignment is new evidence. Finally, the system makes a final decision according to the new evidence based on decision rules.
# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## 5 Implementation Method of Load-Balanced Routing Optimization  
### Route Establishment Process  
In route establishment, nodes need to collect and compute link states and neighbor information, adding this information to the HELLO packets. Nodes periodically broadcast these packets within their one-hop radius. Nodes can establish a neighbor relationship table upon receiving HELLO messages from neighboring nodes. Each node selects a subset of nodes as MPR nodes based on the MPR selection algorithm and maintains an MPR_Selector table. Nodes only forward broadcast messages from senders that are included in their MPR_Selector set. Upon receiving measured information from neighbor nodes, MPR nodes add this information to TC packets and periodically broadcast them network-wide, allowing distributed collection and maintenance of the network's topology information. Nodes in the network acquire complete topology control information and the load condition of each node, thereby calculating each node's load cost matrix. The Dijkstra algorithm is employed during route updates to compute and generate the minimum cost paths for nodes [31].  
Figure 7: Feasible link diagram with 140 devices and SF = 7.  
At the initial stage, each node initializes its load cost matrix to an all-zero matrix. Nodes extract topology control information from TC packets sent by neighbor nodes, including the destination addresses, the previous-hop address in the packet, communication distances, data transmission rates, and the load of terminal nodes. The node load matrix is calculated according to Equation (36), and the load cost matrix of the node is updated according to Equation (37). Through this process, the load cost to reach nodes in the MPR_Selector set from the current node, with the previous hop being a neighbor, is updated. Upon completion, if the current node is an MPR node, the previous hop address is set to the node itself, and this TC packet is broadcasted network-wide. After a period, the load cost matrices for reaching other nodes in the network are updated. This approach ensures that the network dynamically adjusts the routing based on load conditions, thereby optimizing load distribution and improving network performance.The routing establishment process is shown in Figure 8.
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 5 Result Analysis
### Domestic Passenger  
The forecasting results and comparisons with other models for domestic passenger flow are presented in Table 2 and Figure 6. From the results, it can be concluded that:  
1. The EMD-SARIMA model forecasts with the best accuracy, followed by the SARIMA model, the Holt-Winters model, and the naive model, especially for the local maximum and minimum values;
2. Due to greater seasonality of the domestic passenger data, the performance of the SARIMA model is the second best, superior to the Holt-Winters model. This is again consistent with previous research claiming that the Holt-Winters model and the SARIMA model are comparable based on different time series [41];
3. Again, the EMD-SARIMA model forecasts with better accuracy consistently regardless of whether it is single- or multi-step forecasting.
## Results  
### Detectability of Plant Water Status Over GSD and Time  
Leaf water content samples for all treatments (Tx1, Tx2, Tx3, Tx4, C, and W) and pre-dawn and midday leaf water potential for treatments Tx3, Tx4, C, and W are shown in Table 2.  
The relationship between flying height (and GSD) and time of day on detectability of leaf water status NDVI and NDRE values is plotted in Figure 2. Spectral indices vary with spatial resolution for several reasons, including the fact that lighting changes throughout the day, as well as leaf moisture. While it is possible to separate treatments and control at all resolutions (i.e., 2.2-8.7 cm GSD), the best separation between all treatments, including the water and control was at 2.2 cm and 4.3 cm GSD flown at altitude 30 m and 60 m, for both NDVI and NDRE (Figure 2a,b). At slightly coarser resolutions (7.2 cm and 8.7 cm GSD) the difference between control and water was less pronounced. NDVI and NDRE values by time of day are shown in Figure 2c. The best separation between treatments was found in the morning imagery for NDVI (between 10:00 and 12:00), and in the early morning for NDRE  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Samples**} & **Day 1** & **Day 2** & **WC** & **Day 1** & **Day 2** & **Midday** & **Day 1** & **Day 2** & **Pre-dawn** \\  & **WC** & **WC** & **Averages** & **Midday** & **WP** & **WP** & **Pre-dawn** & **Pre-dawn** & **WP** \\  & & & **WP** & **WP** & **(average)** & **WP** & **WP** & **(average)** \\ \hline Tx1 & 4.49 & 2.18 & 3.33 & NA & NA & NA & NA & NA & NA \\ \hline Tx2 & 4.33 & 3.34 & 3.83 & NA & NA & NA & NA & NA & NA \\ \hline Tx3 & 12.91 & 3.33 & 8.12 & \(-\)5.20 & NA & \(-\)5.20 & \(-\)2.72 & \(-\)4.78 & \(-\)3.75 \\ \hline Tx4 & 36.25 & 51.39 & 22.13 & \(-\)2.83 & \(-\)5.07 & \(-\)3.95 & NA\({}^{*}\) & \(-\)4.02 & \(-\)4.83 \\ \hline Control & 56.29 & 52.82 & 56.05 & \(-\)2.63 & \(-\)2.43 & \(-\)2.53 & \(-\)0.50 & \(-\)0.65 & \(-\)0.61 \\ \hline Water & 56.63 & 53.00 & 54.82 & \(-\)2.30 & \(-\)2.25 & \(-\)2.28 & \(-\)0.37 & \(-\)0.52 & \(-\)0.47 \\ \hline \end{tabular} \({}^{*}\)NA Tx4 was conducted at 10 am Day 1 and was not yet available for sampling.  
\end{table}
Table 2: Acquired water content (WC) and water potential (WP) samples and averages for Day 1 (25 July) and Day 2 (26 July) for each treatment.  
(between 8:00 and 10:00). The drier plots (e.g., Tx1 and Tx2) were less separable as the day progressed within both indices. The NDVI for Tx4 declined throughout the day, as we would expect, given this treatment had been enacted one day prior to the flights represented.