esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Student Model Evaluation  
To further optimize the classification module, three DeiT architectures, DeiT-Tiny, DeiT-Small, and DeiT-Base, were evaluated. Table 3 shows the performance metrics for each model.  
While DeiT-Base offered the highest validation accuracy and average precision, it has a significantly larger model size and longer inference time, which may not be ideal for real-time deployment in resource-constrained environments. DeiT-Tiny, on the other hand, provided a commendable validation accuracy of 98.7% and an average precision of 0.990 with the lowest inference time and smallest model size. DeiT-Small presented a middle ground but still falls short in computational efficiency compared to DeiT-Tiny.  
Given the marginal performance gains of DeiT-Base and DeiT-Small over DeiT-Tiny, coupled with their increased computational demands, DeiT-Tiny was selected as the student model. This choice ensures a balance between maintaining high classification accuracy and achieving the necessary computational efficiency for real-time deployment in broiler farms.
## 3 Formation of Heterogeneous Groups
### Clustering Techniques for the Formation of Homogeneous Clusters
#### 3.1.1 K-Means  
The algorithm uses a K number of clusters (to be set before execution), for which you want to divide the dataset and assign a centroid to each cluster, which must represent the central point of each cluster.  
To determine the appropriate number of clusters, a function was developed in Python, using the \"Elbow Method\" [32; 33], an interpretation method within the cluster analysis to find the appropriate number of clusters in a dataset. The method allows to generate a graph, having in the axis of the abscissas a range of values of K (i.e., the number of clusters in which the dataset is divided) and on the ordinate axis the sum of the distances of observed data from cluster centroids, called WithinCluster-Sum-of-Squares (WCSS). The number of K (value in the abscissa axis) where, in the graph, the decrease in the value of WCSS causes a significant drop in speed of increment (when K increases), it is called \"elbow\". This value represents the optimal number of clusters to build based on the dataset provided.  
The next step is the execution of the clustering algorithm. This takes the number K of clusters obtained from the elbow method for subdividing the dataset and assigning a centroid to each cluster. The choice of the centroid, in the first iteration, occurs randomly. The proximity of a vector (set of data relating to a certain student) to the centroid of a cluster establishes the belonging of that vector to that particular cluster. The algorithm calculates the Euclidean distance between each vector \(x\) and each centroid, assigning the vector to the centroid \(c\) for which distance is minimum:  
\[c{=}\operatorname*{argmin}_{c_{i}\in C}\,dist(c_{i},x)^{2}\]  
where \"\(c_{i}\)\" represents a centroid of the set \(C\) (set of centroids), \(x\) represents the input vectors, while \"dist\" is the standard Euclidean distance. Then, the values of the centroids are recalculated.  
The new value of a centroid will be the average of all vectors that are been assigned to the centroid cluster. The execution continues by iteration (a maximum number of iterations are defined a priori for avoid an infinite loop) and ends when:  
* no vector of the dataset changes clusters;
* the sum of the distances is reduced to a minimum;
* a maximum number of iterations is reached.  
The steps in order to achieve clustering using K-means algorithm are described here:  
1. Select the number of clusters(K) with Elbow Method in order to obtain the data points.
2. Insert the centroids c_1, c_2, c_k randomly.
3. Repeat 4 and 5 points, until convergence or the end of a fixed number of iterations.
4. for each data point x_j:
5. -find the nearest centroid (c_1, c_2, c_k).
6. -assign the point to that cluster.
7. for each cluster j = 1, k
8. -new centroid = mean of all points assigned to that cluster
9. End.
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
###### Abstract  
The popularity of the Power Line Communication (PLC) system has decreased due to significant deficiencies in the technology itself, even though new wire installation is not required. In particular, regarding the request for high-speed throughput to fulfill smart-grid requirements, Broadband Power Line (BPLC) can be considered. This paper approaches PLC technology as an object of simulation experimentation in the Broadband Power Line Communication (BPLC) area. Several experimental measurements in a real environment are also given. This paper demonstrates these experimental simulation results as potential mechanisms for creating a complex simulation tool for various PLC technologies focusing on communication with end devices such as sensors and meters. The aim is to demonstrate the potential and limits of BPLC technology for implementation in Smart Grids or Smart Metering applications.  
network; broadband; power; line; communication; standard; simulation; adaptive; measurement +
Footnote †: journal: Sensors
## 3 Results and Discussions
### RF Characteristics of MS lines
For a lossy transmission line with a relatively low resistive loss, such as 0.25 \(\Omega/\square\) or 0.32 \(\Omega/\square\), the characteristic impedance \(Z_{0}\) can be written as [13]  
\[Z_{0}=\sqrt{\frac{R+j\omega L}{G+j\omega C}} \tag{1}\]  
where \(R\), \(G\), \(L\), and \(C\) are the resistance, conductance, inductance, and capacitance per unit length of the MS line, and \(\omega\) is the angular frequency of the RF signal. Given a load impedance of \(Z_{\mathrm{L}}\), The reflection coefficient \(\Gamma_{0}\) is written as [13]  
\[\Gamma_{0}=\frac{Z_{\mathrm{L}}-Z_{0}}{Z_{\mathrm{L}}+Z_{0}} \tag{2}\]  
From Equation (1) and (2), we can deduce that the presence of sheet resistance leads to a complex characteristic impedance. For our test systems, the \(Z_{\mathrm{L}}\) is typically set as a standard impendence of 50 \(\Omega\). The complex \(Z_{0}\) leads to a nonzero \(\Gamma_{0}\), which implies the reflection of the RF signal. This reflection will be more significant if we further increase the resistive part for the MS lines. Thus, minimizing the resistance could reduce the reflection of the RF signal for an MS line. Otherwise, complicated matching networks are indispensable to reducing reflection for practical applications. In our case, sheet resistance below 0.32 \(\Omega/\square\) with a corresponding laser power of 150 mW is preferable for interconnection applications. On the other hand, the transmission and reflections can be tuned via a simple variation of the laser power, which is beneficial to a more flexible design for RF devices. Figure 5: S\({}_{2\mathrm{I}}\) spectra at various sheet resistances: (**a**) simulation results; (**b**) experiment results. This variation may also simplify the fabrication of resistive components and other functional structures by integrating them into a single step, leading to flexible and low-cost manufacturing.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 3 Metal Selenide Thin Films
### Antimony Selenide Films  
The Sb\({}_{2}\)Se\({}_{3}\) thin films were deposited on glass substrates by using antimony potassium tartrate (K\({}_{2}\)Sb\({}_{2}\)(C\({}_{4}\)H\({}_{2}\)O\({}_{6}\))\({}_{2}\)), tartaric acid (complexing agent), and sodium selenosulfate [121] under immersion cycles (350 times). The reaction mechanisms are reported in Equations (35)-(38).  
\[\mathrm{Na}_{2}\mathrm{SeSO}_{3}+\mathrm{OH}^{-}\rightarrow\mathrm{Na}_{2} \mathrm{SO}_{4}+\mathrm{HSe}^{-} \tag{35}\]  
\[\mathrm{HSe}^{-}+\mathrm{OH}^{-}\rightarrow\mathrm{H}_{2}\mathrm{O}+\mathrm{ HSe}^{2-} \tag{36}\]  
\[(\mathrm{Sb}^{3+}\{\mathrm{tartaric acid}\})\rightarrow\mathrm{tartaric acid}+\mathrm{Sb}^{3+} \tag{37}\]  
\[2\mathrm{Sb}^{3+}+3\mathrm{Se}^{2-}\rightarrow\mathrm{Sb}_{2}\mathrm{Se}_{3} \tag{38}\]  
XRD studies showed the average crystallite size was 3 nm, with the orthorhombic phase and the strongest peak corresponding to the (211) plane. Atomic force microscopy (AFM) and SEM analysis indicated rough morphology and that the grains covered well to the substrate, respectively. The band gap, activation energy, and room temperature resistivity were 1.8 eV, 0.21 eV, and 10\({}^{5}\)\(\Omega\)-cm, respectively.
## 3 Analysis of Experimental Results
### Friction and Displacement of the Pile-Soil Interface  
The frictional-displacement (_f_-s) relationship at the pile-soil interface, where the response is maximum, is depicted in Figure 6. Due to the significant soil settlement exceeding that of the pile group unit, it results in the relative upward movement of the piles. With the increase in \(\theta\) values and water pressure, this relative movement exhibits an exponential growth trend. The deformation zones in the soil are classified based on the slope of the frictional displacement curve (_f_-s). When \(\theta_{1}\) = 45 degrees, a heavy plastic region (\(A2\)) and a mild plastic region (C2) are formed, with \(L\) = 3.5 m serving as the boundary. When \(\theta_{2}\) = 60\({}^{\circ}\), noticeable changes in the slope of the _f_-s curves occur at \(L\) = 4 m and \(L\) = 8 m (\(L\) represents the pile length). This leads to the formation of a heavy plastic region (A1), a moderate plastic region (\(B1\)), and a mild plastic region (\(C1\)). When \(\theta_{3}\) = 90\({}^{\circ}\), the plastic regions \(A3\) and \(C3\) are separated by \(L\) = 5 m.  
Figure 5: Bending moment distribution of the piles.  
Figure 6: Friction-displacement curves of the pile–soil interface.  
After the earthquake, the maximum settlement zone of the soil around the pile is located in the heavy plastic region \(A\). In this area, the bending moment on the pile is also at its highest. The vertical displacement decreases in a linear manner. When the frictional reaches a critical value, the vertical displacement decreases. This is due to the mobilization of the bearing capacity end of the pile and the frictional side of the pile to resist axial forces [46]. The soil around the pile is in a state of force equilibrium. In the lower part, within the mild plasticity region C, the frictional undergoes minimal variations. However, there is a sudden and steep reduction in settlement. In this region, which is closer to the seismic sources, the soil experiences a high degree of compaction. This results in an increase in effective stress. Additionally, the \"X-shape\" pile group exhibits a similar frictional to the hexagonal pile group. However, it does not display the intermediate plasticity region \(B\). In the \"X-shape\" pile group, the boundary of the heaviest plasticity region with the most deformation is shallow, at 0.5 m. This indicates that the pile spacing differences in the \"X-shape\" pile group may result in an incomplete group pile effect [48]. Under the same seismic conditions, differences in pile spacing result in non-uniform soil compression. This phenomenon is closely related to the location of the peak moments on the pile sides.
## 3 Results
### Evaluation of Model 1: Cropland_3 Subgroup  
Four predictors were selected in the _cropland_3_ subgroup model to differentiate misclassified savanna from cropland: Fraction\({}_{\text{veg}}\), Fractionsoil, Fraction\({}_{\text{Litter}}\), and the seasonal difference in near-infrared reflectance (DifferenceB4). The likelihood ratio test, evaluating the four predictors as a group, was highly significant (\(G^{2}\)162.65, df = 4, \(p<\) 0001). The Hosmer-Lemeshow goodness-of-fit (\(X^{2}\)0.519, df = 8, sig. = 1.000) as well as two pseudo R-square values (McKelvey and Zavoina R\({}^{2}\) = 0.803 and Nagelkerke R\({}^{2}\) = 0.803) provided further support for the model fit. A comparison of nested and non-nested models offered positive evidence in favor of the nested (4 predictor) model over the full (10 predictor) model (difference in BIC = 3.816). All predictors contributed significantly to the model at a significance level of 0.001. A one unit increase in the difference of near-infrared reflectance increased the log odds of a pixel being savanna by 0.06, whereas the soil, vegetation, and litter fractions all decreased the log odds of a pixel being savanna, in order of decreasing magnitude (Table 3a).  
Of the 80 known savanna plots within the _cropland_3_ thematic category, 74 were classified correctly, and 89 of the known 96 cropland plots classified correctly for an overall accuracy of 92.6% (Table 3b). Positive and negative predictive values (PPV and NPV) were calculated (%) with accompanying confidence intervals (Equation A1). PPV and NPV values indicated high probability of a plot being savanna when the test is positive, and high probability of a plot being non-savanna when the test is negative. Recoding pixels with \(p\geq\) 0.5 reduced the spatial extent of the _cropland_3_ thematic category by 53.15%, from roughly 294 thousand hectares to 156 thousand while, simultaneously, reducing commission error of cropland and omission error of savanna.
# Prediction of Power Fluctuations of Gaussian Beams After Transmission Through Turbulent Atmosphere
## 3 Construction of the TSLSTMFCPN
As shown in Figure 6, the Time Series Long Short-Term Memory Fully Connected Processing Network (TSLSTMFCPN) architecture consists of two LSTM layers and a fully connected layer. In each time step of data acquisition, five meteorological parameters, including temperature, humidity, wind speed, atmospheric pressure, and ambient illuminance, are measured and stored simultaneously. The data from 32 consecutive time steps are grouped into a batch as a single input for the model. Then, the TSLSTMFCPN outputs eight values of laser power predictions for the 8 time steps that just followed the previous 32 time steps of the input. For the training of the TSLSTMFCPN, a Root-Mean-Square Error (RMSE) function is used as the loss function, which is defined by  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\lvert y_{i}-\overline{y_{i}} \rvert^{2}} \tag{8}\]  
In Equation (8), \(y_{i}\) represents the measured value at time \(i\), and \(\overline{y_{i}}\) represents the predicted value at time \(i\). In order to minimize the training error while avoiding the occurrence of local minimum points, an Adam optimizer was utilized, which has an adaptive learning rate for back-propagation. Meanwhile, normalization was applied to the data of laser power and meteorological parameters for data preprocessing, and L2 regularization was applied to the TSLSTMFCPN to reduce overfitting. Figure 7 illustrates the prediction error rates of the TSLSTMFCPN after being trained for different rounds of training epoch (training iteration), in which (a) was obtained using the data collected in the turbulence pool experiment and (b) in the outdoor experiment. From both curves, it can be seen clearly that the model's prediction error decreases with an increase in training epoch, and remains constant as the training epoch reaches about 30 rounds.
## 1 Introduction
For desiccant cooling, there is existing work on a simulation-based PMV prediction for a liquid desiccant system operated in an office space [17]. Besides PMV, there is another index of thermal comfort that can be compared quantitatively, i.e., the effective draft temperature (EDT). The EDT index is commonly used for the evaluation of thermal comfort in air-conditioning spaces with ventilation, such as desiccant cooling systems [18]. EDT has been frequently studied as an indicator of thermal comfort in East Asia, where Korea is situated. However, in the EDT index, measuring the local airflow velocity distribution required for EDT calculation, which is mostly obtained through computational fluid dynamics (CFD), is difficult [19; 20]. The distribution of airflow generated by the air conditioner is greatly influenced by the discharge angle. Recently, studies that analyzed the effect of discharge angle on flow distribution, energy efficiency and thermal comfort by CFD have been published [21; 22; 23; 24]. For floor standing type air conditioners, the optimum discharge angle considering energy consumption and thermal comfort was calculated to be \(30^{\circ}\) upward [21]. [PERSON] et al. [23] reported that the flow rate varies up to \(20\%\) depending on the discharge angle for the wall mounted type, but the thermal comfort was left as a future work. In the case of the cassette type [24], it was reported that the optimum supply angle was \(30^{\circ}\) from the ceiling when considering ventilation and PMV. The desiccant cooling system analyzed in this study has a discharge type close to that of a standing type air conditioner. However, the return air intake is located on the ceiling. In a conventional air conditioner, the supply port and the suction port are located close to each other for mixing ventilation [21; 24]. However, this system has been developed for the purpose of multi-room control, and has a hybrid air distribution system in which mixing and displacement ventilation occur simultaneously [25], as the suction port is installed on the ceiling. Therefore, in the present study, the change in thermal comfort according to the discharge angle in a cooling space with hybrid air distribution was investigated. Because the factors of EDT are affected by PMV, EDT and PMV have a strong correlation in the laboratory environment without direct sunlight [26]. However, in a residential environment with windows and direct sunlight, it was found that the two factors locally conflict with each other due to different distributions of radiant temperature and air current. Therefore, in the present study, a discharge angle with optimal thermal comfort was found between PMV and EDT in a residential space where desiccant cooling was performed.
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## 3 Results
### Assessing Limitations for Storm Prediction
NUCAPS is highly correlated with radiosondes for other stability parameters, such as total precipitable water (88%), lifted index (64%), total-totals index (69%), the K-Index (79%), and the Galvez-Davison Index (78%) [28]. However, for severe weather forecasting, a bias of a few degrees at the surface can significantly alter CAPE, which in turn can affect a forecasters assessment of severe weather potential. NUCAPS lapse rate measurements are more reliable and particularly useful for mid-level  
Figure 7: Reponses to the question “Did any of the following prevent you from using NUCAPS products in your analysis?”. convection above 850 mb, above the surface layer. Promisingly, recent work explored improving NUCAPS-based CAPE by combining retrievals with model-based surface observations [36]. There are current research efforts to improve the boundary layer depiction (and thereby CAPE estimation) for operational forecasting. Display problems were not ranked as a significant issue, but this contrasted with conversations we had with forecasters. Many vocally disliked the National Center Sounding and Hodograph Analysis and Research Program (NSHARP) sounding display in AWIPS, saying it was difficult to remember which profile they were inspecting or had previously inspected. It was also difficult to display side-by-side comparisons with Skew-Ts from other sources, such as models and radiosondes. Some were satisfied with the pop-up menu, but others found those displays to be not useful. However, this feedback would not have been received based on the survey alone, emphasizing the value of direct communication with the forecasters.
## 3 Results
### Normalized Gain  
Figure 5 shows the cumulative distribution functions (CDFs) of \(G_{m}^{N,K}\) in the layout matching that of Figure 4. Each plot in Figure 5 presents a CDF for \(K=1\) (black), \(K=2\) (red), \(K=5\) (blue), and \(K=10\) (green). In addition, the 95 th percentile of each CDF is marked with a vertical dashed line of the same color. Table 1 lists the 95 th percentiles for all possible combinations of the studied parameters. Additionally, the table cell background color saturation is proportional to its numerical value, ranging from white for zero to deep blue for one. This gives a visual cue to how different parameter combinations affect the normalized time-averaged gain.  
Figure 3: A flowchart of the procedure used to generate distributions of the time-averaged gain values \(G_{m}^{N,K}(T)\). The complete procedure yields \(N_{emp}\cdot N_{s}\) time-averaged gain evaluations.  
Figure 4: DOD (\(\varphi,\theta\)) of the maxima of the time-averaged BS array patterns as observed over 2500 cases (100 simulations with randomly distributed UEs in 25 different environment samples) when serving \(K=5\) UEs simultanously, with connection time \(T=60\) s. Each maximum direction is marked with a black circle. The circle size is proportional to the corresponding value of the time-averaged maximum gain (normalized to the maximum of all samples of the respective parameter combination). The circle opacity is proportional to the number of maxima found in the corresponding \((\varphi,\theta)\) direction. Left, center, and right columns show data for CB, MRT, and ZF transmission schemes, respectively. In the first, second, and third rows, scenarios with 2-by-2, 6-by-6, and 10-by-10 base station arrays are depicted, respectively. The ZF transmission with 2-by-2 BS array (\(N=4\)) is undefined and was omitted. The dashed line depicts the cell boundary. The normalized time- and sample-averaged BS array patterns are shown in blue.  
Figure 5: Cumulative distribution functions (CDFs) of the normalized 6 min average BS array gain \(C_{CB}^{N,K}\) (first column), \(C_{MRT}^{N,K}\) (second column), \(C_{ZF}^{N,K}\) (third column) with \(T=60\) s. Scenario with base station array size \(N=4\), \(36\), and \(10\) are shown in the first, second, and third rows, respectively. Solid lines show CDFs for the number of simultaneously active UEs \(K=1\) (black), \(K=2\) (red), \(K=5\) (blue), and \(K=100\) (green). Dashed lines of matching color mark the \(95\)th percentile of each distribution.