esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Requirements for the Calibration Data Management and Data Formats
#### 2.3.4 Harmonization Challenges for Developing Digital Solutions
* Level 1: Digital format (e.g., PDF) allowing automatic management and display of the document. * Level 2: Machine-readable format. The structure of the document can be digitized and certain granular content can be exported (chapters, graphics, definitions, etc.). Content and presentation are separated. * Level 3: Machine-readable and -executable content. All essential granular information units can be clearly identified and their reciprocal relationships recorded and made available for further processing or partial implementation. * Level 4: Machine-interpretable content. The information in a standard is linked with implementation and usage information in such a way that it is implemented by machines directly or interpreted and combined with other information sources so that complex actions and decision-making processes take place automatically. The German Institute for Standardisation (DIN) and German Association for Electrical, Electronic & Information Technologies (DKE) have presented an additional level that goes beyond Level 4 in the IEC model [64]:  
* Level 5: Machine-controllable content. The content of a standard can be amended by machines working unassisted and adopted by automated (distributed) decision-making processes. The content adopted in this way is automatically reviewed and published via the publication channels of the standardization organizations. The model emphasizes well the difference between machine-readable, -executable, -interpretable, and -controllable formats. Ideally, for the development of digital calibration management processes, the requirements would have to be machine-interpretable, enabling requirement-specific data and criteria such as allowance limits to be directly interpreted by the management systems as process parameters, making further automation of the processes management possible. Currently, when the requirements are not yet available in such a format, this kind of specific information must be included in the systems through human interpretation, which means that revisions of the requirements will require updating of the systems accordingly.
# Applying Spectral Clustering to Decode Mobility Patterns in Athens, Greece
## 2 Data and Methods  
The analysis is based on 1327 reported trips, examined using Spectral Clustering. The trips exhibit four key dimensions: purpose, transport mode, departure time, and distance. These attributes are processed before running the cluster analysis. The interpretation of clusters is later performed by estimating descriptive statistics of the socio-demographic characteristics of travelers (the \"trip owners\") included in each cluster.
## 5 Simulation Results  
The controller is tested on a simulated redundant 9-degrees-of-freedom continuum robot, shown in Figure 2.  
The robot was simulated using Matlab's Robotics Toolbox [28] and DQ Robotics Toolbox [29]. The actuation primitives used were joint position command signals to drive the control points towards the target poses. The Denavit-Hartenberg parameters of the robot is listed in Table 1.  
The controller was programmed as described in Section 4 using \(k=4\) nearest neighbors that comprise of actuation primitive and observation pairs. Since the experiments were conducted in simulation, a tracking camera was not needed to determine the poses of the control points. In addition, actuators discovery was also omitted in the simulation. Instead, the simulator provided these information directly. Furthermore, gravity was omitted in the simulation to keep the focus on multi-point pose control, deferring gravity compensation to future work.  
Three experiments were performed to study the capabilities of the proposed kinematic-model-free multi-point controller. The first experiment involved end-effector pose control for the continuum robot with increasing number of degrees of freedom enabled. This experiment shows the degrading performance of the controller when increasing the number of degrees of freedom using a single control point. In the second experiment, the 9-degrees-of-freedom continuum robot was controlled using 1, 2, and 3 control point(s). This experiment highlights the capability of the kinematic-model-free multi-point controller to control robots of high degrees of freedom and resolve redundancies. Lastly, the robustness of the kinematic-model-free multi-point controller was measured by executing the simulation experiment 100 times with randomly-generated configurations.
# Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities
## 4 Problem Formulation  
We conceptualize and formalize the problem in this section. Our objective was to derive metrics for the matching of the descriptions of device capabilities against requests for their services for the two approaches and provide recommendations on the usability of the two approaches in the RF domain based on these metrics.  
Figure 2 is a data flow diagram that represents the evaluation process at a high level. As shown in this figure, a set of device descriptions (and their capabilities) is generated and represented in the respective data representation languages, i.e., XML in the XML-based approach and RDF/OWL in the OWL-based approach. The descriptions of device capabilities are stored in respective datastores. Requests for device capabilities are generated and expressed in two query languages--XQuery in the XML-based approach and SPARQL in the OWL-based approach. The requests represented in the respective query languages are forwarded to the appropriate matchers for matching of the device capabilities against the requests. The matching results of each query expression returned from the matchers are then evaluated by comparing with the ground truth of the matching devices against the query, and metrics are computed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **OWL-Based** & **XML-Based** \\ \hline
**Background Knowledge** & OWL & XSD \\
**(Schema Level)** & & \\
**Device Descriptions** & & \\
**(Data Level)** & & \\
**Requests for Services** & & \\
**(Query Level)** & SPARQL & XQuery \\ \hline \hline \end{tabular}
\end{table}
Table 2: Languages used in the two approaches for each level.
## 3 Results  
### Unconfined Compressive Strength (UCS)  
The unconfined compressive strength of the samples is shown in Figure 2. A general trend of strong growth was observed when adding limestone powder to a clayey soil as a ternary element together with lime or cement. Moreover, the samples made with cement had greater values than the samples made with lime at all ages. For the lime samples, the UCS increased from 7 days to 6 months for all samples (with and without limestone powder and using distilled water and the sulfate solution). Therefore, for the lime samples without limestone powder and those made using distilled water, the UCS increased by 249% from 7 days to 3 months and by 303% from 7 days to 6 months. The UCS of the lime samples with limestone powder and those made using distilled water increased by 86% from 7 days to 3 months and by 124% from 7 days to 6 months. Moreover, the UCS was greater for the samples composed of 25% limestone powder than for those without the industrial by-product. This effect can be seen from 7 days to 6 months. The lime samples with limestone powder and made using distilled water had a UCS value 127% greater than the samples without the industrial waste at 7 days, with a reduction in this increase to 21% at 3 months and 26% at 6 months. The samples made with the Na\({}_{2}\)SO\({}_{4}\) solution showedvery similar behavior to those made with distilled water. However, the UCS of the samples made with the Na\({}_{2}\)SO\({}_{4}\) solution was 5% greater than those made with distilled water for both types of samples (with and without limestone powder) at 6 months.  
Regarding the cement samples, the UCS increased from 7 days to 6 months for the samples made without limestone powder and with the industrial waste and sulfate solution. Nevertheless, the UCS of the samples made with limestone powder and those made with distilled water decreased slightly. Therefore, the UCS increased by 127% from 7 days to 3 months and by 175% to 6 months for cement samples without the industrial waste and those made with distilled water, respectively. The UCS of the cement samples with the industrial waste and distilled water increased by 105% from 7 days to 3 months and by 91% from 7 days to 6 months. Concerning the samples made with the Na\({}_{2}\)SO\({}_{4}\) solution, the samples without the industrial waste increased their UCS by 165% at 3 months and by 181% at 6 months. Meanwhile, the increase in the UCS of the cement samples with the industrial by-product was smaller, rising by 79 and 110% at 3 and 6 months, respectively. Similar to the lime samples, the UCS values of the cement samples made with the Na\({}_{2}\)SO\({}_{4}\) solution were greater than for those made with distilled water.
# Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models
###### Abstract  
The energy discourse is multifaceted, encompassing energy creation, storage, and conservation. Beyond the imperative of conserving energy consumption, effective energy management is a critical aspect of achieving overall energy efficiency. Despite being traditionally regarded as low electricity consumers, retailers play a pivotal role in economic activity. While categorized as non-productive energy users, the retail industry operates numerous establishments, facing substantial energy costs that make energy management integral to its operations. Historically, smaller retail stores have lacked awareness of energy saving. However, by connecting these stores, even modest reductions in individual electricity consumption can yield significant overall energy savings. This study aims to investigate the feasibility of implementing the demand-side management (DSM) aggregator model in the retail industry. Through surveys on awareness of energy saving and the application of deep learning techniques to analyze the effectiveness of the Aggregator model, the results reveal that the mean squared prediction error (MSPE) of this research is below 2.05%. This indicates substantial accuracy and offers meaningful reference value for Energy Service Company (ESCO) providers. The findings contribute practical recommendations for the sustainable and competitive implementation of DSM energy management practices in smart cities.  
demand-side management; energy management; energy-saving awareness; ESCO; aggregator Article
# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 4 Results
### Presentation of Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Runs** & & **Precision** & **Recall** & **F1-Score** & **Sensitivity** & **Specificity** \\ \hline \multirow{2}{*}{Run 1} & Malignant & 0.94 & 0.89 & 0.92 & 0.89 & 0.92 \\  & Benign & 0.85 & 0.92 & 0.88 & 0.85 & 0.94 \\ \hline \multirow{2}{*}{Run 2} & Malignant & 0.93 & 1.00 & 0.96 & 1.00 & 0.88 \\  & Benign & 1.00 & 0.88 & 0.93 & 0.88 & 1.00 \\ \hline \multirow{2}{*}{Run 3} & Malignant & 0.9 & 1.00 & 0.95 & 1.00 & 0.85 \\  & Benign & 1.00 & 0.85 & 0.92 & 0.85 & 1.00 \\ \hline \multirow{2}{*}{Run 4} & Malignant & 1.00 & 0.88 & 0.93 & 0.88 & 1.00 \\  & Benign & 0.88 & 1.00 & 0.94 & 1.00 & 0.88 \\ \hline \multirow{2}{*}{Run 5} & Malignant & 1.00 & 0.88 & 0.94 & 0.88 & 1.00 \\  & Benign & 0.88 & 1.00 & 0.94 & 1.00 & 0.88 \\ \hline \multirow{2}{*}{Run 6} & Malignant & 0.87 & 0.93 & 0.9 & 0.93 & 0.88 \\  & Benign & 0.94 & 0.88 & 0.91 & 0.88 & 0.93 \\ \hline \multirow{2}{*}{Run 7} & Malignant & 0.97 & 0.93 & 0.95 & 0.93 & 0.97 \\  & Benign & 0.94 & 0.97 & 0.95 & 0.97 & 0.93 \\ \hline \multirow{2}{*}{Run 8} & Malignant & 0.82 & 0.96 & 0.89 & 0.96 & 0.82 \\  & Benign & 0.97 & 0.82 & 0.89 & 0.82 & 0.96 \\ \hline \multirow{2}{*}{Run 9} & Malignant & 0.97 & 0.94 & 0.96 & 0.94 & 0.96 \\  & Benign & 0.93 & 0.96 & 0.95 & 0.96 & 0.94 \\ \hline \multirow{2}{*}{Run 10} & Malignant & 0.94 & 0.97 & 0.96 & 0.97 & 0.93 \\  & Benign & 0.96 & 0.93 & 0.94 & 0.93 & 0.97 \\ \hline \multirow{2}{*}{**Average**} & Malignant & 0.934 & 0.938 & 0.936 & 0.94 & 0.92 \\  & Benign & 0.935 & 0.921 & 0.925 & 0.91 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance metrics for the best CNN for 10 runs (batch size = 8, dropout = 0.7). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Title 1** & **Malignant** & **Benign** \\ \hline Malignant & 31 & 1 \\ Benign & 2 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Best confusion matrix for the proposed CNN. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Grayscale** & **CNN Architecture (2,4,8), Epochs = 200, Batch Size = 8, Dense = 16, Drop-Rate = 0.2, Pixel Size (\(150\times 150\))** \\ \hline \multirow{2}{*}{**Runs**} & **Accuracy** & **Loss (Validation)** & **Accuracy (Testing)** & **Loss (Testing)** \\ \hline Run 1 & 85.94 & 0.31 & 85.71 & 0.42 \\ Run 2 & 92.19 & 0.28 & 80.36 & 0.45 \\ Run 3 & 78.12 & 0.52 & 75 & 0.51 \\ Run 4 & 87.5 & 0.26 & 78.57 & 0.61 \\ Run 5 & 84.37 & 0.33 & 89.28 & 0.3 \\ Average & 85.62 & 0.34 & 81.78 & 0.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: CNN performance for grayscale images.
# Accurate Constant Phase Elements Dedicated for Audio Signal Processing
## 5 Discussion
Approximation of CPEs using a fully passive ladder circuit is also not restricted to a fixed frequency band. For example, each designed CPE dedicated to audio signal processing can be transformed into a subsonic or ultrasonic frequency band by introducing frequency scaling factors \(\gamma<1\) and \(\gamma>1\), respectively. This scaling factor is a real positive number that reduces the value of the \(k\)-th capacitor to \(C_{b}/\gamma\), and decreases the value of the \(k\)-th inductor to \(L_{b}/\gamma\). The values of the resistors remain unchanged. Of course, transformed CPEs are implementable if values of accumulation elements are still reasonable, off-the-shelf components. Each designed CPE undergoes Monte Carlo analysis, namely 1000 runs of AC sweep in Orcad Pspice for a normal distribution of the random values of the passive components. It turns out that the high-precision passive components need to be considered for CPE design. Larger fabrication tolerances (0.5%, 1%, etc.) cause too large phase errors. This means that the phase frequency response is heavily rippled, and neighborhood peaks of the phase pantle can add up, leading to the maximal phase deviation being significantly raised. Based on personal experiences of author, a maximal phase error greater than \(3^{\circ}\) over the approximated frequency range renders designed CPEs useless, because individual orders become undistinguishable. Of course, the usability of designed CPEs depends on the concrete application.
## 3 Experimental Results and Analysis
### Impact of Data Enhancement on Recognition Accuracy  
Although PETA is currently a large pedestrian attributes dataset containing 19,000 images, the sample data scale is not large enough for each attribute. Therefore, the dataset is expanded to 190,000 images by means of horizontal translation, vertical translation, horizontal folding, scaling transformation, and random rotation of a certain angle. Table 2 shows the effect of data enhancement on recognition accuracy.  
The experimental results show that mA can be improved by 0.36% through data enhancement. The results of the loss and accuracy in the training process are shown in Figure 5, where Figure 5a is without data enhancement, and Figure 5b with data enhancement, respectively. It can be seen that after the data enhancement, the jitter of the loss reduction in the whole training process becomes smaller and also tends to be earlier, and the accuracy of the training set or test set is relatively high. Therefore, it can be concluded that data enhancement has a good impact on recognition accuracy and training convergence.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Enhancement or Not** & **Loss** & **mA** \\ \hline no & 0.2181 & 91.52\% \\ yes & 0.2167 & 91.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The impact of data enhancement on recognition accuracy.  
Figure 5: The loss and accuracy of the training process with data enhancement on the PETA dataset: (**a**) without data enhancement; and (**b**) with data enhancement.
## 3 Materials and Methods: Workforce Capacity and Workload
### A Few Definitions
In the TPS, since its conception in the 1950s, the term _heijunka_ (translated as production leveling) refers to distributing the production of different models evenly over a time period (e.g., a day) and entails, beyond the traditional line balancing approach, the concept of leveling or equating the work load to be performed to the capacity of operators or machines to complete that work [40]. Hence, the concepts that we operationalize in this paper stem from the very heart of LM. In Equation (4), workload becomes related to capacity and time units are avoided. If we examine Equations (1) and (3) together, we realize that load (\(q\)) and _takt_ time (or desired cycle time) are reciprocal concepts as well as capacity and operation time (or station time). For this reason, an analysis based on load and capacity is equivalent to the traditional study based on operation time and cycle time [41]. \[Wu_{ik}=\frac{q_{k}}{CP_{ik}} \tag{4}\]  
In Equation (5), the time that a given workstation spends (e.g., during the day or available time \(AT\)) performing task \(i\) in the production of model \(k\) (\(TP_{ik}\))--simply computed as \(q_{k}\) times the operation time \(t_{ik}\)--becomes related to capacity (\(CP_{ik}\)), by means of Equation (3), and especially to the unit workload of the workstation (\(Wu_{ik}\)), thanks to Equation (4). This way, temporal magnitudes may be regained when necessary. In Equation (5), \(Wu_{ik}\) can be isolated and then described as a fraction of the available time (Equation (6)). \[TP_{ik}=t_{ik}\cdot q_{k}=\frac{AT}{CP_{ik}}\cdot q_{k}=AT\cdot Wu_{ik} \tag{5}\]  
\[Wu_{ik}=\frac{TP_{ik}}{AT} \tag{6}\]  
According to its definition, when \(Wu_{ik}\leq 1\), an operator is enough to complete a given job within the time allotted. If \(Wu_{ik}<1\), there is some idle time, and more operations could be assigned to the operator. Alternatively, if \(Wu_{ik}>1\), more people are necessary to complete the task. This approach is not only valid for assembly lines. Regardless of the type of layout or manufacturing strategy, the value of \(Wu_{ik}\) (rounded up to the next integer, if part-time work is not considered) gives the theoretical minimum number of operators (\(w\)) to complete the scheduled work.
# Hourglass 3D CNN for Stereo Disparity Estimation for Mobile Robots
## 3 Methodology
### Disparity Regression  
Disparity regression [9] is utilized to estimate the disparity map and is calculated as Equation (1).  
\[\hat{d}=\sum_{d=0}^{D_{\text{max}}}d\times\sigma(-c_{d}) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Layer Description** & **Output Tensor Dim** \\ \hline  & Cost volume & \(1/16D\times 1/16H\times 1/16W\times 1\) \\ \hline
1 & 3D conv, 3 \(\times\) 3 \(\times\) 3, F features & \(1/16D\times 1/16H\times 1/16W\times F\) \\
2 & 3D conv, 3 \(\times\) 3 \(\times\) 3, F features & \(1/16D\times 1/16H\times 1/16W\times F\) \\
3 & 3D conv, 3 \(\times\) 3 \(\times\) 3, 2F features, & \(1/32D\times 1/32H\times 1/32W\times 2F\) \\  & stride 2 & & \\
4 & 3D conv, 3 \(\times\) 3 \(\times\) 3, 2F features & \(1/32D\times 1/32H\times 1/32W\times 2F\) \\
5 & 3D conv, 3 \(\times\) 3 \(\times\) 3, 4F features, & \(1/64D\times 1/64H\times 1/64W\times 4F\) \\  & stride 2 & & \\
6 & 3D conv, 3 \(\times\) 3 \(\times\) 3, 4F features & \(1/64D\times 1/64H\times 1/64W\times 4F\) \\
7 & 3D deconv, 3 \(\times\) 3 \(\times\) 3, 2F features, & \(1/32D\times 1/32H\times 1/32W\times 2F\) \\  & stride 2, add layer 7 and 4 & & \\
8 & 3D deconv, 3 \(\times\) 3 \(\times\) 3, F features, & \(1/16D\times 1/16H\times 1/16W\times F\) \\  & stride 2, add layer 8 and 2 & & \\
9 & 3D conv, 3 \(\times\) 3 \(\times\) 3, F features & \(1/16D\times 1/16H\times 1/16W\times F\) \\
10 & 3D conv, 3 \(\times\) 3 \(\times\) 3, 1 features & \(1/16D\times 1/16H\times 1/16W\times 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of our proposed architecture. Each 3D convolutional layer represents a block of convolution, batch normalization, and ReLU (except the final layer).  
where \(\hat{d}\) is the predicted disparity and is calculated as the sum of each disparity probability \(d\). Finally, \(d\) is calculated from the cost function (\(c_{d}\)) based on a softmax operation (\(\sigma(\cdot)\)).  
As in [9], using the above disparity regression achieves better results in comparison with stereo-matching methods based on classification.
# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
At the same time, the network operators also need to reduce the operation and maintenance expenses (OPEX). According to the Federal Communication Commission (FCC), more than one-third of fiber disruptions are caused by fiber-cable problems [14]. These issues can include failures of connectors or power supplies, fiber breaks, macro bends, or even problems with Optical Line Terminal (OLT) or Optical Network Unit (ONU) transceiver problems. Consequently, a remote and automatic monitoring or diagnosing mechanism for the fiber links would be very beneficial to reduce the mean time to repair (MTTR), increasing customer satisfaction. The main contribution of this paper is as follows:  
1. We propose a smart resilience mechanism architecture and operations in Next-Generation Ethernet Passive Optical Network (NG-EPON). 2. We introduce a novel Resilience Dynamic Bandwidth Allocation (RDBA) mechanism, ensuring the Quality-of-Services (QoS) of real-time and tactile internet applications. 3. We build a supervised AI model using Multi-Layer Perceptron (MLP) for detecting any anomalies or faults in the branches. The extensive simulation results demonstrate that the resilience of AI-enhanced anomaly and fault detection effectively manages delay for real-time and tactile internet applications. The remainder of this paper is organized as follows. Related work is presented in Section 2. The SDN-Enabled Broadband Access (SEBA) architecture is discussed in Section 3. Section 4 introduces the proposed smart resilience architecture. Section 5 presents the performance evaluation. Finally, Section 6 concludes our work.