esa-sceva/satcom-chunk-collection huggingface dataset:
# Service Process Modeling in Practice: A Case Study in an Automotive Repair Service Provider
## 3 Materials and Methods
### Case Study Description and Rationale  
The case study organization is a large, authorized dealership service center. It employs a significant number of technicians and service advisors and has a substantial number of service bays. The organization provides a comprehensive range of after-sales services, including routine maintenance, complex repairs, and warranty work.  
This organization was selected due to its accessibility and willingness to provide detailed service activity reports. It represents a typical example of a large, authorized dealership service center in the region, making it relevant to the study's aim of understanding common service process challenges and improvement opportunities.  
While this case study focuses on a single authorized dealership, many of the service processes and challenges identified (e.g., vehicle scheduling, customer communication, and parts management) are common across other types of automotive service providers, such as independent repair shops and multi-brand service centers. However, the specific implementation of these processes may vary.  
A key limitation of this single case study is the limited generalizability of the findings. The results may not be directly transferable to smaller independent repair shops or service centers with different organizational structures or customer demographics. Further research involving multiple cases is needed to confirm the generalizability of these findings.  
The large size of the case study organization may have led to a higher degree of formalization in its service processes compared to smaller workshops. This formalization could influence the effectiveness of certain improvement measures. Additionally, the focus on a single brand might affect the findings related to spare parts management and technical expertise.
# Hybrid-Margin Softmax for the Detection of Trademark Image Similarity
## 2 Materials and Methods  
### Hybrid-Margin Softmax  
The peculiarity of TM images is crucial to the TMISD task. We compared the FR and the TMISD task to have a better view of the latter:  
1. The compositions of images in an FR task are constant. The principal parts of the input pairs of samples are human faces that always come from one exact person or different ones. The features extracted from the input are fixed generally, such as the shapes of faces, eyes, and noses. Plus, there are external interfering terms that should be considered including gestures, illuminations, ages, image noises, etc.
2. The TMISD task is aimed at detecting the similarity of TM images. Generally, a TM design consists of a single element or several ones. The elements of the disputed TM image will not be identical to the legal one but partly similar in contours, colors, and textures, as shown in Figure 1. It is common for there to be both similar and different elements between two TM images in disputed cases. It should be noted that new outlines can be formed by the varying placements of elements. Furthermore,interfering terms mentioned in the FR task are no longer to be considered, since TM images are artificially designed in most cases.  
To sum up, compared to the FR task, there are supposed to be more margin penalties on the decision boundary to tolerate a wide variety of element design changes in pairs of similar TM images. Meanwhile, the detection model should extract more information from input images to have a full understanding of the similarity degree and avoid false alarms. Therefore, the detection model should be further penalized, and the learnable parameters should be preserved as much as possible.  
Given the characteristics of the TMISD task, a hybrid-margin softmax (HMS) is proposed as follows:  
\[L_{3}=-\frac{1}{N}\underset{i=1}{\overset{N}{\underset{i=1}{\sum}}}\log\frac{e^ {s\parallel\left\|W_{y_{i}}^{T}\right\|\parallel x_{i}\parallel\ \left(\cos\left(\theta_{y_{i}}-d_{1}\right)-d_{2}\right)}}{e^{s\parallel W_{y_{ i}}^{T}\parallel\ \left\|x_{i}\parallel\ \left(\cos\left(\theta_{y_{i}}-d_{1}\right)-d_{2}\right)}+ \underset{j=1,j\
eq y_{j}}{\overset{n}{\sum}}e^{s\parallel W_{j}^{T} \parallel\ \left\|x_{i}\parallel\ \cos\theta_{j}\right.}} \tag{3}\]  
where \(s\) is a global scale factor. \(W_{y_{i}}\) are the weights of the fully connected layer, and \(x_{i}\) is the feature vector of \(i\)-th sample extracted by SNN. The weights and feature vectors are not normalized, and the biases are set to zero. Additive margin \(d_{1}\) and \(d_{2}\) are attached to the angle term and the cosine term, respectively.  
The decision boundary of HMS loss is as follows:  
\[\|W_{1}\|\cos\theta_{1}=\|W_{2}\|(\cos(\theta_{2}-d_{1})-d_{2}) \tag{4}\]  
The decision boundary still can be considered as laying in the angular space with a varying amplitude of the cosine curve, as shown in Figure 2a.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using SAS software, v. 9.4 (SAS Institute, Cary, NC, USA). A descriptive statistical analysis of the superficial microbial contamination of all surfaces and each type of surface under study was performed in terms of the median and interquartile range (25-75%) at T0 and T1. The Wilcoxon signed-rank test was used to evaluate the difference between the RLU values at T0 and T1 for all surfaces and for each type of surface under study. Differences between the disinfection at T0 and T1, expressed by the luminometer as categorical data \"approved\" or \"not approved\" for all surfaces and each type of surface under study, were analyzed by the McNemar test. To evaluate the differences in the superficial microbial contamination among the three surfaces under study, at T0 and T1 separately, the Kruskal Wallis test and the Chi-Square tests were performed. The appropriate test was chosen based on the type of disinfection data, continuous or categorical, respectively. The level of significance (\(p\)) was set to \(<\)0.05.  
Data registered before seating the patient and starting the dental procedure ensured that the surfaces under study were cleaned but were not included in the analyses, since the study aimed to evaluate the effectiveness of the overheated dry-saturated steam system alone in the disinfection of the dental chair surfaces.
## 3 SWIFTNET: Protocol for Fast-Reactive Monitoring
### Learning Technique: Normalized Least-Mean Square Filter  
In order to realize our goal of an aggressive data gathering process with minimal data transmission, we anchor our design on a light-weight learning method. The aim is to apply this model as a predictor in a distributed manner on both the sensor nodes and the base station. The Normalized Least-Mean Square (NLMS) filter is known to be an effective learning model, which could yield optimal results. NLMS is a class of adaptive filters that are well-known as an alternative solution to perform prediction on timeseries data without requiring knowledge of the statistical properties of the phenomenon being monitored. The algorithm has three parts as discussed in [48] namely:  
1. The training sample: this includes the input signal vector denoted as \(x(n)\) and the desired response denoted as \(d(n)\).
2. Adaptive parameter: this includes the learning rate denoted as the \(\eta\) and the weight initialization \(\hat{h}\), which is set as \(\hat{h}(0)\) = \(0\).
3. The computation: this includes the computation of the error signal, the weight adaptation and the prediction. The error signal is calculated as: \[e[n]=d[n]-\hat{h}^{T}[n]x[n]\] (12) The weight vector is then updated as: \[\hat{h}(n+1)=\hat{h}(n)+\frac{\eta\;x(n)e(n)}{\left\|x(n)\right\|^{2}}\] (13)  
The NLMS [49] is a variant of the stochastic gradient descent LMS filter shown in Figure 2. The difference is that the NLMS offers guaranteed convergence by normalizing the filter with the power of the input as shown in Equation (13). Basically, an adaptive filter takes in a sample \(x[n]\) of input signal \(x\) at each step \(n\) to compute the filter output \(y[n]\) given as:  
\[y[n]=\sum_{i=0}^{N-1}\hat{h}_{i+1}[n]\times x[n-i] \tag{14}\]  
A prediction algorithm can be constructed with a linear combination of the past \(N\) samples of the input signals with the weight adaptation \(h_{i}[n]\) from the filter. The output signal \(y[n]\) is then compared with the desired signal \(d[n]\) which the filter tries to adapt to. These weights are computed to satisfy the optimality criterion which is normally the minimization of the Mean-Squared-Error (MSE). The weights are updated at each \(n\) step with the aim to iteratively converge at the minimum. The error signal \(e[n]\) is used in the adaptation rule in Equation (13) in order to reduce the errors in the subsequent step \(n+1\).  
Using the prediction algorithm does not meet our need for an adaptive prediction. Since a prediction model uses a fixed sampling interval, we are interested in following the evolution of the signal at a faster rate when necessary. Thus, to satisfy this need, we integrate an adaptive sampling strategy into the prediction model, which we discuss next.  
Figure 2: LMS Filter.
## 3 Method
### Ordinal Loss  
[PERSON] et al. [41] suggested that there is a relationship between different severity grades in knee osteoarthritis. A higher loss should be obtained if the predicted and actual grades are farther apart in order. For example, knee osteoarthritis has five grades, and the weight loss for misclassifying grade 2 as grade 1 and grade 0 should be different. Therefore, on the basis of the focal loss, an ordinal loss is proposed to detect osteoarthritis.  
ACL tears are divided into three grades: grade 0 being no tear, grade 1 being partial tears, and grade 2 being complete tears. The ordinal loss also applies to this case since the different tear grades are correlated in order. A 3 \(\times\) 3 matrix of ordinal loss weights **W** is assumed. \(w_{ij}\in\textbf{W}\) represents the ordinal weight of the predicted class \(i\) for the true class \(j\). The ordinal weight ought to be greater the further apart the predicted class is from the actual class. The loss is similar to the cross-entropy loss, as follows:  
\[loss=\sum\limits_{j=1}^{n}w_{ij}q_{j} \tag{6}\]  
where \(q_{j}=\left\{\begin{array}{cc}p_{j}&j\
eq i\\ 1-p_{j}&j=i\end{array}\right.\) and \(p_{j}\) is the predicted probability value. The weights are re-weighted to simplify the computation and to make the form closer to cross-entropy loss. The new weight matrix is \({w_{ij}}^{\prime}=\left\{\begin{array}{cc}0&i=j\\ w_{ij}+1&i\
eq j\end{array}\right.\), where the loss is equivalent to \(loss=\sum\limits_{j=1}^{n}{w_{ij}}^{\prime}p_{j}\).  
As shown in Figure 5, suppose \(\textbf{W}=\left[\begin{array}{ccc}1&3&5\\ 3&1&3\\ 5&3&1\end{array}\right]\); then, \(\textbf{W}^{\prime}=\left[\begin{array}{ccc}0&4&6\\ 4&0&4\\ 6&4&0\end{array}\right]\). The actual grade \(j\) is grade 1 and the predicted grade \(i\) is grade 1; then, \(loss=4\times 0.025+4\times 0.203=0.912\).  
Ordinal loss is used in ACL classification to optimize the hard-to-train problem in the classification task, which is caused by the small number of samples and sample imbalance.  
Figure 5: Process of ordinal loss.
# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
###### Abstract  
With the emergence of the 5G network, the count of analysis papers associated with the 6G Internet of Things (IoT) has rapidly increased due to the rising attention of researchers in next-generation technology, 6G networks and IoT techniques. Owing to this, grasping the overall research topics and directions is a complex task. To mutually address the significant issues of 6G cellular IoT, i.e., information transmission, data aggregation and power supply, we proposed a variance-based integrating model for the 6G-IoT approach that considers energy, communication and computation (ECC). Initially, the base station (BS) charges huge IoT devices concurrently utilizing WPT in the downlink. After that, IoT devices gather the energy to perform the communication task and the computation task in the uplink in a similar spectrum. Also, the model integrates the optimization of transmit beams via the Improved Ant Colony Optimization (IACO) model to balance the system performance, power consumption and computational complexity. Further, this study exploited activated Remote Radio Units (RRUs) to improve the network performance and energy efficiency in the downlink model. The simulation outcomes evaluate the performance of the proposed work over the conventional models concerning error analysis. From the results, the MSE value in the IACO work is much lower, around 0.011, while the compared schemes achieved comparatively higher MSE values.  
6G Internet of Things; beamforming; communication; energy; improved ant colony optimization 2023  
[PERSON], [PERSON], [PERSON], [PERSON], G.C. Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System. _Sustainability_  
1  
Sustainability
# Dynamical Networks Modelling Applied to Low Voltage Lines with Nonlinear Filters
## 3 Linear and Nonlinear Filters  
### Overview  
In the example above, we see that capacitance and inductance can alter the wave time speed (Equation (2)), and capacitance will filter high frequencies (example in Figure 5). This capacitance filtering will vary linearly with the wave frequency. That is, the linear filter has an output that is a linear function of its input. On the other hand, a nonlinear filter has an output that is not a linear function of its input.  
In our model, the capacitances and inductances, as well the losses, are described within the node model. Any kind of disturbances or interferences on the line will hit the Receiver R. What kind of filters could be applied to the system that will not affect the normal signals, but only when these interferences appear? Nonlinear filters are suitable for this purpose because they will not be active when the signals are those normal ones expected but will be activated when unexpected and abnormal ones are surging. An appropriate model for this kind of nonlinear filter must be defined and introduced to the iterative process, giving to the network a dynamical behaviour, according to each level of abnormality.  
For metallic transmission lines, the semiconductors are considered powerful nonlinear filters, and for high levels of voltage and/or energy, the metal-oxide variable resistors, or varistors, are among the most useful. Their physical characteristics and parameters, as well as their typical curves, are described below and will enable us to build their dynamic models. Although this is a very specific electrical component, the model is generic and can be applied to any other nonlinear elements, in any other dynamical systems that use this network approach.  
Figure 9: A nonlinear filter applied to one or more points of the metallic line (**top**) and its representation as applied to the respective network node (**bottom**). The filter will be temporarily part of the structure according to unexpected functional feature variances (for example, alien attacks).
## 4 Model and Methods
Specifically, we calculated statistical features such as standard deviation, gradient, minimum, maximum, and amplitude for each SpO2 and heart rate sequence. These features were chosen based on their potential to capture signal variability, which is known to correlate with apnea events. Among these, standard deviation and amplitude features contributed significantly to recall improvement by providing the model with a sense of recent signal fluctuation. Feature selection experiments indicated that the addition of these derived features increased recall by approximately 5% in the validation set, indicating their effectiveness in aiding in the detection of apnea events. Further preprocessing steps included the removal of SpO2 values close to zero to eliminate potential measurement errors. For each signal parameter, statistical features such as standard deviation, gradient, minimum, maximum, and amplitude were calculated. A sliding window approach was employed to generate sequences with the window size defining the length of each sequence. Additionally, a time feature was computed by dividing each row index (in seconds) by 43,200 (the total number of seconds in 12 h) to incorporate temporal context into the model. Each sequence was labeled based on the occurrence of an apnea event in the subsequent epoch, which was recorded in the csv_events file. This approach, along with preprocessing adjustments, ensured that the data were well prepared and appropriately structured to optimize the detection performance and robustness of the model.
## 4 Seismic Data Interpretation
### Time Contour and Time Surfaces Maps of Dunghan Formation  
The time contour map of the Dunghan formation points out a monoclinic structure dipping in the eastward orientation. The reflection time increases from 0.93 to 1.14 s, as shown in Figure 8a. The time contour surface of the Dunghan formation was generated by joining the points of equal time. These time values were assigned a color bar, which can be used as a guide for interpretation. On the color bar, red shows the shallowest point, and purple-blue shows the deepest point (Figure 8b). The 3D view of the particular formation surface helps to comprehend the monoclinic structure in the Punjab platform region northward.  
Figure 8: (**a**) Time contour map of the Dunghan formation, (**b**) Time surface map of the Dunghan formation.  
The counter map is generally defined as the line showing the same parameter (depth elevation) on a 2D map. The contours are made with the help of the time values, which represent the time of the wave to reflect from a dense point reflector such as R1, R2, R3, and R4. The time counters are generated on the map, taking the value of reflector time filling a line. In the current research, the reflectors of interest are Dughgan, Samana Suk, Datta, and Warcha formations, and these are plotted on the counter section referring to their reflection time values. The reflection time is plotted in order to give an idea about the structure and time delay of the seismic wave response due to the depth and other deflections in the path of the wave.
## 4 Problem Formulation
### Problem Constraints
#### 4.2.2 GFU Technical Constraints  
\(\bullet\) **Power Generation**  
The power generation of GFU is limited by maximum and minimum values, which are indicated in (6). Moreover, to prevent setting values in off-times, the binary variable \(I_{t}^{GF}\) is defined.  
\[P^{GF,Min}I_{t}^{GF}\leq P_{t}^{GF}\leq P^{GF,Max}I_{t}^{GF} \tag{6}\]  
\(\bullet\) **Ramping Up/Down**  
Ramping up and ramping down in producing power of GFU between two consecutive hours are defined as (7) and (8), respectively, in which the binary decision variables, i.e., \(X_{t}\), \(Y_{t}\) are obtained based on a unit commitment in the scheduling period. Otherwise, these binary variables are starting-up and shutting-down indicators defined as (9) and (10), respectively.  
\[P_{t}^{GF}-P_{t-1}^{GF}\leq(1-X_{t})R^{up}+X_{t}P^{GF,Min} \tag{7}\]  
\[P_{t-1}^{GF}-P_{t}^{GF}\leq(1-Y_{t})R^{dn}+Y_{t}P^{GF,Min} \tag{8}\]  
\[X_{t}-Y_{t}=I_{t}^{GF}-I_{t-1}^{GF} \tag{9}\]  
\[X_{t}+Y_{t}\leq 1 \tag{10}\]  
\(\bullet\) **Minimum Up/Down Time**  
GFUs cannot start-up and shutdown in each hour that we want to commit. Therefore, linearized minimum uptime and minimum downtime of units should be considered as (11)-(14) and (15)-(18), respectively.  
\[UT=Max\Big{\{}0,Min\Big{(}N_{T},\Big{(}T^{ON}-IS_{t=0}^{ON}\Big{)}I_{t=0}^{ GF}\Big{)}\Big{\}} \tag{11}\]  
\[\sum_{t=1}^{UT}\left(1-I_{t}^{GF}\right)=0\quad\quad;\,\forall t=1,\ldots,UT \tag{12}\]  
\[\sum_{t^{\prime}=t}^{t+T^{ON}-1}I_{t^{\prime}}^{GF}\geq T^{ON}\times\,X_{t} \quad\quad;\forall t=UT+1,\ldots,N_{T}-T^{ON}+1 \tag{13}\]  
\[\sum_{t^{\prime}=t}^{UT}\left(I_{t^{\prime}}^{GF}-X_{t}\right)\geq 0\quad\quad; \,\forall t=N_{T}-T^{ON}+2,\ldots,NT \tag{14}\]\[DT=Max\Big{\{}0,Min\Big{(}N_{T},\Big{(}T^{OFF}-IS^{OFF}_{t=0}\Big{)} \Big{(}1-I^{GF}_{t=0}\Big{)}\Big{)}\Big{\}} \tag{15}\] \[\sum_{t=1}^{DT}I^{GF}_{t}=0\qquad;\;\forall t=1,\ldots,DT\] (16) \[\sum_{t^{\prime}=t}^{t+T^{OFF}-1}(1-I_{t^{\prime}}{}^{GF})\geq T^{ OFF}\times Y_{t}\quad;\forall t=DT+1,\ldots,N_{T}-T^{OFF}+1\] (17) \[\sum_{t^{\prime}=t}^{DT}(1-I_{t^{\prime}}{}^{GF}-Y_{t})\geq 0\quad; \forall t=N_{T}-T^{OFF}+2,\ldots,NT \tag{18}\]  
\(\bullet\) **Gas Fuel Consumption**  
Gas fuel function reported in the second term of (3) is defined as Equation (19) in which the power produced by GFU follows gas consumption and is restricted by the maximum allowed daily gas consumption, which is stated as (20).  
\[F^{GF}(P^{GF}_{t})=aP^{GF}_{t}+b \tag{19}\] \[\sum_{t=1}^{NT}\Bigg{(}F^{GF}(P^{GF}_{t})-\sum_{k=1}^{NGS}G^{dis, GSS}_{k,t}\Bigg{)}\leq G^{Max,daily} \tag{20}\]  
\(\bullet\) **Maximum Allowed Sold Power**  
The dispatched power of WPU, and also the generated power of GFU, could be sold to the energy market by the transmission line, which is depicted in (21). This means that, according to their nominal capacities, each facility could generate maximum allowed power to transmit.  
\[P^{WT}_{t}+P^{GF}_{t}\leq PT^{Max} \tag{21}\]
## 4 Validation and Discussion
### Construction of the Test Platform
#### 4.1.2 Data Transmission Test  
For the data transmission test, the cloud server was connected through the remote desktop of the local computer, and the communication was carried out between the upper computer program and the cloud server. After the connection was successfully established, the upper computer would receive a prompt from the cloud server and then send the vehicle speed data, and at the same time, the data received by the server was observed, as shown in Figure 10. The test results show that the communication status is good.