esa-sceva/satcom-chunk-collection huggingface dataset:
# MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
## 1 Introduction  
Electric power grids are currently undergoing a transition from large centralized power stations to distributed generation, which includes significant renewable components [1]. Utilizing a high percentage of intermittent renewable energy creates new engineering problems, requiring greater flexibility from controllable power sources and large-scale energy storage systems. Information technology is also shaping changes to the energy sector, allowing for automated and reliable tracking of transactions between many buyers and sellers [2]. The new ability for small prosumers to trade directly with each other creates potential to improve renewable energy utilization. This work explores an optimal dispatch algorithm that controls all energy flows within two prosumers (small scale producers and consumers, such as homes with rooftop PV) and determines when they should share energy.
## 3 Results and Discussion
### Waste Collection Efficiency of Current Model  
The population of Lahore city is 10.3 million (excluding catchment areas) [114], which generates 5563 tons of solid waste per day (0.54 kg/capita/day) in areas under the jurisdiction of Metropolitan Corporation Lahore (MCL). The average value of waste collection and disposal at the Lakhodair site is 4700 tons/day (October 2021 to September 2022) by LWMC, excluding catchment boards and private entities. Primary collection is performed mainly through CBC (Figure 12a) mode by LWMC, while the secondary collection function is performed on the outsourced model. Based on waste generation, collection, and disposal figures (Figure 12b), the waste collection and disposal efficiency are determined as \(\pm\)84%. The remaining waste is scavenged by waste pickers or illegally dumped in depression areas along the ring road and the Ravi River by the private housing societies.
# Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers
## Appendix A
387, 469), it can be rotated into a diagonal matrix by the unitary transformation \(\mathbf{R}_{\boldsymbol{\phi}\boldsymbol{\phi}}=\mathbf{Q}\Lambda\mathbf{Q}^{ \mathrm{H}}\), in which \(\mathbf{\Lambda}=\text{diag}(\lambda_{1}\quad\lambda_{2}\quad\cdots\lambda_{N})\) is the diagonal matrix of real and positive eigenvalues of \(\mathbf{R}_{\boldsymbol{\phi}\boldsymbol{\phi}}\) (see [68], p. 471), in the form of \(\lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{N}\), and \(\mathbf{Q}\in\mathbb{C}^{N\times N}\) is the orthonormal matrix of eigenvectors that diagonalizes \(\mathbf{R}_{\boldsymbol{\phi}\boldsymbol{\phi}}\) through a similarity transformation [69]. Then, rotating \(\mathrm{E}(\mathbf{V}[k])\) by the matrix of eigenvectors \(\mathbf{Q}\)--i.e., \(\tilde{\mathbf{V}}[k]=\mathrm{E}(\mathbf{V}[k])\mathbf{Q}\)--decouples the evolution of its coefficients. By means of this rotation, we can express the modes of convergence (see [70], p. 77) of (106) as  
\[f_{\mathbf{Y}}(\mathbf{y})=\sum_{m=1}^{M}f_{\mathbf{X}}(\mathbf{x})\big{|}I \Big{(}\mathbf{g}_{m}^{-1}(\mathbf{y})\Big{)}\big{|}. \tag{10}\]2  
As \((\mathbf{I}-\eta_{w}\mathbf{\Lambda})\) is diagonal and the \(n\)th row of \(\tilde{\mathbf{V}}[k]\) represents the projection of \(\mathrm{E}(\mathbf{V}[k])\) onto the \(n\)th eigenvector of \(\mathbf{R}_{\boldsymbol{\phi}\boldsymbol{\phi}}\), all elements of \(\tilde{\mathbf{V}}[k]\) evolve independently. Hence, (107) converges to zero if \(|1-\eta_{w}\lambda_{n}|<1\) (see [69], p. 84). As the fastest mode of convergence corresponds to the maximum eigenvalue \(\lambda_{max}\), using the identity \(\lambda_{1}=\lambda_{max}\leq\text{tr}(\mathbf{R}_{\boldsymbol{\phi} \boldsymbol{\phi}})\) (see [70], p. 77), the condition for the convergence in the mean becomes  
\[f_{\mathbf{Y}}(\mathbf{y})=\sum_{m=1}^{M}f_{\mathbf{X}}(\mathbf{x})\big{|}I \Big{(}\mathbf{g}_{m}^{-1}(\mathbf{y})\Big{)}\big{|}. \tag{10}\]3As the trace of \(\mathbf{R}_{\phi\phi}\) is equal to the product of the number of neurons outputs and the respective signal power, the adaptive step bound is given by  
\[f_{\mathbf{Y}}(\mathbf{y})=\sum_{m=1}^{M}f_{\mathbf{X}}(\mathbf{x})\big{|}I \Big{(}\mathbf{g}_{m}^{-1}(\mathbf{y})\Big{)}\big{|}. \tag{10}\]4  
Note that the convergence in the mean of both PT-RBF and complex LMS is similar (see [70], p. 77), and it is natural in some way, considering that after the trans-dimensional transformation step of the PT-RBF, both algorithms have comparable architectures. However, in view of Corollary 1, \(\mathrm{E}(\lvert\phi[k]\rvert^{2})\) can be difficult to obtain. To circumvent this issue, using Corollary 1, we can replace \(\phi[k]\) by its maximum value (\(1+j\)) into (100), which yields  
\[f_{\mathbf{Y}}(\mathbf{y})=\sum_{m=1}^{M}f_{\mathbf{X}}(\mathbf{x})\big{|}I \Big{(}\mathbf{g}_{m}^{-1}(\mathbf{y})\Big{)}\big{|}. \tag{10}\]5  
which is the adaptive step bound for the convergence in the mean.
# Fraud Detection Using Neural Networks: A Case Study of Income Tax
## 3 Methodology
### Model Evaluation  
As the loss function and optimizer, binary cross-entropy was used. The binary cross-entropy, also called log-loss, is a loss function that is well suited for binary classification tasks, as it compares predicted probabilities to actual classes by measuring the distance between the prediction and actual output when training and the computed log-loss shows how close or far the prediction is to the actual values [32].  
\[\text{Loss}\ =-\frac{1}{N}\sum_{i=1}^{N}y_{i}\times\log(p(y_{i}))+(1-y_{i}) \times\log(1-p(y_{i}))=-\sum_{i=1}^{\text{N}}y_{i}\times\log(p(y_{i})), \tag{2}\]  
where \(y_{i}\) is the actual class and \(\log(p(y_{i}))\) is the probability of that class and \(p(y_{i})\) is the probability of 1 while \((1-p(y_{i}))\) is the probability of 0.  
When training, an optimizer is used to minimize the loss, and Adamax was found to be the best optimal as per the grid search hyperparameter tuning performed and was eventually used in this study. Accordingly to [PERSON] [33], Adamax, which is based on infinity norm, is a variant of Adam optimizer which is an efficient stochastic method that computes individual adaptive learning rates for different parameters. This optimizer is sometimes superior to Adam especially when representing many discrete features which makes it well suited for bigger datasets with discrete variables like in this research.
# How to Make Sense of Team Sport Data:
## 2 Team Sport Data
### Video and Sensor Data  
Video recordings are ubiquitous in invasive team sports and there is an increasing demand for professional analyses. Video recordings range from television recordings from mass media with various perspectives (e.g., Sky TV has 24 cameras on the ground for soccer matches [1] while for the NFL up to 70 cameras are used during a super bowl match [2]) to professional recordings carried out by the teams themselves. Consequently, video data generally can be considered as the most available data source and for example can be obtained by capturing television recordings of professional matches. Companies like STATS [3] or Opta [4] offer the service of extracting movement, event, and statistic data based on their own recordings. Working directly with video data is much cheaper than assigning a professional company to track a team's players, events, and statistics. However, the extraction of player movement from video recordings is a non-trivial task. Nevertheless, due to recent progress  
Figure 2: Relations and hierarchies between different data types in team sport. Most data can be extracted either from video or sensor data. Additional information is provided by supplemental context data.  
in the field of image and video processing, recent publications showed the feasibility of extracting movement data based on video sources [5; 6; 7].  
Another possibility recording team sports is capturing the players' movement with sensors directly attached to players or game objects (e.g., ball, sidelines, targets, etc.). Practically, the applicability of this acquisition modality is depending on the legislation adopted by the sports associations. This data acquisition may be partially restricted in some invasive team sports (as it was for example in soccer until 2015) while allowed for others. For instance, the NFL [8] allows active tracking by sensors placed on the player shoulders in cooperation with Zebra Technologies [9]. Sensors may allow real-time capturing of data via wireless data transfer. The dataset of the ACM DEBS 2013 Grand Challenge is a perfect example for these kinds of sensor data [10]. Video data is often the basis for movement extraction. Nevertheless, videos can be enriched with analysis results visualized in the raw video data respecting perspectives and angles.
## 1 Introduction  
The prevalence of thyroid nodular disease is significant [1], with a consistent increase concerning differentiated carcinomas [2], regardless of the severity or size, and a steady prevalence of other forms of thyroid cancer [3]. The current recommendations propose risk stratification by means of anannesis, clinical evaluation, and ultrasound characterization [4; 5; 6]. Positive history, some clinical features, and specific ultrasound (US) patterns are considered high risk.  
The conventional diagnostic algorithm recommends fine needle aspiration (FNA) biopsy for the further management of cases. It is recognized as the gold standard for diagnosing thyroid neoplasia [4; 6]. Currently, the Bethesda reporting system is recommended [7]. However, it is generally accepted that FNA has unclear results in Bethesda categories III and IV, and there is no consensus about the management of these particular cases: whether to follow-up, refer to surgery, or complete the evaluation with molecular genetic tests [4; 6]. Even in experienced centers [8], inconclusive results represent up to 30% of all cytology results. FNA has shown valuable diagnostic value and a favorable overall sensitivity between 70% and 85% [9; 10]. The risk of malignancy in the intermediate cytological category covers a wide range, with a mean of 16%, ranging between 6% and 48% [11; 12; 13]. Our center has a previously reported rate of 28.8% [14] in this particular cytological category.  
Ultrasound diagnostic techniques are widely used in the pursuit of improved diagnostic reliability in the risk stratification of thyroid nodules. Several US characteristics are used different risk evaluation algorithms [15; 16; 17; 18; 19]. Besides conventional US patterns such as a markedly hypoechoic solid nodule, a nodule shaped taller than wide, a solid nodule with intranodular inhomogeneity, irregular borders, intranodular microcalcifications, or suspicious regional lymph nodes [4; 5; 6; 20], increased strain [19; 21; 22] on elastography, and marked perinodular vascularity measured by three-dimensional (3D) Doppler [23; 24] are also described as suggestive for malignancy.  
Multimodal US is being used with increasing frequency, with the purpose of achieving an improved accuracy in the pre-surgical risk prediction [25; 26; 27].  
In cases of intermediate cytology, there are results showing that the sonographic characteristics should influence the further management of these cases [28] by considering molecular diagnostic tests or surgery [29].  
Thus, the aim of this study was to determine the diagnostic value of multiparametric ultrasound evaluation in the assessment of thyroid lesions with indeterminate cytology results, with respect to post-FNA attitude.
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ2. What Are the Identified Solution Directions?
#### 5.2.9 Decomposition  
Study [AB] focuses on deciding the right size of microservices and provides a conceptual methodology to decompose business capability based on domain-driven design principles. To evaluate the usage of this methodology, a case study is conducted on the weather information dissemination domain. Evaluation results show that the weather information dissemination system is partitioned into different microservices successfully.  
Study [U] proposes a systematic approach using functional decomposition and based on functional requirements. This approach aims to build high cohesive and low coupled decomposition. To evaluate this approach, they have compared microservices implementations by three independent teams. The evaluation results show that it achieves identifying microservices much faster.  
The authors [AP] stated that the decomposition process is so challenging task and it should be supported with an approach, so they suggest a data flow-driven decomposition approach to handle the decomposition problem of MSA. They aim to obtain independently deployable and scalable microservices, so they defined a four-step decomposition procedure consisting of business requirement analysis, building fine-grained data flow diagrams, extracting dependencies between processes and finally identifying microservices by clustering processes. They conducted a case study to validate this approach and it has been observed that microservice candidates are determined by taking coupling and cohesive constraints into consideration.  
Study [CC] proposes an approach for identifying microservices by analyzing dependencies between business processes thanks to control, data, and semantic models. Further, it also provides a clustering method to identify potential microservices. To validate this approach, the authors carried out a case study. The results of the case study demonstrate its doability. Additionally, it also achieves better results than existing approaches in terms of microservice identification.  
In summary, the better we decompose the business capability into microservices, the more powerful microservices we have so we can say that this challenge is the primary among other challenges. Despite this fact, we could not find enough studies to work on this topic deeply.
## 4 Experiments and Results
### Implementations
#### 4.1.2 Evaluation Protocol  
The performance of our method is evaluated by recognition accuracy. Six state-of-the-art methods, including Replay [47], iCaRL [41], Wa [48], Podnet [49], ICAC [40], and CBesIL [15], were used to carry out comparative experiments. In our experiments, the backbones of iCaRL, Replay, Wa, ICAC and Podnet are all Resnet18. A brief description of these methods is as follows.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Target**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-5}  & **Num** & **Angle** & **Num** & **Angle** \\ \hline BMP2 & 233 & 17 & 195 & 15 \\ BTR70 & 233 & 17 & 196 & 15 \\ T72 & 233 & 17 & 196 & 15 \\ BTR60 & 256 & 17 & 195 & 15 \\
2S1 & 299 & 17 & 274 & 15 \\ BRDM2 & 298 & 17 & 274 & 15 \\ D7 & 299 & 17 & 274 & 15 \\ T62 & 299 & 17 & 273 & 15 \\ ZIL131 & 299 & 17 & 274 & 15 \\ ZSU234 & 299 & 17 & 274 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of ten-class targets under SOC.  
Figure 3: SAR images of ten classes of vehicle targets.  
* Replay: Replay retains a small amount of old category data as an instance set. Then, the instance set is used for training in the process of updating new data to review old knowledge.
* iCaRL: iCaRL builds and manages an exemplar set, which is a representative sample set of old data. After the representation learning of these data, iCaRL classifies the sample with the nearest-mean-of-exemplars rule.
* Wa: Wa is an improvement of iCaRL. On the basis of iCaRL, Wa normalized the classifier amplitude. This operation could eliminate the deviation in the model updating process.
* Podnet: Inspired by the representation learning, Podnet improves the distillation loss with pooled outputs distillation (POD). In addition, it represents every class with several proxy vectors, so it has better performance in incremental learning.
* ICAC: ICAC adaptively adds new anchored class centers for new classes, and the features of new class will be clustered around the corresponding center. For old classes, ICA retains key samples for each class. In addition, ICAC proposes an SL (Separable Learning) strategy to address the class imbalance between new and old classes.
* CBesIL: To reduce data storage pressure, CBesIL proposes a class boundary selection method to build the exemplar set. When performing incremental learning, CBesIL employs a boundary-based reconstruction method to rebuild the key data to avoid catastrophic forgetting.  
As presented in Table 2, we set up three different scenarios for the MSTAR dataset to carry out incremental learning experiments, and they are Scenario_A, Scenario_B and Scenario_C. The initial classes of three scenarios are all 4. However, the incremental classes (step sizes) of them are 1, 2 and 3, respectively. The settings of three scenarios are consistent with that in Reference [40].
# Robust Speed Tracking of Induction Motors: An Arduino-Implemented Intelligent Control Approach +
## 1 Introduction
In this regard, we propose an Arduino rather than a Field-Programmable Gate Array (FPGA) (e.g., [34; 35; 36; 37]) for the implementation of the proposed speed controller. Our main reasons for this choice are given as follows. Firstly, the use of a software programming language to develop a control scheme is simpler and more user-friendly than that of a hardware description language. Secondly, the use of an Arduino may save more computational resources. Thirdly, an Arduino is much more economical than an FPGA. The advantages of the proposed control scheme for speed tracking of a V/f controlled Nikki Denso NA21-3F 0.14-hp squirrel-cage IM are evaluated. Despite various motor horsepower, induction motors have a similar internal functional structure. Thus, a control design methodology for the 0.14-hp induction motor is in general applicable to the control design of induction motors with larger horsepower. Experiments with/without load torque disturbances are performed by PC-based and Arduino-based control. The PC and the Arduino plus the circuits which we design cost around US $600.00 and US $32.00, respectively. The experimental results consistently demonstrate that the proposed scheme can noticeably outperform several control schemes such as a Z-N PID and a modified Z-N PID in speed tracking and robustness. It is noted that the Arduino-implemented modified Z-N PID+Fuzzy for speed control of the V/f controlled IM yields slightly higher tracking errors than the PC-based modified Z-N PID+Fuzzy. Nevertheless, the Arduino-implemented controller is preferable in consideration of price, size, and robust tracking performance. The outline of this paper is as follows. Section 2 describes the framework for speed control of a V/f controlled IM. Section 3 presents the proposed intelligent control method. The experimental setups are described in Section 4. The experimental results of PC- and Arduino-implementation of the proposed control design method and other schemes are provided. Conclusions are given in Section 5.
## 3 Findings
### Prognostics and Health Management (PHM) for Unit Systems
#### 3.1.4 Workload Strategy  
A dynamic workload adjustment technique has been proposed by [36] to locate the most extreme workload machinery. In their work, the highest degraded machines were identified to satisfy the production necessities on parallel configurations. With various benchmark instances, simulation tests have been conducted to assess the degradation rate. [PERSON] et al. [35] explored the effects of various workload adjustment methodologies on  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Literature Review on Residual Life Distribution in the Context of Flexible Unit Systems**} \\ \hline
**Sl. No.** & **References** & **Findings** \\ \hline
1 & [35] & Investigates the effects of various workload strategy methodologies on manufacturing system performance by a mathematical study. \\
2 & [36] & A workload adjustment has been proposed to find the extreme workload to the remaining working units to fulfil the manufacturing prerequisites. \\
3 & [37] & Focuses on the dynamic workload adjustment to manage the degradation of all the units in a compound system. \\
4 & [38] & Works on dynamic workload adjustment strategy to control the degradation of units. \\ \hline \hline \end{tabular}
\end{table}
Table 8: Literature review on workload strategy related to flexible unit systems.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Literature Review on Workload Strategy in the Context of Flexible Unit Systems**} \\ \hline
**Sl. No.** & **References** & **Findings** \\ \hline
1 & [30] & To predict the Residual Life under time differing conditions, the degradation rate changing and unexpected signal bounds at condition change points have been proposed. \\
2 & [29] & In this paper, an attempt was made to audit and sum up the ongoing demonstrating improvements of Wiener process models for assessing the Residual life. \\
3 & [31] & A data-driven technique for Residual life expectation depends on a Bayesian approach that has been proposed. \\
4 & [32] & In this paper, remaining useful life prediction of slightly degraded parts with co-dependent degradation processes have been shown. \\
5 & [33] & Describes the fundamental steps needed to execute the Prognostics and Health Management System, so that the remaining useful life of CNC milling cutters can be predicted. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Literature review on residual life distribution related to flexible unit systems.  
a system agent-based simulation approach. To prevent the overlap of machine failure within a period of time, [PERSON] et al. [39] developed a method to control the degradation and predicted failure time of each machine by adjusting the workload. Similarly, the allocation of buffer capacity is especially important in order to obtain an acceptable throughput and work-in-progress, as mentioned in [40].
# eJamar: A Novel Exergame Controller for Upper Limb Motor Rehabilitation
## 2 Related Work  
Although there are numerous studies that incorporate exergames as part of rehabilitation treatments, around 26% utilize specialized controllers for this purpose [33]. Among the GC designed for exergames, three categories can be distinguished based on the interaction mode: active, wearable, and standard. Active interaction controllers involve the user making natural movements in front of a camera without direct contact with a device, such as the Kinect [34], Leap Motion Controller (LMC) [35], and webcams [36]. Wearable controllers are attached to a part of the limb using straps, for example, glove-type controllers [16], wristbands [37], head-mounted devices [38], and exoskeletons [39]. Standard controllers are handheld, such as end-effector robots [40], GC platforms [41], a mouse [42], and various prototype controllers. GCs use different sensors, such as accelerometers, IMUs, encoders, force, pressure, and EMG, to detect the user's movements, strength, or speed in the upper limb, measuring tilt or motion in the hand or forearm [33].  
Over the past decade, various specialized controllers have been developed and used for exergames, primarily falling into the wearable and standard categories. For example, the RAPAEL Smart Glove allows independent movements of the fingers and hand, and has been used to rehabilitate stroke patients [16; 17]. The Bright Brainer Grasp is a controller that uses force sensors to measure hand strength and incorporates several aspects of comfort for hand and forearm movements, making it one of the few to do so [43]. Another specialized controller is the Arm Assist [31], a robotic device designed for comfortable hand and wrist movements, with ergonomic considerations. [PERSON] et al. developed a cylindrical-shaped controller equipped with an IMU sensor that detects pronation and supination movements of the forearm, transmitting data via Bluetooth [30]. These devices have been developed to meet the need for accurately measuring hand strength and movement, something that most commercial platform controllers do not offer.