esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study Results
### The Application of [PERSON]'s Five Forces Model to the AeroLogic Joint Venture
#### 4.5.4 Bargaining Power of Buyers  
Customers that have a high level of market power competition in the air cargo industry and they can place pressure on air cargo rates, whilst at the same time demanding higher quality or the extension of a carrier's existing product/serviced range [175]. As noted earlier, around 90% of world air cargo traffic is provided by freight forwarders [120]. These firms are powerful because they are often purchasing standardized or undifferentiated services, the costs associated with changing airlines is low, and the products/services provided by carriers are often easily exchanged or substituted [175].  
Today, there is a relatively small number of international freight forwarders, such as DHL Global, Panalpina, and Schenker, who account for a major portion of the industry revenues and employees [90,186]. Certainly, there are still, and will continue to be, small- and mid-sized freight forwarding firms, as they can offer a level of specialisation or a combination of scope and flexibility that the large global operators cannot provide. Nonetheless, the global air cargo industry has never seen such a high degree of forwarder concentration as exists today. Given the large volumes of air cargo traffic that these large firms control, they can have considerable influence over an airline's routing decisions, and they are using that influence to develop cargo hubs at airports that they want [187] (p. 140).  
Some freight forwarders have gone beyond merely booking cargo space on behalf of their customer on passenger and freighter services provided by the airlines. They are now offering shippers their own dedicated freighter services. Switzerland-based Pandipina Global Transport pioneered this trend in the 1990s with the introduction of a service from Luxembourg Airport to Huntsville, Alabama-the service was known as the _'Dixie Jet'_[187] (p. 140). The actual flying for these services is performed on a charter basis from existing airlines, such as Cargolux and Atlas Air, though Panalpina controls the whole capacity of the aircraft [188].  
Once again, as AeroLogic only transports cargoes on behalf of its two JV partners, it is not affected by the concentration of the freight forwarders nor their strategies to operate their own aircraft services, as in the case of Panalpina and its _'Dixie Air'_ service from Luxembourg to Huntsville, Alabama. This is another unique aspect of the AeroLogic business model.
## 5 Experimental Progress
### NRRS Task Planning  
Due to the limited number of in-orbit satellites in the test constellation (hence the limited area for communication), NRRS can not be achieved anytime and anywhere. In order to validate this presented NRRS architecture, task planning should be carried out on the basis of existing satellite and station resources. Based on the experiment illustrated in Figure 12, the workflow of NRRS task planning can be generated in Figure 13. The constraints for filter and match are listed as follows:  
1. The demand collection needs to be within the communication arc of the satellite GS2, and the duration should be \(\geq\)2 min.
2. Command generation and upload time \(\geq\)3 min.
3. Command upload should be in the TT&C arc of satellite BP01.
4. The time from the end of command upload to the start of remote sensing should be \(>\)450 s.
5. The imaging time lasts for 2 min and should be kept within the data-receiving arc of BP01.
6. The total duration of the file upload and image processing \(\geq\)7 min.
7. Image data distribution is completed in the communication arc of AP03.  
Figure 12: Illustration of the NRRS experiment.
# AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion
## 2 Related Work  
### UNet  
The UNet backbone network, proposed by [PERSON] et al. [23], is a classical end-to-end network that exploits deep networks, residual learning, and regularization techniques. The UNet architecture adopts a conventional encoder-decoder network architecture that has a remarkable learning ability to preserve high-level semantic information within the pre-trained backbone (i.e., the encoder), while leveraging its decoder to provide detailed, fine-grained information. As shown in Figure 1, the UNet architecture comprises two primary components: the contracting and the expansive paths. The contracting path is responsible for capturing image context information, whereas the expansive path is responsible for precise image reconstruction. The contracting path includes a sequence of convolutional layers followed by max-pooling layers that reduce the spatial resolution while simultaneously increasing the number of channels in the feature maps. Conversely, the expansive path comprises multiple deconvolutional layers that enhance spatial resolution while reducing the number of channels. The use of skip connections enables the contracting and expansive paths to connect their corresponding layers, permitting the network to reuse learned features from the contracting path during the reconstruction stage. The primary benefits of the UNet architecture include its ability to handle images of different resolutions and scales, capture both local and global feature representations of input images, and increase the effective size of the training set owing to its ability to manage samples with limited data. However, since the UNet architecture lacks simultaneous feature extraction and fusion mechanisms, it can only achieve early or late fusion when used for the fusion of IR and visible images, while a more effective medium fusion cannot be directly achieved. Therefore, the baseline network is selected for the proposed fusion network, and significant modifications are made to enable the fusion network architecture to successfully attain feature extraction and fusion. These modifications are introduced and elaborated on in the following sections.
## 5 Evaluation Measures
### Integrity Measures
#### 5.3.2 Transparency  
Transparency is a well-discussed topic in research that addresses the challenges posed by AI systems that function as black boxes. By making AI systems' decision-making processes more visible, transparency allows stakeholders to evaluate whether these processes align with ethical standards and societal values. Moreover, transparency should ensure accountability, predictability, trustworthiness, and user acceptance. [PERSON] et al. [142] define the concept by stating that _\"transparency in AI refers to the degree to which the AI system reveals the details required for a person to accurately anticipate the behavior of the system\"_.  
Transparency is also an essential topic in XAI, where explainability is seen as the key to achieving transparency by providing clear, comprehensible explanations of how AI systems work and how they reach their conclusions. In our sample, several studies also addressed transparency as an explicit evaluation measure [15; 32; 37; 55; 56; 57; 58; 61]. Upon closer analysis, it becomes evident that transparency is typically conceptualized and operationalized by the constructs of understandability and controllability.  
Regarding conceptualizing the construct, [PERSON] et al. [15], for instance, mention that transparency aims to \"explain how the system works\" [15]. Similarly, [PERSON] [37] defines transparency in terms of users' interest in _\"understanding what questions are asked, why, and how they affect the outcome\"_[37]. In the context of newsfeed algorithms, [PERSON] et al. [55] consider transparency as a quality of explanations that helps users \"become more aware of how the system works\" and enables them to assess whether the system is biased and if they can control what they see. Concerning recommender systems, [PERSON] et al. [32] also emphasize that _\"transparency and control are not independent of each other\"_[32].  
Also, in the operationalization of the construct, the close link to understandability is evident. For instance, the transparency questionnaire suggested by [PERSON] [37] includes items such as _\"I understood why an event happened\"_ and _\"The response helps me understand what the [result] is based on\"_. Similarly, [PERSON] et al.'s [58] questionnaire includes the item _\"it helps me to understand what the recommendations are based on\"_, while [PERSON] et al. [57] use the item _\"the system explains how the system works\"_, and [PERSON] et al. [56] use the item _\"I find that [the system] gives enough explanation as to why an exercise has been recommended\"_ for this purpose. These examples demonstrate that transparency can be measured through self-reported assessments of the system's understandability.
# Integration of the 3D Environment for UAV Onboard Visual Object Tracking
## 1 Introduction
The proposed approach allows us to shift from tracking in 2D image space to tracking in 3D scene space (see Figure 3). (3) We improve the processing of false associations--i.e., distractors--through the usage of a multimodal state estimator. (4) We create a new dataset called AU-AIR-Track, designed for visual object tracking from a UAV perspective. The dataset includes 90 annotated objects as well as annotated occlusions and two 3D reconstructions of the static scenes observed from the UAV. (5) We demonstrate the effectiveness of our pipeline through quantitative results and qualitative analysis. The paper is structured as follows. In Section 2, an overview of related work on single visual object tracking and their corresponding benchmarks is provided. The complete system and the individual components are explained in Section 3. We give a detailed description of the edited dataset used and  
Figure 1: Distribution of bounding box sizes w.r.t. their image resolution. A comparison between the Online Tracking Benchmark (OTB) [4] in red (ground-level perspective), and the AU-AIR-Track dataset in blue (unmanned aerial vehicle (UAV) perspective). A detailed description of our dataset AU-AIR-Track is given in Section 4.1.  
present metrics with the evaluation protocol for assessing the performance of our approach in Section 4. Finally, in Section 5, we analyze quantitative as well as qualitative results and present the benefits of the design choices made. In the end, we discuss modifications that can be added to our approach in the Section 6 and give a conclusion in Section 7.
# Mechanical Faults Analysis in Switched Reluctance Motor
## 1 Introduction  
### Electrical Motor Diagnostics and Switched Reluctance Motor Characteristics  
In recent years, electric drives have increased in popularity in various industries, especially in transport, i.e., passenger cars, buses, and other utility and military vehicles. On the one hand, this phenomenon entails their miniaturization and automation. On the other hand, challenges with their reliability and safety usage are rising. While failures or downtime of electric motors in everyday household appliances are usually not of great importance, in places such as production lines, construction machines, railways, or mines, they can result in significant financial losses and even human casualties. Therefore, proper monitoring and reliability management are essential.  
Identifying the variety of motor faults and their severity is challenging, and an electric machine is only a single component of the entire electric drive. Nevertheless, each specific fault has its unique features in the measured signal. These physical attributes can facilitate identifying the degree of specific fault and even the range to the critical point of stopping the motor considering the specific kind of electrical machine.  
A switched reluctance motor (SRM) was selected for this research regarding the high durability requirements for special applications. Among its advantages is that the SRM is built of iron and does not have permanent magnets. Therefore, there is no risk of demagnetization due to a strong magnetic field or high temperature. It has a suitable compact design, thanks to which it can rotate at a speed of over 10,000 rpm [1].
## 3 Materials and Methods
### Electrical Impedance Mammography (EIM)  
EIM is an imaging technique that, unlike X-ray mammography, which is based on the differential absorption of radiation in tissues, takes advantage of the differences in the electrical conductivity and permittivity between tissues, making it possible to infer structural and functional information without using ionizing radiation [28]. Malignant cells tend to have a higher presence of water and electrolytes (such as sodium, calcium, and potassium), presenting higher conductivity than healthy cells. As a result, tumor regions exhibit a lower impedance to the passage of an electric current, whereas normal tissue has a higher impedance [29; 30]. While EIM does not replace conventional mammography, it has the advantage of being radiation-free, making it a promising complementary tool for detecting malignant lesions, especially in patients with a high breast density. To promote its application in diagnosis, an impedance scale has been developed to assess the risk of cancer based on the electrical properties of the tissue [31], as shown in Table 1.
# Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems
###### Abstract
The results indicate that the model can predict the various impairments with high accuracy, except under strong ASE noise, where the model demonstrated lower accuracy in predicting light turbulence parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Channel Type** & **ML Model** & **Training Feature** & **Monitored Parameter** \\ \hline
[12] & Fiber & ANN & Egy diagrams & CD \\  & & & & DGD \\  & & & & OSNR \\
[13] & Fiber & ANN & AAH & CD \\  & & & & DGD \\
[14] & Fiber & ANN & empirical moments & CD \\  & & & DGD \\  & & & & OSNR \\
[15] & Fiber & ANN & ADTS & CD \\  & & & & DGD \\
[16] & Fiber & CNN & ADTS & OSNR \\  & & & & OSNR \\
[17] & Fiber & DNN & Eye diagrams & CD \\  & & & & DGD \\
[18] & FSO & CNN & OAM & \(C_{2}^{2}\) \\
[19] & FSO & CNN & OAM & \(C_{n}^{2}\) \\  & & & & \(C_{n}^{2}\) \\
[20] & FSO & CNN & OAM & OSNR \\  & & & & Pointing errors \\
[21] & FSO & CNN & OAM & Visibility range \\  & & & & Visibility range \\
[22] & FSO & SVM & AAH & OSNR \\  & & & & Pointing errors \\  & & & & OSNR \\ This work & Hybrid Fiber/FSO & GPR & AAH & CD \\  & & & & \(C_{n}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review. The paper is structured as follows. Section 2 provides an overview of the system and the ML technique employed. The results' analysis and discussion are presented in Section 3. Finally, the paper concludes in Section 4.
## 6 Simulation Setup and Results
### Uplink Use Case
#### 6.7.1 Baseline  
The SINR results for the baseline scenario simulation run are presented in Figure 10. As expected, the satellite connections are not stable in the baseline, as all links reuse the same channel for operation. During the convergence phase (see blue regions), both NGSO links suffer from interference but then recover to their normal operation. The GSO link is only slightly impacted by the interference from the NGSO links: we can observe that the SINR of the GSO receiver maximally decreases by 0.5 dB, which is within the 1 dB safety margin. The FWA links are impacted as the ESIMs are moving closer to them, aligning with the FWA beam. The impact on FWA 8 is much smaller, however, not more than 1.5 dB, whereas FWA 10 is seriously impacted by ESIMs passing close to its position. The interference impact of NGSO-ESIM 3 stopping behind the FWA 8 node is visible from 120 to 250 s of simulation time (yellow regions), whereas the passing of the other two ESIMs behind the FWA receiver is also quite visible in the SINR graph as deep spikes of SINR drops (green areas). Once interference occurs in the second part of the simulation run, the FWA links are heavily impacted and unable to operate.
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
## 5 Conclusions and Future Work  
This paper presents a novel localization approach that uses hybrid RSS-AOA time series measurements and considers the varying measurement noise distributions among different types of anchor nodes. This improves the positioning accuracy and reliability in heterogeneous anchor node scenarios. We propose a two-stage LWLS method that uses weights based on the residual variances to handle different types of anchor nodes.The proposed method consists of two stages. In the first stage, we use the range-based LWLS method to estimate a rough target location. In the second stage, we estimate the residual variances of LWLS equations and use them as the LWLS weights to reapply the LWLS method and obtain a more accurate location. Simulation experiments show the superior performance of the proposed method. It achieves more accurate and reliable localization results in heterogeneous anchor node scenarios than existing methods. Moreover, the proposed method does not increase the complexity of the localization system significantly, making it practical for real applications.  
Figure 8: Comparison of RMSE as time steps, \(T\).  
Figure 7: Comparison of RMSE as the number of anchor nodes, \(N\).  
The simulation results show that using the residual variances of LWLS equations as weights for localization is an effective approach in heterogeneous anchor node scenarios. Our future work will focus on three main aspects: (1) Improving the accuracy of estimating the residual variances of LWLS equations to further enhance the localization performance. (2) Developing noise models for the unit vectors in spherical coordinates to better reflect real-world conditions. (3) Reducing the number of time steps required in the time series, making the proposed method more efficient and adaptable to various application scenarios. We will also investigate the applicability of the proposed method in real-world application scenarios and evaluate its performance in practical environments.  
All the authors are contributed equally to prepare this article. Conceptualization, W.D., L.L. and S.C.; Formal analysis, W.D., L.L. and S.C.; methodology, S.C. and L.L.; writing--original draft, W.D., L.L. and S.C.; writing--review and editing, W.D., L.L. and S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Scientific Research Program of Zhejiang Provincial Department of Education under Grant Y202454865 and Zhejiang Provincial Higher Education Key Project of \"14 th Five-Year Plan\" for the Second Batch of Undergraduate Teaching Reform under Grant JGZD2024078.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 3 Preliminaries and Problem Definition
### Problem Definition  
In this subsection, the problem of explainable sentiment analysis in the stock market domain is formally defined. A document related to the stock market consists of numerous sentences and aspects. While the sentiment of each sentence or aspect may be different, there is usually a single overarching conclusion that comprehensively considers the influence of various aspects. For example, a securities review article may include positive or negative reviews about the company's operating performance, economic environment, market sentiment, etc. Subsequently, an overall recommendation regarding the stock is conducted through comprehensive analysis.  
Definition 1: Let a stock-related review document with \(m\) sentences be represented as \(d=\{s_{1},s_{2},\ldots,s_{m}\}\in\mathbb{D}\), where \(\mathbb{D}\) denotes the total set of documents employed in our analysis. In document \(d\), let \(i\)-th sentence with \(n\) words be represented as \(s_{i}=\{w_{i1},w_{i2},\ldots,w_{in}|w_{it}\in V\}\), where \(V\) is the vocabulary. The problem of document sentiment analysis is to learn a function \(f\) that can predict the overall sentiment polarity \(r\in\{positive,neural,negative\}\) of document \(d\).  
Definition 2: Given a document \(d\) for which the function \(f\) predicts sentiment polarity \(r\), the challenge of explainability is to find a suitable basis for interpreting the decisions of function \(f\). A common approach is to visualize attention weights in order to identify the relative importance of inputs to the entire model.  
Definition 3: An artificial query set containing \(k\) queries is designed to enhance the attention mechanism and improve its interpretability. Let the query set be represented as \(\mathbb{Q}=\{q_{1},q_{2},..,q_{k}\}\), where each query is commonly defined as a set of keywords relevant to the data domain in order to represent a distinct analytical perspective. On the sentence level, the \(i\)-th query with \(j\) words is similarly represented as \(q_{i}=\{w_{i1},w_{i2},\ldots,w_{ij}|w_{it}\in V\}\). The size \(k\) of the query set and length \(j\) of each individual query can be arbitrary. By incorporating domain-specific knowledge, this query set aims to provide a more informed and focused analysis of the data. Through careful selection of relevant keywords, queries can be used to direct attention to specific features and aspects of the data, thereby enhancing the overall performance of the model.
## 3 Use Case. CEDER-CIEMAT Microgrid
### Monitoring of Elements  
The second step of the methodology consists of monitoring the elements involved in energy management and integrating them into a control system that allows operating commands to be issued to each of the dispatchable generation sources and storage systems. As explained in the previous section, NodeRed is used to monitor all these parameters, and Home Assistant is used to develop the control system.  
Figure 4 shows one of the screens of the human-machine interface of the control sys-tem for Lead-acid batteries PEPA I, where the following information is displayed: Main values (battery power; battery voltage level; and power setpoint); Charging/Discharging (type of charge: normal, deep, floating, or equalisation; selection of the type of charge; selection of power setpoint; and button to send power setpoint command); State of charge in %; State (information about whether the battery is on or off; boolean button to select On/Off, indication of stopped, charging, or discharging; Temperature, state of charge, and indication of full load); Errors (shows the different errors that can occur in the battery).  
Figure 5 shows one of the screens of the human-machine interface of the control system for LFP batteries, where the following information is displayed: Rack (battery power; battery voltage level, battery current; and buttons to reset the battery and start); Temperatures (shows maximum and minimum temperatures and the cell in which they occur); Inverter (shows state of the inverter: on/off; and buttons to turn the inverter on and off); Command (to introduce the power setpoint and button to send the value); Voltage (shows maximum and minimum voltage levels and the cell in which they occur). Figure 5 also shows two graphs with the maximum and minimum voltage levels of the cells and the power of the battery.