esa-sceva/satcom-chunk-collection huggingface dataset:
# Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users
## 2 Related Work  
### Accessibility in IoT Dashboards  
IoT dashboards typically rely on rich visualizations like line graphs, scatter plots, or heat maps to convey sensor readings and alerts in real time [20]. While these graphical representations are effective for sighted users, they can be nearly unusable for individuals with visual impairments if robust textual alternatives are not provided. The Web ContentAccessibility Guidelines (WCAG) 2.1 [21] specify that images and charts should have meaningful alternative text so that users of assistive technologies can perceive the content. In practice, however, many IoT solutions ignore this guideline or implement it in a minimal way. It is not uncommon to find a complex chart with an alt attribute that says only, for example, \"Line chart of sensor data\"--a description too vague to support any interpretation or data analysis task by the user. Without more detailed context (such as what variables are plotted, what the trends are, and any noteworthy values), a blind user is effectively excluded from the information that the chart is meant to convey.  
Some modern web dashboard frameworks and UI toolkits offer partial accessibility features. These may include keyboard navigation support, focus management, or high-contrast color schemes to assist low-vision users. While such features are helpful, they seldom address the core challenge of non-visual data representation. A screen reader can tab through a dashboard's interactive elements or announce the presence of a chart, but unless the chart is accompanied by a textual description or an alternative format (like a data table), the user gains little insight. Prior studies on the accessibility of data visualizations during events like the COVID-19 pandemic have noted that, even when alt text or simple data tables are provided, they often lack the depth needed for thorough understanding [5].  
Recognizing these gaps, researchers have started to explore ways to make dashboards more accessible. For example, [PERSON] et al. [22] developed Azimuth, a system that automatically generates screen-reader-friendly dashboards from existing visual dashboard specifications. Azimuth uses a combination of co-design with blind users and algorithmic generation of textual summaries for charts. In user studies, their generated dashboards--which included structured headings, concise summaries, and interactive filtering through text--enabled blind users to navigate and analyze data more effectively than standard dashboards. This and similar efforts underscore that making dashboards accessible is feasible and can greatly enhance the user experience for blind or low-vision users. However, most prior solutions either require authors to manually write long descriptions or focus on static datasets rather than live IoT data streams. AccessiDashboard builds on insights from these works, pushing further into real time, dynamic data accessibility with the help of AI-generated content.
# A System for Controlling and Monitoring IoT Applications
## 1 Introduction
### Wireless Sensor Network (WSN)  
Nowadays, continuous technological advances have encouraged the development of devices with wireless communication capabilities, which are distributed in many locations, and are increasingly small, autonomous, more powerful, and have more efficient battery consumption [1]. With regards to the batteries, they are similar to those in mobile phones. However, they can be recharged through solar cells and from here the idea of the wireless sensor network (WSN) arises [2, 3].  
A WSN is composed of low cost and low power consumption devices named nodes or motes (see Figure 1). These nodes are able to acquire any information type from the environment where they are located, and process and transmit it by means of wireless links to a central node. The latter acts as a coordinator or a gateway. The nodes act as communication infrastructure elements to send the transmitted packages through more distant nodes to the coordination center [4]. These can be fixed or mobile depending on the need. A WSN is designed to realise a set of high level processing information tasks, such as detection, monitoring or classification.
# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 2 Methodology
### Segmentation
#### 2.2.4 Consensus Clustering Using Voting Schema  
In this section, the segmentation results are combined using voting schema. Let \(n\) be the number of voxels presented \(X=\{x_{1},x_{2},x_{3},\ldots,x_{n}\}\) and \(t\) be the set of clustering algorithms considered for clustering the \(n\) voxels, i.e., \(\Pi=\{\pi_{1},\pi_{2},\ldots,\pi_{t}\}\). Each clustering algorithm \(\pi_{i}\) maps \(x_{i}\) to \(c\) clusters. The problem of consensus clustering is to find a new \(\pi^{*}\) that best summarizes the clustering ensemble. In the proposed work, the input brain images voxels are segmented using the above-discussed four clustering algorithms. After convergence of each algorithm, each voxel is assigned to its corresponding cluster based on the membership value. Let \(U_{1}\), \(U_{2}\), \(U_{3}\), and \(U_{4}\) represent the membership matrix of RSKFCM, GSKFCM, MIFCM_S, and MIFCM_Y, respectively. From these membership matrices, a label for each pixel is computed. The pixel \(x_{j}\) is assigned a label of a cluster for which it has maximum membership value. Let \(P_{1}\), \(P_{2}\), \(P_{3}\), and \(P_{4}\) be the label matrix created for RSKFCM, GSKFCM, MIFCM_S, and MIFCM_Y, respectively. From these label matrices, consensus results are produced using a voting method. The pixel \(x_{j}\) is assigned to a cluster based on the maximum number of cluster labels, i.e., \(label=argmax_{i}\Big{(}P_{(l)}^{(i)}\Big{)}\), where \(l=\{1,2,3,4\}\) and \(1\leq i\leq c\). Algorithm 1 presents the individual steps involved in the proposed method.  
```
Data: Input image \(X=\{x_{1},\ldots,x_{j},\ldots,x_{n}\}\), Stopping criteria \((\varepsilon)\), \(m\), number of clusters \(C\) Result: Segmentation results, Cluster centers
1 Obtain membership matrix for each clustering algorithm
2 Construct label matrix for each algorithm i.e \(P_{1}\), \(P_{2}\), \(P_{3}\) and \(P_{4}\)
3 The pixel \(x_{j}\) is assigned to a cluster based on maximum number of cluster label, i.e., \(label=argmax_{i}\Big{(}P_{(l)}^{(i)}\Big{)}\), where \(l=\{1,2,3,4\}\) and \(1\leq i\leq c\).
4 Update the cluster centers by considering new cluster assignments
```  
**Algorithm 1**Consensus Clustering using voting scheme
# Design and Testing of a New Bionic Corn-Ear-Picking Test Device
## 3 Design of Key Components of a New Bionic Test Device for Corn Ear Picking
### Parameter Design for Key Components  
#### 3.3.1 Design of Clamping Conveyor  
Because the ear-picking device was a fixed test bench, a clamping conveyor should be designed to drive the movement of corn plants with the purpose of simulating the field operation of the machine. There are three design purposes: (i) the corn plants must be held and transported obliquely to the rear of the machine; (ii) the corn straw must not fall or be clamped, with the guarantee of clamping force being applied during the ear-picking process; (iii) the favorable trafficking of the corn straw must be ensured, so as to prevent blockages occurring.  
Based on Figure 7, the left and right clamping frames were designed to satisfy the requirements of the clamping conveyor. The left clamping conveyor frame was equipped with six fixed tension rollers, whereas the right one was equipped with six movable tension rollers to prevent the conveyor belt from loosening during transmission. Specifically, the amount of tension was determined by a tension spring on the staggered left and right rows of the tension rollers. The tension clamping gap can be automatically adjusted in line with the straw diameter during conveying.
# Electric-Field-Coupled Resonator Antenna for 5G Applications
## 3 Modelling of the ELC Resonator  
The length and width of the ELC resonator are 13 mm and 19 mm, respectively, while the thickness of each side is 1 mm. The length and width of each arm of the capacitive gap are 5 mm and 0.6 mm, respectively, while the gap distance is 0.6 mm. The substrate material used here is RT Duroid 5880 with a dielectric constant of 2.2 and a thickness of 1.52 mm.  
Since the resonator is anisotropic, i.e., it shows different resonance characteristics for different applied field orientations, it needs to be investigated for different boundary conditions [21; 22; 23; 24]. However, the boundary conditions where the magnetic field is perpendicular  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values (mm)** & **Parameters** & **Values (mm)** \\ \hline L\({}_{1}\) & 23 & W\({}_{2}\) & 19 \\ L\({}_{2}\) & 13 & W\({}_{3}\) & 12 \\ L\({}_{3}\) & 08 & W\({}_{4}\) & 7.0 \\ L\({}_{4}\) & 09 & W\({}_{5}\) & 4.6 \\ L\({}_{5}\) & 6.5 & W\({}_{6}\) & 5.0 \\ L\({}_{6}\) & 3.0 & W\({}_{7}\) & 2.5 \\ L\({}_{7}\) & 0.6 & W\({}_{8}\) & 1.52 \\ W\({}_{1}\) & 23 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed dimensions of the proposed antenna.  
Figure 1: The configuration of the proposed antenna. (**a**) Top view; (**b**) Bottom view; and (**c**) Side view.  
to the resonator are not discussed here. This is because the symmetry of the ELC structure belongs to the C2h group, which does not possess magnetoelectric coupling [25].  
For this reason, the presented structure is modeled by two boundary conditions. In one boundary condition, the electric field is applied along the y axis, and the magnetic field is applied along the x axis (Setup 1), whereas in another boundary condition the field orientations are opposite to the former one (Setup 2) (see Figure 2).  
The resonator with these two setups is simulated using a time-domain solver using CST Microwave Studio software. Since in Setup 1 the electric field is polarized along the split (see Figure 2(a)), it induces a current in the structure that circulates in two loops as shown in Figure 2(b), resulting in two different current paths. Due to these current paths, two resonances are found at 2.67 GHz and 8.03 GHz frequencies (see Figure 3(a)).  
On the other hand, when the electric field is polarized along the non-split bearing sides (see Figure 4(a)), the induced current is confined to the opposite sides of the resonator. For this reason, it cannot complete a loop and results in a reduced electrical length as shown  
Figure 4: S21 spectrum of the ELC resonator for (**a**) Setup 1 and (**b**) Setup 2.  
Figure 3: Distribution of (**a**) electric field and (**b**) surface current at 2.67 GHz for Setup 1.  
Figure 2: Simulation setup of the ELC unit cell. (**a**) Setup 1; (**b**) Setup 2.  
in Figure 5b. For this reason, a single resonance occurs at 4.23 GHz (see Figure 4b), which is higher than the previous orientation (2.67 GHz).
## 2 Materials and Methods
### Process of Collecting Scenarios
#### 2.1.2 Highly Automated Vehicles  
Structured in-depth _interviews_ were carried out with three onboard operators of automated shuttles (SAE Level Four [13]) integrated into Hamburg's public transport system as part of the HEAT project [11]. These interviews focused on control center tasks, disruptions, work experience, current and future workplace design [25]. From the disruptions mentioned, scenarios were derived.  
_Videoclips_ from the EU CityMobil2 project [1, 3] were _analyzed_ focusing on the interaction of AVs with other road users to generate scenarios. The main objective of CityMobil2 was to implement different demonstrations of AVs in five European cities as a part of local public transport [12]. Before the analysis, literature research and workshops with four traffic experts led to a set of categories that were applied to analyze the videos of AVs on the road. The categories were chosen to represent the events of interest as exhaustively as possible. The categories were mutually exclusive, precisely defined and their wording was simplistic. They included interactions with vehicles, pedestrians, cyclists, and infrastructure. In accordance with this categorization scheme, naturalistic video clips from the AV demonstrations were evaluated. They showed highly automated shuttles from the cities of La Rochelle, France, and Trikala, Greece, and were recorded as part of the EU project CityMobil2. The videoclips were shot independently from the raters who categorizedthem and therefore not influenced by them. In order to categorize them, the videos were analyzed regarding incidents, the main events were noted down and grouped regarding the interactions of the AV with other road users, including vulnerable road users (VRUs), and the infrastructure, e.g., traffic lights. These interaction categorizations served as a basis for generating scenarios.
## 6 Discussion
### Insights on Chosen Datasets  
To rigorously evaluate our XAI framework, we chose the following datasets: NSL-KDD, CICIDS-2017, and RoEduNet-SIMARGL2021. These datasets were specifically chosen due to their varied and comprehensive representations of network intrusions, which range from DoS to Bot attacks. By dissecting each dataset, we highlighted inherent challenges, such as imbalanced classes, varied attack signatures, and differing traffic patterns. The analysis results of the application of XAI not only underline the relevance of these datasets to real-world network environments but also showcase how our XAI framework effectively identifies and explains key features across such diverse conditions. This depth of analysis ensures that our framework's capabilities are tested against scenarios that closely mimic the complexities encountered in actual network security operations, especially when considering RoEduNet-SIMARGL2021 since CICIDS-2017 and NSL-KDD are considered benchmark datasets for network intrusion detection tasks.  
In particular, the selection of these three datasets for evaluating our XAI framework was driven by their distinct characteristics and widespread recognition in the network security community. NSL-KDD is an improved version of the KDD'99 dataset and addresses some of the inherent problems of its predecessor. CICIDS-2017 is valued for its realistic network traffic scenarios, which include a wide array of modern attacks. This dataset mimics the state of current network environments more accurately, providing rich testing security technologies. Lastly, RoEduNet-SIMARGL2021 offers data from a real operational network, adding valuable insights into the complexities and nuances of real-world network traffic that laboratory-created datasets may not replicate.
## 2 Materials and Methods
### Statistical Analysis
#### 2.4.2 Agreement of Segmentation Methods  
Each breast mass was segmented _via_ two different delineation approaches: (_i_) manual segmentation, performed by two distinct radiologists with 5 years of experience; (_ii_) an automated method based on spatial FCM clustering that was already tested and validated in [21]. The inter-observer agreement of the two segmentation methods (automated and manual) was quantified by calculating the ICC using a two-way random-effects model and the mean differences of the two observers. Regarding the manual segmentation, two distinct series of delineations were analyzed and independently performed by two different raters [31]. For this reason, three ICC series were computed, according to Equations (3)-(5):  
\[ICC_{automated-manual1}=ICC(Segm_{automated},Segm_{man1}), \tag{3}\]  
\[ICC_{automated-manual2}=ICC(Segm_{automated},Segm_{man1}), \tag{4}\]  
\[ICC_{manual1-manual2}=ICC(Segm_{man1},Segm_{man2}), \tag{5}\]After determining the features showing excellent robustness, we aimed to identify the most relevant features for the analysis at hand by evaluating the dependence on the segmentation method. To this aim, we considered \(binCount=32\), which guarantees the greatest number of features (43) with excellent robustness.  
The Shapiro-Wilk test [32] was used to assess the normality of the 129 distributions of the differences (obtained considering 43 features and three segmentations) among the segmentation raters, considering a 0.05 confidence interval. All distributions passed the normality test, obtaining \(p\)-values \(\ll\) 0.0001.  
To evaluate the robustness of the MRI radiomic features, a six-level scale (ranging from 0 to 5) was defined, based on a combination of the ICCs coefficient and the standard deviation (SD) of the mean percentage differences between the three raters (i.e., auto, man1, man2), according the conditions in Table 2. Percentage differences were evaluated according to Equation (6).  
\[\text{PercentageDifference}(Segm_{i},Segm_{j})=\frac{abs(Segm_{i}- Segm_{j})}{mean(Segm_{i},Segm_{j})}\times 100 \tag{6}\]  
\[\forall i,j\in\{auto,man1,man2\}.\]
## 4 Results
### The Process of Green Tide Development and Drift  
Using eight satellite images from 2021 (see Table 1), the green tide pixels were extracted using the method in Section 2.2.2. Then, the envelope of the green tide was obtained using the buffer analysis method. The green tide distribution areas for the eight dates were 387 km\({}^{2}\), 8217 km\({}^{2}\), 30,490 km\({}^{2}\), 43,709 km\({}^{2}\), 42,501 km\({}^{2}\), 24,834 km\({}^{2}\), 8615 km\({}^{2}\), and 1247 km\({}^{2}\), respectively. The corresponding daily green tide growth rates for the seven time periods were 1119 km\({}^{2}\)/day, 2784 km\({}^{2}\)/day, 1102 km\({}^{2}\)/day, \(-\)37 km\({}^{2}\)/day, \(-\)1963 km\({}^{2}\)/day, \(-\)901 km\({}^{2}\)/day, and \(-\)368 km\({}^{2}\)/day, respectively. It can be seen from the above results that the green tide's distribution area went through rapid growth, faster growth, stabilization, a rapid decline, and a slow decline to disappearance. The eight envelopes clearly show the complete process of the green tide emergence--development--explosion--decline--disappearance (see Figure 9). It was detected in the Subei shoal on 10 May, and it proliferated for one month while drifting to the north, reaching its explosion stage on about 6 June, and then reached the south coast of Shandong. Around 9 July, the green tide entered the decline period, with the envelope area gradually decreasing until the green tide died out in late August [4,5].  
Figure 8: Simplified and original envelopes for 17 May (**A**), 6 June (**B**), 9 July (**C**), and 5 August (**D**) in 2021. The 5 August image zooms in some details of the envelope, showing a good fit between the simplified and original envelopes.
## 3 Active Guidance Controller Design for IRWs Based on MARL
### MADDPG Algorithm  
The MADDPG conforming to the Dec-POMDP is an extension of the DDPG algorithm and comprises critic and actor networks for each agent [15; 17]. By operating as an off-policy algorithm, MADDPG employs a replay buffer, which is denoted \(D\), archiving all agents' historical interactions with the environment, including global states, actions, subsequent states, and rewards, formatted as \((s_{t},\,a_{1,t},\,\ldots,\,a_{N,t},\,s_{t+1},\,r_{1,t},\,\ldots,\,r_{N,t})\) tuples. To optimize the critic network \(Q_{i}(s_{t},\,a_{1,t},\,\ldots,\,a_{N,t})\) and its parameters \(\omega_{i}\), the loss function \(L(\omega_{i})\) is defined in Equation (10).  
\[L(\omega_{i})=\frac{1}{|D_{s}|}\sum_{j\in D_{s}}\,(y_{i}^{j}-Q_{i}(s^{j},a_{1} ^{j},\ldots,a_{N}^{j}))^{2} \tag{10}\]  
Figure 3: Block diagram of the decentralized partially observable [PERSON] decision process (Dec-POMDP).  
where \(D_{s}\) is a small batch from the replay buffer, \(j\) is the index of the samples, and \(y_{i}^{j}\) is the TD target, computed as per Equation (11).  
\[y_{i}^{j}=r_{i}^{j}+\gamma Q_{i}^{\prime}(s^{j+1},a_{1}^{j+1},\ldots,a_{N}^{j+1} )\Big{|}_{a_{k}^{j+1}=\mu_{k}^{\prime}(a_{k}^{j+1})} \tag{11}\]  
where \(Q_{i}^{\prime}\) and \(\mu_{i}^{\prime}\) are the critic and actor networks defined in the target network, respectively.  
The actor network's update considers the gradient of \(Q_{i}\) with respect to \(a_{i}\), and the policy gradient for agent \(i\) is shown in Equation (12).  
\[\
abla_{\theta_{i}}J(\theta_{i})\approx\frac{1}{|D_{s}|}\sum_{j\in D_{s}}\
abla _{\theta_{i}}\mu_{i}(o_{i}^{j})\
abla_{a_{i}}Q_{i}(s^{j},a_{1}^{j},\ldots,a_{N} ^{j}) \tag{12}\]  
where \(a_{k}^{j}=\mu_{k}\left(o_{k}^{j}\right)\), \(k=1,\ldots,N\), and \(Q_{i}(\cdot)\) is agent \(i\)'s action value function under policy \(\mu_{i}\). The gradients \(\
abla_{\theta_{i}}(\cdot)\) and \(\
abla_{a_{i}}(\cdot)\) correspond to the policy parameter gradient and the action gradient, respectively.
## 3 Implementation of the System  
The primary goal of this work is to design and analyze a comprehensive and well-structured FL framework to train machine learning algorithms using a federated approach involving heterogeneous FL clients. Here, we outline the important steps considered during the implementation of the FL process.  
The considered FL system is based on a client-server architecture, with the server represented by a Windows PC and a set of Raspberry Pi devices acting as FL clients. In addition, a set of virtual clients are also considered, to build an FL framework with heterogeneous clients. Given the importance of network programmability and virtualization technologies in the 5G/6G networks, defining the FL framework with a set of hardware/software clients can have added advantages. Finally, inter-device communication is carried out through the local network, to which all hardware nodes are connected via Wi-Fi.  
As mentioned above, the main objective is to evaluate the efficacy of FL in the context of devices with limited and heterogeneous resources.  
We have considered a typical image classification problem and aim to build a proper Deep Neural Network (DNN) with the help of the considered FL framework. The well-known CIFAR10 dataset is considered during model training operations. It should be noted that to induce data heterogeneity over different FL clients, the original dataset is split into different datasets. Also, though the analysis is performed with a specific ML task with predefined datasets, this can be extended to any generic ML problem. The FL framework is built with the help of Python programming language. In particular, the PyTorch library is considered to train the neural network model, while Flower, a specialized library for federated machine learning, is considered to automate the client-server interactions more efficiently.  
In the following, we describe in detail the various configurations used in client/server parts of the considered FL model. Figure 1, presents the basic elements of the considered FL framework that includes a set of Raspberry Pi devices, virtual machines, and an FL server.