esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Visualization
### Abstract Clustering Results and Visualization  
Four clustering iterations are conducted in the iterative clustering phase. An iteration stops when a cluster contains fewer than 50 abstracts. In total, 24 clusters formed from 593 abstracts, each with 10 to 46 abstracts. Table 2 shows the number of abstracts and silhouette scores of each cluster in each iteration. The first iteration generates 5 small clusters (#1 to #5) with fewer than 50 abstracts; 2 large clusters with 62 and 107 abstracts, respectively; and 287 outliers, which have a lower density indicated by the negative silhouette score (\(-0.76\)). In the second iteration, the 2 large clusters (62 and 107) are split into 6 subclusters (#6 to #11) and the 287 outliers from the first iteration are divided into 5 subclusters (#12 to #16). From 187 outliers in the second iteration, 5 subclusters (#17 to #21) are generated in the third iteration and 106 outliers are fed to the fourth iteration. In the fourth iteration, three more subclusters (#22 to #24) are formed. Figure 4: An example of an article abstract [55] highlights our extracted keywords in the text and lists the author-assigned keywords under the text. The silhouette scores in Table 2 reflect the quality of the clusters in each iteration. A cluster with a positive score close to 1 suggests that the abstracts within this cluster are highly similar to each other. By contrast, the outliers are typically associated with a negative score and each iteration always produces a number of outliers. We will discuss the reason for not discarding the outliner in the next section. In general, 24 abstract clusters appear to be well defined in 3 bands: 17 clusters (65% of abstracts) with good scores above 0.5, 5 clusters (27% of abstracts) with medium scores between 0.5 and 0, and 2 clusters (8% of abstracts) with negative scores. On average, the abstract cluster has a score of 0.55. To visualize the clustering results, the dimension of an abstract embedding, produced by GPT-3 Similarity Embedding Model, is reduced using UMAP. The reduced embedding is then projected to the position (x, y) on a plane of two dimensions, one for the x-axis and one for the y-axis. The visualization uses the Plotly JavaScript open-source graphing library ([[https://plotly.com/javascript/](https://plotly.com/javascript/)]([https://plotly.com/javascript/](https://plotly.com/javascript/)), accessed on 1 January 2022) to draw a scatter plot, with each abstract represented as a dot that corresponds to its projected position (x, y). The dot color encodes the clustering membership of each abstract in the first iteration. The blue dots are of the five clusters #1 to # 5 in Table 2; the green dots the cluster #6, #7, and #8; and
# Formalism-Driven Development: Concepts, Taxonomy, and Practice +
## 4 Formalism-Driven Development in Practice
### Seniz
#### 4.2.2 Verification Generator  
Seniz provides a verification generator that translates Seniz programs developed in the abstraction stage into Promela programs for the verification stage.  
A state declarator is translated into a _macro_ and an _inline_. A _macro_ is defined as the conjunction of all state variables identified in the declarator and additional critical variables inferred by the compiler.  
**Example 13** (State Declarator Translation for Promela).: _For the acquiredLock state declarator in Example 12, it is translated to a macro and an inline shown in Listing 5._  
```
1#defineACQUIREDOCKloc[_pid]==3
2
3 inline_acquiredLock(){
4 atomic{
5 locl_pid}=3;
6}
7}
```  
**Listing 5**. Translated _acquiredLock_ state declarator in Promela.  
The transition relations, used to construct the control flow, are translated into a _prototype_.  
**Example 14** (Transition Relation Translation for Promela).: _A prototype is generated according to the translation relations programmed in Example 12, which is shown in Listing 6._  
Figure 7: Visualized system graph of the transaction client in Seniz.  
```
1 protypeWorker(){
2 processing();
3 do
4::PROCESSING->readTyWrite();
5::READYTOWRITE->
6 atomic{
7 if
8::_s>0->_decreaseS();acquiredLock();
9 fi;
10}
11::ACQUIREDLOCK->atomic{_increaseS();releasedLock();}
12::RELEASEDLOCK->processing();
13d;
14}
```  
**Listing 6**. Translated transition relations in Promela.  
The translation of propositions and temporal properties is trivial on account of the similar syntax between the Seniz language and the Promela language.  
The generated Promela program can be directly used for verification by model checkers such as the SPIN model checker [1]. For the Promela program generated from Example 12, the checking report implies property _mutexHolds_ is satisfied while both fairness properties are violated.
###### Abstract  
The dairy industry generates substantial quantities of wastewater, primarily whey wastewater, posing environmental challenges. Current treatment methods involve physical, chemical, and biological processes, but efficient solutions are still sought. Biological treatments using microalgae are gaining attention due to their potential to remove pollutants from wastewater and generate valuable products, making them an alternative way to improve environmental sustainability. The physicochemical characterization of whey effluents reveals a high organic content, an acidic pH, and elevated nutrient levels. This study investigates the potential of _Chromochloris zofingiensis_ (formerly known as _Chlorella zofingiensis_) for treating whey wastewater using three concentrations, 10%, 20%, and 50%, over a 7-day culture period. The optimal concentration of whey wastewater for biomass, nutrient removal, astaxanthin, and lipid production was found to be 10%. At this concentration, _C. zofingiensis_ achieved a biomass of 3.86 g L\({}^{-1}\) and a removal efficiency of nutrients between 77.08% and 99.90%. Analysis of pigment production revealed decreases in chlorophyll and carotenoid production with increasing whey wastewater concentration, while lipid and astaxanthin production peaked at the 10% dilution. The chlorophyll a, chlorophyll b, total carotenoid, astaxanthin, and lipid contents were, respectively, 11.49 mg g\({}^{-1}\), 4.56 mg g\({}^{-1}\), 4.04 mg g\({}^{-1}\), 0.71 mg g\({}^{-1}\), and 30.49% in 10% whey wastewater. The fatty acid profiles indicated the predominance of saturated and unsaturated fatty acids, enhancing the biofuel potential of _C. zofingiensis_ cultivated in whey wastewater. These findings demonstrate the dual benefit of using _C. zofingiensis_ for sustainable whey wastewater treatment and high-value bioproduct generation, supporting the development of circular biorefinery systems.  
Keywords: whey wastewater; _Chromochloris zofingiensis_; astaxanthin; lipid; biorefinery +
Footnote †: journal: Applied Sciences
# Validation of a Horizontally Dynamic Armrest for Joystick Controlled Mobile Equipment
## 1 Introduction
It is hypothesized that this was because a stationary armrest impeded the natural downward motion of the elbow during aft joystick movements. A translating dynamic armrest designed for joystick use was studied by [PERSON] et al. [1], and it was determined that this armrest lowered the UT loading. However, it also increased muscle activation in the flexor carpi radialis (FCR) and extensor carpi ulnaris (ECU), which are important during inward and outward joystick movements. It may not have been entirely successful because it only translated in the fore-aft direction and did not follow natural forearm pendulation (i.e., the up-and-down movement of the elbow and wrist during joystick controller use). [PERSON] and [PERSON] [26] designed an armrest that translated in the fore-aft direction and followed forearm pendulation during fore-aft joystick motion. A validation study showed that the armrest successfully reduced muscle activation in the UT, AD, and posterior deltoid (PD) [27]. It should be noted that neither of these dynamic armrests fully supported the arm during inward and outward movements. As indicated by [PERSON] et al. [1], the muscles responsible for inward and outward joystick movements may be negatively impacted by these armrest designs. It is hypothesized that a horizontally (inward-outward) dynamic addition to a fore-aft dynamic armrest that follows the forearm during inward and outward movements could be beneficial in reducing the activation of the wrist flexor and extensor muscles. Therefore, the purpose of this study was to determine if a horizontally dynamic addition [28] to the fore-aft dynamic armrest designed by [PERSON] and [PERSON] [26; 27] could \begin{table}
\begin{tabular}{c c c} \hline \hline
**Joystick Movement** & **Stage** & **Muscles Involved** \\ \hline Fore & 1 & Anterior deltoid, extensor carpi radialis, triceps \\  & 2 & Upper trapezius, posterior deltoid \\ Aft & 1 & Upper trapezius, posterior deltoid, pectoralis major \\  & 2 & Variety (at low levels) \\ Inward & 1 & Flexor carpi radialis, pectoralis major \\  & 2 & Extensor carpi radialis \\ Outward & 1 & Extensor carpi radialis, pectoralis major \\  & 2 & Flexor carpi radialis, pectoralis major \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Muscles involved in four different joystick movements [14]. Stage 1 involves movement from a neutral joystick position to the end point in the specified direction, and stage 2 involves joystick movement from the end point back to neutral position. reduce muscle activation in upper limb and neck muscles involved in joystick motion. If a reduction in muscle activation is achieved, the armrest could give more comfort to operators of large mobile machines and potentially reduce the incidence of work-related shoulder and neck musculoskeletal injuries. Lastly, this study served to ensure that the addition of a horizontal dynamic component did not reduce or negate benefits of the [PERSON] and [PERSON] [26, 27] fore-aft dynamic armrest.
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 3 Cotton Boll Disease Detection Algorithm and Improvement
### Improved Cotton Boll Disease Detection Algorithm Model Framework
#### 3.3.1 Introducing the EMA Mechanism  
During cotton ball disease detection, leaf occlusions and ambient lighting variations frequently lead to false negatives and false positives. Additionally, visual similarities between desiccated foliage and diseased cotton bolls frequently induce misclassification. These challenges stem primarily from the model's limited capacity to extract and filter discriminative disease features. To address this, an EMA mechanism [11] is introduced into the Backbone of YOLOv11n. By emphasizing important features and suppressing irrelevant regions (such as leaves), the EMA mechanism improves the detection accuracy of the model. By replacing the ordinary attention mechanism in C2 PSA with EMA, the model better extracts and fuses multi-scale features, thereby enhancing its target detection capability in complex scenarios. The EMA mechanism, proposed by [PERSON] in 2023, is an efficient multi-scale attention approach based on cross-spatial feature learning. It reshapes part of the channel dimension into the batch dimension and performs grouped operations across channels without dimensionality reduction, thus avoiding the loss of channel-wise feature information. This design reduces computational overhead while maintaining high accuracy and a low parameter count [12]. The structure of the EMA mechanism is illustrated in Figure 6.  
The EMA mechanism enhances pixel-level attention accuracy in the feature map through 1 \(\times\) 1 and 3 \(\times\) 3 branches and a cross-spatial learning module. The 1 \(\times\) 1 branch establishes cross-channel interactions through global average pooling and 1 \(\times\) 1 convolutions, while the 3 \(\times\) 3 branch extracts multi-scale features. The cross-spatial learning module pools and applies Softmax activation to the outputs of the 1 \(\times\) 1 and 3 \(\times\) 3 branches, generating two spatial attention maps via dot product. These maps are then fused, which strengthens the model's contextual understanding. By using convolutions at different scales in parallel, EMA effectively integrates multi-scale features, significantly improving the model's object detection capability.  
Figure 5: YOLOv11n-ECS model structure. Note: Conv2 d is the convolutional layer, Concat is the feature connection module, Upsample is the upsampling module, Detect is the detection head, MaxPool2d is the maximum pooling, Bottleneck is the convolution module containing residual connections, and Split is the feature layering.
# eJamar: A Novel Exergame Controller for Upper Limb Motor Rehabilitation
## 3 Materials and Method  
The architecture of the proposed system is presented in Figure 1, where the main actors are the player, the ejamar game controller, and the exergame. The ejamar serves as an interface facilitating the transmission of commands to the gaming system and, thereby, enables users to command the exergame actions effectively. The ejamar can identify the arm movement and grasping actions, which are mapped with specific actions in the exergame. For this study, two exergames were designed to encourage the game-user interaction based on movements needed to perform activities of daily living. The ejamar controller connects to the computer program running the games through a Bluetooth wireless interface.  
To establish this wireless connection, the computer's Bluetooth module must first be activated. Subsequently, the onboard Bluetooth module of the ejamar device must be linked with the computer where the exergames will be executed. It should be noted that each ejamar unit is assigned a unique identifier and it allows to connect two ejamar devices to play, and promote bimanual training. During game initialization, the program connects the exergame to the ejamar controller, identifying which exergame is currently running. The ejamar controller is compatible not only with the two exergames developed for this study in the Unity game engine, but also with other ones.  
The modalities and functions of the proposed exergames encourage the player to perform various hand and wrist movements, as well as repetitive and sustained grips. The aim of these game control functions is to replicate exercises commonly performed in hand and wrist rehabilitation sessions. In order to develop effective systems, it necessitates ergonomic design and should accommodate a wide range of user mobility [44]. Thus, a preliminary usability study with healthy volunteers serves as a step toward validating the controller with actual patients.
## 1 Introduction  
Alzheimer's disease (AD), the most common type of dementia, is characterized by the accumulation of abnormally folded amyloid-\(\beta\) (A\(\beta\)) proteins in the brain. A\(\beta\) monomers are produced from amyloid precursor protein (APP) via \(\beta\)- and \(\gamma\)-secretase activity [1]. Next, they begin to aggregate into oligomers, fibrils, and plaques. These misfolded A\(\beta\) proteins can cause neuronal degeneration in regions of the brain associated with memory and cognitive functions, eventually leading to symptomatic onset in patients with AD [1, 2]. Therefore, confirming the presence of A\(\beta\) aggregation has become an important hallmark for AD diagnosis.  
Several histological dyes are used for A\(\beta\) detection in postmortem brain sections. Thioflavin S (ThS) specifically binds to the \(\beta\)-sheet rich structure of A\(\beta\) aggregates, leading to increased fluorescence intensity of its emission spectrum, which enables the visualization of aggregated A\(\beta\) via fluorescence microscopy [3, 4]. However, ThS detects other \(\beta\)-sheet rich protein complexes, including tau [5, 6]; therefore, new dyes that specifically validate A\(\beta\) aggregate detection are required. A\(\beta\)-specific antibodies, such as 6E10 and A11, are used to visualize A\(\beta\) aggregates via immunohistochemistry; however, these require expensive and time-consuming methods. New histological staining dyes that use natural substances that easily label and visualize A\(\beta\) aggregates should be studied because they are cost effective and require a simple process.  
Fucoxanthin is a carotenoid extracted from the microalgae _Phaeodactylum tricornutum_[7]. It reduces inflammation, decreases oxidative stress, and has an antiobesity effect, demonstrating its safety for use in animals and humans [8; 9; 10; 11]. Moreover, fucoxanthin reduces A\(\beta\)-induced oxidative stress and proinflammatory cytokines in microglial cells and hippocampal neurons [12; 13], inhibits A\(\beta\) aggregation, and alleviates cognitive impairment in vivo [14]. Additionally, the fluorescence spectrum of fucoxanthin has absorption bands at 448, 476, and 505 nm and emission bands at 630, 685, and 750 nm [15]. Therefore, we hypothesized that fucoxanthin will directly interact with A\(\beta\) aggregates and be a suitable fluorescent dye to detect A\(\beta\) accumulation in the brain.  
Here, we report that fucoxanthin extracted from _P. tricornutum_ can be used as a fluorescent dye targeting A\(\beta\) aggregates in different AD transgenic mouse models. Histological analysis was performed on the fixed mouse brain tissue of APP/PS1 and 5\(\times\)FAD mice, which exhibit remarkable A\(\beta\) production and eventually excessive accumulation of A\(\beta\) in the brain. Brain tissue from aged mice was stained with different concentrations of fucoxanthin using two types of washing solutions. Fucoxanthin colocalized with ThS regardless of the type of washing solution, which demonstrated that it can detect A\(\beta\) via fluorescent visualization. This can verify the detection of A\(\beta\) with ThS.
# Applying Spectral Clustering to Decode Mobility Patterns in Athens, Greece
## 4 Discussion
### Clusters' Interpretation and Main Findings
Regarding temporal mobility trends, it can be concluded that morning trips are mainly conducted by car and train, while afternoon and evening trips are primarily carried out by walking, motorcycle, and bicycle. In relation to the age of commuters, young people use public transport significantly more compared to older age groups, who prefer using cars. The analysis reveals that reported trips exhibit meaningful repetitions, as evident from the distinct characteristics observed within each cluster (i.e., group of trips). The existence of such repetitions was a key hypothesis of this study, and its confirmation aligns with previous research [30], further supporting the robustness of the findings. Overall, this study focuses on clustering trips rather than travelers, allowing for a more dynamic understanding of mobility patterns without restricting individuals to a single cluster. One individual can \"own\" trips from more than one cluster. Considering the findings, this assumption appears to be quite reasonable. Previous approaches focused on defining traveler profiles and understanding the trip generation process [25; 26; 27; 28; 29]. While they successfully uncovered the underlying reasons for travel behavior, overall mobility patterns remained largely unknown in the past. The applied approach effectively decodes urban mobility in a city, resulting in six highly meaningful clusters. In general, it provides a more comprehensive understanding of the possible range of alternatives--something that initially seemed unfeasible given the limited sample size. This, of course, has policy implications.
# Safety and Security Considerations for Online Laboratory Management Systems
## 3 Literature Review
### Security in Remote Laboratories and OLMS
#### 3.4.1 Classification of Security Attacks in OLMS  
Security attacks in remote laboratories can be classified into three primary categories: network-based, access-based, and device-based attacks.  
* Network-based attacks target the infrastructure that enables communication between users and remote laboratories, aiming to interrupt data flow or block users from accessing and controlling experiments. Typical network-based attacks, such as denial-of-service (DoS) and distributed denial-of-service (DDoS), can cause system outages, while more sophisticated threats, like man-in-the-middle (MITM) attacks, put at risk data integrity and confidentiality, compromising sensitive information [65; 66; 67; 68].
* Access-based attacks involve unauthorized attempts to gain entry or privileges within the OLMS by exploiting weaknesses in authentication or authorization processes. These attacks can target some components, including hardware, software, and communication channels, exploit vulnerabilities in devices or networks to gain control, manipulate or hijack experiments, or cause serious harm to the integrity of OLMSs. Some examples include attempts to connect using protocols like RDP, WMI, and FTP, or exploiting vulnerabilities to gain unauthorized control over experiments in remote labs [67; 68].
* Device-based attacks target physical devices to undermine their functionality, security, or integrity. These attacks may involve tampering with the hardware, exploiting weaknesses in the device's software or firmware, or causing damage through manipulation or environmental influences. Examples include physical damage, hardware compromise, and attacks exploiting vulnerabilities in remote laboratory devices [67; 68].  
Table 19 includes an overview of security attacks classification (See Appendix A).