esa-sceva/satcom-chunk-collection huggingface dataset:
# Verifying Mutual Exclusion Algorithms with Non-Atomic Registers
## 4 Modeling Non-Atomic Registers in Uppaal
### Modeling MWMR Safe Registers  
The most challenging case concerns modeling MWMR safe registers. This paper proposes the introduction of decoration variables to reflect both the case of multiple writes on a scrambled register and that of multiple reads of a flickered register. As an example, Figures 7 and 8 show, respectively, the Peterson for 2 processes (Peterson-2) model [10] under atomic memory and its decorated version with scrambling and flickering. Scrambling is modeled according to the Raynal assumption [3] that multiple simultaneous writes assign a non-deterministic value to the register.  
As in other cases, in the models of Figures 7 and 8, \(i\) denotes this process and \(j=3-i\) indicates the partner process. The Peterson-2's solution uses two output boolean variables \(flag[.]\) to testify the interest (\(flag[i]==true\)) or not (\(flag[i]==false\)) of each process to enter the critical section. The shared non-output \(turn\) variable is used for yielding to the partner the grant for entering its CS. In a possible case, both processes can write simultaneously to \(turn\) (_scrambling_). The following decoration variables are used: (scalar) \(wt\) and (array) \(wf[pid]\). As shown in Figure 8, a writer who senses the other writer is not writing to \(turn\) sets \(wt\) to true and assigns the ID of the partner to the \(turn\) variable. Would the second writer arrive simultaneously at the SCR location, it can see \(wt\) is true. In this case, a non-deterministic value is assigned to \(turn\). The writer who assigned true to \(wt\) is also responsible for lowering it to false in the next step. Of course, being two processes,  
Figure 8: Peterson-2 process model with MWMR safe registers.  
Figure 7: Peterson-2 process model with atomic registers.  
in the case of scrambling on the \(turn\) register, no simultaneous read can occur. Instead, a write operation on the output variable \(flag[i]\) or the \(turn\) non-output variable can always occur simultaneously with a concurrent read operation issued by the partner process in the busy-waiting (BW) location.  
From the model checking of the models in Figures 7 and 8, it emerged that Peterson-2 is correct under atomic memory and it is MWMR-safe under non-atomic memory. This is because being the \(turn\) of type \(int[1,2]\), that is, the domain includes only the two values 1 and 2 of the process IDs, scrambling cannot generate random values, which can cause the algorithm to lose its mutual exclusion, enter a deadlock state, and so forth.  
The following TCTL query (satisfied) guarantees effectively that the two processes can simultaneously be in their SCR location, thus generating scrambling in the \(turn\) variable:  
E\(\Leftrightarrow\) Process(1).SCR & Process(2).SCR
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
###### Abstract  
This paper aims to explore the possibility of utilizing vision transformers (ViTs) for on-edge medical diagnostics by experimenting with the Kvasir-Capsule image classification dataset, a large-scale image dataset of gastrointestinal diseases. Quantization techniques made available through TensorFlow Lite (TFLite), including post-training float-16 (F16) quantization and quantization-aware training (QAT), are applied to achieve reductions in model size, without compromising performance. The seven ViT models selected for this study are EfficientFormerV2S2, EfficientViT_B0, EfficientViT_M4, MobileViT_V2_050, MobileViT_V2_100, MobileViT_V2_175, and RepViT_M11. Three metrics are considered when analyzing a model: (i) F1-score, (ii) model size, and (iii) performance-to-size ratio, where performance is the F1-score and size is the model size in megabytes (MB). In terms of F1-score, we show that MobileViT_V2_175 with F16 quantization outperforms all other models with an F1-score of 0.9534. On the other hand, MobileViT_V2_050 trained using QAT was scaled down to a model size of 1.70 MB, making it the smallest model amongst the variations this paper examined. MobileViT_V2_050 also achieved the highest performance-to-size ratio of 41.25. Despite preferring smaller models for latency and memory concerns, medical diagnostics cannot afford poor-performing models. We conclude that MobileViT_V2_175 with F16 quantization is our best-performing model, with a small size of 27.47 MB, providing a benchmark for lightweight models on the Kvasir-Capsule dataset.  
vision transformers; Kvasir-Capsule; TensorFlow Lite; quantization +
Footnote †: journal: Medical Imaging  
**Citation:**  
* [PERSON]; [PERSON]; [PERSON] On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset. _Appl. Sci._**2024**, _14_, 8115. [[https://doi.org/10.3309/app1488115](https://doi.org/10.3309/app1488115)]([https://doi.org/10.3309/app1488115](https://doi.org/10.3309/app1488115))
## 5 Conclusions  
The findings of this study, along with supporting evidence from other research, emphasize the critical role of running speed and degree of unloading in determining muscle activity patterns when using an anti-gravity treadmill. The relationship between speed and unloading is non-linear, with higher speeds causing more pronounced reductions in muscle activity. This suggests that the use of an anti-gravity treadmill is most beneficial at lower running speeds, where significant unweighting can be applied without disrupting the natural muscle activation patterns. This makes the technology particularly valuable in early rehabilitation for individuals recovering from lower limb injuries. While anti-gravity treadmills effectively reduce the mechanical load on the musculoskeletal system and minimize the risk of further injury, they also allow athletes to maintain fitness and train at higher intensities with reduced impact on bones and soft tissues. However, the influence of body weight support on biomechanical variables, such as cadence and stride length, should be closely monitored to prevent long-term changes in running form. The ability to manipulate cadence during unweighting offers an additional method to preserve more natural muscle activation patterns during rehabilitation.  
In conclusion, anti-gravity treadmills provide a versatile and effective tool for both rehabilitation and athletic training, enabling safe and progressive recovery while minimizing injury risk. These treadmills allow for adjustments that optimize muscle activity and running mechanics, which is particularly beneficial in the early stages of recovery and for endurance athletes looking to maintain performance while reducing impact-related stress.  
Conceptualization, P.P. and A.G.; methodology, P.P. and A.G.; software, M.G.; validation, R.R. formal analysis, R.R.; investigation, P.P.; resources, R.R.; data curation, R.R.; writing--original draft preparation, P.P.; writing--review and editing, M.G.; visualization, A.Z.; supervision, A.Z.; project administration, P.P.; funding acquisition, A.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The research was approved by the Ethics Committee of The Academy of Physical Education in Katowice (3/2021) and executed according to the ethical standards of the latest version of the Declaration of Helsinki, 2013.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient to publish this paper.  
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
# A Survey of Electromagnetic Techniques Applied to Cultural Heritage Conservation
## 2 EM Techniques Applied to CH
### Ionizing Radiation vs. Traditional Methods  
If compared with traditional methods, ionizing radiation does not leave chemical residuals; hence, there is no need of quarantine; moreover, their efficacy is preserved longer, and they act simultaneously on different type of biodeteriogens and at different stages of the life cycle. In the \"Irradiation Protocol for cultural heritage conservation treatment\", [PERSON] et al. [69] specify the most evident disadvantages of traditional disinfection methods, namely:  
* Alcohol 70% eliminates fungi but it has no effect on spores;
* Fumigation is effective but carcinogenic;
* Anoxia does not guarantee penetrability;
* Freezing is effective on fungi, but not on spores;
* Dry sanitization performs only a superficial removal [70].  
Fumigation is conducted by means of poisonous gases, usually ethylene oxide, which may be emitted over time from treated objects, and methyl bromide, which is carcinogenic and extremely flammable. Moreover, gases hardly penetrate materials; hence, the effectiveness of treatment is compromised [71].  
Ethyl and isopropyl alcohol act quickly on fungi but, as said above, they are ineffective against bacteria spores. An effective liquid against fungi, bacteria, and spores is formaldehyde, but it is a carcinogen and may cause respiratory problems and skin irritations. Moreover, any liquid should not remain in contact with a CH artifact for a prolonged period [72].  
Anoxia requires long exposure time under strict environmental conditions (low oxygen level), and is ineffective on fungi [73].  
One advantage of ionizing radiation over traditional methods is the penetrating capability. This allows treatment of thick and dense objects, but because CH objects may have complex shapes, a critical parameter to manage together with the absorbed dose is the uniformity of radiation delivered, which is highly dependent of the type of irradiation facility. These are two key factors that make the success or failure of the application.  
Also, the IAEA outlines the validity of radiation techniques in CH applications, even in comparison with more traditional methods like chemical and physical ones. \"Chemical and physical methods have been developed to treat and restore cultural heritage artifacts. However, chemical methods may leave undesirable chemicals in the material, whereas physical methods are generally quite extreme and therefore not suitable for all types of materials. In contrast, radiation technologies do not cause any harm to the artifacts themselves. They can be used to destroy parasites [...] and to assess any internal damage that was caused by them\" [74]. (Treatment and restoration, (c) IAEA, [[https://www.iaea.org/topics/cultural-heritage-restoration](https://www.iaea.org/topics/cultural-heritage-restoration)]([https://www.iaea.org/topics/cultural-heritage-restoration](https://www.iaea.org/topics/cultural-heritage-restoration)), accessed on 3 December 2024).
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 2 Datasets
### Feature Extraction  
To shed insights into the CIRs from two experiments, two features are extracted from the CIRs. The CIR shows a representation of the multi-path channel between the source and the receiver as a convolutional system, with copies of the transmitted signal arriving at the receiver with different delays (sometimes referred to as arrivals). In order to use traditional supervised learning methods with the relatively long CIR, it is necessary create low dimensional representations, which are known as features. The first feature generated is an estimate of the SNR, in which the CIR before the first arrival is used to estimate the noise variance and everything over three times the noise standard deviation is considered to be an arrival. The delay spread is also calculated, which measures the time difference between the first arrival and the last arrival in milliseconds. An example of this processing can be seen in Figure 1, which shows data from an arbitrarily selected CIR from the KWAUG14 dataset. The two significant arrivals are identified by a marker; in this example, SNR is calculated to be 14.1 dB and the delay spread is 1.17 milliseconds.  
To better understand the distribution of these features for both datasets, crossplots of the extracted features and the BERs are generated and shown in Figures 2 and 3. Due to the high density of points, density contours are shown on the lower off-diagonal crossplots to aid interpretation. There are some obvious trends, such as a weak inverse relationship between the SNR and the BER and different regimes of delay spreads that don't correlate well to BER.  
For KWAUG14, due to the relatively static channel condition, a fairly consistent delay spread and low BERs can be observed in Figure 2. The trend between the SNR and the BER is not strong, but shows the slight negative trend one would expect.  
Figure 1: An example of feature extraction from the KWAUG14 channel impulse response. Significant arrivals have a marker.  
Figure 3: SPACE08: extracted feature distributions with kernel density estimate contours on lower left off-diagonal plots.  
Figure 2: KWAUG14: extracted feature distributions with kernel density estimate contours on lower left off-diagonal plots.  
For SPACE08, one can observe from Figure 3 that the CIR is much more complex and includes arrivals with a longer delay spread than KWAUG14. In addition, the CIRs vary substantially between measurements. The average BER is higher, but the weak negative trend between SNR and BER is still visible.
# Recent Trends and Progress in Corrosion Inhibitors and Electrochemical Evaluation
## 3 Types of Corrosion Inhibitors
### Composite-Based Corrosion Inhibitors
#### 3.4.7 Metal-Organic Frameworks  
By altering the metal ions, organic ligands, or the interaction between the two, inorganic metals and organic ligands can be combined to form coordination polymers called metal-organic frameworks (MOFs). Previous studies have shown that aromatic rings and heteroatom-containing organic molecules, such as N, O, and S, are efficient corrosion inhibitors. Due to their significant loading capacity, biocompatibility, and simplicity of synthesis under mild conditions, the ability of the organic frameworks to perform sacrifices is improved with the inclusion of more electropositive metals. On the other hand, the organic structure produces a protective layer over the metal surface, slowing corrosion [108]. [PERSON] et al. [109] reported that the use of MOF-based CI names, such as \(\mathrm{s}\)zolitto imidazolate framework-8@[Mo\({}_{132}\)] (ZIF-8@[Mo\({}_{132}\)]), can act as a good green CI. The electrochemical results showed that there is much higher corrosion efficiency for ZIF-8@[Mo\({}_{132}\)] than the ZIF-8 structure. In another study from 2020, [PERSON] et al. used composite material which consisted of inhibitor benzotriazole (BTA)-loaded MOFs and further loaded with tetraethyl orthosilicate (TEOS), and the resulting composite was further combined with graphene oxide (GO). The results showed that the BTA-OF-TEOS-GO incorporated into the epoxy coatings displayed the most excellent impedance values up to 8.6 \(\times\) 108 cm\({}^{2}\), indicating the most superior and long-lasting anticorrosion characteristics [110]. In another study, a hybrid nano-compound was developed by [PERSON] et al. [111] by encapsulating BTA in Ce-MOFs and then adding TEOS to create a film to surround the Ce-MOFs. Figure 10 shows that Ce-MOFs@TEOS epoxy coating has the biggest impedance arc, while the epoxy coating has the lowest impedance arc, and this corresponds to the superior dispersion of Ce-MOFs@TEOS in epoxy coatings. However, because CIs have active corrosion properties, [PERSON] et al. [112] investigated the CI efficiency of composite materials made of Cu-MOF/graphene, Cu-MOF/graphene oxide, Mn-MOF/graphene, and Mn-MOF/graphene oxide on carbon steel. The 3% Cu-MOF/GO and 3% Mn-MOF/GO composite coatings on the carbon steel surface had moderately high total polarization resistances at 55,097 and 55,729 cm\({}^{2}\), respectively. According to electrochemical tests and results, the carbon steel could be effectively protected against corrosion by these coatings.  
Figure 10: Nyquist plots of different epoxy coatings after 2 h in a 3.5 weight% NaCl solution [111].
# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
###### Abstract  
This research addresses the use of dual-polarimetric descriptors for automatic large-scale ship detection and characterization from synthetic aperture radar (SAR) data. Ship detection is usually performed independently on each polarization channel and the detection results are merged subsequently. In this study, we propose to make use of the complex coherence between the two polarization channels of Sentinel-1 and to perform vessel detection in this domain. Therefore, an automatic algorithm, based on the dual-polarization coherence, and applicable to entire large scale SAR scenes in a timely manner, is developed. Automatic identification system (AIS) data are used for an extensive and also large scale cross-comparison with the SAR-based detections. The comparative assessment allows us to evaluate the added-value of the dual-polarization complex coherence, with respect to SAR intensity images in ship detection, as well as the SAR detection performances depending on a vessel's size. The proposed methodology is justified statistically and tested on Sentinel-1 data acquired over two different and contrasting, in terms of traffic conditions, areas: the English Channel the and Pacific coastline of Mexico. The results indicate a very high SAR detection rate, i.e., >80%, for vessels larger than 60 m and a decrease of detection rate up to 40% for smaller size vessels. In addition, the analysis highlights many SAR detections without corresponding AIS positions, indicating the complementarity of SAR with respect to cooperative sources for detecting dark vessels.  
automatic identification system (AIS); cross-comparison; detection; dual-polarization; synthetic aperture radar (SAR); vessels +
Footnote †: journal: Computer Science and Technology  
0000-0001-5000-0000  
0000-0001-5000-0000][PERSON] 00000-0001-5000-0000][PERSON] 0000-0001-5000-0000][PERSON] 0000-0001-5000-0000][PERSON] 0000-0001-5000-0000][PERSON] 0000-0001-5000-0000][PERSON] 0000-0001-5000-0000][PERSON] 0000-0001-5000-0000]Curent address: 41, rue du Brill, Belvaux, Luxembourg.
## 3 Materials and Methods
### Classification Models
#### 3.3.4 K-Nearest Neighbors  
The K-Nearest Neighbors (KNN) algorithm [31] is a supervised machine learning method that can be used for both regression and classification tasks. Unlike other supervised learning algorithms, the KNN classifier does not have a distinct training phase. For this reason, it is also referred to as a lazy learner. KNN does not focus on building a general model; instead, it stores all examples corresponding to the training data in an n-dimensional space. KNN is highly robust against noisy training data and can also be applied to multi-class classification problems [32]. KNN has proven to be a very effective classifier in various applications and is therefore widely used in many fields [33].  
When classifying new data with the KNN algorithm, the distances between the new point and all points in the training set are calculated. Different metrics such as Euclidean, Manhattan, and Minkowski are used for distance calculation in machine learning [34]. In the KNN algorithm, the letter \(K\) represents the number of nearest neighbors considered when classifying new data. For \(K=1\), the new data are assigned to the class of the nearest neighbor, while for \(K=3\), the three closest neighbors are taken into account and the new data are assigned to the class that has the majority among these three neighbors. The number of neighbors (K) and the distance metric used are important parameters that influence the performance of the KNN algorithm. Typically, these parameters are determined through an optimization process. This study uses grid search and 10-fold cross-validation for parameter optimization.
# Support of Advanced Technologies in Supply Chain Processes and Sustainability Impact
## 3 Conceptual Model Proposed  
The use of advanced technologies is strategic in a context where better business processes are developed to support higher efficiency and faster response. In addition, dynamic complexity has overtaken the possibility of human intervention to identify and solve many system issues and, being digital, SCs can remove many of the persistent inefficiencies. As such, it is harder to achieve performance improvements through the traditional means and companies are compelled to develop newer solutions arising from technology and business-model based innovations. Furthermore, the costs of instrumentation have declined dramatically in recent years and smart devices are being deployed everywhere [99] contributing to a massive use of advanced technologies.  
The Supply Chain Operations Reference (SCOR) model offers standard guidelines for companies [33] to aid in SC configuration, identification and measurement of metrics and continuous application of best practices. It is considered an analytical tool that gathers performance processes, metrics, best practices, and people into an integrated structure, being frequently used as a deductive framework to study SC topics, as mentioned by [PERSON] and [PERSON] [100].  
Several authors [41; 67] have argued that the digital connectedness and information development and sharing associated with digitalization may have contradictory impacts on triple bottom line sustainability. The digitalization of manufacturing and lean business processes together with deployment of smarter machines and devices may offer numerous advantages such as manufacturing productivity, resource efficiency, and waste reduction [76; 89; 90; 91]. Therefore, a SC can be considered as showing a sustainable behavior if sustainability concerns are identified at each level of the SC [20], the existence of a performance measurement system formed by economic, environmental and social indicators being very important to assess this kind of information [21].  
Attending to the literature review the following conceptual model is proposed in this study (Figure 1).
## 4 Methodology
### User Preferences Estimation  
In the POI recommendation, user preferences are influenced by various contextual information. To enhance the accuracy of preference estimation, we propose a method that integrates geographical and temporal contexts. Specifically, for a user \(u\), the representation of user preferences is calculated as follows:  
\[\mathbf{H}_{u}^{U}=\sum_{p\in\mathbb{C}_{u}}g_{p}\cdot\mathbf{H}_{p}^{\text{F} }+\sum_{p\in\mathbb{C}_{u,l}}t_{p}\cdot\mathbf{H}_{p}^{\text{F}} \tag{5}\]  
where \(g_{p}\) and \(t_{p}\) are aggregation weights to control the contributions of geographical and temporal factors.  
In this paper, we employ the following strategies to calculate \(g_{p}\) and \(t_{p}\): (1) for \(g_{p}\), we compute the frequency of a user's visits to each region and utilize this frequency as \(g_{p}\). Intuitively, the higher the frequency with which a user visits a particular region, the greater the attraction of the POIs in that region to the user. (2) for \(t_{p}\), taking into account the dynamic nature of user preferences, we use recent visit records to calculate user preferences:  
\[t_{p}=\exp(\frac{1}{t_{p}-t_{0}}) \tag{6}\]where \(t_{0}\) and \(t_{p}\) are given timestamp and the check-in timestamp of POI \(p\). The principle of Equation (6) is based on the assumption that POIs visited in the distant past have less influence on the user preferences, while recently visited POIs have a greater impact on the user preferences.