esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Keeping drinking water safe is a critical aspect of protecting public health. Traditional laboratory-based methods for evaluating water potability are often time-consuming, costly, and labour-intensive. This paper presents a comparative analysis of four transformer-based deep learning models in the development of automatic classification systems for water potability based on physicochemical attributes. The models examined include the enhanced tabular transformer (ETT), feature tokenizer transformer (FTTransformer), self-attention and inter-sample network (GAINT), and tabular autoencoder pretraining enhancement (TAPE). The study utilized an open-access water quality dataset that includes nine key attributes such as pH, hardness, total dissolved solids (TDS), chloramines, sulphate, conductivity, organic carbon, trihalometanes, and turbidity. The models were evaluated under a unified protocol involving 70-15-15 data partitioning, five-fold cross-validation, fixed random seed, and consistent hyperparameter settings. Among the evaluated models, the enhanced tabular transformer outperforms other models with an accuracy of 95.04% and an F1 score of 0.94. ETT is an advanced model because it can efficiently model high-order feature interactions through multi-head attention and deep hierarchical encoding. Feature importance analysis consistently highlighted chloramines, conductivity, and trihalomethanes as key predictive features across all models. SAINT demonstrated robust generalization through its dual-attention mechanism, while TAPE provided competitive results with reduced computational overhead due to unsupervised pretraining. Conversely, FTTransformer showed limitations, likely due to sensitivity to class imbalance and hyperparameter tuning. The results underscore the potential of transformer-based models, especially ETT, in enabling efficient, accurate, and scalable water quality monitoring. These findings support their integration into real-time environmental health systems and suggest approaches for future research in explainability, domain adaptation, and multimodal fusion.  
water potability; transformer models; deep learning; physicochemical features; tabular data classification; attention mechanism; environmental monitoring
## 2 Materials and Methods
### Adsorption Experiments  
The adsorption equilibrium was carried out with 300 mL glass bottles containing 200 mL of Co(II) solution (0-10 mg/L) at pH 7.0 \(\pm\) 0.1. After 0.1 g of either p-HdA or b-HdA was added into each bottle, the bottles were placed in a shaking incubator (WIS-20, DAIHAN Scientific Co., Ltd., Seoul, Korea) at 150 rpm under different temperatures (293, 303, and 313 K) for 30 h to ensure that saturated adsorption took place. The adsorption kinetic studies were performed by adding 0.1 g of adsorbents to 40 mL of a Co(II) solution (10 mg/L) and having the experiment proceed for 30 h under 293 K. The competitive adsorption of Co(II) in the presence of Ni(II), Sr(II), Cs(I), or U(VI) was investigated by introducing 0.05 g of adsorbents to 40 mL of adsorbate mixtures with different initial concentrations (0.1, 1.0. and 5.0 mg/L). All samples were taken at predetermined intervals, filtered through a 0.22 \(\upmu\)m polyvinylidene fluoride (PVDF) syringe filter (Hyundai Micro), and acidified to pH 3.0 using concentrated HNO\({}_{3}\). An inductively coupled plasma mass spectrometry (ICP-MS) method (Agilent ICP-MS 7700S, Agilent, Santa Clara, CA, USA) was used to analyze the concentrations of the radionuclides.  
The adsorption capacity of radionuclides (\(q_{e}\) (mg/g)) was calculated using the following mass balance equation [28]:  
\[q_{e}=\frac{(C_{o}-C_{e})V}{m} \tag{1}\]  
where \(C_{o}\) and \(C_{e}\) (mg/L) are the initial and equilibrium concentrations of the adsorbate, respectively, \(V\) (L) is the volume of the adsorbate solution, and \(m\) (g) is the mass of the b-HdA used.
## 4 Evaluation
### Experimental Results
The LBA has an obviously lower average SFD-to-controller latency and packet loss rate relative to the LIB in the same SDN deploy ratio. When the number of SFDs is the same as the number of controllers, the latency and packet loss rate decrease to zero for directly linked. Deploying more devices deployed with SDN allows more paths to be used to forward control messages, and thus reduces the chance they pass through the LEDs. Hence, a lower average latency and packet loss rate would be obtained. With more controllers deployed in the network, the curves in Figure 9 show a decreasing trend. For the same reason as Figure 7, some curves in Figure 10 have ups and downs. However, LBA still has lower packet drop rate relative to LIB. Figure 6: Average SFD-to-controller latency with different number of controllers. Figure 7: Loss rate with different number of controllers. In conclusion, to achieve an optimal controller assignment with the lowest average SFD-to-control latency, the LFDs in the hybrid SDNs cannot be ignored. The performance evaluation shows that the proposed scheme has a better performance for both average SFD-to-control latency and message loss rate. It is obvious that, with more SDN controllers deployed in the network, a lower average latency can be obtained. Figure 8: Average SFD-to-controller latency improvement compared with LIA in two different topologies. Figure 10: Loss rate with different SDN deployment ratio. Figure 9: Average SFD-to-controller latency with different SDN deployment ratio.
## 4 Discussion
It is to note that this study is exploratory by design. It has limitations in terms of small sample size and qualitative nature. The study findings suggest possible differences between the lifestyle and medication groups, but future studies will be necessary to replicate and support the observed findings. Moreover, the study focused only on two opposite segments (medication and lifestyle) but it will be important to address if, and to what extent, the observed findings can be extended to patients from intermediate segments or sub-segments. Specifically, it will be important to conduct further workshops with a more regular composition of patients (e.g., constant number of patients in each workshop, balanced number of males and females). Moreover, it will be useful to address standardized measures before and after the workshops, including follow-up measures at predetermined intervals (e.g., after one week, one month, and three months). Examples of relevant measures include surveys to address health status (e.g., the EuroQol EQ-5D-5L, which includes perceived overall health and dimensions of mobility, self-care, usual activities, pain/discomfort, and anxiety/depression), patient attitudes and behaviors (e.g., the DIAB-Q, Diabetes Intention, Attitude, and Behavior Questionnaire), and individual self-efficacy and disease management (e.g., the DMSES, Diabetes Management Self-Efficacy Scale). The addition of specific measures of perceived health, individual attitudes, and self-efficacy is also important per se to support a better understanding of the specific behavioral characteristics and attitudes of patients in the medication segment and could support future development of interventions tailored to the needs of these patients. In addition, in a follow-up study it will be important to incorporate the proposed peer-to-peer intervention into a clinical prospective study to address the clinical characteristics of patients in the different segments and investigate the changes in biomarker values and the individual control of disease following the intervention. It is acknowledged that the results of this study are preliminary; nevertheless, they provide interesting insights for the future implementation of peer-to-peer interventions for patients with T2D. Our study indicates that, particularly in patients who are more willing to follow lifestyle-based interventions, evidence from successful strategies within a patient's own segment presented in a positive manner without shaming can help people create their own choice architecture, sparking collective intelligence to foster better health. Moreover, the proposed approach is potentially scalable as it uses a fully virtual process, it does not require healthcare provider supervision, and is based on EMR data that are widely used globally. In the future, ad hoc digital tools could be developed (e.g., a mobile app or web portal) to let people with similar behaviors and attitudes work collectively towards a shared goal.
## 2 Monte Carlo Point Probability Model and the Modeling of Light Sources
### Monte Carlo Point Probability Method
\(\Omega\) is the spatial solid angle composed of all possible scattering directions from point \(S_{n}\) that can be detected by the receiver. The total probability that a photon reaches \(R\) after \(n\) scattering is \(P_{n}=W_{n}P_{1n}P_{2n}\), where \(W_{n}\) refers to the survival probability before reaching \(S_{n}\), and \(W_{0}\) is the initial survival probability and is equal to 1. \[W_{n}=(1-P_{1n})\exp(-k_{e}|S_{n}-S_{n-1}|)W_{n-1}, \tag{13}\]  
Therefore, the total probability of a single photon reaching the detector can be expressed as  
\[\mathrm{P}=\sum\limits_{n=1}^{N}P_{n}=\sum\limits_{n=1}^{N}W_{n}P_{1n}P_{2n} \tag{14}\]
## 5 Case study: Social Smart Mobility for Electric Vehicles  
A _smart mobility_ case study referring to power management of Plug-in Electric Vehicles is discussed to highlight the capabilities and benefits of the proposed framework in dynamic environments. Figure 6 depicts the envisioned scenario. _A 2017 BMW 13 plug-in electric taxi (\(T_{1}\)) usually serves \"district \(A\)\" of a large city. It has friendship relations with vehicles of its PEV taxi company and some of the charging stations in the city.  
A possible \(\mathcal{ALN}\) formalization of electric vehicle power management aspects (reported in OWL 2 Manchester syntax [49] with reference to the reference domain ontology) is in Figure 7. For the sake of conciseness, figures in this section show only some relevant classes of the ontology.  
Figure 6: Social smart mobility scenario.  
Figure 8: Semantic annotations of taxi request and friends’ services.  
Figure 7: Electric taxi profile semantic description  
The request cannot be completely satisfiedSo taxi taxi fleet agents autonomously cooperate in order to share useful information about electricity suppliers among their friends and suggest the services they offer.A new charging station service \(CS_{3}\) in \"district \(Z\)\", summarized in Figure 9, is selected and tagged (i.e., suggested) by the friend \(T_{3}\). Service description has a dispatching distance higher than the desired maximum but a non-exact match is tackled by means of non-standard reasoning services for semantic matchmaking recalled in Section 2. Furthermore, the experience factor _suggest_\(S(D)\) described in Section 4.3 is exploited to refine social relationships. Let us suppose the taxi becomes a frequent visitor of \"district \(Z\)\", and where it often needs charging. If \(CS_{3}\) service of device \(D_{new}\) is frequently tagged, \(S(D_{new})\) value will increase during the social object's lifetime and a new friendship relation will be suggested to directly retrieve and use the services of the charging station.  
This is just a small example to highlight the benefits of the proposed semantic-based SIoT framework. In real smart mobility scenarios, more complex requests and service descriptions are expected, and non-standard inferences will be even more useful for service ranking and composition to satisfy detailed requests.
# MoCoformer: Quantifying Temporal Irregularities in Solar Wind for Long-Term Sequence Prediction
## 4 Results
### Multi-Mode Decomp Block Decomposition Experiment  
The number of layers in the Multi-Mode Decomp Block is of significant importance in influencing the prediction process and the resulting outcomes. An increase in the number of layers allows for a more precise capture of diverse frequency components and modal features, thereby enhancing the modal information extracted from the original signal. The selection of the optimal layer count necessitates a delicate balance in order to ensure optimal model performance. A deficiency in the number of layers may result in the loss of information, whereas an excess could introduce noise or irrelevant details. The results of the experiments conducted on the layer count in the Multi-Mode Decomp Block are presented in Figure 8, which illustrates the impact of this variable on the performance of the model.  
The experimental results, corroborated by Figure 9, highlight the significance of choosing an optimal number of decomposition layers. The figure illustrates that when  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**96**} & \multicolumn{2}{c}{**192**} & \multicolumn{2}{c}{**336**} & \multicolumn{2}{c}{**720**} \\  & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline MoCo-rM & 0.747 & 1.016 & 2.407 & 0.746 & 1.025 & 2.554 & 0.747 & 1.061 & 2.382 & 0.771 & 1.026 & 2.411 \\ MoCo-rMrSTL & 0.769 & 1.038 & 3.027 & 0.776 & 1.043 & 2.724 & 0.768 & 1.048 & 2.686 & 0.788 & 1.045 & 2.593 \\ MoCo-rCrT & 0.715 & 0.938 & 2.230 & 0.720 & 0.988 & 2.258 & 0.735 & **0.986** & 1.997 & 0.739 & **0.966** & **1.844** \\
**MoCoformer** & **0.699** & **0.926** & **2.408** & **0.711** & **0.968** & **2.156** & **0.731** & 0.988 & **1.983** & **0.733** & 0.968 & 1.850 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of ablation studies results for univariate prediction based on the omni-IMF dataset.  
the number of IMF layers is set to six, optimal outcomes are achieved, with the essential information captured while avoiding noise and unnecessary details. The acquisition of information may be inadequate with five or fewer layers, resulting in less accurate predictions. Conversely, the introduction of seven or more layers may result in the inclusion of excessive details or noise, which could have a detrimental impact on the performance of the model. This underscores the pivotal importance of determining an optimal number of layers for specific conditions.
###### Abstract  
A transmitarray antenna is evaluated to generate a multi-focusing spot area in the Fresnel region of the antenna in the Ka-band. The antenna is designed to focus the radiated field at a certain point using a central feeding configuration. The number of feeds is increased to create as many focusing spots as feeds. The feeds are placed along an arc defined in the principal planes of the transmitarray, radiating independent near-field spots and providing a solution with a wide-angle spot scanning without an antenna displacement and a high isolation between feeds. To validate this concept, a transmitarray based on dielectric-only cells is designed and simulated under full-wave conditions. Then, this design is manufactured using a 3D printing technique, and the prototype is measured in a planar acquisition range. Measurements are performed for different feed positions in order to validate the multi-focusing capability of the antenna. Measurements and simulations show a high agreement and validate the proposed design technique.  
dielectric transmitarray; 3D printing; near-field focusing; near-field multi-focusing 21 422[[https://doi.org/10.3390/s21020422](https://doi.org/10.3390/s21020422)]([https://doi.org/10.3390/s21020422](https://doi.org/10.3390/s21020422))
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 4 Analysis of Influencing Factors of Cable Damage Identification
### Influence of Model Error  
Model error, for example, unintentional eccentricities of the load (producing torsion) or an imperfect symmetry of the geometric or mechanical distribution of masses or stiffnesses may occur errors. It can also compromise the ability of the proposed method to identify cable damage. When the cable is damaged, it only affects the deflection of the damaged side of the cable, and the model error will increase or decrease the deflection difference of the beam body without affecting the location identification of the damaged cable. As the damage degree identification is related to the deflection difference, it has an impact on the damage degree identification; however, the error is small.  
Figure 17: Identification results for considering stiffness degradation.
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 2 Study Area and Data
### Experimental Data
#### 2.2.3 Other Auxiliary Data  
ZY3-02 sensor-corrected products were acquired on 18 July 2018, provided by the China Centre for Resources Satellite Data and Application (CRESDA, [[http://www.cresda.com/CN/](http://www.cresda.com/CN/)]([http://www.cresda.com/CN/](http://www.cresda.com/CN/))), since the planimetric and vertical accuracies of the ZY3-02 sensor-corrected products are better than 2.5 m and 2 m, respectively [49]. We used the forward/backward stereo images to assist with Luojia 1-01 image geometric correction and extract the building digitization.  
In addition, the Hefei city Regional Map of the study area can be obtained from the Tianditu net ([[http://map.tianditu.gov.cn](http://map.tianditu.gov.cn)]([http://map.tianditu.gov.cn](http://map.tianditu.gov.cn))). In order to maintain consistency between different data sources, all remote sensing data were first converted into a WGS_1984_Albers projected coordinate system. The study area, as captured by the different data sources, is shown in Figure 3.
# Novel Extensions to Enhance Scalability and Reliability of the IEEE 802.15.4-DSME Protocol
## 5 Performance Assessment  
In particular, this section assesses three performance indexes, i.e., scalability, queuing delays for the aperiodic messages transmitted in the CAP, and packet loss ratio. In Section 5.1 we assess  
Figure 3: Example using miGTS.  
the scalability comparing the number of GTSs required to accommodate all the periodic flows in the case of Shareable DSME-GTS extension enabled and in the case of plain DSME, respectively. For this reason, a network with a star topology is chosen, in which each node generates two periodic flows, with periods multiple of the multi-superframe duration. The simulation is performed increasing the number of nodes. As the assessed metrics, i.e., the number of GTSs required to accommodate all flows, does not depend on the DSME parameters, in this scenario no settings of BO, SO, etc. are needed. Conversely, in the second scenario, discussed in Section 5.2, the average queueing time for the aperiodic messages transmitted in the CAP is assessed. The aim of this assessment is to give some quantitative insights on the improvement in terms of average delays for aperiodic messages when the Shareable DSME-GTS extension is adopted. In this scenario we consider a fixed number of nodes that transmit aperiodic messages and in each simulation run we vary the number of nodes that transmit periodic messages in the GTS. This way the multi-superframe duration varies and, as a consequence, the CAP frequency also varies. Finally, in the third scenario, presented in Section 5.3, the packet loss ratio results of the Mirror D-DSME and the plain DSME protocol, under harsh channel conditions, are compared varying the distance between the end-nodes and the _PAN-Coordinator_.
# Automatic Detection of Tomato Diseases Using Deep Transfer Learning
## 2 Materials and Methods
### Performance Evaluation Metrics  
The metrics used to evaluate the performance of the CNN models are shown in Equations (1)-(6). In these equations, \(T_{P}\) represents the true positive (i.e., a leaf image correctly classified in one of the nine disease states), \(F_{N}\) represents the false negative (i.e., a leaf image classified as healthy, but, in reality, it was drawn from one of the disease classes), \(F_{P}\) represents the false positive (i.e., a healthy leaf image wrongly classified as representing a disease), and \(T_{N}\) represents the true negative (i.e., a healthy image classified correctly as such). _Recall_ (i.e., true positive rate (TPR) or sensitivity) measures the ability of the model to identify a leaf image as belonging to the correct disease class out of all the positive images, which is affected by the existence of false negatives. Moreover, _Specificity_ measures the  
Figure 2: Sample images from the nine disease classes and the healthy one. (**a**) bacterial spot; (**b**) early blight; (**c**) late blight; (**d**) leaf mold; (**e**) mosaic virus; (**f**) septoria leaf spot; (**g**) spider mites; (**h**) target spot; (**i**) yellow leaf curl virus; (**j**) healthy.  
ability of the model to identify a leaf image as belonging to the healthy class, which is affected by the existence of false positives. High sensitivity indicates that the model easily recognizes leaf images as representing a disease but may include a large number of false positives. _Precision_ measures the ratio of false positives to all cases identified as positive (i.e., false positives included). The _Accuracy_ measures the ratio of the sum of true positives and true negatives to the total number of testing images. However, since different classes have a different number of images (i.e., class imbalance), the F1 score is considered a more reliable measure of the model classification performance [34]. The Matthews Correlation Coefficient (MCC), see Equation (6), is another metric of great importance. MCC and its multiclass generalization provide a more correct reflection of the classification performance in comparison to the accuracy and F1 score because the size imbalance of the different classes is taken under consideration [35]:  
\[Accuracy=\frac{T_{P}+T_{N}}{P+N} \tag{1}\]  
\[Precision=\frac{T_{P}}{T_{P}+F_{P}} \tag{2}\]  
\[Recall=\frac{T_{P}}{T_{P}+F_{N}} \tag{3}\]  
\[Specificity=\frac{T_{N}}{T_{N}+F_{P}} \tag{4}\]  
\[F1=2\times\frac{T_{P}}{2\times T_{P}+F_{P}+F_{N}} \tag{5}\]  
\[MCC=\frac{T_{P}\times T_{N}-F_{P}\times F_{N}}{\sqrt{(T_{P}+F_{P})(T_{P}+F_{N })(T_{N}+F_{P})(T_{N}+F_{N})}} \tag{6}\]