esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Sidr (_Ziziphus_ spp.) Honey from Different Geographical Origins
## 4 Discussion  
This research revealed that monofloral honey obtained from the _Ziziphus_ spp. nectar of different geographical origins contained more than 70% of a specific pollen. The most important physicochemical parameter of honey is the water content. Low water content increases honey shelf life while high levels promote its fermentation during storage. The water content of the investigated honey samples was within the accepted range which, in general, should not exceed 20% [12; 35]. During honey production by bees, the water content is affected by the relative humidity and temperature. Water content results for honey samples of different geographical origins have been documented previously for their water content [34; 36], being 18.32 \(\pm\) 0.67 g/100 g for Egyptian, 16.28 \(\pm\) 0.22 g/100 g for Yemeni, 15.64 \(\pm\) 0.30 g/100 g for Saudi and 14.73 \(\pm\) 0.3 g/100 g for Kashmiri honey. The sugar content of honey is widely used to assess the authenticity and the overall quality of honey [37]. Honey adulteration by mixing the honey with other cheaper sugar syrups is a frequent problem in the worldwide market [38]. For this reason, the sugar analysis of honey is an indicator of whether the honeybees were fed naturally with flower nectar or were fed with sugar solution. The use of artificial feeding is evident when the glucose content of honey is much higher than its fructose content [39]. The results of this investigation revealed that the sum of glucose and fructose (content of reducing sugars in honey) was within the accepted range and is consistent with the standardization and authenticity of honey as observed by [PERSON] et al., (2018) [40]. It has been demonstrated that the most dominant sugar in honey is fructose [39] and [PERSON] et al., (2021), in their study of the winter feeding of honeybees, observed the fructose to glucose ratio (F/G) was higher than 1.00 indicating the natural feeding of honeybees [41]. In all the honey samples in this study, sucrose content did not exceed 5%, which is the accepted level for honey to be considered authentic as observed by [PERSON] et al., (2021) [42]. A higher content of sucrose may indicate the artificial feeding of bees with some types of sugar syrup or adulteration of the honey [38]. Two important parameters used to prove the freshness of honey are hydroxymethylfurural content and diastase activity [43; 44]. There are many factors affecting diastase activity, including the physiological period of the colony, age of the bees, the quantity of nectar and its sugar content as well as the nectar collection period [45]. The WHO _Codex Alimentarius_, the European Union and the Gulf Technical Regulation on honey (GSO 147:2008-Standards Store-GCC Standardization Organization) [46] recommend that the maximum level for HMF content in honey does not
###### Abstract
We study required received optical power (ROP) for a certain bit error rate (BER) for the different effective number of bits (ENOB) and extinction ratio (ER) at the transmitter. This allows estimating what implementation penalty to expect when designing high-speed transmitters. Besides, we also show achievable transmission reach in C-band for 100 Gbaud OOK, PAM4, and eight-level PAM (PAM8) signals over standard single-mode fiber with a simple decision feedback equalizer (DFE). This paves the way for cost-effective short-reach optical interconnects with high-speed multilevel modulation formats. The remaining parts of the paper are organized as follows. In Section 2, the experiment setup is detailed, and corresponding results and discussion are presented. In Section 3, we show the simulation setup and results along with a discussion. The conclusions are drawn in Section 4.
## 1 Introduction
### State of the Art
Metrics such as Daylight Autonomy (DA) [54] and Circadian Stimulus Autonomy (CSA) [55; 56] have been employed to evaluate lighting adequacy, taking into account architectural factors like spatial layout, window placement, and local climatic conditions. Despite advancements in understanding circadian lighting, gaps remain in translating research into practical nursery applications. While studies have explored circadian lighting in hospitals [55; 57], educational facilities [58], and workplaces [59], research focusing specifically on nurseries is comparatively sparse. Future research should focus on the integration of dynamic circadian-friendly lighting systems with architectural design principles [60] to create holistic environments that support both the health and developmental needs of children and the functional requirements of caregivers. By bridging advancements in air-quality monitoring, ventilation strategies, and circadian lighting design, childcare facilities can evolve into environments that not only minimize health risks but actively promote the physical and cognitive development of children. Long-term studies and the refinement of simulation tools will be instrumental in achieving this goal, enabling evidence-based interventions that optimize indoor environmental quality in nurseries.
## 4 Results
### Characteristic Analysis  
Figures 6 and 7 showcase the units that have benefitted from the VESS operation. Figure 6 displays the percentage of the participants that are using the VESS service among all SEC units. As the implemented VESS capacity increases, the participation ratio also increases. This is because the VESS service cost is reduced with increase in the implemented VESS capacity due to the economics of scale. In the case of VESS operation by **P0**, where the economics of scale is the main driver of the benefit, this trend appears more clearly. When the proposed VESS operation of **P1** is applied, most units participate in the VESS service because participants achieve higher savings on the demand bill due to the dynamic operation, as shown in Figure 4.  
Figure 7 shows the Pearson's linear correlation coefficient (PLCC) between the characteristics of the participants and allocated VESS capacity to the participants [38]. The results indicate that the allocated VESS capacity, which is the usage of VESS, has a higher correlation with the peak demand in Figure 7b than with the electricity bill in Figure 7a. This means that the demand bill according to the peak demand acts as a dominant factor in the decision to use the VESS, as shown in the results in Figure 4, as well. Moreover, as the implemented VESS capacity increases, the PLCC of the results by applying **P0** also increases, but it is lower than that obtained by the proposed VESS operation. This means that the proposed VESS operation using **P1** better reflects the characteristics of the participants than the VESS operation using **P0**.  
Figure 4: Savings on electricity bills of the participants when the implemented VESS capacity is 150 kWh.  
Figure 5: Peak demand reduction ratio with varying implemented VESS capacity.
# ODIN112-AI-Assisted Emergency Services in Romania
## 5 Discussion
### Speech Emotion Recognition  
Our results on the EMO-IIT dataset surpass the current state of the art, as shown in Table 9. To our knowledge, there are no other results for GSM-coded emotional speech recognition in Romanian. We chose to compute the weighted accuracy in order to compensate for any imbalance in the distribution of emotion classes. Our results for the EMO-DB dataset from Table 10 are similar to other current architectures. To our knowledge, the only paper with experiments on the effect of bandwidth reduction on SER is [PERSON] et al. [30], where a pretrained AlexNet neural network is trained on log Mel spectrograms extracted from the EMO-DB corpus. Our model follows a similar approach to [PERSON] et al. [30], though employing a more complex VGG16 type of neural network. The results from other works employing VGG16 range from an accuracy of 71% [44] to 92% [29]. The best results on the EMO-DB corpus (95,89% accuracy) [45] were achieved using a combination of convolutional and recurrent neural networks; our result is not far behind but employs a simpler neural network model.  
The train and test datasets contain high-quality entries with little noise. This is usually not the case in emergency calls. Therefore, we will consider adding noise to the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Set** & **Train Set** & **SWARA** & **RSC** & **Echo** & **All** \\
**[\%WER]** & **[\%WER]** & **[\%WER]** & **[\%WER]** & **[\%WER]** \\ \hline SWARA & 3.28\% & 6.01\% & 4.42\% & 2.99\% \\ RSC & 13.30\% & 4.36\% & 4.40\% & 3.32\% \\ Echo & 37.21\% & 11.90\% & 4.97\% & 5.29\% \\ All & 35.38\% & 13.89\% & 4.82\% & 5.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Word error rates when training and testing various Romanian models. All four models were trained using only the “train” subset, whereas the evaluation was performed exclusively on the “test” subsets.  
test recordings in future work. However, this is not a simple task. For this, we have to determine the minimum signal-to-noise ratio (SNR) for which a human still perceives emotions from speech and build noisy datasets with a range of SNRs around that value, followed by thorough testing of the trained models with the noisy datasets.
## 3 Test Results
### Variation Patterns of Various Factors on SRNR Performance  
As indicated in the previous analysis, various factors have varying impacts on the surface texture characteristics of EAC, which directly correlate with the SRNR performance of EAC surfaces. Therefore, it is necessary to analyze the influence of each factor on the SRNR performance of EAC surfaces based on their effects on the surface texture characteristics. Figure 9 illustrates the variation patterns of the average pendulum value and average noise value of EAC surfaces under different factor levels.
# Performance of Graph and Relational Databases in Complex Queries
## 5 Test Queries
### Task 5: Recursive Queries for Invoice Chain  
The recursive queries find all the invoices related to a given invoice id. The task is useful to test the recursive query capabilities of the databases. In SQL, Common Table Expressions are used to make the query. In Cypher, there is a way to optimize a recursive query by negating irrelevant relationships. The optimized query does not return exactly the same result as the basic query. While the basic query returns a set of individual nodes, the optimized query returns a list structure containing nodes. However, it still returns similar  
Figure 5: Queries for Task 3 (Invoice price).  
results and as such it is a relevant query. Figure 7 presents the queries for finding invoices recursively related for a given invoice.  
SQL (Task 4)  
SELECT q1.customerId, q2.inviceId, SUM(q3.price) AS invoicePrice FROM (SELECT customer.id AS customerId, invoice.id AS invoiceId FROM invoice INNER JOIN customer ON invoice.customerId=customer.id) AS q1 INNER JOIN  JOIN ```  
SELECT workflow.inviceId, workflow.workId  
COM workinvoice.inviceId = invoice.id) AS q2 USING (invoiceId)  
INNER JOIN (  
SELECT workflows.workId AS workId, SUM((worktype.price * workhours.hours * workhours.discount) + (item.purchaseprice * useditem.amount * useditem.amount * useditem.amount) AS price FROM workflows INNER JOIN workinvoice ON workinvoice.workId = useditem.workId INNER JOIN item ON useditem.itemId = item.id GROUP BY workinvoice.workId) AS q3 USING (workId)  
WHERE q1.customerId=0 GROUP BY q2.inviceId  
Cypher (Task 4)  
MATCH (c:customerId=-[PAYS]->(inv:inv:noice) WHERE c.customerId=0 WITH c, inv)  
OPTIONAL MATCH (inv)-[WORK INVOICE]->(inv:noork)  
WITH c, inv, w WITH c, inv, w  
SUM((h.hours*h.discount*vt.price)+(u.amount*u.discount*i.purchaseprice))  
AS workPrice RETURN c, inv, SUM(workPrice) as invoicePrice  
Cypher with CALL (Task 4)  
MATCH (inv:invice) WHERE inv.customerId=0 CALL (WITH inv) MATCH (c:customer)-[PAYS]->(inv) RETURN c)  
Call (WITH c, inv, MATCH (inv)-[WORK INVOICE]->(w:work) RETURN v)  
CALL (WITH w MATCH (w:worktype)-[h:WORKINGUS]->(w)-[u:USED ITEM]->(i:item) RETURN SUM(h.hours*h.discount*vt.price)+(u.amount*u.discount*i.purchaseprice))  
AS workPrice)  
RETURN c, inv, SUM(workPrice) AS invoicePrice  
Sql (Task 5)  
WITH RECURSIVE sequential invoices AS (SELECT id, customerId, state, duedate, previousinvoice FROM invoice WHERE id=0000 UNION ALL SELECT i.id, i.customerId, i.state, i.duedate, i.previousinvoice FROM invoice AS i INNER JOIN sequential invoices AS j ON i.previousinvoice = j.id WHERE i.previousinvoice <> i.id) SELECT * FROM sequential invoices  
Cypher (Task 5)  
MATCH (i:invoice { invoiceId:10000 })-[p:PREVIOUS INVOICE *0..]->(j:invoice) RETURN *  
Cypher optimized (Task 5)  
MATCH inv=(i:invoice { invoiceId:10000})-[p:PREVIOUS INVOICE *0..]->(j:invoice) WHERE NOT (j)-[p:PREVIOUS INVOICE]->() RETURN nodes(inv)  
Figure 6: Queries for Task 4 (Invoice prices for a given customer).
# Learning an Efficient Convolution Neural Network for Pansharpening
## 4 Experiment  
### Experimental Settings  
#### 4.1.1 Datasets  
Our experiments were implemented on datasets from WorldView-2 and IKONOS respectively. Each of the datasets is sufficient to prevent overfitting, and some of them are available online ([[http://www.digitalglobe.com/resources/product-samples](http://www.digitalglobe.com/resources/product-samples)]([http://www.digitalglobe.com/resources/product-samples](http://www.digitalglobe.com/resources/product-samples)); [[http://glcf.umd.edu/data/ikonos/](http://glcf.umd.edu/data/ikonos/)]([http://glcf.umd.edu/data/ikonos/](http://glcf.umd.edu/data/ikonos/))). Given the absence of HRMS at the original scale, the CNN model cannot be trained directly; as a conventional method, we followed [PERSON]'s protocol [27] for network training and experiment simulation. Specifically, we smoothed the MS and PAN with an MTF kernel [8; 28] to match the sensor properties, and down-sample the smoothed component by a factor of 4. Subsequently, the degraded MS was up-sampled with bicubic interpolation to obtain HRMS; accordingly, the original MS image was regarded as HRMS. Figure 5 provides a pictorial workflow of training dataset generated based on [PERSON]'s protocol.  
Figure 4: (**a**) Comparison of loss error on the validation dataset. (**b**) Performance-to-epoch curve of the proposed model and corresponding residual-free model.
## 7 Nanodiagnostics and Imaging
### Compressive Self Referencing Interferometry and Metrology  
[PERSON], [PERSON] and [PERSON]  
University of South Florida, Tampa, FL 33620, USADirect measurement techniques are interferometry techniques for acquiring images of complex fields. These types of measurements can be very useful in fields such as metrology as the phase of reflected fields can be used to compute distances and surface profiles. Single pixel cameras are cameras which use a single pixel and a series of masks to obtain an image from multiple measurements. This type of imaging can be very useful when the cost of making an array of detectors is high or impossible. Compressive sensing techniques have also been investigated for single pixel cameras to reduce the number of measurements required to obtain a full image. Previous works have combined these ideas to form compressive direct measurement, which is a direct measurement imaging technique utilizing a single polarization detector in what is known as a weak-strong measurement. This can be useful for performing profile measurements of fast moving but time periodic surface disturbances such as surface acoustic waves as the detector can directly obtain a waveform corresponding to each compressive sensing mask which contains information about the surface profile at different times throughout a period. In this work, we expand on this idea by proposing a new formulation which allows for stronger (strong-strong) measurements and reduces the noise in the resulting images. We also propose a new compressive sensing prior which can improve recovered image quality in certain situations. Experimental results with our methods conclude that strong measurements greatly improve the SNR of the results compared to weak measurements, and our proposed Fourier based prior was able to obtain higher fidelity reconstructions compared to previously used priors in certain situations.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Node Centric
#### 3.1.2 Time-Driven  
Time detection is a central component in numerous WSNs applications, especially those with a content aspect. In time-driven protocols, sensed data are sent periodically, and the reporting period may be preconfigured or set during operation, depending on the requirements of the application. Technically, time-driven communication is easy to implement. In addition to that, sending nodes to sleep between transmissions help to conserve energy and consequently extending the network lifespan, but time-driven techniques need various synchronization and coordination mechanisms. Among the representative protocols of this class, we find:* **Well-Balanced-Threshold sensitive Energy Efficient sensor Network _protocol (WB-TEEN)_** This protocol is designed as a time-driven protocol with a distributed clustering model [40]. In fact, it is an improved version of the TEEN protocol [41]. Authors in [42] found that this protocol enhances energy conservation, but load balancing between nodes is not well handled.  
Table 2 establishes a comparison between \"application type\"-based routing protocols [43].
## 5 Strength Deterioration Mechanism Analysis and Discussion
It can be seen that under reciprocating shear, the block particles distributed on the shear surface of the soil are broken, the number of fine particles significantly increases, the roundness of the soil particles increases, and the interlocking friction between the soil particles decreases significantly due to the decrease in particle size. In addition, after repeated shear deformation, the compactness of the shear surface is weakened, and the frictional impedance effect of the soil is weakened by the increasing number of fine particles directionally arranged on the shear surface. Although the contact mode and directional arrangement of the soil particles will continue to change during the process of reciprocating shear deformation, the interlocking and friction impedance effects between the soil particles will deteriorate. However, with the soil subjected to repeated reciprocating shear, the shear surface of the soil gradually penetrates and is in a loose state, and the ability of the soil to resist shear failure tends to be stable. Therefore, the complex physical-mechanical behavior response of argillaceous siltone rockfill materials during reciprocating shear causes them to exhibit significant strength deterioration characteristics. Both forward and reverse shear strengths decrease with the number of reciprocating shears. The development and evolution of the soil sample's hysteresis loop area, damping ratio, and shear stiffness are similar to those of its shear strength, all exhibiting different degrees of deterioration with the number of reciprocating shears.
## 2 Computational Methods  
This paper employed a bidirectional CFD-CSD coupled method to solve the deformation and stress distribution of blades. The Navier-Stokes equations were utilized to compute the flow field within the transonic axial flow compressor blades. Once the flow field calculation converged, the pressure field distribution across the compressor blades was obtained. The conservative interpolation method was applied to map the pressure field onto the structural mesh nodes of the blade. Subsequently, the structural static equilibrium equations of the blade were solved to obtain the structural deformation. If the structural deformation did not converge, the deformation on the structural nodes was transferred back to the flow field mesh nodes using the conservative interpolation technique. Subsequently, the Motion Mesh method was employed to adjust the mesh positions, ensuring that the aspect ratio and skewness of the mesh were maintained at approximately 1 and 0.8, respectively. The adjusted mesh was then used to recalculate the flow field. After the flow field converged, the pressure field was again transferred to the structural nodes through the coupling interface, and the structural deformation was recalculated. This iterative process was repeated until the structural deformation of the compressor blade converged. The computational flowchart is shown in Figure 1.  
Figure 1: Process diagram of the bidirectional CFD-CSD coupled method.
# Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation
## 4 Experiments
### Results  
In this section, we report the accuracy of the proposed method (abbreviated as 'FGLSF' for Fuzzy Graph Learning regularized Sparse Filtering) and other state-of-the-art works, the results are shown on Table 2. From experimental results, we have the following observations:  
* FGLSF vs. NN. According to the results, FGLSF is significantly better than NN. NN cannot handle the domain discrepancy, thus results in unsatisfying performance. On the other hand, it indicates that our method is able to learn transferable representations.
* FGLSF vs. CORAL, JDA. FGLSF is superior to CORAL and JDA. This two methods are classical distribution matching works, but they have limited considerations on the discrimination of learned representations.
* FGLSF vs. MASF. MASF is another framework based on sparse filtering, which adopts a modified \(\mathcal{A}\) distance for domain alignment. Compared to our method, it cannot deal with the problem of conditional distribution matching.
* FGLSF vs. CAPLS, SPL. Objectively speaking, our method FGLSF has comparable performance when compared state-of-the-art selective hard labeling method, only a 0.3% improvement is gained. It shows that the proposed fuzzy graph regularization is also valid for alleviating the negative effects caused by wrong labeling.  
Another interesting finding is that the proposed method works well in ImageCLEF, achieves best performance on all subtasks, but when it refers to Office-Caltech10, it seems that SPL works better. For these two datasets, the biggest difference is the class weights, ImageCLEF is an absolutely class-balanced dataset, which means that each object has the same number of pictures, but Office-Caltech10 has class weight bias, which makes it a more complicated problem. When we infer the pseudo labels, the object who has more examples is expected to be dominant, and vice versa. For the proposed method, we only use source labels for obtaining pseudo labels, so it may easily influenced by the class weight bias. SPL considers both source labels and manifold structures, thus achieves better performance on Office-Caltech10.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**No.** & **Task** & **NN** & **JDA** & **CORAL** & **CAPLS** & **MASF** & **SPL** & **JAN** & **DAN** & **FGLSF** \\ \hline
1 & C\(\rightarrow\) A & 85.69 & 89.77 & 92.00 & 90.90 & 90.81 & 92.80 & 91.90 & 92.00 & **93.32** \\
2 & C\(\rightarrow\) W & 66.10