esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 3 Intelligent Handover Decision for V2X (IHD-V2X)
**Select an Action (\(a_{v}\))** At each state, Q-Learning must choose an action (\(a_{v}\)) [30]. 3. Stay: The UE remains connected to its current small cell. 4. Handover: The UE switches to another small cell. Equation (1) prescribes the set of actions for the UE at movement \(m\): \[a_{v}[m]=\{a_{0},a_{1},a_{2},\ \ldots,a_{Z-1}\},\ s.t\sum_{z=0}^{Z-1}a_{z=1}\] (1) where \(a_{0}\) represents stay at the current small cell. 5. \(a_{z}\)_for \(z\
eq 0\) represents the UE changing its connection to a better network. Determining a balance between exploration and exploitation is a challenging task in Q-Learning. The agent might not find better actions that ultimately result in larger rewards if it always exploits (chooses the most well-known action). However, if the agent never settles on the best course of action and keeps trying different things, it might never maximize its total benefits. The decision follows an \(\varepsilon\)-greedy policy to ensure that the system learns from experience while still testing the new handover policies. In this tactic, (1) the agent chooses a random action with probability \(\varepsilon\) (e.g., 0.1 or 10%) and tries different handover actions to discover new strategies. (2) The agent selects the action with the small cell with the highest Q-value by exploiting with probability \(1-\varepsilon\) (e.g., 0.9 or 90%). This strategy allows the agent to learn effectively by exploring new actions while leveraging the learned knowledge to optimize handovers.
# Warpage Behavior on Silicon Semiconductor Device:
## 3 Warpage Measurements and Physical Analysis
### Physical Analysis  
Differential Scanning Calorimetry (DSC) analysis is employed to detect the occurrence of thermally driven phenomenon during the heating process. A sample identical to the ones considered for warpage measurements is heated from room temperature to 185 \({}^{\circ}\)C with an heating rate of 10 \({}^{\circ}\)C/min. DSC analysis reported in Figure 7 highlights the starting of an exothermic reaction at 160 \({}^{\circ}\)C. It could be indicative of an irreversible modification of the copper layer that it will be further investigated by microscopy.  
Figure 6: Warpage profile at 150 \({}^{\circ}\)C during heating for “W/o plateau” along cutline.  
Some samples are cross-sectioned and analyzed by Focused Ion Beam (FIB) technique [25]. Devices under test are milled with high precision by FIB and without splashing the copper metal and creating artefact due to sample preparation. The FIB-prepared samples are analyzed with Scanning Electron Microscopy (SEM). It works in a similar way as FIB, adopting a electron beam to hit the target. Information about the surface topography and composition can be extracted by the interaction between beam electrons and the the atoms on the surface sample. Comparative pictures are shown in Figure 8 for samples without thermal treatment (a), heated at 150 \({}^{\circ}\)C (b) and at 250 \({}^{\circ}\)C. Microscopy analysis points out the increase of grain size between samples treated at 150 and at 250 \({}^{\circ}\)C. Grains of thick copper layer can be distinguished among them by the different gray tonality in SEM analysis. Investigation is focused on the FIB-opened section of ECD copper film; in fact, the original not-removed thick copper film is visible on the right and left edges of Figure 8b,c. According to sample description reported at the end of Section 2, SEM points out, respectively, the metal (gray) and the TEOS (black) layers underneath ECD copper. The observed micro-structure modification is coherent with DSC result (Figure 7) that detects an irreversible thermal process at around 160 \({}^{\circ}\)C. Moreover, this observation helps to explain the warpage behavior (Figure 5), in terms of residual warpage after thermal process and slope modification in warpage/temperature curve observed at around 150 \({}^{\circ}\)C. The experimentally observed grain growth due to temperature increase is supported by literature Chaudhari model for grain growth [26].  
Figure 7: DSC analysis on a thick copper samples which shows the starting of an exothermic process at about 160 \({}^{\circ}\)C.  
Figure 8: Scanning electron microscopic on FIB-prepared samples without thermal treatment (**a**), heated at 150 \({}^{\circ}\)C (**b**), and at 250 \({}^{\circ}\)C (**c**). Grains can be distinguished by grey tonality.
# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
## 5 Derivation and Validation of Shipborne GNSS-Based Sea Surface Heights
### Validation and Least-Squares Adjustment of the Results
With crosses are shown minimum and maximum residuals and their differences at intersections in (**a**,**b**), respectively. deviation. The most significant residual differences were detected between campaigns C2 and C3, resulting in standard deviation estimates of 4.4 cm and 5.0 cm. The compared intersections were primarily detected in the relatively shallow Vainameri (cf. Figure 2), which could imply errors in squat estimation, but Vainameri also has more than 300 islands and islets, which may have influenced the HDM-embedded DT modeling. Similarly to Figure 14a, Figure 15 suggests biases in the data (notice mean values). As described earlier, errors in tape measurements likely contributed significantly to these estimates. Hence, to increase the consistency of the residuals (and reduce the potential influence of tape measurement errors), the bias differences between campaigns were estimated by free network least-squares adjustment:  
\[\hat{X}=\left(A^{T}PA\right)^{-1}A^{T}PL, \tag{22}\]  
where vector \(L\) denotes the estimated residuals' mean differences between campaigns at intersections (i.e., the mean values shown in Figure 15) and design matrix \(A\) corresponding comparisons. Thus, a system of \([6!/(6-2)! ]-4=26\) linear equations was compiled (order matters, due to the second intersection search criterion, and for 4 comparisons, no intersections were detected). Weights \(P\) were assigned according to the number of detected intersections between campaigns (cf. Figure 15). The estimated bias corrections denoted by vector \(\hat{X}\) (consisting of 6 estimates, one for each campaign) were then subtracted from the filtered and corrected residuals. This approach resulted in least-squares adjusted residuals, denoted as \(r_{CM}^{F+C+A}(\varphi,\lambda,t)\) in the following. Figure 16 presents the comparisons between the initial (filtered and corrected) and least-squares adjusted residuals. Since free network adjustment was used, the average value of the six mean residual estimates (i.e., the average of the 6 values shown in Figure 16a) remained unchanged. It can be noticed that the mean residuals of campaigns agree better after adjustment (Figure 16a) and that the results are more consistent (histograms in Figure 16b combine residuals of all six campaigns). However, since some biases still  
Figure 15: Statistical properties of filtered and corrected residuals’ differences at intersections between campaigns. The black lines are mean values, and colored bars denote standard deviation estimates. Crosses show minimum and maximum differences. Note that color shows the validated campaign and the X-axis validation dataset. In square brackets are the total number of detected intersections for each comparison. exist (which cannot be detected due to lack of intersections), the combined data standard deviation in Figure 16b exceeds that of most individual campaigns in Figure 14a.
## 5 Experimental Evaluation and Analysis
### Experimental Results
#### 5.2.2 Impact of Buffer Size  
To evaluate the impact of buffer size on the protocols' performances, the buffer size varies from 200 to 1,000 packets. The performance of the two protocols with various buffer sizes is shown in Figure 11. In this section, we investigate the effects of buffer size on the average delivery ratio, mean delay, and network life. From Figure 11(a), we find that the data delivery ratio of the SFRRP is higher than for the Flooding method for all of the tested buffer sizes. Furthermore, the data delivery ratio of both protocols goes up considerably when the buffer size of the nodes increases. The reason is that much more buffer space leads to more data packets to be transmitted to the base station. Indeed, Flooding is more sensitive to the buffer size compared to the SFRRP routing protocol. In Figure 11(b), it can be seen that the average delivery delay of the SFRRP protocol is less than that of Flooding routing. In the Flooding protocol, a larger node buffer size leads to more packet exchange between nodes. Furthermore, it causes the probability of network congestion to increase. Nevertheless, SFRRP needs less buffer size, and all generated packets are transmitted to the base station. Thus, average delivery delays of packets are identical. Figure 11(c) illustrates that network life based on SFRRP is higher compared to the network life.  
Figure 10: Impact of data generation rate, (**a**) average delivery rate; (**b**) average delay; and (**c**) network life.  
based on the Flooding routing protocol. Note also that Flooding routing needs much more redundant packets to operate, reducing the network life.
## 4 Experimental Setup
### Ultrasonic Water Flow Imaging System  
In the experiments, a medical B-type ultrasound instrument, SIUI APOGEE 1200, is deployed. The ultrasound probe is fixed just touching the water surface to avoid affecting the flow field, as shown in Figure 6. Emission frequency of the probe is 5.0 MHz, the temporal resolution is 60 frames/s. In order to monitor the motion of all particles in the imaging plane, the probe is placed in the same direction as the sand-containing water flow. The ultrasound instrument is connected to a computer, which converts the echo signals collected by the probe into ultrasound images and transmits them to the ultrasound particle velocimetry software in the computer for analysis, processing, and display of flow field information.  
To calculate the actual particle flow velocity, it is necessary to convert the pixel units of the ultrasonic particle image into actual distance units. We place standard metal parts of different widths at different locations in the water, identify their pixel widths through image processing, and compare them with their standard physical widths to obtain a calibrated rate relationship. According to the rate relationship, the flow velocity field obtained by the ultrasonic image velocimetry algorithm can be calibrated to the actual flow velocity field.  
Figure 6: Ultrasonic particle image velocimetry system physical picture.  
The schematic diagram of the calibration experiment is shown in Figure 7, and the software interface of the calibration algorithm we wrote is shown in Figure 8.
## 3 Results
### Selection of Starter Culture and Fermentation Conditions
#### 3.1.2 Selection of Fermentation Conditions  
Under different inoculation volumes of _L. plantarum_ TUST-232, the R-B mixed juices varied in their pH values and viable cell counts (Figure 1A). At an inoculation volume less than 0.2%, the organic acids produced by _L. plantarum_ TUST-232 were positively correlated with the inoculation volume, in contrast to no significant difference among indicators related to fermentation performance at an inoculation volume above 0.2%. Under the three inoculation volumes (0.2%, 0.3%, and 0.4%), _L. plantarum_ TUST-232 in the R-B mixed juices all entered the stationary phase within 14 h. A higher inoculation volume can avoid the contamination of the R-B mixed juice caused by pathogenic microorganisms in the environment in the early stage of fermentation, while it can also produce more volatile compounds during the fermentation [34]. Therefore, the inoculation volume was selected as 0.3% for further experiments. The pH values and viable cell counts under different fermentation temperatures were compared to determine the fermentation temperature. When fermented at 37 \({}^{\circ}\)C, the R-B mixed juice showed the lowest pH value and highest viable cell count of 3.42 and 8.91 log CFU/mL, respectively (Figure 1B), in contrast to a higher pH value and lower viable cell count at a temperature lower or higher than 37 \({}^{\circ}\)C. Therefore, 37 \({}^{\circ}\)C was determined as the final fermentation temperature for further experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Strain**} & \multicolumn{2}{c}{**24 h**} & \multicolumn{2}{c}{**48 h**} \\ \cline{2-5}  & **pH** & **Viable Cell Count** & **pH** & **Viable Cell Count** \\  & & **(Log CFU/mL)** & **(Log CFU/mL)** \\ \hline TUST-BS & 4.17 \(\pm\) 0.08 b  & 3.41 \(\pm\) 0.11 c  & 3.51 \(\pm\) 0.05 b  & 7.83 \(\pm\) 0.11 c  \\ TUST-232 & 3.43 \(\pm\) 0.19 a  & 8.90 \(\pm\) 0.10 a  & 3.29 \(\pm\) 0.06 a  & 8.69 \(\pm\) 0.19 a  \\ TUST-392 & 4.7 \(\pm\) 0.16 c  & 3.10 \(\pm\) 0.29 c  & 3.79 \(\pm\) 0.12 c  & 4.74 \(\pm\) 0.08 d  \\ TUST-354 & 3.99 \(\pm\) 0.12 b  & 4.64 \(\pm\) 0.14 b  & 3.31 \(\pm\) 0.11 a  & 3.31 \(\pm\) 0.11 a  & 8.42 \(\pm\) 0.08 b  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fermentation performance of different strains used in the R-B mixed juice.  
In Figure 1C, the pH values of the fermented R-B mixed juice were shown to decrease gradually with the prolongation of fermentation time. After 14 h, the pH values began to decline slowly, and the growth of _L. plantarum_ TUST-232 began to slow down, entering the stationary phase. Therefore, 14 h was defined as the final fermentation time.
## 3 The Proposed Method
### The LWAS Framework
#### 3.1.1 Differentiable Architecture Search  
Considering the efficiency superiority, LWAS follows the differentiable NAS methods, where the discrete search space is relaxed into continuous with a weighted summation as shown in  
\[o(x)=\sum_{k=1}^{|\mathcal{O}|}c_{k}o_{k}(x) \tag{1}\]  
where \(c_{k}\in(0,1)\) is the weight of the \(i\)-th operation \(o_{k}(\cdot)\) in the operation set O and \(o_{k}(\cdot)\) is the mixed operation.  
The weight \(c_{k}\) is generated by one re-parameterization trick \(c_{k}=g(\mathbf{\alpha}),\mathbf{\alpha}\in\mathbb{R}^{|\mathcal{O}|}\) and \(\mathbf{\alpha_{k}}\) is the corresponding architecture parameter for \(c_{k}\). And in this paper, LWAS employ the Gumbel-Softmax [24] re-parameterization trick:  
\[c_{k}=g(\mathbf{\alpha})=\frac{\exp((log\mathbf{\alpha_{k}}+\mathbf{G_{k}})/\lambda)}{ \sum_{k^{\prime}=1}^{|\mathcal{O}|}\exp((log\mathbf{\alpha_{k^{\prime}}}+\mathbf{G_{k^ {\prime}}})/\lambda)} \tag{2}\]  
where \(\mathbf{G_{k^{\prime}}}=-log(-log(U_{i}))\) is the Gumbel random variant, \(U_{i}\) is a uniform random variant, and \(\lambda\) is the temperature of Softmax. After relaxing the search space, the mixed output of candidate architectures and the loss can be obtained, on top of which the gradient descent can be applied to optimize the architecture parameter since the loss is differentiable w.r.t. to \(\mathbf{\alpha}\). And in this way, light-weight architectures in expectation will be searched by learning \(\mathbf{\alpha}\).
## 3 Methodology
### SFWC Design  
The proposed SFWC (Figure 4) was inspired by the basic structure of an SFWC, whose main element is the topic classifier. The topic classifier determines if the content from a web page is related or not to the target topic or domain. Traditional approaches integrate a domain ontology in the topic classifier step. The domain ontology provides a predefined knowledge about the domain or topic. It describes the relationship between domain elements and could define some rules and restrictions. The KRS is an alternative to the use of domain ontologies, providing a simple schema to represent a topic specific content.  
Figure 3: The KRS construction steps.  
The proposed SFWC takes a seed web page as input to start the process and a queue of links to collect the URLs related with the target topic. In general, the proposed SFWC is divided in the following steps:  
* S0. KRS construction: It represents a previous step in the SFWC process.
* S1. Web page retrieval: It downloads the web page to be locally analyzed.
* S2. HTML content extraction: It extracts the web page content.
* S3. Topic classifier: It processes the web page content and analyzed it to determine if it is similar or not with the KRS.
* S4. URLs extraction: It extracts the URLs (enclosed in <a> tags) from the original web page content.
* S5.Crawler frontier: It collects and stores the extracted URLs in a queue of links. The crawler frontier sends the next URL to the web page retrieval step.  
The first and second steps (S1 and S2) are focused on getting the content from a seed web page. One of the core components of the proposed SFWC is the topic classifier, which is constructed in the third step. Like the domain ontology-based approaches, the topic classifier performs a content analysis of web pages to determine their similarity with the KRS. The topic classifier begins with the text preprocessing and the NNE tasks. These tasks have the same purpose as in the KRS construction. The IDF value for each enriched noun is computed from the KRS using SPARQL queries. SPARQL is the SW standard to query RDF triples from a KB or an RDF graph. In this work, a query retrieves the number of documents containing the noun extracted from the web page content. The retrieved results must match the noun anchor text and they must be described by the same URL.  
The similarity measure, described in the next section, calculates the arithmetic mean with respect to the extracted IDF from each enriched noun. In this work, it is established that a web page content is similar to the KRS if the arithmetic mean is within a threshold.  
The last two steps (S4 and S5) extract the corresponding URLs and store them in a queue links. The process from S1 to S5 is repeated until the queue is empty or the process reaches a predefined number of iterations.
Few studies have investigated the functional role of specific volatile compounds in plant-pollinator-predator-microorganism interactions. In this study, we focused on a semiochemical of honey bees, 2-heptanone (heptan-2-one or methyl n-amyl ketone). This methylketone secreted by the mandibular glands of worker honey bees has a debated role; it is thought to signal aversive situations by inducing defensive responses [10], to act as a chemical marker on visited flowers [11] or even as an adaptative modulator of learning and memory [12]. It has been recently discovered that honey bees use their mandibles to bite and remove _Varroa_ mites from the bodies of their nest mates and that the released 2-heptanone acts as a local anesthetic, causing paralysis and death of this parasite [13]. The use of 2-heptanone as a mite repellent has been proposed, taking into consideration that this compound would not be toxic towards bees or contaminate hive products, since it already exists in hives [14; 15]. Methylketones can be synthetized by bacteria, fungi, plants, insects and mammals and have been found in numerous natural environments, having various biological roles [16]. In plants, for example, these compounds are highly effective for protection against pests [17]. 2-heptanone produced by lactic acid bacteria (i.e., _Lactobacillus casei_ and _Lactobacillus pancasei_) is known to contribute to aroma development in many dairy products [18]. Furthermore, this volatile compound was found to be synthetized by species of _Bacillus_ during host-pathogen interactions [16] and by _Bacillus amyloidatefaciens_ with antifungal properties against _Fusarium oxysporum_[19]. Even though this volatile compound has various bioactive roles of potential practical interest, its biosynthetic pathway has been scarcely investigated. In 2005, two methylketone synthetases from wild tomatoes (_Lycopersicon hirsutum_) were reported to use intermediates of the fatty acids biosynthetic pathway to synthesize methylketones [17]. In bacteria, a methylketone synthase involved in the production of 2-heptanone was identified in _Bacillus nematocida_[16]. The mediation of extracellular bacterial metabolites in beneficial interactions with honey bees has been reported [20; 21], but little is known on VOCs produced by bacterial populations inhabiting hives. The microbiota associated with honey bees has lately been the subject of an increasing number of studies, and the most frequently reported taxa include Lactobacillusaceae, Bacillaceae, Acetobacteraceae, Bifidobacteriumiaceae, _Gilliamella_ and _Fructobacillus_[22; 23; 24; 25; 26; 27; 28]. Hence, the aims of this study were to (i) establish and quantify the production of 2-heptanone in the culture filtrate by bacterial species isolated from honey bees and (ii) investigate the potential of honey bee-associated bacteria to produce this natural compound with a protective role against hive pathogens by using biocomputational tools.
###### Abstract  
Satellite image-based farmland classification plays an essential role in agricultural monitoring. However, typical tiling-based classification approaches, which extract patches at fixed offsets within each image during training, often suffer from structural issues such as patch duplication, limiting training diversity. Additionally, farmland classification frequently exhibits class imbalance due to uneven cultivation areas, resulting in biased training toward majority classes and poorer performance on minority classes. To overcome these issues, we propose Class-Balanced Random Patch Training, which combines Random Patch Extraction (RPE) and Class-Balanced Sampling (CBS). This method improves patch-level diversity and ensures balanced class representation during training. We evaluated our method on the FarmMap dataset, using a validation set from the same region and year as the training data, and a test set from a different year and region to simulate domain shifts. Our approach improved the F1 scores of minority classes and overall performance. Furthermore, our analysis across varying levels of class difficulty showed that the method consistently outperformed other configurations, regardless of minority-class difficulty. These results demonstrate that the proposed method offers a practical and generalizable solution for addressing class imbalance in tiling-based remote sensing classification, particularly under real-world conditions with spatial and temporal variability.  
remote sensing; farmland classification; tiling-based classification; class imbalance; random patch extraction; class-balanced sampling Article