esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The Whale Optimization Algorithm was improved and combined with support vector regression to construct an IWOA-SVR seepage parameters inverse analysis model. An inverse analysis of the hydraulic conductivity of various media was carried out based on seepage monitoring data. The hydraulic conductivity values obtained from the inversion were utilized for three-dimensional seepage calculations to obtain the contour map of the groundwater level and hydraulic head, which verified the feasibility of the model. Based on the results of the seepage analysis, the working conditions of the dam can be understood. Subsequently, it can provide a reference for the reinforcement of the weak parts of the seepage control system. The main conclusions are as follows.  
1. The performance of IWOA is improved compared to WOA due to three improvement strategies. The good point set method replaces the random initial population method to expand the search range. The cosine-based nonlinear convergence factor strategy balances search range and search precision. The Levy flight and Quasi-oppositional learning strategy are employed to overcome the local optimum of the algorithm. These three improvement strategies balance the algorithm's global optimization and local development performance.
2. A new hydraulic conductivity inversion method was proposed by combining IWOA with SVR. Numerical experiments proved that the improvement for IWOA was effective. The IWOA was utilized to search for optimal parameters, while SVR was employed to establish the mapping relationship between hydraulic conductivity and hydraulic head at monitoring points. The IWOA-SVR inversion model was constructed.
3. The hydraulic conductivity values of target materials were obtained by the IWOA-SVR inversion model. The hydraulic conductivity values were substituted into the finite element model to obtain the hydraulic head at each monitoring point. The maximum absolute and relative errors of the hydraulic heads were \(-2.13\) m and \(-1.94\%\). The results show that this inversion model is reliable and can be applied in future engineering calculations.  
Conceptualization, H.L. and L.X.; methodology, Z.S.; software, Y.W.; validation, Y.S. ([PERSON]) and H.L.; formal analysis, Y.W.; investigation, J.T. and Y.S. ([PERSON]); resources, Z.S.; data curation, Y.S. ([PERSON]) and Y.W.; writing--original draft preparation, H.L.; writing--review and editing, J.T. and Y.S. ([PERSON]); visualization, H.L. and [PERSON]. ([PERSON]); supervision, Z.S.; project administration, Z.S.; funding acquisition, Z.S. and L.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key R&D Program of China (Grant No. 2019 YFC1510802) and the National Natural Science Foundation of China (Grant No. 52179130).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Sensor-Based Solid Waste Handling Systems: A Survey
## 3 RFID Based Solid Waste Management Systems  
RFID technology is an automated identifying system that is primarily based on RF microwave transmission and is seen as a natural extension of previous identification systems such as bar codes, magnetic cards, and smart cards [17]. Despite the fact that the RFID system's theory of operation was conceived at the beginning of the radio frequency communications era, its use is steadily increasing today [18]. RFID is a technology that allows users to read information from tags and communicate it to an information-processing system over a radio frequency range without the need for a physical connection. An antenna, a tag, and a reader are the three parts of an RFID system. The antenna transmits a signal that activates the transponder using radiofrequency waves. When the tag is engaged, it sends data back to the antenna. The transmission ranges of low-frequency RFID systems are short, while those of high-frequency RFID systems are longer. Contactless payments [19], automobile [20], library [21], retail supply chain management [22], pharmaceutical [23], industry [24], ticketing [25], and solid waste management systems [26] are only a few of the applications where RFID technology is used.  
The main objectives of RFID based solid waste management systems are as follows:  
* To obtain information on the waste collection area and the respective collecting time;
* To develop a system for monitoring and tracking of waste collection trucks and waste bins;
* To obtain information on the quantity of solid waste inside the bin and the surroundings.
## 3 Materials and Methods
### Monitoring of the Transport of Suspended Sediment in the River Drava in Slovenia
#### 3.1.1. Suspended-Sediment Samplers  
Suspended-sediment samplers are used to collect representative water samples in the stream. These samples are then analysed in a laboratory setting to determine the suspended-sediment concentration levels. To ensure the most reliable sampling, some conditions should be met, such as the following: water should enter the nozzle isokinetically (where the velocity and direction of water entering the sampler are the same as in the environment); stream flow patterns should be disturbed as little as possible; standard bottle sizes should be used (e.g., 1 pint or 1 L); and the equipment should be easy to operate and maintain [4; 31; 32].  
There are two different types of samplers for suspended-sediment collection, namely depth-integrated and point-integrated samplers. The depth-integrated sampler is designed to obtain water samples from a vertical column of water by lowering it to the bottom of a stream and, afterwards, raising it back to the surface. The point-integrated sampler, on the other hand, collects the water at a specific point in the stream, which is made possible by the use of a valve that can be activated electrically. Figure 2 shows a point-integrated sampler US P-72 with appropriate equipment, which can be used for the isokinetic sampling of water at any point in the stream and was used for sampling at some of the monitoring sites investigated in this study.  
The sampler is made of aluminium, has a length of 0.7 m, and weighs 18 kg. The hydrodynamic shape of the sampler allows it to rotate in the direction of the flow and be immersed at any depth. The diameter of the nozzle is 5 mm. Inside the housing there is a space for a collection bottle with a capacity of 0.95 L. On the side, there is a hole through which air can escape from the bottle while it fills with water. The sampler can be used in streams with a velocity between 0.5 and 2.0 m/s and a depth of 20 m.
# Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz
## 1 Introduction
### Related Work
The measurement campaign carried out in a dense urban scenario [30] shows that consistent coverage can be achieved with a cell radius of 200 m. Furthermore, coverage and capacity analysis in [31] for cellular communication at 28 GHz shows huge potential in terms of coverage and capacity. Additionally, the 28 GHz band, with spectrum allocation of more than 1 GHz of bandwidth, is currently available for use [30]. Therefore, in this paper, communication characteristics are analyzed in 28 GHz for V2V communication.
## 6 Conclusions  
In this study, a true-color camera loaded on a UAV was used to collect images of cotton plants. The collected images were processed to obtain the stitched image of the study area, and the SVM and maximum likelihood classification method were used to discriminate the cotton plants from the stitched image. The SVM performed better than the maximum likelihood classification method, with an accuracy of 96.65% compared to 87.85% of the maximum likelihood classification method. The method used in the study to count the overlapping cotton plants presented an increased detection accuracy of 6.3% over the method that did not consider overlapping. The accuracy of the proposed method is 91.13%, and the mean germination rates for manual interpretation, non split and split were 57.7%, 46.3%, and 52.6%, respectively. The resolution of the flight images was opposite to the cost, and the appropriate resolution for monitoring the budding rate of the cotton should not be greater than 12 mm/pixel. While the use of plastic film in the field allowed the monitored budding rate to be unaffected by weeds, it also caused a decrease in the apparent monitored cotton budding rate. However, the apparent budding rate is more useful in practice for the grower to replant and manage the cotton field.  
Data curation, L.X., L.C., G.X., Y.W. and T.Y.; Investigation, T.Y.; Methodology, L.X., R.Z. and Y.H.; Validation, G.X. and Y.W.; Writing--Original draft, L.X. and L.C.; Writing--Review & editing, L.X., R.Z., L.C. and Y.H.  
This research was funded in part by the National Key R&D Program of China (2016 YFD0200701-2). The National Natural Science Foundation of China (31601228), [PERSON]'s Beijing Nova Program (Z181100006218029), and BAAFS Innovation Ability Construction Program 2018 (KJCX20180424). We thank [PERSON] and her team from Xinjiang Agricultural University for supporting the experiment.
## 4 Standards for Evaluating the Bearing Capacity of Frozen Sandy Soils Using the CPT  
The relationship between soil \(q_{\text{c}}\) and CBR values can be significantly influenced by climatic conditions, engineering classifications of soil, and regional factors. For geotechnical surveys in different permafrost regions, it is crucial to establish the correlation between CPT and CBR values through comparative tests. The CBR test, a commonly used geotechnical testing method for evaluating the bearing capacity of soils, has developed into a mature evaluation system. However, direct application in permafrost areas is challenging due to environmental and technical constraints. The CPT, known for its portability and stability, allows for the quick acquisition of mechanical parameters of permafrost layers, although it lacks standardized mechanical parameter evaluation criteria. Therefore, by controlling the experimental conditions, it is possible to establish a correlation between the two, using the CBR test as a reference for evaluating the soil's bearing capacity. Preliminary standards for CPT test indices can be established accordingly. According to the \"Specifications for Design of Highway Subgrades\" (JTG D30-2015), the minimum CBR values for subgrade fill materials are converted to corresponding cone tip resistances (\(q_{\text{c}}\)) as shown in Table 4. For higher-grade highway embankments, the minimum CBR values typically range from 3% to 4%. Previous studies have shown that soft soil CBR values generally range from 0.5% to 2.5% [25], necessitating stabilization treatments to enhance the soil's bearing capacity. Consequently, soil CBR values are categorized into three levels: less than 3%, corresponding to weak soil layers; 3% to 4%, corresponding to soil layers with some bearing capacity; and more than 4%, corresponding to soil layers with strong bearing capacity. Based on this, standards for evaluating the bearing capacity of frozen sandy soils using CPT are established: a \(q_{\text{c}}\) greater than 7 MPa indicates a strong bearing capacity, representing a bearing layer; a \(q_{\text{c}}\) between 4 MPa and 7 MPa indicates moderate bearing capacity, representing a weak bearing layer; and a \(q_{\text{c}}\) less than 4 MPa indicates poor bearing capacity, representing a weak soil layer. This study constructs the correlation between indoor CPT and CBR tests for frozen sandy soils, demonstrating that CPT data can be used to quickly and easily assess the bearing capacity of frozen sandy soils. This validates the feasibility of using CPT for bearing capacity evaluation in permafrost regions, enhancing the engineering application value of in situ test results and providing a theoretical basis for geological surveys and evaluations in permafrost areas.
## 2 Related Work
### Transformer-Based Road Extraction Methods  
Transformer-based methods, originally introduced for natural language processing tasks, have recently gained attention in remote sensing imagery interpretation tasks, including road extraction. Transformers can capture long-distance dependencies in images, thus effectively modeling global context information. This enables the model to better understand the location and shape of the road in the whole image and improve the quality of road extraction. In the field of road extraction, transformer-based models leverage self-attention mechanisms to capture contextual information and generate accurate road predictions. Examples of transformer-based architectures applied to road extraction include Vision Transformer (ViT) [20], Swin Transformer [21], Swin-UNet [22], BDTNet [23], and RoadFormer [24]. These models have shown promising results in capturing global context and improving the accuracy of road extraction. However, due to the large number of parameters in Vision Transformer, the generalization ability of the model may be limited for small data sets, and it is easy to over-fit.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 6 Control Strategies for EV Systems
Remember that cooler temperatures and multi-stage charging can enhance battery life. Ultimately, the best control method for battery life requires balancing efficiency, speed, and the battery's characteristics. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Control Methods (Non-Isolated)** & **Benefits** \\ \hline Voltage Mode Control & Improves charging efficiency, fast response, regulation protects the battery. \\ \hline Current Mode Control & Enhancing charging efficiency, ensuring the charging current remains within a predetermined threshold, reducing charging times, minimizes energy losses, and improves efficiency. \\ \hline Peak Current Control & It prevents overloading the converter and charging infrastructure, regulates and limits the peak current to optimize the charging process, prevents the current from exceeding the peak limit, ensures efficient energy transfer, and provides overcurrent protection. \\ \hline Average Current Control & Optimizes energy transfer and improves charging, ensures optimal charging performance and power quality, and reliable, safe, fast charging operations. \\ \hline
**Control Methods (Isolated)** & \\ \hline Voltage Mode Control & Maintaining a stable output voltage provides efficient energy transfer, stability from the grid to the EV battery, and compatibility with charging infrastructure. \\ \hline Current Mode Control & Provide efficient energy transfer, stability from the grid to the EV battery, fast dynamic response to load variations, and excellent adaptability to load variation conditions. \\ \hline Peak Current Control & Regulates and maintains a stable average output current, demand for rapid and reliable EV charging increases, compatibility with grid constraints, maintaining safety, optimizing energy transfer, and increasing charging efficiency demand for EV fast charging. \\ \hline Average Current Control & Regulates and maintains a stable average output current; it ensures compatibility with charging infrastructure and provides flexibility in adapting to changing load conditions, allowing the system to accommodate different charging scenarios, optimizing energy transfer, reducing charging times, and creating reliable and high-performance charging infrastructure. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Control strategies for EV systems techniques for isolated and non-isolated DC-DC converters [10; 158; 159; 160; 161].
# Cross-Camera Erased Feature Learning for Unsupervised Person Re-Identification
## 3 Materials and Methods
### Erased Partial Feature Learning  
Images of the same pedestrian under different cameras may look different in global appearance, since they have different camera views, lightings, etc. However, since they are of the same pedestrian, their local details could be similar. Therefore, for each training sample, we erase a part of the pedestrian image and train the model to learn from the retained partial image. In BFDL branch, batch feature eraser (BFE) network is proposed to randomly erase a part of feature maps in each mini-batch. In supervised person re-identification learning method, elements of same classes are closer in the feature space and farther away from different classes to enhance the discrimination of elements [35,36]. In CEFL, we propose to draw similar erased features closer while pushing dissimilarity erased features away in the feature space, as shown in Figure 4.  
The erased features are extracted by BFE network, as shown in Figure 2. Then we need to compare each unlabeled erased feature with other features to find similar features, which is difficult to handle in mini-batch optimization of deep learning. Therefore, we used erased feature memory bank [36] to store these erased features. At the same time, erased feature memory bank is updated during the training process. Function of the erased feature memory bank is as follows:  
\[F_{j,\epsilon}=\begin{cases}(1-r)\times F_{j,\epsilon}+r\times F_{j,\epsilon} \times 0,\\ F_{j,\epsilon},\epsilon=0,\end{cases} \tag{2}\]  
\[F_{j,\epsilon}=\begin{cases}F_{j}\end{cases}_{j=1}^{N} \tag{3}\]  
where \(N\) is the number of images in the training dataset,\(e\) is the training epoch, and \(r\) is the updating rate of \(F_{j,\epsilon}\), \(F_{j,\epsilon}\) is the latest erased feature extracted by BFE. \(e=0\) means the erased feature memory bank is in initial state. The erased feature memory bank is updated in batches during the training phase.  
Then the erased features are extracted and normalized by the subsequent network layer, as shown in Figure 2. For each erased feature, the loss function is defined as follows:  
\[L_{B}=-\log\frac{\sum_{F_{j}\in K_{i}}e^{-\frac{1}{2}\|b_{i}-F_{j}\|_{2}^{2}}}{ \sum_{j=1,j\
eq i}^{N}e^{-\frac{1}{2}\|b_{i}-F_{j}\|_{2}^{2}}} \tag{4}\]  
where \(K_{i}\) is the nearest-neighbor feature set: \(k\) features closest to feature \(b_{i}\)(Euclidean distance) in the erased feature memory bank. By minimizing the loss function \(L_{B}\), EFDL can bring the feature \(K_{i}\) that similar to the feature \(b_{i}\) closer, while pushing the dissimilar feature \(\left\{F\middle|F_{j}\
otin K_{i}\right\}\) away.  
Figure 4: Similar classes are drawn closer and dissimilar classes are pushed away.
## 3 Design Space for Auditory Information Browsing  
[PERSON] and [PERSON] [38] explained information exploration behavior by searching and browsing. Searching is a behavior that starts with target identification followed by query formulation, search, extraction, and evaluation. On the other hand, browsing is a behavior that begins with context, followed by chaining or linking, and then by extraction and evaluation. The existence of the explicit query (specificity of information needs) distinguishes searching and browsing.  
This work aims to support information browsing behavior on lecture audio data. Figure 1 presents the design space for an auditory information browser. We considered four design subspaces according to two dimensions: (1) the level of the playback skip control and (2) the type of cue about the current playback.  
First, the design for the auditory information browser can be specified by the level of the playback skip control. The user should control the audio playback position to find and link relevant information. The time-level skip control (e.g., skip 10 s) has been widely used in conventional audio seekbar, but it does not consider the current playback contexts. Some studies have proposed semantic-level skip control. For example, [PERSON] [39] presented the segment-level keyword search function by the timeline representing the linear-structure of the audio content and visualizing relevant keywords.  
Second, providing appropriate cues is important in the design to increase awareness of the current playback. Audio content has diverse advantages, but the audio is often limited to identifying the current playback's position and surrounding contexts because the user depends on hearing only and process the content sequentially. Therefore, the auditory information browser should provide appropriate feedback about the audio playback.  
Figure 1: Design space for auditory information browsing.  
We classified the awareness cue into two modality types: (1) visual cue and (2) auditory cue. There are many graphical user interfaces for audio playback. The most common one is an audio seekbar. It can help users monitor the current playback and easily move to the targeted position using its slider bar. In addition, there have been graphical interfaces for visualizing keywords in audio [39]. Most of the studies in this design subspace utilize texts corresponding to the audio content.  
On the other hand, the auditory cue can be helpful in particular situations. The auditory cue can be delivered easily, even on a simple device without a screen, and also it allows users to have multitasking. Especially, it would be helpful for the visually-impaired people's information behavior. However, providing auditory cues for information browsing is more challenging. Information browsing behavior requires more dynamic processes, and the limitations become more serious when audio content is not well structured or does not have available meta-data.  
In this design space, HearIt aims to provide semantic-level audio skip control with auditory cues. The following section describes the details about the implementation of HearIt.
## 4 MARL-Based Routing Solution
### State Space  
In DRL, the environment must provide relevant information to the agent so that it can learn an optimal policy. This information represents a partial observation of the current state of the environment. At each time step, the environment sends the following information to the agent:  
* The source and destination node identifiers (TFC or TMS) specified in the service request (_src_ and _dst_).
* The current node, which is the node up to which the path has been established.
* The maximum delay bound specified in the service request and the accumulated delay in the path formed up to that point.
* A state vector related to the resource status of the links outgoing from the current node. This vector has as many elements as nodes in the network. Each element of the vector generally contains the delay of the link connecting the current node to a specific node in ascending order (e.g., the first element contains the delay of the link from the current node to node 0 and so forth). If no link exists between the current node and another node, the corresponding element in the vector is \(-1\). Similarly, if a link exists but is congested and cannot transmit data frames, the value for that link is also set to \(-1\). This vector provides the agent with enough information to know whether a link has enough resources to be assigned to a specific data flow, thus avoiding congestion issues or selecting failed links. The reason why any link cannot be used to assign resources for service requests is transparent to the agent, as it can only observe whether the link is available or not.