esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Technique for Accelerating the WSN Convergence Process
## 5 Proposal--Design and Implementation of a Quick Recovery Mechanism (Step 3)  
Acceleration of the AODV protocol is realized by the implementation of BFD. The testing could not be performed in real conditions, so the available simulation environments that can be used for the simulation of WSNswere examined, and the OMNeT++ simulator was subsequently selected. The BFD protocol was implemented in the form of Asynchronous mode according to RFC 5880.  
Figure 7 shows the integration of BFS fast failure detection into the hierarchy of the AODV protocol. In view of the fact that each verification of a specific link through BFD means an excessive load and consumption of electricity, the BFD session is activated only on links that are critical from the point of view of the infrastructure and are determined by the administrator.  
The BFD protocol is a failure detection protocol that is often used in routing protocols for fast path failure detection. The AODV protocol is designed without direct support for BFD; therefore, for the implementation of BFD in AODV, the following was necessary:  
* Get to know the BFD and AODV protocols in detail.
* Identify places in AODV where BFD could be integrated.
* Create an independent BFD module that ensures communication between neighboring nodes and performs outage detection functions.
* Integration of the BFD module into AODV--the created BFD module must be integrated into the existing AODV code, that is, adding BFD functions at appropriate AODV times and ensuring proper coordination between BFD and AODV operations.
* Testing and debugging the integrated BFD in AODV and verifying the proper functioning of outage detection.  
Because modifying an existing routing protocol can affect its performance, reliability, and correctness, thorough validation and testing, is important to ensure proper network functionality. In the following sections, a short description of the actual implementation of the BFD module in the OMNeT++ INET library is presented.  
Figure 7: Integration of fast BFD Hello messages.
# Revolutionizing Palm Dates Harvesting with Multirotor Flying Vehicles
###### Abstract  
This study addresses the challenges of traditional date palm harvesting, which is often labor-intensive and hazardous, by introducing an innovative solution utilizing multirotor flying vehicles (MRFVs). Unlike conventional methods such as hydraulic lifts and ground-based robotic manipulators, the proposed system integrates a quadrotor equipped with a winch and a suspended robotic arm with a precision saw. Controlled remotely via a mobile application, the quadrotor navigates to targeted branches on the date palm tree, where the robotic arm, guided by live video feedback from integrated cameras, accurately severs the branches. Extensive testing in a controlled environment demonstrates the system's potential to significantly improve harvesting efficiency, safety, and cost-effectiveness. This approach offers a promising alternative to traditional harvesting methods, providing a scalable solution for date palm cultivation, particularly in regions with large-scale plantations. This work marks a significant advancement in the field of agricultural automation, offering a safer, more efficient method for harvesting date palms and contributing to the growing body of knowledge in automated farming technologies.  
drones; Pixhawk; harvesting; date palm; robotic arm; 3D printers +
Footnote †: journal: Applied sciences  
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## 3 Design of a Digital Satellite-Borne Base Station for Quick Functional Reconfiguration
### Network Model
#### 4.2.3 Migration Precomputing and Real-Time Computing Synergy  
The main migration overhead in VNF migration comprises the time overhead of calculating the VNF migration scheme and the time overhead of implementing the VNF migration scheme. When the number of nodes increases, the time overhead of performing migration scheme calculations increases sharply. To reduce the time overhead of migration scheme calculations, this paper proposes a migration algorithm that collaborates with migration precomputation and real-time computation, and sets the corresponding light overload threshold and overload threshold for the system to reduce the overhead of VNF real-time computation. To ensure the validity of the migration precomputation results, a time stamp is added to the migration computation results, and a reasonable timeout expiration time is set. In addition, the migration computation is performed again when the migration scheme in the result set is invalid after the timeout.  
The implementation process of the migration algorithm with collaborative migration precomputation and real-time computation is as follows: the resource occupation of the node is monitored. When the node is overloaded, the node migration scheme result set is checked. If it is not empty and the expiration time has not expired, migration is performed, the node computation result set is set to empty immediately, and the node state continues to be monitored; if the node is overloaded and the node migration scheme result set is empty or non-empty, but the expiration time has expired, then the responsive migration strategy is used, the migration scheme is calculated in real time, and migration is implemented; when the node exceeds the light overload threshold but not the overload threshold, the node migration scheme result set is checked, and if the node migration scheme result set is empty or the expiration time has expired, the node migration scheme is re-calculated to ensure the validity of the migration scheme in the migration result set.  
The system uses the same migration algorithm for precomputation and real-time computation, and the parameters that the algorithm receives include the node's state, the occupancy information of the resources of the node that the VNF is installed on, and the pertinent state information of other nodes in the system.
## 3 Methodology
### Models  
#### 3.2.1 ET Model  
The SEBS model, a classical single-source surface energy balance approach with a clear physical mechanism, was used to estimate turbulent heat fluxes and daily ET through a combination of satellite remote sensing data and meteorological data [72]. The calculation of sensible heat flux (H) was used to establish the following nonlinear equations and solve them iteratively based on the Monin-Obukhov similarity theory (MOST) and the bulk transport equation:  
\[H=ku_{*}\rho C_{p}(\theta_{0}-\theta_{a})\left[\ln\left(\frac{z-d_{0}}{z_{0h}} \right)-\psi_{h}\left(\frac{z-d_{0}}{L}\right)+\psi_{h}\left(\frac{z_{0h}}{L} \right)\right]^{-1}, \tag{3}\]  
\[u=\frac{u_{*}}{k}\left[\ln\left(\frac{z-d_{0}}{z_{0m}}\right)-\Psi_{m}\left( \frac{z-d_{0}}{L}\right)+\Psi_{m}\left(\frac{z_{0m}}{L}\right)\right], \tag{4}\]  
\[L=-\frac{\rho C_{p}u^{3}\theta_{v}}{kgH}, \tag{5}\]  
where \(z\) is the reference height; \(u_{*}\) is the friction velocity; \(k\) is the von Karman constant; \(\rho\) is the density of the air; \(C_{p}\) is the specific heat of the air at constant pressure; \(u\) is the  
Figure 2: Workflow of information processing used to generate high-resolution (daily, 100 m) ET, GPP, and WUE.  
wind speed; \(d_{0}\) is the zero-plane displacement height; \(g\) is acceleration due to gravity; \(\theta_{v}\) is the potential virtual temperature; \(z_{01}\) and \(z_{0m}\) are the roughness heights for the heat and momentum transfer, respectively; \(\Psi_{h}\) and \(\Psi_{m}\) are the stability correction functions for the sensible heat and momentum transfer [73; 74; 75], respectively; and \(L\) is the Monin-Obukhov length.  
In this study, the optimized parameterization scheme of the SEBS model was adopted [61], and the lack of optical remote sensing data was overcome by using the AWLST data products due to frequent cloud contamination and other poor atmospheric conditions [57]. Instantaneous LE/ET was then calculated from instantaneous net radiation (R\({}_{\text{n}}\)), H, and surface soil heat flux (G\({}_{0}\)) by substituting in the energy balance. On this basis, the scaled-up time-scale method of instantaneous to daily surface evapotranspiration (the constant solar radiation ratio, SolRad [76]) was used to calculate daily surface evapotranspiration.
## 4 SkyDRL for Multi-Cloud Service Composition Problem
### State and Action  
We model the cloud service composition problem as a Markov Decision Process (MDP), where a cloud service composition based on MDP is defined as a 5-tuple \(\langle State,s_{0},s_{\gamma},A,R\rangle\)[41]:  
* \(State\) is a set of finite states;
* \(s_{0}\) is the initial state of the agent, and the execution process of the cloud service composition starts from this state;
* \(s_{\gamma}\) is a set of terminal states. The execution of the cloud service composition will terminate when a state in \(s_{\gamma}\) is reached;
* \(A(s)\) is the set of executable actions in state \(s\). In the cloud service composition problem, the action set is a set of candidate cloud service sets. At each state, there is a set of candidate cloud service sets from which we can select and execute cloud services;
* \(R\) is the reward function. Assuming the current state is \(s_{i}\), a service is selected and called, and the state transitions to \(s_{i+1}\). Afterward, we will receive a reward for executing this action from the environment.  
For each state, we can define it as \(s_{i}=\langle t,cs,cld\rangle\), where \(t\) represents the state at the time \(t\); \(cs\) represents the selected cloud services in the current state, and \(cld\) indicates which cloud environment the selected cloud service belongs to. In this problem, the  
Figure 4: Example of skyline services.  
environment can include cloud environments, service providers, user demands, and other factors. Specifically, the environment will provide a set of currently available cloud services, calculate corresponding reward or punishment signals based on the cloud services selected by the agent, and provide the agent with the current state information to help the agent make decisions.  
The cloud service composition process starts with an initial state, in which no cloud service has been selected for subtasks yet, and ends with a final state, in which all subtasks have selected specific cloud services. Each time a specific cloud service is selected for a subtask, a state transition occurs, as shown in Figure 5. Agent executes an action in the state, which refers to the cloud service selection process, and the action set of the state refers to its corresponding candidate cloud service set.  
This simple example in Figure 5 illustrates the process. Specifically, in Figure 5, the current state is \(s_{i-1}\). The agent selects an action from the action set corresponding to the state and calls it, and then the state transits from \(s_{i-1}\) to the next state \(s_{i}\). The environment calculates a reward based on the action called and sends it to the agent. This process is equivalent to a subtask \(T_{i}\) selecting a specific cloud service. Repeat this process until the final state is reached, where all subtasks have selected specific cloud services, and the cloud service composition process ends.
# Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation
## 4 Experiments
### Baselines  
We compared our proposed framework with the following four baseline methods:  
**Fixed Word Embedding Vector Model (FIXED) [4]:** the original recommendation model with embeddings of uniform and fixed sizes. We adopted the embedding size of 128 for all the users and items.  
**Differentiable Architecture Search (DARTS) [18]:** the traditional DARTS method that adjusts embedding sizes by learning the weights for the six candidate embedding sizes \(2,4,8,16,64,128\) for each user or item. The embedding of a user or an item is represented as the weighted sum of the embeddings of the six candidate sizes.  
**AutoML-based end-to-end framework (AutoEmb) [19]:** a DARTS-based method that trains a controller to dynamically assign weights for the six candidate embedding sizes \(2,4,8,16,64,128\) based on the frequency of a given user or item. The embeddings are also represented by soft selection.  
**Embedding Size Adjustment Policy Network (ESAPN) [4]:** this model determines the dimension size of the embedding based on the frequency of items and users. Its policy network is responsible for selecting the embedding size of the user or item embedding. The recommendation layer is responsible for accepting the input and making predictions and finally updating the policy with the activation function. It uses a hard selection method to implement the embedding size search technology and uses the multi-layer perceptron technology to capture the feature relationships between users and items.  
Among them, the FIXED model does not use multi-dimensional embedding vectors and linear models such as matrix factorization. The other three models use multi-dimensional embedding vectors but do not introduce linear models. Therefore, in the following section, the TMMNN model proposed in this paper will be compared with these four models so that the overall performance of the two-layer structure can be examined. Moreover, the ESAPN is also the comparison target of the TMMNN model under a small data volume or a low-frequency data area to verify the effect of introducing matrix factorization.  
The experimental data and analysis results presented in the following sections are based on the model's sufficient learning of the given data to predict user behavior. The two datasets with different frequency distributions are divided according to size and frequency. As a result, the model's performance in various application scenarios is tested, which can reflect the generalization ability of the model to the data, so no new dataset for testing will be introduced in this paper.
# A Multicomponent Face Verification and Identification System
## 2 Materials and Methods
### Deep Learning Model for the Face Identification and Verification
#### 2.2.4 Similarity Measure and Evaluation Methodology  
Cosine similarity [70] is a measure used to gauge the similarity between two entities, typically represented as vectors in a multi-dimensional space. In the context of face recognition, both for identification and verification, cosine similarity assesses how closely aligned the feature vectors of different face images are. The closer the cosine similarity is to 1, the more similar the faces are. In our application, we utilize cosine similarity due to its effectiveness in high-dimensional spaces, like those involved in facial recognition. It helps in differentiating good results from poor ones by providing a clear metric for similarity. Additionally, we employed a normalized cosine distance, calculated as \(1-\)cosine similarity, expressed as a percentage. This approach aids users by quantifying the degree of resemblance between faces, offering an intuitive and user-friendly way to interpret the similarity between the faces being compared or verified.  
The performance of the face recognition algorithms was assessed using objective evaluation metrics:  
* Face identification: This entails matching a given face image to a known identity in a database. The Rank-1 identification rate [71], defined as the percentage of test images for which the correct identity is ranked first, served as the evaluation metric. The Rank-5 identification rate represents the percentage of test images for which the correct identity is within the top 5 matches (or ranks) from the database.
* Face verification: This involves determining whether two given face images belong to the same person. The area under the receiver operating characteristic curve (AUC-ROC) [72], the false negative rate (FNR), and the false acceptance rate (FAR) [73] served as the evaluation metrics.
## 2 ITS Architecture and Applications
### ITS Applications
#### 2.3.1 Road Safety Applications  
Road safety applications exploit wireless V2X communications between surrounding ITS entities (e.g., vehicles, road infrastructures, _etc._) to reduce traffic accidents and to protect the drivers and pedestrian from various road hazards. To that end, each ITS entity periodically broadcasts safety messages to notify its neighborhood about its context and location information. Furthermore, depending on specific events (e.g., accidents, detected road hazards), each ITS entity may also trigger the transmission of notification messages to nearby vehicles and/or emergency services using multi-hop communications. As shown in Table 1, the critical latency (or end-to-end communication delay) represents one of the most important system requirement for road safety applications, which typically should not exceed one hundred milliseconds. Figure 3: Classification of ITS applications. Three typical examples of emerging ITS road safety applications are shown in Figure 4. The first example consists in the pedestrian crossing warning application in which drivers are notified in case pedestrians are crossing the road (_cf._ Figure 4a). To that end, sensors are deployed along the sidewalks to detect the presence of people, and the corresponding sensory events are collected at the road side units. These RSUs can thus detect and/or predict the occurrence of potential accidents and notify the incoming vehicles. The second example consists in the left turn driver assistance application, as shown in Figure 4b. In this scenario, two vehicles might approach an intersection without seeing each other due to visual obstructions (e.g., trees, building, _etc._). The objective of such an application is thus to assist the driver in making a safer left turn at the intersection. To that end, RSUs collect information about the in-vehicles OBUs and/or from the deployed road sensors to detect the occurrence of such an event and, thus, to provide timely recommendations and notifications to the involved drivers. Finally, the third application is related to the approaching emergency vehicle warning application, as shown in Figure 4c, in which an approaching emergency vehicle (e.g., ambulance, police or fire fighter) requests the surrounding vehicles to form a corridor and to provide a clear path. Eventually, the emergency vehicle could communicate with the surrounding road infrastructures to set the traffic lights to green, hence minimizing the emergency response time. Other examples of road safety applications include: emergency electronic brake lights warning, stationary vehicle indication, roadwork warning, intersection collision avoidance, lane change warning and many others [28, 29, 30]. A case study related to the collision risk warning (CRW) road safety application will be analyzed in more detail in Section 5.
## 3 Analysis of the Lignosulfonate Reinforcement Mechanism  
### Mineral Composition Analysis  
X-ray diffraction tests show that the diffraction peaks of lignocellulosic-modified loess and plain soil are basically the same and their mineral compositions are basically the same [46; 47; 48], as shown in Figure 3.  
[PERSON] [46] tested and analyzed the mineral composition of loess modified by calcium lignocellulosic with different dosages and concluded that calcium lignocellulosic and albite (NaAlSi\({}_{3}\)O\({}_{8}\)) in loess undergo decomposition and replacement reactions at the initial stage to form quartz (SiO\({}_{2}\)), thereby improving the mechanical properties of the improved soil. Ca\({}^{2+}\) in calcium lignocellulosic reacts with loess to form calcite (CaCO\({}_{3}\)) and dolomite (CaMg(CO\({}_{3}\))\({}_{2}\)). Carbonate is an inorganic compound with good filling and cementing ability, and its content is the largest when the content of calcium lignocellulosic is 1% to 1.5%. The content of clay minerals increases first and then decreases with the addition of calcium lignocellulosic, and the content is the largest when the dosage of calcium lignocellulosic is 1%. Clay minerals can not only improve the bonding strength between particles but also enhance the water-holding capacity of modified loess due to their hydrophilicity [46].  
[PERSON] et al. [49] compared the mineral composition of loess modified with 3% calcium lignocellulosic or 3% sodium lignocellulosic. It was concluded that Na\({}^{+}\) in sodium lignocellulosic produces the clay mineral albite (NaAlSi\({}_{3}\)O\({}_{8}\)) in this reaction. The increase in quartz content can improve certain mechanical properties of the modified soil, but the carbonate in the modified soil does not increase significantly compared with calcium lignocellulosic, the cementation effect on loess is weaker, and sodium lignocellulosic leads to excessive Na\({}^{+}\) in the soil.
###### Abstract  
Introduction:This study investigated the influence of stimulus composition for three speech intelligibility word lists and two scoring methods on the speech accuracy judgments of five tracheoesophageal (TE) speakers. This was achieved through phonemic comparisons across TE speakers' productions of stimuli from the three intelligibility word lists, including the (1) Consonant Rhyme Test, (2) Northwestern Intelligibility Test, and (3) the Weiss and Basili list. **Methodology:** Fifteen normal-hearing young adults served as listeners; all listeners were trained in phonetic transcription (IPA), but none had previous exposure to any mode of postlargectomy laryngeal speech. Speaker stimuli were presented to all listeners through headphones, and all stimuli were transcribed phonetically using an open-set response paradigm. Data were analyzed for individual speakers by stimulus list. Phonemic scoring was compared to a whole-word scoring method, and the types of errors observed were quantified by word list. **Results:** Individual speaker variability was noted, and its effect on the assessment of speech accuracy was identified. The phonemic scoring method was found to be a more sensitive measure of TE speech accuracy. The W&B list yielded the lowest accuracy scores of the three lists. This finding may indicate its increased sensitivity and potential clinical value. **Conclusions:** Overall, this study supports the use of open-set, phonemic scoring methods when evaluating TE speaker intelligibility. Future research should aim to assess the specificity of assessment tools on a larger sample of TE speakers who vary in their speech proficiency.  
tracheoesophageal speech; intelligibility; laryngectomy; postlargectomy rehabilitation; laryngeal voice +
Footnote †: journal: Applied Sciences  
1 Otolaryngology--Head and Neck Surgery, Division of Laryngology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA 1  
Voice Production and Perception Laboratory, Rehabilitation Sciences, Western University, London, ON 1N6G 1H1, Canada; [EMAIL_ADDRESS]  
Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI 48824, USA; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]  
tracheoesophageal speech; intelligibility; laryngectomy; postlargectomy rehabilitation; laryngeal voice