esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reliable Handoff Mechanism for Mobile Industrial Wireless Sensor Networks
## 6 Evaluation and Experiments
### Evaluation of Related Parameters  
Before implementing the proposed mechanism, some related parameters should be determined, such as the threshold of the moving state evaluation \(R_{T}\) and the threshold of handoff triggering degree \(\mu_{T}\). During movement, \(MN\) determines \(R\) at each end of the superframe by monitoring infrastructure nodes nearby. The number of monitored nodes is uncertain in practice, which is why we carry out several tests to describe the relationship between mobile speed and the value of \(R\). We set a different number of nodes around \(MN\) for each mobile speed and calculate the corresponding \(R\), the results of which are shown in Figure 10. The result with the same mobile speed and different number of neighbor nodes is represented by different colors. To decide the threshold of \(R\), we should begin by ensuring a speed value as the boundary. In this paper, we consider a node as moving when its speed exceeds 2 m/s since the movement of welding machine is caused by the workers who need to change their work position. Therefore, we select the average value of \(R\) in 2-m/s mobile speed as the \(R_{T}\), which is 7.7, and such threshold is denoted by the red dash line.  
The handoff is triggered by comparing the output of mobility awareness and the predefined threshold \(\mu_{T}\). The window of measurement is set to 5 since \(\lambda=0.8\) is used to convert \(l_{t}\) and \(l_{p}\) to the performance of packet delivery. The maximum speed supported in this paper is 5 m/s. Different handoff trigger thresholds have influence on not only packet delivery but also energy consumption. Therefore, we conduct the experiments to obtain RLP-\(V\), REP-\(V\), REP-\(MN\) and energy consumption of \(MN\) with eight different \(\mu_{T}\) to decide the preferred threshold. The experiments are implemented in our testbed and the environment is described in the following section. The results in Figure 11 show that RLP-\(V\), REP-\(V\) and REP-\(MN\) are all decreasing until \(\mu_{T}\) reaches 85. As \(\mu_{T}\) moves up from 85, the value of REP-\(MN\) tends to be moderate, however, both RLP-\(V\) and REP-\(V\) have a growing tendency. Since the larger the \(\mu_{T}\) is, the more frequent the handoff, which has a great effect on network topology. However, the performance of the packet delivery is compensated by energy consumption. As the \(\mu_{T}\) increases, the \(MN\) consumes more energy. It is better to select the lower value as \(\mu_{T}\) while the network reliability is also guaranteed. Thus, we select 85 as the threshold of triggering degree.  
Figure 10: Relationship between \(R\) and mobile speed.
## 2 Materials and Methods
### Model Evaluation and Validation  
The study employed the ROC method for model evaluation and validation. The AUC is used as the metric to evaluate the quality of the model. The susceptibility map was divided into 25 equal area classes with decreasing susceptibility and the percentage of landslide in each of class is calculated. The cumulative landslide percentage was plotted against the cumulative area to derive the ROC curve.  
The landslide inventory was partitioned into two groups, i.e., training dataset and test dataset. The SRC is acquired when ROC curve of the same susceptibility map is plotted against the training set. The PRC is generated from the ROC curve derived from the susceptibility map generated from the training set against landslide points in the test set. The performance of the model is represented by a value range under the curve of 0.5 to 1.0, where 0.5 represents a test with accuracy no better than chance while value close to 1.0 which suggests an ideal model with a perfect fit [19].  
The whole 2014 landslide inventory was used to generate a landslide susceptibility map and the SRC is plotted to assess the model's compatibility. Spatial cross-validation was done on the same inventory. The study area was partitioned into alternating linear zones (zone A and zone B) measuring 1 km in width as to represent the smallest geological unit in Kuala Lumpur known as schist. Another reason for the selection of the dimension is to ensure consistency since the topographic map of Malaysia has a 1 km grid. The zones are orientated east-west, perpendicular to the north-south geological and geomorphological trend. This is to ensure the inclusivity of the factors involved in both sub-regions. The validation was done twice. First zone A was used as the training set and zone B as the test set, and in second validation, the role of zones A and B is reversed. The SRC and PRC were plotted for both cases.
## 3 Experiments and Results
### The Other Experiments
#### 3.5.2 Exposure Dataset  
In multiple Exposure datasets, Exposure dataset has a total of 19 color images with different exposures. In this paper, these color images are converted into grayscale images for experiments and comparison. The specific experimental results are as follows:  
The red font in Table 5 indicates that this indicator has the highest score. The network model proposed in this paper has the highest score in 4 of the 7 evaluation indicators. In Figure 7, the background in the blue box of image A is under-exposed, while the background in the red box is under-exposed. In general, image A is under-exposed. The background in the blue box of image B is exposed, while the red box is over-exposed. In general, image B is over-exposed. Other images are obtained through various image fusion algorithms. CNN, Densefuse, and IFCNN images are over-exposed. However, the images fused by the network model proposed in this paper can be seen in the blue box, and also conform to the distribution of light sources. In the red box, you can see the grid under the light without over-exposure to the light effect.  
In conclusion, the network model proposed in this paper can better keep more information in the under-exposed and over-exposed images. Thus, the model proposed in this paper is effective and has achieved good results in other areas.
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 2 System Model
### Achievable Secrecy Sum-Rate
#### 2.3.1 Internal Eavesdropping Scenario  
Eavesdroppers are assumed to be internal nodes such as compromised base stations. Thus, we assume that eavesdroppers operate same as the base station and thus use the receive random beamforming technique. Similar to the desired base station, each eavesdropper independently decodes its received signal in (3) by using receive random beamforming based on (1). From the perspective of \(s\)-th receive random beamforming, information leakage by all eavesdroppers (i.e., the achievable rate by eavesdroppers) is given by  
\[r_{\text{Int}}^{[s]} =\max_{k\in\mathcal{K}}\left\{\log\left(1+\frac{|\mathbf{u}_{k}^{ [s]\intercal}\mathbf{g}_{\pi_{s}k}|^{2}\rho}{1+\sum_{l=1,l\
eq s}^{S}|\mathbf{ u}_{k}^{[l]\intercal}\mathbf{g}_{\pi_{l}k}|^{2}\rho}\right)\right\}\] \[=\log\left(1+\max_{k\in\mathcal{K}}\left\{\frac{|\mathbf{u}_{k}^{ [s]\intercal}\mathbf{g}_{\pi_{s}k}|^{2}\rho}{1+\sum_{l=1,l\
eq s}^{S}|\mathbf{ u}_{k}^{[l]\intercal}\mathbf{g}_{\pi_{l}k}|^{2}\rho}\right\}\right), \tag{5}\]  
where subscript 'Int' indicates an internal eavesdropping scenario, \(\mathcal{K}\) denotes eavesdropper index set (i.e., \(\mathcal{K}\triangleq\{1,\cdots,K\}\)), \(\mathbf{u}_{k}^{[s]}\) represents \(s\)-th receive random beamforming vector defined in (1), and subscript \(k\) in \(\mathbf{u}_{k}^{[s]}\) indicates receive random beamforming vectors at \(k\)-th eavesdropper. (5) is represented as maximum of each eavesdropper's achievable rate among \(K\) eavesdroppers since we assume each eavesdropper operates independently.  
Therefore, for internal eavesdropping, the achievable secrecy sum-rate is given by  
\[R_{\text{Int}}^{\text{sec}}=\sum_{s}^{S}\left[r_{\text{BS}}^{[s]}-r_{\text{ Int}}^{[s]}\right]^{+}, \tag{6}\]  
where \(r_{\text{BS}}^{[s]}\) and \(r_{\text{Int}}^{[s]}\) are defined in (4) and (5), respectively.
## 3 Experimental Results and Discussion
### Spatial Distribution of EMPs
#### 3.2.2 Distribution of EMPs on Different Angles  
The angular distributions of the EMPs were measured at different angles ranging from \(-120^{\circ}\) to \(110^{\circ}\), as shown in Figure 4a, and ranging from \(-120^{\circ}\) to \(0^{\circ}\), as shown in Figure 4b. The other experimental parameters were as follows: in Figure 4a, \(E=100\) mJ, \(t=30\) fs, and \(d=20\) cm; in contrast, in Figure 4b, the \(E=300\) mJ, \(t=30\) fs, and \(d=30\) cm. Figure 4a shows that the main portion of the power of the EMPs occurred near \(\theta=70^{\circ}\), which is approximately more than twice as high as at \(\theta=0^{\circ}\) and nearly twice as high as at \(\theta=90^{\circ}\). In contrast, in Figure 4b, the \(E\) was increased from \(100\) mJ to \(300\) mJ, and the angular distribution of EMP power ranging from \(-120^{\circ}\) to \(0^{\circ}\) was measured. As a result, we observed the following: the EMP power increased significantly, from \(6\times 10^{-5}\) (a.u.) to \(3\times 10^{-3}\) (a.u.), the EMP energy was more widely distributed with respect to the detection angle, covering \(-80^{\circ}\) to \(-20^{\circ}\), the strongest signal appeared at \(\theta=-30^{\circ}\), which was approximately twice as high as that at \(\theta=0^{\circ}\) and \(-90^{\circ}\). In addition, we also measured a strong EMP signal in the backward of the target (near \(-120^{\circ}\)). The corresponding frequency domain signals are shown in Figure 4c. Moreover, the main frequency domain was at \(0.19\) GHz, and there was also a significant EMP signal near \(0.45\) GHz.  
Figure 3: Typical frequency oscillogram of EMP at six values of the distance, where \(E=170\) mJ, \(t=30\) fs, \(d=15\)–\(40\) cm, \(\theta=0^{\circ}\) (the inset shows the measured values of the EMP intensity at different detection distance).
## 4 Performance Evaluations
### Simulation Setup  
The sensing space for UASN is a rectangular parallelepiped of 1 km \(\times\) 1 km \(\times\) 100 m, in which 100 sensor nodes are randomly deployed. A mobile beacon is assumed to move linearly at a velocity of 1 m/s and broadcasts a beacon at every beacon interval, _i.e._, each 1 s. To fairly compare LoMoB and LDB, we assume that a mobile beacon has a directional transceiver and just four beacon points are used for localization because LDB works for a system using a directional transceiver and uses four beacon points. The angle of the directional transceiver's beam is \(60^{\circ}\). Because it is more convenient to move a mobile beacon on the sea surface than underwater in real underwater environments, the mobile beacon is assumed to move on the sea surface.  
In LoMoB and LDB, localization begins by selecting beacon points. Any projected beacon points that are not exactly on the circle formed by the transceiver's beam for a sensor node cause an estimation error of the distance between the projected beacon point and the sensor node, which results in an error in the estimated location. The beacon distance causes projected beacon points to be located not exactly on the circle formed by the beam. In addition, the phenomena such as reflection, diffraction, and refraction in underwater environments cause irregularities in the radius of the circle formed by the beam. To improve the reality of our simulations, irregularities in the radius of the circle are modeled as \(\hat{r}=r+e_{r}\), where \(e_{r}\) is a random value with the Gaussian distribution \(N(0,\sigma_{r}^{2})\). Because the GPS error in a mobile beacon, water fluctuations, and tidal currents can cause errors in the location of the mobile beacon, we assume the location information broadcasted from a mobile beacon is error prone [24]. The location error of a mobile beacon is modeled as \(\mathbf{\hat{x}_{m}}=\mathbf{x_{m}}+\mathbf{e_{m}}\), where \(\mathbf{\hat{x}_{m}}=[\hat{x},\hat{y}]^{\mathrm{T}}\) is the measured location of the mobile beacon located at \(\mathbf{x_{m}}=[x,y]^{\mathrm{T}}\) and \(\mathbf{e_{m}}=[e_{x},e_{y}]^{\mathrm{T}}\) is a random vector whose \(e_{x}\) and \(e_{y}\) are independent and identical random values with the Gaussian distribution \(N(0,\sigma_{m}^{2})\). We use Matlab to perform these simulations.
## 3 The Self-Adaptive Mobile Clustering Algorithm (SAMCA)
### Phase II: Cluster Reformation  
The proposed cluster reformation strategy considers several cases (e.g., a sensor joining/leaving a cluster, cluster merging/splitting, and clusterhead/gateway reselection) and maintains clusters in a fully distributed way. In order to keep the hierarchical structure efficiently, load for each cluster should be equivalent. Thus, the cluster size is a key parameter to achieve balanced load among clusters. However, due to node movement, the cluster size changes as time proceeds. In the proposed approach, referring to the neighboring and its cluster sizes, each clusterhead triggers the cluster merge/split process if necessary. Let the upper bound _UB_ and the lower bound _LB_ represent the constraints of the cluster size for managing the topology change. When the cluster size is over the upper bound _UB_, the cluster is divided into smaller ones, while when the cluster size is under the lower bound _LB_, two neighboring clusters are merged into one cluster. Thus, each cluster size is adjusted autonomously.  
Moreover, in sensor network applications, it is possible that the energy level in the clusterhead is below the threshold or the clusterhead may malfunction during the network operation. As a result, reselecting a new clusterhead may be required. For maintaining the network connectivity, gateway reselection may be necessary as well in many situations such as discovering new clusters, the link down events of the distributed gateways, merging and splitting clusters, the energy issues and so on. Therefore, this self-adaptive organization is essential in mobile wireless sensor networking systems.  
Joining/Leaving A Cluster  
When a sensor node wants to join a cluster, it may check all the links with its neighbors and measure the distances by the received signal strength. Then it chooses the nearest neighbor and joins its cluster when the cluster size constraint is satisfied. Otherwise, the sensor forms a cluster of its own, which may be merged by nearby clusters later. When a sensor leaves a cluster, this link down event can be  
Figure 3: The initial cluster formation of random networks of 100 (left) and 200 (right) sensors with \(R/\ell=0.15\) and \(R/\ell=0.105\), respectively. A connection between a pair of distributed gateways is indicated by a dashed line.  
detected by not receiving periodical broadcasting messages and the neighboring sensors can update the knowledge of its neighborhood. Other possible schemes for handling new admissions and releases of sensors in a cluster can be found in [23] and [24].
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 4 Implementation Strategy
### Hybrid Forwarding Mechanism  
Forwarding in this system employs a hybrid approach that combines the reinjection of packets into LwIP together with the use of raw sockets, depending on the routing requirements determined by the DTN Module. This ensures that the system balances compatibility with standard networking behaviors and the flexibility required for delay-tolerant networking. Packets processed by the DTN Module are either returned to LwIP or forwarded directly via raw sockets. When reinjected into LwIP, the packet follows the normal forwarding pipeline, which includes default routing, ICMPv6 generation, and protocol-specific handling. This path is used for traffic that does not require specialized routing or modifications to its headers. For packets requiring custom routing decisions, raw sockets provide a direct forwarding path. The DTN Module must construct the necessary headers, sets the appropriate destination address, and specifies the outgoing network interface. The raw socket bypasses LwIP's routing logic, ensuring that the packet adheres to the DTN Module's routing decision. This approach is particularly advantageous for scenarios involving opportunistic forwarding, experimental protocols, or non-standard traffic. The choice between reinjection and raw socket forwarding is dynamically determined based on the packet's routing requirements. This hybrid mechanism enables the system to handle both standard and DTN-specific traffic efficiently, ensuring seamless integration of advanced routing functionalities without compromising compatibility with the existing networking stack.
# Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure
###### Abstract  
Extensive unsupervised livestock farming is a habitual technique in many places around the globe. Animal release can be done for months, in large areas and with different species packing and behaving very differently. Nevertheless, the farmer's needs are similar: where livestock is (and where has been) and how healthy they are. The geographical areas involved usually have difficult access with harsh orography and lack of communications infrastructure. This paper presents the design of a solution for extensive livestock monitoring in these areas. Our proposal is based in a wearable equipped with inertial sensors, global positioning system and wireless communications; and a Low-Power Wide Area Network infrastructure that can run with and without internet connection. Using adaptive analysis and data compression, we provide real-time monitoring and logging of cattle's position and activities. Hardware and firmware design achieve very low energy consumption allowing months of battery life. We have thoroughly tested the devices in different laboratory setups and evaluated the system performance in real scenarios in the mountains and in the forest.  
animal monitoring; low-power wide area networks; LoRaWAN; wearable devices design +
Footnote †: journal: Applied sciences  
1 Aragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, Spain;  
arturovhache@unizares (A.H.); [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (T.B.); jvvy@unizares (F.J.Z-S.)
# CFD-Simulink Modeling of the Inflatable Solar Dryer for Drying Paddy Rice
## 4 Discussion  
The current study investigated the airflow patterns in the ISD. The results obtained with the CFD simulation model helped to visualize the airflow distribution (Figure 7). A velocity reduction is expected due to an abrupt expansion from the fan to the dryer. These results are likely to be related to a turbulent regime [43] and generated vortex in the heating area, as seen in Figure 7a. A similar flow structure has been shown in another study [44].  
Previous studies evaluating the airflow patterns in solar dryers have been conducted using a CFD approach [21, 45, 46], where the study of the turbulence effect was proportionally related to the rate of heat transfer. This effect was also observed in the ISD where the presence of higher velocity in the heating area and lower velocities over the paddy rice spread along the drying area shows a temperature rise across the dryer (Figure 11b).  
The simulated results from the MATLAB/Simulink model are consistent with the experimental data (Figure 11). A similar behavior was also observed from drying various agricultural products using the Hohenheim solar tunnel dryer type [23, 47, 48, 49, 50].  
Several studies have reported different simulation models of forced convection solar drying systems, where a thin layer of drying materials was commonly assumed and described [22, 23, 51]. In this study, we have used the same approach and have further developed the drying kinetics of paddy rice. For example, we show that the prediction of moisture content during drying is more accurate compared to the prediction of temperatures (Table 3). Based on the results, the drying process in the ISD can be computationally evaluated with a good agreement to the empirical measurements.  
The results of the application of the model over the dry and the rainy seasons further support the results of the previous ISD experiments [16], where drying during the rainy season required a long period of time, while in the dry season it was accomplished within 10 h of the drying process. Further research should be undertaken with ANSYS fluent and MATLAB/Simulink to investigate the application of the collaborative model approach with different agricultural commodities and weather conditions.
# Characteristic Volatile Composition of Seven Seaweeds from the Yellow Sea of China
###### Abstract  
Plant volatile organic compounds (VOCs) represent a relatively wide class of secondary metabolites. The VOC profiles of seven seaweeds (_Grateloupia filicina_, _Polysiphonia senticulosa_, _Callithammoni corymosum_, _Sargassum thunbergii_, _Dictyota dichotoma_, _Enteromorpha prolifera_ and _Ulva lactuca_) from the Yellow Sea of China were investigated using multifiber headspace solid phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME/GC-MS), among them, the VOCs of three red algae Grateloupia filicina, Polysiphonia senticulosa, and Callithammoni corymosum were first reported. Principal component analysis (PCA) was used to disclose characteristic categories and molecules of VOCs and network pharmacology was performed to predict potential biomedical utilization of candidate seaweeds. Aldehyde was found to be the most abundant VOC category in the present study and (_E_)-\(\beta\)-ionone was the only compound found to exist in all seven seaweeds. The chemical diversity of aldehydes in _E. prolifera_ suggest its potential application in chemotaxonomy and hinted that divinylbenzene/carbouxan/polydimethylsiloxane (DVB/CAR/PDMS) fiber is more suitable for aldehyde extraction. VOCs in _D. dichotoma_ were characterized as sesquiterpenes and diterpenes and the most relevant pharmacological pathway was the neuroactive ligand-receptor interaction pathway, which suggests that _D. dichotoma_ may have certain preventive and therapeutic values in cancer, especially in lung cancer, in addition to neuropsychiatric diseases.  
volatile organic compounds; seaweeds; characteristic VOCs; chemotaxonomy; biomedical utilization; headspace solid phase microextraction 28 February 2021 25 March 202  
contributions in other areas have been seldom investigated. VOCs are released under biotic or abiotic stresses and have great significance for plant survival and reproduction. In many cases, evolution has resulted in some plant VOCs acting as metabolic safety valves, protective or defense compounds, or communication cues [1]. VOCs can be divided chemically as aldehydes, alcohols, terpenoids, ketones, halogenated compounds, sulfur compounds and hydrocarbons and exhibit multiutdinous functions, such as in being feeding attractants [11], pheromones [12] involved in chemical defenses [13; 14; 15] and acting as antimicrobial agents [16].  
In the present study, VOC profiles of seven seaweeds blooming in the littoral area of Qingdao (the Yellow Sea of China) were investigated, including the first report of VOCs for three red algae species--_Gratelcupia filicina, Polysiphonia senticulosa_ and _Callithamnion corymbosum_. Characteristic categories and molecules of VOCs are discussed from the point of view of their chemotaxonomic significance and bioactivities are discussed in order to further expand these seaweeds' potential utilities in pharmaceutical areas. Our study provides new insights for the chemotaxonomy of algae and for the biomedical application of _D. dichotoma_.