esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This article has modeled and evaluated three MAC protocols based on FSA (namely, FSA-ACK, FSA-FBP and DFSA) that are targeted at massive data collection in IoT scenarios. The analytic models are based on an AMC, which allows to derive the average delay and energy consumption for both the network coordinator and the end-devices. We then have used computer simulations to validate the model and compare each protocol variant in terms of these parameters, and depending on the number of end-devices present in the network.  
Results show the model accuracy and outline the benefits of DFSA with respect to FSA-FBP and FSA-ACK. In terms of delay, DFSA provides a reduction of 17% and 32% compared to FSA-FBP and FSA-ACK, respectively. In terms of energy consumption, for the network coordinator, DFSA provides savings of 23% and 28% compared to FSA-FBP and FSA-ACK, respectively. In contrast, for end-devices, DFSA provides savings of 50% and 24% compared to FSA-FBP and FSA-ACK, respectively.  
Finally, based on the analytic model and simulation results, the article has provided insights to configure each FSA variant optimally. Specifically, the FSA-ACK and FSA-ACK optimum value is \(m=n/2\), that is, when the number of slots per frame is twice the number of nodes in the network. In contrast, for DFSA the number of slots per frame has to be adjusted to \(\rho=1\), that is, be equal to the number of end-devices that contend in every frame. These optimizations are key for massive data collection in IoT scenarios, as these networks rely on low-power wireless communication technologies and have to support a large number of devices with stringent energy consumption requirements.  
Figure 9: Network coordinator and end-device average energy consumption (Joules) depending on the number of end-devices when using the optimal FSA-ACK, FSA-FBP and DFSA configuration.  
**Author Contributions:** [PERSON] has created the analytic model and performed the computer simulations to generate the results. [PERSON] has prepared and reviewed the manuscript. [PERSON] and [PERSON] have validated the results and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is partially supported by Generalitat de Catalunya (SGR-60-2017 and SGR-891-2017) and the Spanish Ministry of Science, Innovation and Universities (SPOT5G <TEC2017-87456-P> and SPOTS <RTI2018-095438-A-100>).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods  
### Preparation of the Optical Filter  
All of the optical filters were designed to be made into a stack of one-dimensional (1D) multilayers on the front and rear of conventional glass substrates with a thickness of 0.145 mm [22; 23]. The materials used in the filter design were SiO\({}_{2}\), with a low refractive index of 1.46, and TiO\({}_{2}\), with a high refractive index of 2.23. All simulations were conducted using the Essential Macleod software. In total, 25, 32, and 29 layers for the photopic filter (V-filter), melanopic action filter (Mel-filter), and circadian filter (C-filter), respectively, were used on the front side of the glass substrates so that light could be transmitted according to \(V(\lambda)\), the melanopic action spectrum (\(s_{\text{mel}}(\lambda)\)), and the \(C(\lambda)\) curves, respectively. In addition, 41 layers on the rear side of the substrate were introduced to block the infrared (IR) region of the incident light. Meanwhile, a refinement process to remove unwanted baseline ripple-shape transmittance was conducted using the aforementioned software. Based on the simulated and refined results, each layer was deposited using an e-beam evaporator. Ultraviolet (UV)-visible spectroscopy (LAMBDA(tm) 365, PerkinElmer, Inc., Waltham, MA, USA) was used to confirm that the fabricated filters matched the corresponding values of \(V(\lambda)\), \(s_{\text{mel}}(\lambda)\), and \(C(\lambda)\). The thickness of each layer of the fabricated filters was confirmed by FE-SEM (JSM-7610F, JEOL Ltd., Tokyo, Japan).
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 3 Methods
### Multinomial Logistic Regression Method  
Multinomial logistic regression was tested as one optional estimation method. The probability of the damage category \(k\) on stand \(p\) was estimated using the model  
\[\text{P}(\text{damage category on stand }p=k|\mathbf{x}_{p}) = \frac{e^{\beta_{\mathbf{k}}\mathbf{x}_{p}}}{1+\sum_{l=1}^{L-1}e^{\beta_{ \mathbf{k}}\mathbf{x}_{p}}},k=1,...,L-1\text{ and }\] \[= \frac{1}{1+\sum_{l=1}^{L-1}e^{\beta_{\mathbf{k}}\mathbf{x}_{p}}},k=L,\]where \(f(k,p)=\beta_{k}\mathbf{x}_{p}\) is a linear predictor function, \(\beta_{k}\) is the vector of the regression coefficient associated with damage category \(k\), \(\mathbf{x}_{p}\) is a vector the set of the explanatory variables associated with observation (stand) \(p\) and \(L\) is the number of the damage categories, here 3.
## 2 Materials and Methods
### Analysis of Rainfall Data during Typhoon Events  
Rainfall data registered during the selected typhoon and tropical storm events were analyzed to find potential relationships between the reactivation and evolution of the landslide area and heavy rainfall. From the CHIRPS data, we extracted daily precipitation for the date when the typhoon was closest to the landslide. For the CWB rain gauge station data, hourly precipitation data allowed us to identify rainfall events during the selected typhoons. We followed the definition of a rainfall event as explained in [PERSON] et al. [5] and [PERSON] et al. [8], where an event starts when there is more than 4 mm of rain registered in the gauge during an hour, and ends when the rain registered is lower than 4 mm for six consecutive hours. We calculated the duration, cumulative rainfall and intensity for each rainfall event. Rainfall duration is defined as the hours the event lasted; cumulative rainfall is the amount of precipitation registered during the entire rainfall event; rainfall intensity is the average amount of precipitation registered per hour during a rainfall event. The rainfall event with the highest duration and intensity within each typhoon event was selected for further analysis.
# Causal Inference for Modality Debiasing in Multimodal Emotion Recognition
## 5 Experiments
### Implementation Details  
**Feature Extraction.** For the CMU-MOSEI dataset [32], features extracted from the raw data are publicly available due to privacy considerations. The text modality is derived from video transcripts and converted into 300-dimensional vectors using pre-trained GloVe word embeddings (glove.840B.300d) [34]. Facial muscle movements in video frames are captured by extracting 35 facial action units using Facet [35]. Audio features are processed with COVAREP [36], including 12 Mel-frequency cepstral coefficients (MFCCs), pitch tracking, and other acoustic parameters, resulting in a 74-dimensional vector. Following previous work [20], we applied the same feature extraction methods used for the CMU-MOSEI dataset to the IEMOCAP dataset.  
**Experimental Setting.** In this study, we conducted experiments using three baseline MER architectures: MuIT [20], PMR [23], and DMD [24]. We re-implemented these three baselines based on the publicly available code and integrated them with our CausalMER framework. In all MER backbones, we utilized the pre-trained ALBERT [29] for the text-only branch and ResNet-18 [30] for both the audio-only and video-only branches, respectively. We used the Adam optimizer, and due to the differing characteristics of the IEMOCAP [31] and CMU-MOSEI [32] datasets, we set different learning rates for each. In the IEMOCAP [31] dataset, the text-only branch uses a learning rate of \(1\times 10^{-6}\), while the audio-only, video-only, and MER backbone branches use \(2\times 10^{-3}\). In the CMU-MOSEI [32] dataset, we trained MuIT [20] and PMR [23] with a learning rate of \(1\times 10^{-3}\), while DMD [24] was trained with a learning rate of \(1\times 10^{-4}\). For the IEMOCAP [31] dataset, we used a batch size of 32. In the CMU-MOSEI [32] dataset, the batch size was 32 for PMR [23] and 16 for both MuIT [20] and DMD [24]. In contrast to various counterfactual inferences that use a grid search strategy [10; 11; 37], CausalMER does not use such an approach. All experiments were implemented using PyTorch (1.13.0) on an NVIDIA RTX A6000 GPU with 48 GB of memory. Additionally, we reported CausalMER with MASK fusion as a key result.
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 3 Multi-Service Adaptive Semi-Persistent Scheduling Algorithm
### Control Mechanism for the Adaptive SPS Period
In order to benefit from the longer SPS periods as well as to avoid excessive packet delay, the adaptation function in Equation (1) should be modified to allocate higher number of PRBs (greater than the PRBs required to serve the latest reported buffer) to the device in a predictive manner to closely match the instanteneous buffered data volume at the device at the next SPS transmission opportunity. Figure 2: Worst case packet delay for traffic class \(S\).
# Discrete Element Analysis of Grouting Reinforcement and Slurry Diffusion in Overburden Strata
## 4 Discrete Element Grouting Diffusion Results
### Grouting Reinforcement Mechanism Analysis
The decrease in porosity near the grouting hole can be attributed to the fact that when the monitoring zone contains either pure cement or pure overburden particles, the porosity tends to be higher; when located in the mixing zone of cement and overburden particles, the porosity is relatively low due to the filling effect of cement particles; as the distance from the grouting hole further increases, the monitoring zones consist entirely of overburden particles, and porosity gradually increases due to pressure transmission. Notably, porosity along the positive vertical axis shows a gradual increasing trend, which is attributed to the combined effects of the slurry entry channel and extrusion near the grouting hole. The coordination number distribution is shown in Figure 9b. As the distance from the grouting hole increases, the coordination number in both the horizontal and inclined directions first increases and then decreases, exhibiting approximate symmetry about the origin. The coordination number ranges from 4.2 to 6.5. However, along the positive vertical axis, the coordination number initially increases and then stabilizes. This behavior occurs because the slurry enters the grouting hole from top to bottom via the grouting pipe. As a result, the monitoring zone above the grouting hole captures the state of cement particles within the pipe, which does not accurately reflect the reinforcement characteristics of the overburden. Affected by the random and uneven distribution of particles on the coverage side, the porosity curve and the coordination number distribution curve are not completely symmetrical with the horizontal axis zero point. Specifically, the coordination number on the left side of the grouting hole is slightly higher than on the right, and the lower-left side exhibits higher values than the upper-right side--indicating a stronger reinforcement effect in the lower-left region compared to the upper-right. Figure 9: Monitoring results of particle contact law after grouting. (**a**) Distribution law of particle porosity after grouting. (**b**) Distribution law of particle coordination number after grouting.
## 4 Application of the Method
### Control Efficiency Analysis  
Two factors are introduced to measure the efficiency of the control \(R_{\text{C}}(t)\). One is the liveness of resilient recovery \(R(t)\), which is proportional to \(R_{\text{C}}(t)\). The other is the controlling cost \(\text{C}(t)\), which is inversely proportional to \(R_{\text{C}}(t)\). The control efficiency \(R_{\text{C}}(t)\) is represented as Equation (20), which can be applied to compare SN recovery strategies.  
\[R_{\text{C}}(t)=R(t)/\text{C}(t) \tag{21}\]  
The supply chain strategies are taken from [44]. There are five recovery strategies in our model (listed below), and the corresponding control costs are shown in Figure 7.  
(1) Emergency management costs. The parameter \(g\) shows the capability of emergency management.  
Figure 6: Skeleton identification for resilient recovery. (**a**) network a, (**b**) network b, (**c**) network c.  
2. Strategy adjustment costs. The parameter \(\mu\) used to identify \(\overline{\alpha}\) indicates the capability for strategy adjustment.
3. Coupled node controlling costs. The coupling relationship will be changed by the structural changes. The parameter \(\Gamma\) is used to control the heterogeneous coupled nodes.
4. Incentive mechanism costs. The feedback matrix \(B_{ik}\) and pulse period \(t\) are concerned with the intensity and frequency of the incentive mechanism, respectively.
5. Reconstruction costs.
## 2 System Description  
### Architecture of the Teleoperation System  
As illustrated in Figure 1, the proposed bimanual robotic teleoperation system consists of two robotic arms, two haptic devices, two robotic grippers, a Leap Motion Controller, communication channels, and a human operator. Two haptic devices and a Leap Motion Controller are set up on the master side while two robotic arms and two robotic grippers are adopted on the slave side. Generally, the overall system is made up of two subsystems, namely the robotic arm teleoperation and the robotic gripper teleoperation. The former subsystem contains two robotic arms and two haptic devices while the latter subsystem consists of two robotic grippers and a Leap Motion Controller.  
In the robotic arm teleoperation subsystem, two UR10 are equipped as the slave robots while two haptic devices of Geomagic Touch (3D Systems, Rock Hill, SC, USA) serve as the master devices. Each Geomagic Touch is used for the teleoperation of one UR10. During the execution, a human operator manipulates specific joints of the Geomagic Touch to generate target movements. Device information such as the position and motion tracking data are recorded and sent to the slave robot as the control command.  
In the robotic gripper teleoperation subsystem, a Leap Motion Controller (Leap Motion, San Francisco, CA, USA) and two 3D printed anthropomorphic robotic grippers are set up on the master and slave sides, respectively. Each gripper is mounted on the end effector of the UR10 for manipulation tasks. Two human hands are used in the gripper teleoperation and each hand controls a gripper. A Leap Motion Controller is adopted for human hands motion tracking. Hand gestures and finger motion information are recorded as the control command and sent to the corresponding robotic gripper.  
Basically, the two subsystems are independent of each other. They can be performed simultaneously in practical teleoperation. The robotic grippers can be used for manipulation tasks while the robotic arms keep moving. No switching mechanism is needed between the robotic arm and gripper control. However, the human operator only has two hands. He can only control two devices simultaneously, i.e., two robotic arms, two grippers, or one robotic arm and one gripper.  
Figure 1: _Cont_.
## 6 Results and Discussion  
Empirical research was carried out; real data were collected from two potential and geographically diverse sites in Saudi Arabia. These two sites are Turaif and Wejh. The former is located in the northern part of Saudi Arabia, while the latter is in the north-western part of the country. Turaif is at a height of 827 meters above sea level, while Wejh is located at a height of 24 meters above sea level. The measurements for wind speed were taken at hub heights ranging from 50 to 140 m. The speed of wind was interpolated or estimated at an interval size of 10 meters using local wind shear exponent. The data for the study were collected over a period of 39 years, from 2 January 1980 to 31 December 2018 and are comprised of hourly average values [40]. Necessary information relevant to this study was extracted from the collected data. Moreover, 15 different turbines from various manufacturers were considered. The rated capacities of the turbines were between 1.5 and 3 MW. Table 2 provides specifications of these turbines.  
The TOPSIS analysis was carried out using the input data. Results were divided into three sets of analysis. In the first set, data for a specific turbine were selected and TOPSIS analysis was performed to identify the best balance between hub height (HH), wind speed (WS), percentage of zero output (ZP), percentage of rated output (RP), and annual energy production (AEP). This procedure was repeated for all turbines and the best tradeoff solution was found for each turbine. In the second analysis, the best solutions for each turbine from the first phase were mutually compared, and the best turbine among all turbines was identified. In the third analysis, results from the two sites were compared and analyzed to observe any specific trends. Details of these analysis are provided in the following sections.
## 4 Discussion
### Analysis of Fatty Acid Composition and Oxidative Stability  
It is well known that microbial oils extracted from the oleaginous yeast _Y. lipolytica_ are rich in oleic and linoleic acid, which has also been confirmed by conducted studies. The composition of all extracted lipids was dominated by these two acids, as mentioned. Differences in fatty acid composition between biomass oil samples obtained from cultures in molasses hydrolysate media and post-frying rapeseed oil media were noticeable as well. In lipid samples from cultures with molasses hydrolysate, no C20:0 and C22:0 acid content was recorded in opposition to C16:0 and C18:1, whose percentages were increased relative to the other samples. In general, microbial oils from molasses-based media were less diverse in composition than samples from cultures with waste rapeseed oil. When [PERSON] et al. [28] cultured three mutant strains of _Y. lipolytica_ overexpressing _DGA2_ in medium with 8% (_w_/_w_) molasses, palmitic acid accounted for 14.0-19.6% of all fatty acids after 3 days of culture. The dominant was oleic acid 53.9-55.7%, but linoleic acid content was significantly lower than in the current experiment with wild strain, ranging from 3.9-9.4%. Very similar results were obtained by the cited authors for cultures in sucrose medium.  
Oils with a high content of unsaturated fatty acids, which include the cellular oils of oleaginous yeast, are particularly easily oxidized. Oxidative stability is one of the most important parameters regarding the asses of oil quality. Oxidation of fatty acids under the influence of heat treatment, extraction conditions or during storage causes quality deterioration [29]. The extracted oil samples were less stable compared to conventional rapeseed oil or olive oil for which T\({}_{\text{max}}\) measured at 120 \({}^{\circ}\)C ranged from 66.61-74.78 min and 134.15-180.07 min, respectively [30,31]. While in our study, T\({}_{\text{max}}\) parameter ranged from 10.04-26.36 min for the analyzed samples. On the other hand, similar oxidative stability was observed for measurements on linseed oil and sunflower oil. The maximum time of oxidative reaction for sunflower oil at 120 \({}^{\circ}\)C was 33.4 min [20]. For linseed oil, especially rich in linolenic acid, oleic acid and linoleic acid, the maximum time of induction was 21.20-24.72 min [32], making it the closest to the values obtained in the current experiment.
## 8 Conclusions  
To monitor valuable objects sensor networks are usually deployed. To pursue profit from the objects, attackers generally capture the object by tracing the source. Therefore, many protocols have been proposed for source location protection. In this paper, we first analyze the limitations of existing works. Then, to address these limitations, we propose SLP to improve the source location security. Considering more powerful attackers with wider fields of vision, we further propose an enhanced protocol named SLP-E. Both theoretical analysis and simulation results show that compared with existing works, both SLP and SLP-E can improve the source location security significantly with low communication cost.
## 1 Introduction
Moreover, given the 2030 objectives to increase sugarcane production, there is a need to assess and compare the VPM performance at distinct sugarcane plantations using MSR and HSR images before it can be applied to perform GPP estimations with HSR images at the regional, continental, and global scales. The core objectives of our investigation were: (1) to evaluate the consistency of satellite-derived vegetation indices (EVI and LSWI) from MSR (MODIS) and HSR (Landsat and Sentinel-2) images in tracking the vegetation phenology and sugarcane crop physiology at two distinct sites; (2) to assess the performance of the VPM in estimating the daily site-level vegetation carbon uptake of sugarcane croplands with different management practices when MSR and HSR images are used, which would shed new light on the advantages of estimating GPP with HSR images; and (3) to analyze the capabilities of the VTM in estimating the daily transpiration of sugarcane croplands.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Cross-Layer Routing  
Cross-layer routing protocol mainly integrates multiple protocol layers, and integrates the advantages of each protocol stack to achieve a better network performance. Experiments showed that the cross-layer method is more adaptable to dynamic WBANs, and the collaboration between different layers can better serve different priority data, provide customized services for each type of data, and achieve a comprehensive network performance with a low latency, high reliability, and energy saving.
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 3 System Model
### Proposed Stackelberg Security-Based Game Theory Algorithm for Securing IoMT Networks Using UAVs
#### 3.2.2 Key Features of the UAV-Assisted Stackelberg Game Model  
The key features of the UAV-assisted Stackelberg game model for securing IoMT healthcare networks are its ability to provide a secure and efficient communication network. One of the main contributions of the model is its use of a Stackelberg game framework to model to secure the UAVs and the IoMT devices' data. This allows the attacker (black hole) or rogue medical device to act as a leader in the game, making strategic decisions to harm the UAV and hack the data. In response, the UAV acts as a defender to secure its data and form its strategies by utilizing the Stackelberg game theory model. This can help improve the accuracy and timeliness of healthcare data, leading to better patient diagnosis and treatment outcomes. Additionally, the model's use of moveable UAVs as assistants between network layer to patient layer devices can help overcome challenges associated with traditional fixed networks, such as limited coverage and interference from other wireless devices.