esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation Scope
### Target Domain
#### 4.1.5 Media Sector  
Explainable recommender systems have also been studied with regard to media contexts, including news, movies, music, books, gaming, and art [26; 31; 32; 33; 34; 55; 69; 71; 95; 96; 97; 98]. These systems have much in common with product recommendation systems, yet there is a significant difference. Mass and social media influence public opinion, inform societal norms, and play a crucial role in shaping individuals' knowledge, attitudes, and values as well as how they perceive and engage with political and social topics. Because of this, recommender systems are at risk of contributing to the formation of filter bubbles and the spread of disinformation and toxic content.  
In this context, [PERSON] et al. [55] and [PERSON] et al. [47] delved into the explanation of news feed algorithms. [PERSON] et al. [46] examined tools designed for social media administrators to detect offensive language in tweets. [PERSON] et al. [60] analyzed explanations concerning the AI-supported detection of toxic social media posts. With regard to disinformation, [PERSON] et al. [31] investigated deceptive practices in reviews, while [PERSON] and [PERSON] [53] and [PERSON] et al. [60] focused on explanations used to support the sentiment classification of movie reviews. [PERSON] et al. [26; 41] evaluated explainable music recommendations regarding their impact on perceived usefulness, satisfaction, and engagement of end-users. In contrast, [PERSON] et al. [33] evaluated the usefulness of explanations for \"debugging\" AI-generated playlists. Regarding film recommender systems, [PERSON] et al. [70] investigated the mental models of end-users, while [PERSON] et al. [71] evaluated different explanation styles regarding trust and satisfaction.  
Empirical studies in this domain also show that users expressed concerns about the amount of information presented, as excessive details can lead to cognitive overload [99; 100]. Moreover, research in gaming and art recommender areas has shown that users prefer prompt hints (explanations) with communicative and user-friendly interfaces [101; 102].
## 3 Q2: What Are the Challenges of the Current UIoT System
### Authenticity  
In UIoT networks, authentication refers to the verification between sender and receiver node. As discussed in Section 3.3.1, the environmental condition is complex. In addition, it is difficult to adapt the terrestrial authentication scheme to the UIoT environment. Therefore, the attacker finds it easier to block the channel. Hence, it is necessary to design a lightweight authentication scheme for UIoT networks.
## 2 Intent-Estimation- and Motion-Model-Based Collision Avoidance Method
### Collision Avoidance  
The trajectory of the autonomous vehicle is planned using a map of the local environment that contains the road structure, static obstacles and moving obstacles. With the predicted trajectory of detected moving vehicles and the planned trajectory of the autonomous vehicle, collision points will be obtained if they exist. Then, the position of the collision points on the map will be marked. Two methods can be applied by the collision avoidance system: avoiding the collision point in the time dimension and avoiding the collision point in the space dimension. The two methods correspond to acceleration and path re-planning. Appropriate evasive maneuvers will be taken to obtain a longer collision distance and greater driving comfort. As shown in Figure 9a, the detected vehicle is moving at a speed of \(v_{DV}\) in the middle lane, and no intent to change lanes is detected. A potential collision point, which is represented by a red point, is obtained using the longitudinal distance and velocity:  
\[d_{collision}=v_{AV}*\frac{D}{v_{AV}-v_{DV}} \tag{26}\]  
where \(D\) is the longitudinal distance, and \(d_{collision}\) defines the position of the collision point. To guarantee timeliness, a lane change is chosen to avoid a potential collision.  
For the scene in Figure 9b, the lane change intent of the detected vehicle can be estimated, and the trajectory is predicted. A collision will occur after \(T\):  
\[d_{collision}=v_{AV}*T \tag{27}\]  
To guarantee the safety of the autonomous vehicle, a deceleration is performed to avoid the collision point.
## 5 Discussion
### Application of GF_Sen  
In this paper, we conducted a study on the GF_Sen dataset, simulated dataset, and multi-scene dataset WHU, among others, and compared the accuracy of the four most mature models, as well as two single models internally, to gain an insight into their performance. In recent years, there have been many advanced deep learning-based super-resolution reconstruction methods. In the future, there is an interest in conducting more comparisons to explore the advantages and disadvantages of different datasets for different models. Although the CSWGAN is trained based on cross-sensor datasets, for the simulated dataset, this paper reconstructed scenes from the WHU dataset and achieved better results than other models for most scenes, demonstrating the universality of the model. The proposal of the cross-sensor remote sensing dataset GF_Sen based on GaoFen-2 and Sentinel-2 supplements the current benchmark pairs of remote sensing image datasets in the field of remote sensing super-resolution reconstruction, providing a benchmark dataset for applications such as fine-grained classification [58], building extraction [59], tree detection [60], and detailed land cover mapping [61]. This dataset addresses the conditions of (1) different sensors, (2) regional diversity, (3) temporal consistency, and (4) challenging scenes, supporting the design of more advanced SR models.
## 3 Multipath Routing in Wireless Sensor Networks
### Motivations for Using Multipath Routing Approach in Wireless Sensor Networks
Alternatively, other techniques such as directed antenna [47] and multi-channel data transmission [48, 49, 50, 51] have been proposed to reduce inter-path interference. However, as location-aware routing protocols demand localization support, they impose significant overhead in terms of communication and computational complexity. Utilizing directed antennas for interference reduction also requires specific hardware equipment that may not be cost-effective in low-cost wireless sensor nodes. In addition, while multi-channel communication can increase network throughput and reduce inter-node interference, it requires a specific MAC layer mechanism that supports channel switching. More importantly, multichannel communication in the frequency band 2.4 GHz is highly affected by the external interference caused by 802.11 networks and Bluetooth devices. Accordingly, these approaches may not be efficient solutions to reduce the negative effects of interference in many applications. [PERSON] _et al._[52] introduced an indicator metric, which is called _correlation factor_, to measure the relative degree of inter-path independency. The correlation factor of two paths is defined as the number of links connecting two paths to each other. Since this approach requires the network connectivity graph to select the minimum interfering paths, using this technique in dense wireless sensor networks imposes high computational overhead. _QoS Improvement:_ QoS support in terms of network throughput, end-to-end latency and data delivery ratio is an important objective in designing multipath routing protocols for different types of networks [8, 13]. Discovered paths with various characteristics can be utilized to distribute network traffic based on the QoS demands of the application for which the multipath routing protocol has been designed. For instance, time critical data packets can be transmitted through higher capacity paths with minimum delay while delay insensitive non-critical data packets can be forwarded through non-optimal paths with higher end-to-end delay. Furthermore, in contrast with the single-path routing techniques multipath routing approaches can preserve QoS demands of the intended application in the case of path failures through directing network traffic to the another active path. Still, due to the link layer issues in single-channel wireless networks, improving network throughput and data delivery ratio through concurrent multipath routing in sensor networks may not be as easy as wired networks.
## 4 Result
### Spatial and Temporal Distribution of TVMPDI  
The Theil-Sen median trend analysis values of the TVMPDI change are shown in Figure 6. In general, the slope of change in all five cities is positive. Except Lhasa, the other four districts are shown in Figure 6a (p \(<\) 0.05). The slope of the TVMPDI in March and December looks higher, which remains around 0.01 (Figure 6b,e, respectively), while the increase in the TVMPDI was insignificant in June and September (Figure 6c,d, respectively).  
Figure 7a demonstrates that the annual mean value of the TVMPDI increased in the southeast of Tibet from 2003 to 2018, which indicates that the drought in this region was decreasing. The region behaved differently according to different seasons: Wet and slightly wet areas in the study area accounted for 93.05% in March (Figure 7b), while the value was just 48.21% in June (Figure 7c). The areas of gradual drying were concentrated on the east and west sides. This situation improved in September (Figure 7d), with only some western parts of the study area becoming slightly dry. In December, 55.86% was wet and 31.85% was slightly wet (Figure 7e).
## 3 Results and Discussion
### Imaging of PAN/Biochar Fibers though the Preparation Process
#### 3.2.2 ESEM Imaging of Carbonized Fibers  
In Figure 6, it can be seen that the grains were well dispersed inside the fiber mat, and in this composition, they occupied a large portion of the material mass. Fiber diameters remained around 1 \(\upmu\)m for all the investigated cases. The dimension of biochar beads was calculated with ImageJ software, and it resulted in below 10 microns in all observed cases. The 50-50% sample, that represented a bold substitution rate, still showed the fiber completely formed, and the biochar particles seemed blended to the carbon structure of the fibers.  
As a primary result, it can be seen from the pictures that biochar beads were successfully jointed to the fibers and that they overcame the thermal treatments. After the carbonization process, the particles did not further dissolve in the carbon fibers structure. The fibers observed with the ESEM resembled a lattice structure with biochar grains dispersed and fixed in position. The same results were found by [PERSON] et al. [13] incorporating the char particles in PAN fibers: \"after the thermal treatments of stabilization and carbonization, the resulting carbon nanofibers well-retained overall morphological structures of their precursor nanofibers\".
## 2 Problem Formulation  
The sensor placement optimization of VSNs is a practical engineering problem which aims to determine the optimal deployment of sensors in the actual physical space. During the research process, it is necessary to abstract the real camera and physical space as mathematical models for quantitative analysis purposes. In this section, the problem is first described as mathematical optimization with some necessary assumptions. Then, we establish some fundamental models as the mathematical foundation of the optimization process, including the sensor model, the space model, the coverage model, and the reconstruction error model, and propose a dimensionality reduction search method.
## 1 Introduction
### Research Objective  
This study aimed to enhance the structural stability of torsion beam suspension for the rear wheels of small SUVs. The objective was to maintain the existing torsion beam suspension shape while improving its stability through modifications to the cross-member reinforcement shape. Based on previous research findings [3], the existing parameters were retained, and cross-member reinforcements were added to the torsion beam suspension to analyze their effects. Using SolidWorks, six different cross-sectional shapes and one reference shape were modeled based on the overall structure of the torsion beam [4]. Material selection for the modeling was carried out, and a stiffness analysis was conductedto examine the stress and stiffness when applying loads to the endpoints of the beam, while considering vehicle rotation.  
This study investigated cross-member shapes that offer driving stability advantages by examining the influence of curvature variation in cross-member reinforcements on the torsion beam suspension. Factors associated with driving stability, smooth road shake (SRS), impact hardness, and memory shake were applied to assess the vibrations transmitted through the torsion beam suspension.  
SRS refers to the vibrations transmitted to the seat and foot floor panel by unbalanced masses in specific speed ranges. These unbalanced masses induce vertical, yaw, and camber movements through the torsion beam suspension, resulting in a negative riding stability for the driver [8]. Impact hardness refers to longitudinal and vertical accelerations when a vehicle experiences sudden impacts. Memory shake refers to the vibrations transmitted to the driver in the vertical and longitudinal directions for a certain period after impact. Although impact hardness and memory shake exhibit contrasting characteristics, when considering memory shake as a reference, they do not necessarily show opposing features [20]. To understand these three quality factors, the side view swing arm (SVSA) angle was used as a key parameter. The SVSA angle is a significant geometric factor that affects the ride comfort performance of a vehicle; a negative SVSA angle indicates superior ride comfort [8].  
This study selected a stable torsional stiffness and optimized the SVSA angle according to the cross-member section with different curvature reinforcements. Figure 1 shows the overall analysis of this study.
# A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets
## 3 Methodology
### BNet: System Description
#### 3.2.1 Embedding Module  
Let \((W,y)\) be the pair of inputs to our system, where \(W=\{w_{1},w_{2},\ldots,w_{l}\}\) is the set of the words in a tweet and \(y\) is the label corresponding to an emotion. The goal of the embedding module is to represent each word \(w_{i}\) by a vector \(v_{w_{i}}\) and the label by a vector \(v_{y}\).  
Our embedding module can be seen as a function that maps a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key. The query is the trainable label embedding, \(E^{Q}(y)\); the keys are the pretrained words embeddings, \(E^{K}(w_{i})\)\(\forall w_{i}\in W\); and the values are the trainable words embeddings, \(E^{V}(w_{i})\)\(\forall w_{i}\in W\).  
As shown in Figure 2, we used the output of \(E^{Q}\) and \(E^{K}\) as inputs to the attention model to find the alignments, i.e., the weights \(\alpha\), between the label \(y\) and the words \(W\) of the input tweet. This step models the relation between the input and the label. As soon as the weights were obtained, we then multiplied each word's vector that came from the embedding \(E^{V}\) by its corresponding weight. Given that, the final representation of a word \(w_{i}\in W\) is given as the following:  
\[v_{w_{i}}=E^{V}(w_{i})\cdot\alpha(w_{i},y) \tag{3}\]  
The function \(\alpha\) is an attention-based model, which finds the strength of the relationship between the word \(w_{i}\) and the label \(y\) based on their semantic similarity. That is, \(\alpha(w_{i},y)\) is a value based on the distance \(\Delta(w_{i},y)\) between \(w_{i}\) and \(y\) as:  
Figure 2: An illustration of the proposed system. Shaded parts are trainable. Fw and Bw refer to the Forward and Backward cells respectively, and FF means Feed-Forward layer.  
\[\alpha(w_{i},y)=\frac{e^{\Delta(w_{i},y)}}{\sum_{w_{j}\in W}e^{\Delta(w_{j},y)}} \tag{4}\]  
Here, \(\Delta(w_{i},y)\) is a scalar score that represents the similarity between the word \(w_{i}\) and the label \(y\):  
\[\Delta(w_{i},y) =E^{k}(w_{i})\cdot{v_{y}}^{T} \tag{5}\] \[v_{y} =E^{Q}(y) \tag{6}\]  
It is worth noting that \(\alpha(w_{i},y)\in[0,1]\) and:  
\[\sum_{w_{i}\in W}\alpha(w_{i},y)=1 \tag{7}\]
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 3 Methodology
### Training Methods of Two Deep Networks  
As illustrated above, the proposed paradigm DeepWL comprises two specially designed deep neural networks, i.e., WLdetectNet and WLgenerateNet. In addition, their architectures have been presented in Section 3.1 and Section 3.2, respectively. Thus, in this section, we focus on their training methods, in which the WLdetectNet is trained in a supervised learning fashion while the WLgenerateNet is trained in an unsupervised learning fashion.
## 1 Introduction
[21] carried out an experiment to investigate the efficiency of PV panels with and without active cooling. Without active cooling, they discovered that the module's temperature was high, and the efficiency of the solar cells was in the range of 8-9%. Meanwhile, the temperature of the PV was diminished significantly when the module was cooled by forced convection, leading to an increase in the efficiency by 12% to 14%. In another experiment by [PERSON] et al. [22], they indicated that, when compared to an asymmetric compound parabolic concentrator (ACPC) with no cooling impact, the total energy output and the conversion efficiency of an ACPC with active cooling was enhanced by 31%. Although active cooling techniques have better cooling efficiency, the energy needed to operate the cooling equipment, and the high cooling-equipment operating and maintenance costs may prevent them from being applied to large-scale solar power plants. To minimize the levelized cost of energy, integration of passive cooling with active cooling can result in a better solution where waste thermal energy can also be utilized to support other applications, such as a thermal membrane distillation [23]. Therefore, the main goal of this research is to experimentally assess the impact of using a passive\(-\)active cooling strategy to maintain a PV panel's optimal operating temperature and improve the overall solar energy conversion efficiency, under the environmental conditions of Riyadh, KSA. The current investigation is based on the performance comparison of four PV panels installed in the same location. The first one is used as a reference without any cooling approach, the second is a PV-PCM system, the third is a combined passive\(-\)active cooling system (PV/T-PCM), and the fourth is an integration of low solar-concentration sheets to the PV/T-PCM (LCPV/T-PCM). In most of the previous studies on PV cooling using PCMs, the phase transition temperatures were in the range of 25-30 \({}^{\circ}\)C for different PCMs. In the current study, the ambient high-temperature conditions of Riyadh city are considered in evaluating the proposed hybrid systems. Therefore, new experimental data are explored and discussed to compare and examine the workability of integrating these systems with a PCM with a high transition temperature of 41-48 \({}^{\circ}\)C.
## 1 Introduction
The advances in computational intelligence have promoted the development of different algorithms, techniques, and procedures to solve issues of location problems such as site suitability, hazard susceptibility mapping, and spatial prediction [12; 13; 14]. In recent times, artificial intelligence and machine learning algorithms have gained wide applicability in the field of geosciences. For example, identifying suitable land for agricultural purposes [15], predicting locations susceptible to flooding [14; 16; 17; 18], landslides [19; 20; 21], forest fire [22; 23], and groundwater potential [24; 25; 26] have been accomplished using prominent algorithms such as artificial neural network (ANN), support vector machine (SVM), multilayer perceptron (MLP), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), fuzzy logic, and logistic regression. Locating a healthcare facility like a hospital is a critical multiciteria location-based problem but based on a thorough literature search by the authors, no work has been reported specifically for a hospital location using these novel techniques to optimize the decision-making process despite its success in similar fields. This study investigates the efficiency of SVM, MLP, and LR models to predict suitable locations to site a hospital in the Gaza Strip. The outcome of this study will provide a methodology to assist in the site suitability assessment of hospitals in the study area for efficient healthcare planning, management, and delivery.
## 1 Introduction
### Clustering for WSNs  
Wireless Sensor Networks (WSNs) are networks composed of distributed micro-devices embedded with various sensing abilities (called sensors), which are used to monitor the environment and send the information back to a central Base Station (BS) [1; 2]. WSNs typically include a large number of sensors that are equipped with limited energy resources, but are required to operate without recharging or replacing batteries for extended periods of time. Energy efficient communication solutions for WSNs are highlighted by many researchers [3; 4]. In order to prolong a networks longevity, clustering techniques have been introduced to achieve energy efficient communication between sensors. A clustering algorithm can partition sensors into different clusters/groups, as shown in Figure 1. In each cluster, a Cluster Header (CH) is elected to be in charge of generating a transmission schedule, gathering data from all the sensors in the cluster and transmitting the assembled data back to the BS. Based on the clustered structure, the system can maintain a longer life by scheduling the duty cycle between the sensors within a cluster, without harming the functionality of the network. In addition to saving energy from scheduling, a sensor can also reduce energy consumption from communication since it only needs to communicate with a local CH rather than a far located BS. Clustering techniques can decompose sensors within a WSN into different clusters. After joining a cluster, a sensor normally only needs to communicate with its own CH. A CH can communicate with the BS directly or through other CHs, as shown in Figure 1. The routing between the sensors in the same cluster is called _intra-cluster routing_. The routing between the CHs and the BS is called _inter-cluster routing_. The routing scheme can be either single hop or multihop, and is dependent upon several factors, such as the objectives of a clustering algorithm or the communication capability of the sensors.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.10. Sensor Information Networking Architecture (SINA)  
This is a database style approach, supporting reprogramming through downloaded scripts [65]. The SEE (Sensor Execution Environment) dispatches incoming messages and executes contained scripts. These are in an extended form of SQL called Sensor Query and Tasking Language (SQTL)--this adds event-based procedural programs, that can be explicitly propagated around the network using the execute primitive, which can select a subset of nodes to propagate the script to. There is no built-in version control, or limits to traffic.  
The database-style model of SINA might provide an alternative framework for highly flexible behavior for autonomous software updates.