esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions  
Global maps need to be conform with international classification schemes, which are of limited applicability in the Arctic. They might be to some extent suitable for treeline monitoring, but not for up-scaling or modeling to the north. New classifications that serve the needs at high latitudes need to be developed separately, but circumpolar and compatible with global maps. Existing methodologies for SAR data could be exploited to widen the thematic detail available from such maps. Winter, as well as summer acquisitions from C-band missions (e.g., Sentinel-1 and Radarsat) might be suitable to complement traditional classifications with C-band based approaches. A spatial resolution around 30 m has been shown suitable for a range of applications. This implies that the current Landsat-8, as well as Sentinel-2 missions would be adequate as input data.  
Of special interest are shrubs and wetlands, as well as the occurrence of mosses and lichens. The identification of shrubs and their height is of interest to permafrost modeling, as this can serve as a proxy for snow distribution. The'shrub' class is available in global land cover products, but it is not mapped at all, or not consistently, at high latitudes. Wetness, as well as moss and lichen coverage are in general sub-features across all traditional land cover classes and, therefore, mostly not used as separate classes in local to regional maps. The predominance of shrubs, tussocks and sedges provides the background information for purpose-driven classification schemes. This should be also considered for circumpolar mapping.  
**Acknowledgments:** This study has been funded by the European Space Agency project DUE GlobPermafrost (Contract Number 4000116196/15/I-NB), which is lead by the Zentralanstalt fur Meteorologie und Geodynamik. Further support was received by the Austrian Science Fund under Grant II401. [PERSON]. [PERSON] was the recipient of a Research Fellowship by the European Space Agency's Climate Office during the writing of the manuscript. We also would like to thank the four anonymous reviewers for their comments on the manuscript.  
**Author Contributions:** [PERSON] developed the concept of the paper and wrote the majority of the manuscript. [PERSON] collected and categorized the literature reviewed in this paper. [PERSON] prepared all land cover figures and supported the global dataset review. [PERSON] contributed to the literature review and discussion in the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
## 4 Routing Protocol Design
### SOV Decision Process  
After sending a _content_request_ message, the SUV will wait for the ACK message responses from candidate SOVs. When the target content is of small volume, the nearest SOV is optimal to serve as the provider; consequently, the sender indicated in the first successfully received ACK response will be chosen. However, the target content is of large volume in this paper; hence, the SOV decision should be done with more than one ACK response. This is because the future network topology will affect the delivery of different parts of the large volume content, while the instantaneous topology information is enough for the delivery of small contents.  
For deciding the optimal SOV, a heuristic weighting method is proposed on the observation that the transmission delay is approximately proportional to the distance between transceivers. This is because the longer the transceiver distance is, the more time will be consumed for constructing the multi-hop routing path using the timer-based routing method described in the next sub-section. Based on this observation, the weighting metric is defined as the average transceiver distance for the whole transmission phase, that is:  
\[w_{k}=\int_{0}^{T_{\text{e2e}}}\frac{(d_{k}^{0}+|v_{k}-v_{\text{SUV}}|t)}{T_{ \text{e2e}}}dt\approx d_{k}^{0}+\frac{1}{2}|v_{k}-v_{\text{SUV}}|T_{\text{ data}}, \tag{6}\]  
where \(d_{k}^{0}\) is the initial inter-vehicle distance between the SUV and the \(k\)-th candidate SOV and \(T_{\text{e2e}}\) represents the overall content transmission time, which can be estimated as \(T_{\text{e2e}}\approx T_{\text{data}}\), given that we address the case of large contents in this work.  
Based on the principle in Equation (6), the SOV with the minimum value of \(w_{k}\) will be selected. Then, the SUV broadcasts a \(SOV\_response\) message to the chosen SOV using a similar method as that in the dissemination of the _content_request_ message.
# Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
## 3 Spatiotemporal Models for Continuous Emotion Recognition  
This paper proposes a two-step approach for continuous emotion prediction. In the first step, to circumvent the lack of sequences of continuous labeled videos, three source datasets, namely ImageNet, VGG-Face, and RAF-DB, are employed to initialize the 2D CNN architectures. ImageNet and VGG-Face datasets, which contain generic object images and face images, respectively, are used for pre-training three 2D CNN architectures: VGG-11, VGG-16, and ResNet50. These architectures were retained because they presented a state-of-the-art performance on discrete emotion recognition [42; 46; 47; 61; 62]. The RAF-DB dataset is closer to the target dataset since it contains face images annotated with discrete (categorical) emotions, and it is used for fine-tuning the 2D CNN architectures previously trained on ImageNet and VGG-Face datasets, as illustrated in Figure 2. Such 2D CNNs will be used as baseline models with the target dataset.  
In the second step, such baseline models are extended to handle spatiotemporal information and perform continuous emotion recognition. Furthermore, such extended models are fine-tuned on a target dataset. There are two strategies to model the sequential information of videos, as shown in Figure 3: (i) a cascade approach, where an LSTM unit is added after the last convolutional layer of the 2D CNNs to form a 2D CNN-LSTM; (ii) inflating the 2D convolutional layers of the 2D CNNs to a third dimension to build an i3D CNN. This second step also includes the pre-processing of video frames, as well as post-processing predictions.  
Figure 2: Overview of pre-training and fine-tuning of 2D CNN architectures for discrete emotion prediction.  
The rest of this section provides additional information on the pre-training and fine-tuning of 2D CNN models, the pre-processing steps to locate face images within video frames and build sequences of frames to feed spatiotemporal models, the DL models for continuous emotion recognition, and post-processing of the emotion predictions.
# Learning an Efficient Convolution Neural Network for Pansharpening
Hence, MRA-based methods, such as decimated wavelet transform (DWT) [6], Laplacian pyramid (LP) [7], and modulation transfer function (MTF) [8], extract spatial information with a corresponding linear decomposition model and inject the extracted component into LRMS. Pansharpening models guided by MRA are characterized by superior spectral consistency and higher spatial distortions. MBO [9; 10; 11] is an alternative pansharpening approach to the aforementioned classes, where an objective function is built based on the degradation process of MS and PAN. In this case, the fused image can be obtained via optimizing the loss function iteratively, which can be time-consuming. All the above-mentioned methods fuse with linear models, and these methods cannot achieve an appropriate trade-off between spatial quality and spectral preservation, as well as computational efficiency [12]. To overcome the shortcomings of linear model, many advanced nonlinear pansharpening models have been proposed, and among them, the convolutional neural network (CNN) based methods, such as pansharpening by convolutional neural networks (PNN) [13], deep network architecture for pan-sharpening (PanNet) [14], and deep residual pansharpening neural network (DRPNN) [15], are some of the most promising approaches. Compared with the previously discussed algorithms, these CNN-based methods significantly improve the pansharpening performance. However, those pansharpening models are trained on specific datasets with deep network architecture, and when generalized to different datasets, they tend to be less robust. In this paper, we adopt an end-to-end CNN model to address a pansharpening task, which breaks the linear limitation of traditional fusion algorithms. Different from most existing CNN-based methods, we have motivated our model as being more robust to the inconsistencies across different satellites. The contributions of this work are summarized as follows:  
1. We propose a four-layer inference network optimized with deep learning techniques for pansharpening. Compared with most CNN models, our inference network is lighter and requires less power consumption. Experiments demonstrate that our model significantly decreases the computational burden and tends to achieve satisfactory performance. 2. To make full use of the features extracted by convolutional layers, we introduce a dilated multilevel structure, where the former features under different receptive fields are concatenated with a local concatenation layer. We also introduce an overall skip connection to further compensate the lost details. Experimental results reveal that with local and overall compensation, our multilevel network exhibits novel performance even with four layers. 3. As our network is shallow and trained with several domain-specific techniques to prevent overfitting, our model exhibits more robust fusion ability when generalized to new satellites. This is not a common feature of other deep CNN approaches, since most of them are trained on specific datasets with deep networks, which lead to severe overfitting problem.
# Chaotic Features of Decomposed Time Series from Tidal River Water Level
## 3 Characteristics of Water Level and Decomposed Time Series  
### Decomposition of Water Level Time Series  
The water level time series from Ulsan gauging station was decomposed into four components of tide, wave, noise, and rainfall-runoff using the wavelet analysis and filtering technique proposed by [2]. The decomposition results are shown in Figure 4 and Table 1. Tide and wave components were separated through a sea surface level time series, and rainfall-runoff and noise components are defined as components excluding tide and wave components from the water level time series. A high pass filter is applied to separate these two components. In other words, the rainfall-runoff component and noise component were separated based on a certain threshold value, which reflects the characteristic that the water level rises rapidly when rainfall occurs. Therefore, rainfall-runoff shows a value of 0 in the no rainfall period, and has a certain value when the water level rises rapidly in the rainfall period.  
The water level time series has similar statistical characteristics with those of the tide component, but has a higher coefficient of skewness and a coefficient of variation due to the rainfall (Table 1). For the tide, it was identified that the mid-period component was well extracted in approximately 15 days for the spring and heap tide. In addition, compared to the other components, the tide shows the narrower ranges for standard deviation and coefficient of variation. On the other hand, the remaining component after the tide component was extracted from the sea-level time series is considered as the wave component, which shows a high coefficient of variation of 1.01 indicating a wide range (Table 1). The rainfall-runoff component occurs by rainfall and so it has zero values if there is no rainfall. Additionally, it corresponds to the sudden rises in a water level time series and has very high variability and skewness as shown in Table 1. The noise component is the last component.
## 4 Syntax and Security Description of CLR-SS-AIBBE
### Security Description of CLR-SS-AIBBE
If \(ID_{i}\in S\), the challenger uses \(Hdr\) to calculate the symmetric key \(CK\). Then, it recovers \(M\) by decrypting \(C\) with \(CK\) and sends it to \(\mathcal{A}\). **Challenge**. \(\mathcal{A}\) gives two messages, \(M_{0}\) and \(M_{1}\), of equal size. \(\mathcal{B}\) selects randomly \(\beta\leftarrow\{0,1\}\). Then, \(\mathcal{B}\) takes \(MP\) and the identity set \(S^{*}=\left\{ID_{1}^{\mathbf{x}_{1}},\ldots,ID_{d}^{\mathbf{x}}\right\}(d\leq l)\) as input. \(\mathcal{B}\) outputs \((Hdr^{*},CK^{*})\). \(\mathcal{B}\) utilizes \(CK^{*}\) to encrypt \(M_{\beta}\) to get the ciphertext \(C^{*}\). \(\mathcal{B}\) sends \((C^{*},Hdr^{*},S^{*})\). **Stage 2**. \(\mathcal{A}\) _can ask \(\mathcal{O}\)-Create, \(\mathcal{O}\)-Reveal, \(\mathcal{O}\)-Decrypt1 and \(\mathcal{O}\)-Decrypt2. The basic limitations are the same as stage 1. Other restrictions are that \(\mathcal{A}\) cannot inquiry the information about \(ID\in S^{*}\) and \(Hdr=Hdr^{*}\). In addition, a leakage inquiry cannot be performed. Since, if a leakage inquiry is allowed, \(\mathcal{A}\) may take the ciphertext, the decryption algorithm and \(M_{0}\) and \(M_{1}\) as the input of the leakage function and obtain a bit output, and win the game in an ordinary way._  
**Guess**. The attacker gives one guess, \(\beta^{\prime}\in\{0,1\}\). If \(\beta^{\prime}=\beta\), \(\mathcal{A}\) wins this game \(\mathrm{EX}_{\text{R}}\). The superiority, that \(\mathcal{A}\) wins this game \(\mathrm{EX}_{\text{R}}\), is defined as \(Adv_{\mathcal{A}}(L_{SK})=\left|\Pr[\beta^{\prime}=\beta]-\frac{1}{2}\right|\). If any PPT attacker can only win negligible advantages in the game \(\mathrm{EX}_{\text{R}}\), the CLR-SS-AIBBE scheme is said to be safety against leakage attack.
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
## 2 Target Peak Enhancement Using Time Reversal Processing
### Detection Performance Comparison  
In this section, the proposed method is compared to the conventional matched filtering with a threshold in terms of detection probability. Since the detection problem is a binary hypothesis testing problem, the likelihood ratio test is employed. For this, the probability density function of the received signal is required. In this development, the clutter signal has the K-distribution while the target signal has a constant intensity \(P_{l}\). A sketch of the derivation for the following probability density function (pdf) is presented in the Appendix.
## 4 Biological Control and the Three Ps: Predation, Parasitism, and Pathogens
### Parasitism  
At the end of the last century, the California Department of Food and Agriculture (CDFA) focused on determining the efficacy of different parasitoids in order to eradicate _B. oleae_ populations in the USA [111]. Several species of the Psyttalia genus were identified as _olf_ parasitoids. For instance, _P. lonsburyi_ is considered to be specific to _B. oleae_, but its effectiveness against this pest is compromised due to its fecundity problems and its difficulty of being reared under laboratory conditions [112]. The _P. concolor_ or _P. humilis_ species are also good candidates because they have already shown their potentials in biological control in the Mediterranean fruit fly, _Ceratitis capitata_, but generalist species would compromise the efficacy of the parasitism [113]. The main problem of this genus is that most of the species have small ovipositors compared to the fruit size in cultivated varieties. This fact decreases the probability of parasitizing the _olf_ larvae [114].  
Regarding parasitism upon the olive moth, efforts to determine the main candidates to perform this ecosystem service are focused on the species that are present during the most sensitive generations, phyllopagous and antibagous. For instance, the braconid _Chelounus eleaophilus_ has been pointed out as an efficient parasitoid with values of parasitism of up to 80% [107]. A relevant role in moth control is also played by the specialist parasitoid _Ageniaspis fuscicollis_ or the generalist _Elasmus flabellatus_[115, 116].  
Despite the enormous impact caused by _Xylella fastdiosa_ and its vectors, very little is known about the biological control agents of aphroporids. Few studies have determined the parasitism in these cercopids. For instance, _Verralia aucta_ is a parasite of the adults but does not have an immediate effect on _Xylella_ transmission given that it kills the vector after several weeks [117, 118]. Recently, the species _Ooctonus vulgatus_ has been proposed as a good candidate to its control due to the overlapping distribution of the parasitoid and its detection via Polymerase Chain Reaction (PCR) in different aphrophorid samples [119].
# A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
## 5 Data Aggregation Overview  
According to [PERSON] [13], the efficient design of the monitoring solution for large public buildings should meet the following requirements: (a) support collaborative real-time monitoring across multiple structures, (b) integrate the different structural monitoring systems via cloud platforms, (c) process and analyze big data, (d) evaluate monitoring data synergy and performance, as well as ensure uniform display of the monitoring results. In other words, smart building monitoring focuses on precise data acquisition from designated targets via an array of sensors and devices, necessitating the complex integration of data from various sources for comprehensive monitoring and subsequent analysis [50]. Therefore, managing large datasets, influenced by factors such as the building's functionality, scalability, and structural characteristics, presents a significant challenge in this sector.  
[PERSON] and [PERSON] [51] explain that effective data management enhances data exchange, facilitating the creation of an interoperable building model and defining the specifications of building components throughout its life cycle. Additionally, smart building applications require combining the data obtained from multiple decentralized systems. Therefore, more advanced computing infrastructures and the capability to handle high data generation demands are necessary. Existing research on computing infrastructures for IoT solutions can be explored [52].
_Applied_  
_sciences_  
_Article_  
The PCL Envelope Lack Sign (PELS) Is a Direct Arthroscopic Sign of Chronic Posterior Cruciate Ligament Insufficiency  
**[PERSON]** 1**, [PERSON] 2, [PERSON]** 3, [PERSON] 4, [PERSON] 5,  
**[PERSON]** 1**, [PERSON] 2 and [PERSON] 3,*  
Footnote 1: [[http://www.mdpi.com/journal/applsci](http://www.mdpi.com/journal/applsci)]([http://www.mdpi.com/journal/applsci](http://www.mdpi.com/journal/applsci))  
Footnote 2: [[http://www.mdpi.com/journals/article/](http://www.mdpi.com/journals/article/)]([http://www.mdpi.com/journals/article/](http://www.mdpi.com/journals/article/))  
Footnote 3: [[http://www.mdpi.com/journals/article/](http://www.mdpi.com/journals/article/)]([http://www.mdpi.com/journals/article/](http://www.mdpi.com/journals/article/))  
Footnote 4: [[http://www.mdpi.com/journals/article/](http://www.mdpi.com/journals/article/)]([http://www.mdpi.com/journals/article/](http://www.mdpi.com/journals/article/))  
Footnote 5: [[http://www.mdpi.com/journals/article/](http://www.mdpi.com/journals/article/)]([http://www.mdpi.com/journals/article/](http://www.mdpi.com/journals/article/))  
Footnote 6: [[http://www.mdpi.com/](http://www.mdpi.com/)]([http://www.mdpi.com/](http://www.mdpi.com/))  
+++ ==WARNING: Truncated because of repetitions==
journals/article/  
++  
symptoms such as vague pain; stiffness; and problems in high knee flexion, descending inclines and stairs, and during deceleration movements [1; 3; 5; 6]. Whereas both MRI and physical examination do not reach the expected sensitivity in chronic PCL tears, stress radiographs and arthroscopic evaluation of the ligament are the diagnostic tools of choice [3; 6; 7]. However, to date, there are no described direct arthroscopic signs of chronic PCL insufficiency. Available indirect signs, such as cartilage lesions in the medial compartment or patellofemoral joint, a \"slack\" anterior cruciate ligament (ACL) sign, or a posteromedial drive-through sign, are vulnerable to inaccuracy [1; 6; 8]. Therefore, direct arthroscopic sign of chronic PCL insufficiency could be very useful for arthroscopic surgeons.  
The aim of this study is to present the arthroscopic \"PCL envelope lack sign\" (PELS) and to calculate its diagnostic characteristics in chronic PCL insufficiency. Our hypothesis was that PELS would be present more often in PCL-insufficient patients than in PCL-efficient patients with good diagnostic characteristics.
# A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data
## 3 Bdmm
### Buffer Resource Allocation Problem Statement  
Existing seamless mobility support solutions based on temporary data buffering only propose data buffering at the access devices (Access Point or access router), and do not clearly define the specific solution design for buffering resource allocation. When buffer resources are unlimited, all packets during handover can be buffered. However, in a real environment, there is an upper limit on the buffer resources of access network devices. Assume that the buffering resources of the access network devices have been consumedand the UE has not completed the handover. In this scenario, subsequent packets sent to the UE continue to arrive, and cannot be buffered. It is assumed that there are two different types of mobile data flows in the UE, such as real-time data flow and non-real-time data flow. If the mobile buffer only adopts the Complete Sharing (CS) strategy [37], the buffer resources will be allocated to the two data flows according to the rate of the data flow. When the real-time data flow rate is much lower than the non-real-time data flow rate, more buffer resources are allocated to the non-real-time data flow. This will result in the loss of a large number of real-time packets during the handover. Real-time services are sensitive to packet loss, and the loss of a large number of data packets will seriously affect the quality of mobile user experience.  
From the above discussion, it can be seen that when there are multiple mobile data flows sharing buffer resources in the network, the buffer resource allocation strategy is related to multiple attributes of the mobile data flows. Therefore, when buffering resources are limited, how to allocate appropriate buffering resources to multiple mobile data flows is a major issue in seamless mobility support.