esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Contributions and Novelty  
Although joint statistic results are essential for performance analysis in communication theory, the previous results are not valid for direct application to the model considered in this study. This is because they can only provide us with the analytical framework and not the required statistical results based on the statistical distribution in consideration, indicating that the problems of deriving the core equations for specific fading environments persist.  
In this study, these unresolved problems are statistically analyzed by providing results in the form of either general closed-form expressions or at least a single integral form instead of the originally complicated multiple-integral form. If the number of RVs is large, even a single integral form is advantageous in terms of computational complexity compared with multifold integration. By adopting the analysis framework introduced in [35; 36], we provide new statistical results that can be used for the performance analysis of wireless communication over fading channels of interest (e.g., Gamma distribution). The novelty of the results of this study lies in the newly derived common functions, which can be immediately applied to analyze the statistical performance of communication systems or algorithms based on any partial sum of ordered RVs over Gamma-distributed fading channels. By using these core equations, we demonstrate how to obtain the required target statistical results.  
As we are not presenting a new scheme to improve the performance, our exact closed-form results are verified by demonstrating their consistency with simulation results. Then, our results can be used for accurate performance analysis without requiring simulation or approximation.
## 3 Case Studies and Analysis
### Test Results and Analysis for PFG at SCP4  
#### 3.5.1 Test Results and Analysis in Two-Dimensional Planes  
Based on the Equation (10), the PFG to CIV1 at SCP4 have two degrees of freedom for regulation. In (a) of Figure 11, the APFG in the normal operation conditions have the similar curves and focus within certain regions, while those of RPFG also have the similar curves and focus within other regions. Both the curves of APFG and RPFG in the severe operation condition stay at different regions with those in the normal operation conditions. In (b), (c) and (f) of Figure 11, the APFG and RPFG to CIV2 at SCP4, and the curves of APFG against RPFG to CIV2 are similar to those at SCP2 and SCP3, which can be deduced from Equations (5), (8) and (11). Similarly, according to (d) and (e) of Figure 11, the PFG to CIV3 at SCP4 have nearly the same changing rules with those at SCP3, which can also be obtained from Equations (9) and (12).  
Figure 11: Curves of PFG to each CIV at SCP4 in planes: (**a**) Curves of the PFG to CIV1; (**b**) Curves of the APFG to CIV2; (**c**) Curves of the RPFG to CIV2; (**d**) Curves of APFG and RPFG to CIV3; (**e**) Curves of APFG against RPFG to CIV1; (**f**) Curves of APFG against RPFG to CIV2.  
Figure 10: Spatial surfaces of PFG to CIV2 at SCP2 in spaces: (**a–d**) Spatial surfaces of PFG to CIV2 in four regulation scenarios.
## 2 Materials and Methods
### Performance Evaluation and Comparison  
The performance of pixel-level classification of the VNIR and SWIR models was evaluated with an FM-pixel detection rate that was calculated by the ratio of the number of correctly identified ROI pixels versus the number of ground-truth ROI pixels. The FM-pixel detection rate was obtained separately for each FM group (polymer, wood, and metal) because the number of samples in each group was different (17, 2, and 4 samples for polymer, wood, and metal, respectively). The performance of blob-level classification was evaluated with the precision (TP/TP + FP), recall (TP/TP + FN; sensitivity), F1 score (2 \(\times\) (precision \(\times\) recall)/(precision + recall)), and Jaccard index (JAC, (TP/TP + FN + FP), also called critical success index or threat score), and an FM-blob detection rate that was (# of correctly classified FM blobs/# of ground-truth blobs in ROIs), where TP (true positive) was the number of blobs that were correctly classified as FM, FP (false positive) was the number of blobs that were mistakenly classified as FM, and FN (false negative) was the number of FMs that were missed from the detection. The FM-blob detection rate was also calculated separately for each FM sample group because the number of samples in each group was different. The performance of the developed algorithm (Fusion model) in detecting FMs was compared with the performance of the VNIR and SWIR models.
## 5 Experiments
### Experimental Results
#### 5.3.3. Uniformity  
We also evaluated the uniformity, as defined in Equation (1), of the original ResNet34 and MB_ResNet34 models. The physical meaning of Equation (1) is similar to that of the variance but slightly modified to better capture the concept of uniformity.  
\[Uniformity\ \triangleq\left(\frac{1}{N}\sum_{i=1}^{N}(time_{i}-minT)^{2} \right)^{\frac{1}{2}}\left(1\right) \tag{1}\]  
In Equation (1), \(time_{i}\) refers to the time the \(i\)_-th_ client takes during the training process, and \(minT\) represents the minimum time spent by any client. By calculating the squared deviation of each client's time from the minimum time and then averaging them, we  
Figure 6: Rounds versus accuracy for different models on the FEMNIST dataset.  
Figure 7: Rounds versus accuracy for different models on the CIFAR-10 dataset.  
measure the uniformity of the training process across selected clients. Taking the result's square root further helps provide a more interpretable value for the uniformity metric.  
By utilizing the formula mentioned above, we can gain valuable insights into the consistency and uniformity of the training process for the original ResNet34 and MB_ResNet34 models, as depicted in Figures 8 and 9, respectively. As a reference, we also included the variance in total training time and the uniformity of computation time and communication time. Evidently, the uniformity significantly decreased from approximately 532 to 313 when implementing the multi-branch network. This fact demonstrates the effectiveness of the multi-branch network in improving the uniformity and consistency of the training process.
## 5 Methods
### Custom CNN with Handcrafted Features
Training DL models with randomly initialized parameters requires a lot of labeled data. In the absence of data, transfer learning is a viable solution because it allows reusing knowledge (the weights) from pre-trained networks on large datasets, such as ImageNet,and achieves good results. The most widely used pre-trained CNN architectures are GoogLeNet, InceptionV3, ResNet, SqueezeNet, DarkNet, DenseNet, Xception, Inception-ResNet, Nasnet, and EfficientNet. In the field of dermatology, although several large public datasets are available, many authors choose to use TL on individual networks [60; 61] or by constructing ensemble models from these [62; 63; 64]. A minority choose to develop custom CNNs based on known networks [65; 66; 67] or totally new ones [68; 69]. In the present work, the custom CNN shown in Figure 2 is developed. The proposed architecture consists of five convolutional layers followed by five levels of maxpooling and batch normalization. After the last normalization layer, two-dimensional features are flattened into one-dimensional arrays and are given to the output layer consisting of one neuron per class. The number of filters in the first two levels of convolution is 32, in the third and fourth is 64, and in the last level is 128. All convolutional kernels are 3 \(\times\) 3 in size. Each convolutional layer is followed by a maxpooling layer with a size of 2 \(\times\) 2 and a batch normalization layer to standardize the inputs. A Leaky Rectified Linear Unit (LeakyReLU) activation function is used in this architecture to introduce nonlinearity into the network. After the last convolutional block, features are flattened by switching from two-dimensional to one-dimensional; thus, features are automatically extracted from the network. To these, handcrafted features acquired using LBP are concatenated, leading to better accuracy than the single-input convolution neural network model (results are discussed in Section 6). In the process of training the proposed architecture, skin lesions are scaled to 224 \(\times\) 224 pixels and are given as input to the CNN. To reduce computational time, no pre-processing was performed.
## 3 Problem Formulation
### Production Policy
#### 3.1.6 Cost Functions  
We recall that our objective is to elaborate an optimal production plan for a finite horizon \(H\cdot\Delta_{t}\). We have processed by minimizing the total cost-function composed of inventory holding, production and the penalty for exceeding the allowed amount.  
\[C_{T}=\sum\limits_{k=1}^{H-1}\left[C_{hold}(k)+C_{prod}(k)+C_{carbon}(k)\right] +C_{hold}(H) \tag{10}\]  
Carbon penalty:  
In order to restrict the amount of emitted carbon by factories during its activities, the government imposes a penalty to be paid after exceeding the allowed quantity:  
\[C_{carbon}(k)=C_{c}\times\left(|Q_{m}(k)|\times\mathbf{1}_{Q_{m}(k)<0}+\left| Q_{r}(k)\right|\times\mathbf{1}_{Q_{r}(k)<0}\right) \tag{11}\]  
Production cost:  
Based on the HMMS model of [22], production cost of manufacturing, remanufacturing and subcontractor units will be expressed by:  
\[\begin{split} C_{prod}(k)=C_{pm}\times[P_{m}(k)]^{2}+C_{pr} \times[P_{r}(k)]^{2}+C_{ps}\times[P_{s}(k)]^{2}\\ C_{prod}(k)=C_{pm}\times[P_{m}(k)]^{2}+C_{pr}\times\left[\begin{array} []{c}(1-b_{k})\times P_{r}(k)\\ +b_{k}\times(1-y)\times P_{r}(k)\end{array}\right]^{2}+C_{ps}\times[P_{s}(k)] ^{2}\end{split} \tag{12}\]  
where \(C_{pm}\), \(C_{pr}\) and \(C_{ps}\) are the unit cost of manufacturing, remanufacturing and subcontracting.  
Inventory holding cost:  
Like the production cost, inventory holding cost will be also based on the HMMS model.  
\[C_{hold}(k)=C_{hm}\times E[w_{m}(k)]^{2}+C_{hr}\times E[w_{r}(k)]^{2} \tag{13}\]  
\(C_{hm}\) and \(C_{hr}\) represent the cost of holding one unit of finished product and returned product.  
The total cost-function will be expressed as follows:  
\[\begin{array}{ll}C_{T}&=C_{hm}\times E[w_{m}(H)]^{2}\\ &+\sum\limits_{k=1}^{H-1}[C_{hm}\times E[w_{m}(k)]^{2}+C_{pm}\times[P_{m}(k)] ^{2}\\ &+C_{c}\times\left(\left|Q_{m}(k)\right|\right)\times\mathbf{1}_{Q_{m}(k)<0} \right]+C_{hr}\times E[w_{r}(H)]^{2}\\ &+\sum\limits_{k=1}^{H-1}[C_{hr}\times E[w_{r}(k)]^{2}+C_{pr}\times[P_{r}(k)] ^{2}+C_{c}\times\left(\left|Q_{r}(k)\right|\times\mathbf{1}_{Q_{r}(k)<0}\right] \\ &+\sum\limits_{k=1}^{H-1}C_{ps}\times[P_{s}(k)]^{2}\end{array} \tag{14}\]
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 5 Experiments
### Experimental Settings  
The parameters of the experiments are listed in Table 7.  
The vanilla AODV routing protocol does not take the energy problem into account, so the authors used Equation (11) to calculate the survival rate of nodes for AODV:  
\[p=\frac{Num_{live}}{Num_{all}} \tag{11}\]  
where \(Num_{live}\) is the number of nodes that are alive at the end of the simulation, and \(Num_{all}\) is the total number of nodes in the simulation.
## 1 Introduction
### The Context of Maritime Safety  
Located in northeastern Europe, the Baltic Sea is one of the largest inland seas in the world, with a surface area of ca. 415,266 sq km. Longitudinal in shape, its length and width can be contained in a rectangle of 1100 km by 1400 km. Its central part, the Proper Baltic, bifurcates in the north into the Gulf of Finland and the Bothnian Sea. In the west, it connects to the North Sea through the Danish Straits--the Sound, the Great Belt, and the Little Belt. The Baltic Sea is relatively shallow, with the average depth of 52 m. The small depth restricts the interchange of water between the Baltic and the North Sea and affects the hydrodynamic conditions and salinity [1; 2; 3].  
Maritime safety in the Baltic region is of utmost geopolitical [4; 5] and economic [6] importance. Busy merchant shipping, including the transportation of energy resources, requires reliable navigational systems, harbor infrastructure, and operational procedures. The hydrological and meteorological conditions strongly affect the safety of navigation, even more so in the transportation and handling of liquefied natural gas (LNG) [7; 8; 9; 10]. The reliability of the maritime infrastructure and the predictability of weather conditions are of key importance to the effective and safe operation of LNG terminals and LNG carriers in the Baltic Sea.  
The southern Baltic, encompassing the territorial waters of Poland and the adjacent water areas of Germany and Russia (the exlave of Krolewiec), has been an important object of analysis for its potential in the transportation and storage of LNG. The ongoing development of the LNG infrastructure in the region, including import terminals in the port of Swinoujscie and the planned investment projects in Gdansk, require a detailed analysis of the hydrological and meteorological conditions, which may affect the shipping, safety, and operation of regasification vessels and LNG carriers [11; 12; 13].
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 4 Results
### ROM Extraction  
All frames of the video sequences were each considered as a two-dimensional array with a depth of three, for the three RGB color channels. Thus, the image dimensions were of the form (width \(\times\) height \(\times\) depth), for example 1280 \(\times\) 720 \(\times\) 3 pixels. Figure 6 consist of sets of three images, with the left image showing the original frame, which is selected when the frame difference reaches a given threshold (as previously described in Section 3), that is, sufficient change is detected. The middle image depicts the identification of a ROM, while the right-hand one shows the resulting image after processing of a selected frame. This resulting image (after compression and then encryption) is then sent to the receiver of the video.  
Figure 6 shows sets of images (A-E) obtained from all five video sequences. Consider Figure 6A, where Figure 6(Aa) shows frame 28 of the video. This frame is selected after detection of prominent change in intensity values with respect to a reference frame, whereas Figure 6(Ab) shows the region where motion is detected. In most cases, a small region of a larger image is detected as a ROM. In the worst-case scenario, when there is more motion and that motion is mostly at the corners of the frame, then a whole frame is sent. For example, the actual size of Figure 6(Aa) was 384 \(\times\) 288 \(\times\) 3 bytes. After cropping, a region of size 267 \(\times\) 286 \(\times\) 3 bytes, as in Figure 6(Ac) was left for transmission. Similarly, considering Figure 6C, where Figure 6(Ca,Cc) also show a decrease in the amount of data, with the image size reduced from 352 \(\times\) 240 \(\times\) 3 to 11 \(\times\) 35 \(\times\) 3 bytes. This ROM is far smaller in size than the actual image. When a region of an image is identified, it is compressed through JPEG encoding, as a result of which an additional reduction in size of transmitted data occurs. Table 1 shows how much data is actually sent for the frames given in Figure 6.
Polymers_**2014**, \(6\), 2552-2572; doi:10.3390/polym6102552  
_polymers_  
**ISSN 2073-4360**  
www.mdpi.com/journal/polymers  
_Article_  
**Tuning the Solubility of Copper Complex in Atom Transfer Radical Self-Condensing Vinyl Polymerizations to Control Polymer Topology via One-Pot to the Synthesis of Hyperbranched Core Star Polymers**  
**[PERSON], [PERSON] and [PERSON] ***  
Department of Chemical Engineering, National Chung Hsing University, 250 Kuo Kuang Road, Taichung 40227, Taiwan; E-Mails: [EMAIL_ADDRESS] (Z.-C.C.);  
[EMAIL_ADDRESS] (C.-L.C.)  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]ADDRESS]; Tel.: +886-422-840-510 (ext. 809); Fax: +886-422-854-734.  
External Editor: [PERSON]  
_Received: 20 August 2014; in revised form: 20 September 2014 / Accepted: 23 September 2014 / Published: 30 September 2014_  
**Abstract:** In this paper, we propose a simple one-pot methodology for proceeding from atom transfer reaction-induced conventional free radical polymerization (AT-FRP) to atom transfer self-condensing vinyl polymerization (AT-SCVP) through manipulation of the catalyst phase homogeneity (_i.e._, CuBr/2,2-bipyridine (CuBr/Bpy)) in a mixture of styrene (St), 4-vinyl benzyl chloride (VBC), and ethyl 2-bromoisobutyrate. Tests of the solubilities of CuBr/Bpy and CuBr/Bpy under various conditions revealed that both temperature and solvent polarity were factors affecting the solubility of these copper complexes. Accordingly, we obtained different polymer topologies when performing AT-SCVP in different single solvents. We investigated two different strategies to control the polymer topology in one-pot: varying temperature and varying solvent polarity. In both cases, different fractions of branching revealed the efficacy of varying the polymer topology. To diversify the functionality of the peripheral space, we performed chain extensions of the resulting hyperbranched poly(St-\(co\)-VBC) macroinitiator (name as: hbPSt MI) with either St or _t_BA (_tert_-butyl acrylate). The resulting hyperbranched core star polymer had high molecular weights (hbPSt-g-PSt: \(M_{\mathrm{n}}=\) 25,000, \(D=\) 1.77; hbPSt-g-PfBA: \(M_{\mathrm{n}}=\) 27,000, \(D=\) 1.98); hydrolysis of the _tert_-butyl groups of the later provided a hyperbranched core star polymer featuring hydrophilic poly(acrylic acid) segments.  
**Keywords:** atom transfer self-condensing vinyl polymerization; copper complex solubility; hyperbranched polymers; amphiphilic star polymers
# La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks
## 4 Dissection of CTP in EH-WSN  
We conducted three artificially-controlled, small-scale simulation experiments, which are hereafter labelled with T-I, T-II, and T-III, respectively. By these three experiments, we try to (1) unravel the basic control principles of CTP; (2) investigate the reaction of CTP when the recharging nodes are going out of and back to the network; and (3) evaluate the performance of CTP and its sticking points in recovering from loops.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 4 Research Methodology
### Research Limitations  
The experiment was limited to candidates from Austria due to time constraints. For a continuous authentication system, which would be deployed world-wide, a bigger participant count from various countries would be necessary for the verification of the effectiveness of the implementation--the conducted experiment hence featured a non-probability sample. Since the proposed solution relies on a HMM using an unsupervised learning approach as the classifier, the experiment can be repeated with data from different participants without relying on our trained model. Furthermore, the training process was carried out on a desktop instead of the mobile device. This was done because an existing implementation of a HMM could be used and the research time was therefore shortened.
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 3 Coverage and Capacity Evaluation
### Number of Bytes per Minute (B)  
The number of bytes \(B\) that the SIoT receiver payload can receive depends on the programmed algorithm for data reception. The receivers are programmed to receive data packets with a maximum packet length of 32 bytes. The receiver MCUs store five packets of 32 bytes of data temporarily from each receiver before forwarding it to the main mission LoRa MCU, using universal asynchronous receiver-transmitter (UART) line every one minute. The number of bytes the satellite can receive per minute is given by Equation (4).  
\[B=n_{\text{p}}\times n_{\text{B}}\times n_{\text{MCU}} \tag{4}\]  
The number of bytes that the satellite can receive depends on the number of packets the receiver can transfer to MCU per minute, np, the number of bytes in a packet, \(n_{\text{B}}\) and  
Figure 14: Satellite passes for KITSUNE in GST countries for 24 h.  
Figure 15: Number of ground passes and their average elevations for KITSUNE in GST countries for one month.  
the number of MCUs, \(n_{\rm MCU}\). Therefore, the number of bytes \(B\) that can be received in one minute is approximately 1280 bytes.  
The LoRa MCU stores the received data in a 1 Gbit flash memory in the OBC/EPS board based on the day number, and each day 64 kB of data storage is assigned in the flash memory. This design enables ease of data download after the satellite is deployed in orbit. This MCU also communicates with the onboard computer (OBC) using the UART protocol. The collected data will be downlinked using another transceiver in the licensed UHF band with a data rate of 4800 bps.
# Implementation and Evaluation of HTTP/3 Connectivity Check Using Happy Eyeballs Algorithm
## 3 Proposed Method
As a result of the above, all requests for a period that do not require reconnection can select the appropriate protocol simply by referring to the correspondence information that is stored in the client. The best approach is to store the client's correspondence information in the memory by creating a dictionary, wherein the key is a host, as described in RFC 3986 [13]. Unlike the conventional Happy Eyeballs, the proposed method uses HTTP/3 as the preferred connection algorithm. As indicated in Figure 3, even once the old HTTP is adopted, HTTP/3 is adopted if the connection by QUIC is completed later, because HTTP/3 has the advantage of being adopted preferentially. Furthermore, as with the conventional Happy Eyeballs, double communication occurs until the connection is established, so there is the problem of increased network traffic volume compared to the connection method that does not also use the proposed method. Figure 3: Flowchart of proposed method.
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 6 Informal Security Analysis
### Authentication  
In our scheme, the employment of a dual signature (DS) ensures the authenticity of the sending party. To compute the DS, in addition to \(\mathcal{Q}\), the secret key of the IoMT (\(\varphi_{\text{IoMT}}\)) and \(\alpha\) are used. However, to accomplish authentication, it is not viable for an attacker to generate a DS because \(\varphi_{\text{IoMT}}\) and \(\alpha\) are only known to IoMT devices.
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 4 Secure WiFi Fingerprint Localization  
The process of the indoor safety localization algorithm based on trusted fingerprint is divided into three steps: The first is the offline testing phase, in which an offline fingerprint database is established, the location of reference anchors nodes is calculated and the RSSI of APs are compared according to localization errors to detect whether the environment or impaired APs have an influence. The second is the offline update phase, in which the location of the reference anchor node is calculated again using the fingerprint after detection to verify whether there is any impact from the environment or the impaired APs. According to the verification result, the APs are voted on, the weight of APs are obtained, then the fingerprint and weight of the APs are updated. The third stage is the online filtering phase. In this stage, the impaired AP is filtered by the RKNN (reliable-KNN) algorithm. The RKNN algorithm calculates the weight of each reference position according to the similarity of reliable fingerprints and selects the K positions with the highest weight to calculate the weighted average  
Figure 6: Impact of impaired AP on RSSI. (**a**) RSSI that are measured in the east. (**b**) RSSI that are measured in the west. (**c**) RSSI that are measured in the south. (**d**) RSSI that are measured in the north.  
value, which is the final positioning result. Furthermore, the destroyed APs and new APs are filtered by comparing the number and type of APs in the offline and online phases.