esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Development Characteristics of Overburden Fissures  
#### 4.1.1 Characteristics of Overburden Fissures  
Owing to the impact of gravel within the caving zone caused by 4\({}^{-2}\) coal seam mining, the HSD5 borehole's final drill depth of 117 m falls short of the predetermined depth. The information of fissures provided by field measurement were applied to analyze the characteristics of overburden fissures. During the drilling process of HSD5 borehole, the boring cores of different depths are gradually extracted. The characteristics of fissures developed in the soil layer cannot be noticed because of the mud pollution during the drilling process of borehole. Only the overburden fissures are examined thought analyzing the core of strata. The boring core image is shown in Figure 5a. As shown in Figure 5a, under the effect of coal mining, the boring core is seriously broken and the integrity is poor (RQD = 9-82%, average 48.2%). In addition, overburden fissures are a result of rock fractures brought on by coal seam mining. The top fissures are situated in the medium-grained stone strata with a depth of 63.2 m. With the increase in drilling depth, the more obvious the rock fracture is, the greater the number of overburden fissures. When the drilling core position is close to the coal seam, the degree of core fragmentation indicatesthat the drilling has reached the caving zone. Thus, according to the boring core image, the top boundary of the caving zone caused by 4\({}^{-2}\) coal seam mining is fine sandstone buried at a depth of 73.8 m, and the height of the caving zone is 30.5 m.  
Previous results show that vertical fissures and transverse fissures are mainly types of overburden fissures [36]. The transverse fissures which have nearly no dip angle are caused by uneven subsidence of adjacent rock stratum. However, the vertical fissures with a considerable dip angle are caused by the fracture of the stratum, which can be regarded as the failed sign of the strata. It can be seen that form the borehole tv images (Figure 5b) that overburden fissures are mainly vertical cracks. The dip angle of overburden fissures is 66\({}^{\circ}\)-90\({}^{\circ}\) (average angle is 79\({}^{\circ}\)), which belongs to large dip angle fissures. Furthermore, below 74 m (depth), the overburden fissures are not developed and the dip angle of vertical fissures increase, and meanwhile, the strata have visible broken blocks, which indicate that the top interface of the caving zone caused by 4\({}^{-2}\) coal seam mining is the rock layer interface with a buried depth of 74 m. So, the height of the caving zone is 30.3 m, which is consistent with the height obtained from the boring core image.
## 4 Experiments and Results
### Evaluation Metrics  
We considered the evaluation metrics such as accuracy, precision, recall and F-measure. The calculation has been carried out in terms of False Negative (FN), True Negative (TN), False Positive (FP) and True Positive (TP). TP is calculated in terms of hate-speech tweets that were correctly predicted as hate speech tweets. TN is calculated in terms of non-hate-speech tweets that were correctly predicted as non-hate speech. FP is calculated in terms of non-hate-speech tweets that were incorrectly predicted as hate speech. FN is calculated in terms of hate-speech tweets that were incorrectly predicted as none-hate speech. Below we show performance measures considered in this study.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FN}+\text{TN}+ \text{FP}} \tag{2}\]  
\[\text{F1 score}=\frac{2*\text{Precision}*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{3}\]
###### Abstract  
hybrid FSO/RF system; communication  
or the jitter of the communication device, which also can degrade the communication performance of the FSO system, even the interruption of communication.  
Therefore, previous works have proposed various mitigation technologies to improve the performance of the FSO communication system. Adaptive optics technology can overcome the beam wander caused by atmospheric turbulence [4]. To solve the misalignment caused by building vibration, the ATP (acquisition, tracking, and pointing) technology is proposed [5]. In addition, the performance of the FSO communication system can be improved by using a high-performance beam [6], spatial diversity [7], hybrid FSO/radio frequency (RF) communication scheme [8], and other technologies [9; 10; 11; 12; 13; 14].  
Compared with the FSO link, atmospheric environment have little impact on the RF link, such as fog, turbulence, and so on. The communication scheme of the hybrid FSO and RF parallel transmission can combine the advantages of the FSO link and RF link to improve the performance of the communication system. [PERSON] et al. [15] proposed a hybrid parallel communication system based on an FSO link with Gamma-Gamma distribution and an RF link with Rayleigh distribution and derived new closed-form expressions for its average bit error rate (BER) and outage probability (OP). [PERSON] et al. [16] proposed a hybrid serial transmission system of the RF link with Rayleigh distribution and the FSO link with the Gamma-Gamma distribution, the system adopted an amplify and forward (AF) relay with variable gain as a relay device and deduced new closed-form expressions of the BER and OP. [PERSON] et al. [17] gave an approximate expression of the ergodic channel capacity (ECC) of the FSO multi-hop hybrid system combined with an RF backup link and AF relay and analyzed the influence of weather conditions on the hybrid system.  
In addition, the hybrid FSO/RF communication system has great potential to improve the performance of the communication system. For example, the hybrid FSO/RF parallel multi-hop system can not only improve the transmission distance but also improve the system performance [18]. The performance of hybrid FSO/RF systems can be further improved by using wavelength division multiplexing (WDM) and spatial diversity in FSO links, or by using multiple input multiple output (MIMO) technology in RF links [19; 20; 21]. Therefore, a comprehensive study of the performance of hybrid FSO/RF communication systems is of great significance to engineering practice and theoretical research.
###### Abstract  
A periodic leaky-wave antenna (LWA) design based on low loss substrate-integrated waveguide (SIW) technology with inset half-wave microstrip antennas is presented. The developed LWA operates in the V-band between 50 and 70 GHz and has been fabricated using standard printed circuit board (PCB) technology. The presented LWA is highly functional and very compact supporting 1D beam steering and multibeam operation with only a single radio frequency (RF) feeding port. Within the operational 50-70 GHz bandwidth, the LWA scans through broadside, providing over 40\({}^{\circ}\) H-plane beam steering. When operated within the 57-66 GHz band, the maximum steering angle is 18.2\({}^{\circ}\). The maximum gain of the fabricated LWAs is 15.4 dBi with only a small gain variation of +/\(-\)1.5 dB across the operational bandwidth. The beam steering and multibeam capability of the fabricated LWA is further utilized to support mobile users in a 60 GHz hot-spot. For a single user, a maximum wireless on-off keying (OOK) data rate of 2.5 Gbit/s is demonstrated. Multibeam operation is achieved using the LWA in combination with multiple dense wavelength division multiplexing (WDM) channels and remote optical heterodyning. Experimentally, multibeam operation supporting three users within a 57-66 GHz hot-spot with a total wireless cell capacity of 3 Gbit/s is achieved.  
5G mobile communications; beam steering; fiber-wireless communication; leaky-wave antennas; millimeter wave wireless; radio-over-fiber; substrate-integrated waveguide +
Footnote †: journal: Electronics Letters
# A Study on Multiple Factors Affecting the Accuracy of Multiclass Skin Disease Classification
## 1 Introduction  
Artificial intelligence (AI), which has profoundly changed our everyday lives, has been extensively studied for several decades [1; 2; 3; 4]. Some prominent applications are: AI-empowered autonomous driving, which has been employed in numerous electric vehicles from various automakers such as Tesla and Ford [5]; AlphaGo developed by Google using artificial neural networks [6]; and the prevailing TikTok app, which has succeeded greatly due to its recommendation algorithms [7].  
An important aspect of AI is machine learning. Artificial neural networks, especially convolutional neural networks (CNNs), are extending the reach of machine learning to a broad range of applications [8; 9]. CNNs are particularly useful for analyzing visual imagery. The convolution kernels used in CNNs are extremely useful for the extraction of image features. Classifying images is a challenging task. However, with the aid of powerful graphic processing units, the classification of images has become efficient using deep CNN architectures. Numerous off-the-shelf architectures are available for use and can be easily adjusted to suit new tasks by using deep transfer learning [10; 11].  
Training deep CNNs usually requires large annotated image datasets. However, in specific areas such as the medical image domain, a large volume of publicly availabledatasets is not always available, and collecting and annotating samples can also be difficult and costly. Therefore, a data augmentation technique has been extensively studied.  
In this paper, multiple factors are studied for potential improvement in the classification accuracy of skin diseases using deep transfer learning. Due to the good generalization ability of deep CNNs, the off-the-shelf architectures AlexNet and ResNet50 are compared for use in the building of transfer learning models for skin disease classification. These deep CNN architectures are initially trained to classify 1000 classes in the ImageNet database. However, as only a few classes are needed for classification in the skin disease dataset, complex deep CNNs seem too heavy. Therefore, the effects of trimming the depths of deep CNNs to reduce the complexity and number of parameters in the networks are evaluated. Comparison is made between using transfer learning and training from scratch. Additionally, different image manipulation techniques for data augmentation are compared. The choice of mini-batch sizes is also discussed. The aforementioned factors are studied through experiments on the HAM10000 skin disease dataset.
## 3 System Model and Problem Statement  
### System Model  
#### 3.1.1 Network Model  
The traditional WSNs can be regarded as an undirected graph \(G=\)\(<\)\(V\), \(E\)\(>\) where \(V\) represents the set of vertices and \(E\) represents the set of edges (or links) [9]. Sink node (or BS) can be placed either inside or outside the area to be monitored. We assume that there are \(N\) nodes randomly scattered in a two dimensional square field [X, Y]. There exists a link \(E(i,\,j)\) between node \(I\) and node \(j\) if the Euclidean distance \(d(i,\,j)\) is not larger than the radio transmission radius \(R\), namely \(d(i,\,j)\leq R\). Here, undirected graph means bi-directional communication link. In other words, if node \(j\) can receive packet from its neighboring node \(i\), it is believed that node \(i\) can receive packet from node \(j\) in a reverse way. The objective in this paper is to find a set of optimal or sub-optimal individual distances during routing process so that the energy is consumed at similar rate for all involved sensors.
# Recent Advances in Hydrothermal Oxidation Technology for Sludge Treatment
## 2 Results and Discussion
### Comparison of Sludge Before and After HTO Treatment
Second, the removal rates of TOC and VSS increase, enhancing the removal of organic pollutants from the sludge. Finally, the products after treatment are mainly volatile fatty acids (acetic acid content is the most), nitrogen (ammonia nitrogen content is the most), phosphorus, carbon dioxide, etc. The development of the above optimization measures can be summarized as follows: first, HTO technology uses oxidants to replace oxygen, avoiding the safety risks brought by oxygen injection. Second, the addition of catalysts reduces the operating temperature of the equipment while enhancing the degradation of organic matter in sludge. Third, supercritical oxidation technology further improves the harmless disposal of sludge. Finally, advancements in reactor design and upgrades play a crucial role. In addition to these optimization methods, pretreatment techniques such as ultrasonic processing or acid/alkali pretreatment can also enhance the efficiency of HTO in sludge treatment. However, due to the complex composition of sludge, some byproducts are inevitably generated after treatment. To reduce processing costs, resource recovery from these byproducts can be employed to improve economic viability.
# Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover
## 2 Materials and Methods  
The system used for data collection comprised one or two Ettus Research (Ettus Research, a National Instruments Brand. 11500 North Mopac Expressway. Austin, TX 78759, USA) Universal Software Radio Peripheral (USRP) devices (model B200/210), which were connected to a multiband cellular antenna and linked to a computer via USB3 for data streaming, processing, and storage. The measurements and observables were generated by the real-time _SoftTurAre REceiver_ (STARE), which is a software-defined radio (SDR) that was specifically developed for measurement and observable generation from LTE and 5G new radio (NR) cellular downlink signals [12; 13]. A diagram of the test setup can be found in Figure 1. The specific software version of STARE used in this study was v.1.4.0, which was, in turn, linked with a UHD, version 4.1.0.5-3.  
The observables generated by STARE were subsequently processed with a custom script written in Julia, which visualizes the observables, performs statistical analysis, and calculates the Allan Deviations, as described in Section 3.4, using the software library AllanDeviations.jl v.0.3.0 [14].  
To maintain a stable local time reference and minimize the measurement errors introduced by local clock instability, a GNSS-disciplined oscillator was used in each test. While, by default, the USRP models used can integrate an oversized quartz oscillator (OCXO) with GNSS disciplining, we, in this study, elected to use an external rubidium (Rb) atomic clock with GNSS disciplining. In the former case, the OCXO oscillator was an Ettus research GNSS-disciplined OCXO, while, in the latter case, the time reference was the Safran (Safran Electronics & Defence, 72-76 Rue Henry Farman, 75015 Paris, France) LNRClok-1500 GNSS-disciplined Rb atomic clock. In all of the tests discussed in this paper, measurements were taken from static base stations while the reception antenna was also kept static throughout the data collection process.  
All data presented in this study were recorded from the author's offices in Strindvegen 4 using only live signals. No simulated data were used.
## 5 Discussion
### The Future Development of InSAR in Landslide Application  
The InSAR technique is suitable for observing slow-moving landslides at a large scale. There are always spatial differences in the deformation of large-scale landslides [43; 44]. The landslide deformation characteristics (direction, intensity, etc.) in different positions and periods are not the same. The displacement obtained by the InSAR technique was a one-dimensional LOS direction (East-West). Nowadays, the LOS displacement is often projected into the steep slope direction to make it consistent with the main sliding direction, which is convenient for deformation analyses. However, this method cannot meet the demands of accurate landslide monitoring. Therefore, it is necessary to develop an extraction method for landslide three-dimensional deformation based on the ascending and descending orbit images and the landslide deformation evolution.  
In the current early warning system, the monitoring methods of surface deformation are mostly the global navigation satellite system (GPS, Beidou, etc.). The remote-sensing techniques, such as InSAR, will be widely applied in landslide monitoring and early warning in the future. The monitoring results of these techniques are not point data but a sort of areal deformation metrics that can provide much more deformation information. Therefore, we need to develop data mining algorithms to obtain more information from the massive motoring data of these novel techniques. It will significantly improve the effectiveness of the landslide early warning system.
# Sensing and Device Neighborhood-Based Slot Assignment Approach for the Internet of Things
###### Abstract  
Concurrent communication constitutes one of the challenging issues associated with IoT networks, as it is highly likely that multiple devices may start communication simultaneously. This issue has become more complex as devices belonging to the IoT networks increasingly become mobile. To resolve this issue, various mechanisms have been reported in the literature. However, none of these approaches has considered the neighborhood information of a server module to resolve this issue. In this paper, a neighborhood-based smart slot allocation scheme for the IoT is presented where member devices are mobile. In this scheme, every CH or server module is bound to maintain two different types of slots, i.e., dedicated and reserved. Dedicated slots are assigned to every device on a First-Come-First-Serve (FCFS) basis, whereas reserved slots are assigned to the migrated devices. Additionally, as long as a device \(C_{i}\) is located inside the server module's coverage area, it is required to use these dedicated slots. Simulation results verified that the proposed neighborhood-based slot allocation scheme performed better than existing approaches and considerably improved various performance metrics, such as 20% in lifetime, 27.8% in slot allocation, and 30.50% in slot waiting time.  
Internet of Things; communication; authentication; TDMA 2023 1 3
## 3 Validation Study Based on Hemisphere Model
### Performance Evaluation of the Proposed Registration Strategy  
The performance of registration was evaluated by SRE and TRE. The SRE was obtained by calculating the average distance between the point cloud in the medical image space (\(i=1\ldots n\)) and the point cloud that was augmented and registered with the proposed strategy (\(i=1\ldots n\)). TRE was obtained by calculating the distance between the target location in the medical image space and the target location in the transformed patient space by applying the transformation matrix obtained through the ICP algorithm [27].  
\[\begin{split} SRE=\frac{\sum_{i=1}^{n}\parallel CT\ point_{i}- Patient\ point_{i}\parallel}{n}\end{split} \tag{8}\]  
\[\begin{split} TRE=\parallel\ Target_{CT}P-Target_{Camera}P\parallel \end{split} \tag{9}\]  
Here, \(CT\ point_{i}\) denotes the \(i\)th point of the medical image and \(Patient\ point_{i}\) denotes the \(i\)th point of the patient after the transformation is applied. In addition, \(Target_{CT}P\) denotes the location of the target measured in the medical image space, and \(Target_{Camera}P\) denotes the points that were generated after applying the transformation matrix of the target measured in the patient space.
## 4 Experimental Results  
The human subject that participated in this research, shown in Figure 10, is a 54 year-old male with paraplegia due to complete spinal cord injury that occurred 27 years ago. The neurological level was at L1 motor and T12 sensory. Although he was diagnosed as ASIA (American Spinal Injury Association) Impairment Scale C, the actual strength of all muscles below the neurological level was feeble by a manual muscle test. Therefore, he cannot stand and walk independently even with conventional KAFO (knee-ankle-foot orthosis) and crutches.  
Since he started wearing the WalkON Suit in January 2018, he has regularly practiced walking, about once or twice a week. As he had experience walking with powered exoskeletons, including  
Figure 9: Images shown on the see-through display of smart glasses: (**a**) a progress chart, (**b**) the number of steps, (**c**) the current motion mode, (**d**) the foot placement, (**e**) a warning message and (**f**) the result analysis.  
ReWalk, he was the right human subject who could immediately adapt to a new robot and check the effectiveness of the proposed UI system.
# Simulating Intraday Electricity Consumption with ForGAN
## 3 Experimental Setup: Data Description and Algorithmic Decisions
### Hyperparameter Choices and a Priori Decisions for the Application of ForGAN  
Various grid searches are performed to make decisions on hyperparameters (i.e., parameters that have to be determined outside the actual algorithm to optimize ForGAN). To explore different hyperparameters (like _window size_ for the size of the condition window, _batch size_ for the size of the batches of real and generated data in training ForGAN, the learning rate in the gradient updates, etc.) across various ranges, other hyperparameters are kept constant. Moreover, the influence of varying one hyperparameter is tested on the performance of ForGAN using a smaller subset of the data.  
Some particular hyperparameter choices are  
* **Size of the condition window:** With regard to the size of the _condition window_ (lookback window), it is found that it should be smaller than 200, as a larger size causes memory issues and significantly slows down computations. However, the results are already quite good, with a condition size of 64. A final choice of 96 is used for both the generator and the discriminator, as this also corresponds to a full day of data.
* **Number of discriminator gradient descent iterations per generator gradient descent iteration:** As already indicated in Section 2.3, different choices for the number \(k\) of sequential updates of \(\theta_{D}\) before the next update of \(\theta_{G}\) are considered. Our experiments led to the choice of \(k=2\).
* **Number of full iterations of parameter updates:** Tests show that the originally intended number of 300 updates is typically too low for the convergence of the generator's objective function. This number is increased to 1000.
* **Choice of the form of the objective function:** Again, as indicated in Section 2.3, the _non-saturating_ GAN loss is used as the objective function, i.e., the discriminator maximizes \(J_{D}(\theta_{D};\theta_{G})\) as given in (2), while the generator maximizes \(J_{G}(\theta_{G})\) as given in (5).  
Figure 8: Heatmap visualizing the average 15 min electricity consumption per day in Germany in 2015–2018 in megawatt hours.  
* **Starting the parameter iterations:** The parameter search is initialized with \(\theta_{D}=0\) and \(\theta_{\rm Q}=0\). As this appears to work well and no specific preference exists for alternative starting values, hyperparameter optimization is omitted in this case.  
A usually recommended, **data transformations** do not improve the model. Using the _scaler_ as a hyperparameter shows that scaling the data does not improve prediction accuracy. This result could have been expected, as all box plots show no outliers.