esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The proposed PUTSS algorithm employs two components to identify the congestion levels on the route. The first component uses statistical data, whereas the second applies an intelligent computer vision service to identify the number of people at each station in real time. Both components are equally important and are described as follows:  
* The Statistical Data Component (SDC) uses historical and real-time statistical data to predict passenger numbers at various stations. The SDC prevents scenarios where passengers arrive at a station just before the bus, which would not allow the algorithm to adjust the fleet frequency in real time. It identifies patterns and trends in passenger flow based on time of day, day of the week, and special events. The advantages are as follows:
* Helps in planning and deploying resources efficiently based on historical data;
* Allows for preemptive adjustments in fleet frequency to manage expected congestion.
* The Real-Time Computer Vision Component (RTCVC) employs intelligent computer vision services to monitor and count passengers at each station in real time. The RTCVC allows for real-time adjustments to fleet frequency, addressing sudden changes in passenger numbers. It handles unexpected situations such as heavy rain, concerts, or other events that cause deviations from the usual statistical patterns. The RTCVC's advantages are as follows:
* Provides immediate insights into the current number of passengers waiting at each station;
* Enables the system to adjust the fleet dynamically, ensuring optimal service even during unpredicted spikes in demand.  
By visualizing data in real time, the algorithm can accurately adjust the vehicle fleet, ensuring optimal transport conditions.
# Implementation of Robots Integration in Scaled Laboratory Environment for Factory Automation
## 1 Introduction  
The automation of warehouses and operation at factory floors is rapidly expanding. This process is enabled by the combination of industrial robots, mobile robotic systems, sensor networks and a central server system that manages and coordinates the work of all machines in the warehouse or across the factory floor. There are many open problems in this domain, such as localization of mobile systems [1], accurate object manipulation for various tasks [2], coordination of the entire system [3], management of alarm events and unwanted events, etc. In many cases, it is also not possible to fully automate the process, and interaction with humans arises as another aspect for research.  
Our work contributes to research in the area of the factory and warehouse automation process, as well as in other segments important for the successful integration of mobile and industrial robots together with cloud-based solutions for the integration and coordination of tasks. Taking advantage of our experience, laboratories can build a scaled version of the factory floor and deal with real-world problems not only in simulation or in a real factory, but also in the laboratory environment, where a lot of realistic situations can be prepared and validated before moving the experiment on the site (Figure 1).  
The paper is organized as follows. In Section 2, we give an overview of mobile and industrial robots used for factory floor automation as well as challenges and solutions for their effective coordination with cloud-based services. Robots are designed and programmed for the automation of pallets transportation and boxes palletizing as one of the most common use cases in many factories. Section 3 gives a detailed description of the hardware design and software architecture in our solution. The robot operating system (ROS) is the backbone of robotic software development. Additionally, the description and evaluation of used simultaneous localization and mapping (SLAM) algorithms are presented, as localization of the mobile robot is of the great importance for successful integration. Section 4 explains a software architecture of mobile and industrial robots relevant for integration with the cloud server. The focus is on interconnection and communication aspects. At the end, in Section 5, we give the final discussion and conclusion, establishing directions for future work.
# Alternative Categorization of Radio Frequency Power Amplifier for Generalized Design Insights
## Appendix A  
Category theory emerged in the 20 th century, and it was developed as a mathematical tool to study generic objects and the relations between them in a highly abstract manner. In general, a category consists of three entities; these are, namely, a class of objects, a class of morphisms, which can be simply thought of as a set of functions acting between the objects, and finally a binary operation (called composition) that admits an identity morphism and is associative. In this work, the class of objects \(ob(\mathcal{C})\) includes the three entities governing the properties of the RFPA: time (physically associated with the waveform itself), frequency, and space. The class of morphisms \(hom(\mathcal{C})\) includes the functions which describe the transformations from time domain to frequency or space domain. In this specific case, one can easily identify the natural morphisms between the time domain and the frequency domain. The function from the time domain \(A\) to frequency \(B\) can be understood as integrating the waveform \(V(t)\) to obtain the corresponding Fourier coefficients:  
\[a_{0}=\frac{1}{T}\int_{0}^{T}V(t)dt\]  
\[a_{n}=\frac{2}{T}\int_{0}^{T}V(t)cos(2\pi nt/T)dt\]  
\[b_{n}=\frac{2}{T}\int_{0}^{T}V(t)\ sin(2\pi nt/T)dt\]  
The set containing all the Fourier coefficients form the abstract object referred to as the 'frequency' component \(B\). The inverse morphism from \(B\) to \(A\) can be easily obtained by simply applying the Fourier series formula. This suggests that this morphism is bijective and thus an isomorphism. The same cannot be said about the morphism from the time object \(A\) to the domain space \(C\) since the RFPA's operating power is directly proportional to the maximum voltage:  
\[P_{out,fo}\;\sim\;max\;\{V(t),0<t<T\}\]  
This suggests that the trade-off between the dissipated power (due to the overlap of the current and voltage waveforms) and the higher harmonics is qualitatively distinct from the relation with the optimal power of operation. This further underlines the valuable insights offered by category theory, not only in terms of analytical predictions but also qualitative factors that could guide the design engineer's efforts.
## 3 Results
### Fatty Acid Concentration and Composition  
The concentrations and composition of fatty acids in the samples are shown in Table 2. The range of total fatty acid was from 1.80% to 10.94%. A total of 18 fatty acids were detected in the 75 pepper fruits tested (Table 2). Of these, seven fatty acids were found in all samples, namely caproic acid (C10:0), palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C18:1n9c), linoleic acid (C18:2n6c), arachidonic acid (C20:0), and behenic acid (C22:0) (Table 2). In the 75 pepper fruits, high levels of fatty acids were found with linoleic acid (C18:2n6c), oleic acid (C18:1n9c), and palmitic acid (C16:0), which together accounted for more than 90% of the total fatty acid content (Table 2). Furthermore, their contents ranged from 47.46% to 80.90% for linoleic acid, 7.18% to 17.50% for oleic acid, and 6.01% to 9.48% for palmitic acid, respectively (Table 2).  
In the current study, linoleic acid (C18:2n6c) was the main polyunsaturated fatty acid, oleic acid (C18:1n9c) was the main monounsaturated fatty acid, and palmitic acid (C16:0) was the main saturated fatty acid (Table 2). Among these accessions, the highest proportions of fatty acids were S03 (_C. annuum_) for linoleic acid (80.90%), S24 (_C. chinense_) for oleic acid (17.50%), and S67 (_C. annuum_) for palmitic acid (9.48%), respectively. Additionally, saturated fatty acids accounted for 9.35% to 25.45%, monounsaturated fatty acids occupied 7.56% to 22.66%, and polyunsaturated fatty acids represented 49.85% to 82.41% in total fatty acids of the pepper fruits.
## 4 Firmware Design
### Localization  
The subclass LocEar is used to execute the localization of the receiver unit based on measured RSS values. Thus, the URSM parameters must be determined in advance through calibration. In order to localize the position of the receiver unit, different estimation algorithms are implemented, i.e., EKF and PF, which fuse the measured RSS values to an estimated position of the receiver unit. The estimated  
Figure 13: Communication between the digital video broadcasting-terrestrial (DVB-T) dongle and the single board computer (SBC). The RSL2832U chip-set is interfaced by the library librtlsdr. The wrapper pyrtlsdr provides convenient access to the library function. Both, librtlsdr and pyrtlsdr run on the SBC.  
position and its uncertainty can then be transmitted to other algorithms, i.e., to allow closed-loop trajectory following.
## 1 Introduction  
The advent of high throughput single nucleotide polymorphism (SNP) genotyping and latterly, next generation sequencing (NGS) technology including whole exome sequencing (WES) have revolutionisedour approach to genetic diagnostics and novel gene discovery in the genodermatoses--a group of inherited skin disorders.  
Prior to this, technologies including linkage analysis using genome wide microsatellite panels in combination with candidate gene screening by PCR and Sanger sequencing have been the primary method for discerning new skin disease-associated loci. Successes with this approach include Hailey-Hailey Disease (OMIM #169600) [1], Netherton Syndrome (OMIM #256500) [2], Darier-Disease (OMIM #124200) [3], and Dyschromatosis symmetrica hereditaria (OMIM #127400) [4]. Candidate gene screening approaches have also yielded success, particularly in deciphering the keratin disorders [5]. However, clinical and likely genetic heterogeneity of skin diseases and the availability of DNA from probands only, or from small families, have hindered disease gene discovery for many disorders [6]. This can now be surmounted with high-density SNP homozygosity mapping for consanguineous recessive disorders, and in particular NGS and WES for dominant and recessive disorders, which has facilitated our understanding of some of the genetic make up of common diseases.  
Skin diseases are ideal for determining genotype-phenotype correlations because of the relative ease with which clinical and histological examination can be made. In addition, inflammatory pathways involved in the pathogenesis of skin diseases such as psoriasis vulgaris (PV) are relevant to a number of other immune-mediated diseases including inflammatory bowel disease and rheumatoid arthritis [7].  
The genetic bases of many monogenic skin diseases have been unravelled and in this review we focus on examples of discoveries in cutaneous genetics, applying different strategies such as SNP microarray, microsatellite linkage analysis, targeted NGS and WES. Equally, it has also been informative in understanding the significance of _de novo_ mutations including the unusual phenomenon of revert mosaicism in the skin, where spontaneous correction of a disease-causing mutation in a somatic cell occurs [8]. We have also gained insights into complex trait diseases and will explore what contributions these have made to mechanistic insights, diagnosis and treatment of common skin diseases including psoriasis, atopic dermatitis (AD) (eczema), and skin cancer.
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 2 Context-Based Lossless Compression for Sensor Signals
### Context-Based Encoding and Decoding
This sub-module is named lstm-compress, and can only work with a batch size of 1. Another deep learning-based lossless compressor, called tensorflow-compress [18], is a general version of lstm-compress, which can work with different recurrent units and an arbitrary batch size. Compared to the static DZip method, lstm-compress and tensorflow-compress do not require pre-training of their deep learning predictor; thus, they do not need to include the neural networks into the compressed data provided that both the compressor and the decompressor are initialized with the same random predictors. During encoding and decoding, the context information is extracted and stored in network state, and the RNN predictor is updated (indicated by red dashed line in Figure 1) via a standard back-propagation through time (BPTT, [24]) algorithm. In this paper, we refer to this kind of context-based technique as _dynamic deep recurrent compressor_ (dynamic DRC). Although the aforementioned compression techniques are promising, they can not fully exploit the special characteristics of sensor signals. For example, the predictor resolution \(R_{p}\), defined in Section 2.1, is usually fixed to 8 in these general compressors. In the case that ADC resolution \(R_{a}\
eq R_{p}\), re-grouping the bit sequence will lose the original data property. Even though the predictor resolution can be arbitrary for some RNN-based compressors, setting \(R_{p}\) to \(R_{a}\) will result in formidable model sizes when the signal sequence is generated from a high-resolution ADC. To address these problems, we propose a new dynamic DRC with a novel recurrent predictor that is specially designed for compressing sensor signals. The network detail is presented in the following section.
# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 2 Materials and Methods
### Privacy Assurance Evaluation at Third Party Data Processing Level  
For third-party data processing, the focus is only kept within the PM domain. Primarily because the privacy concerns relating to the Internet of Things (IoT) with recent healthcare wearables, Artificial Intelligence (AI), and clinical research are already well renowned [71; 72]. Moreover, the concerns increase with the availability of disease-specific online EHR with sensitive information of patients, nurses, and specialists to reduce mobility for both patients and home care providers [73]. This research focuses on PM data analytics by third-party data processors who work remotely instead of working on the field within a healthcare organization's premises. Unfortunately, the former experiences a trust deficit, whereas the latter is often easily trusted for the benefit of both. For example, MRON, Radiology East Netherlands [74] employees are located (along with their radiology machines) and are working within the premises of Medisch Spectrum Twente (Enschede and Oldenzaal) and Ziekenhuisgroep Twente (Almelo and Hengelo) hospitals in Overjysel province of Netherlands, where big data analytics is an integral part of their routine proceedings for efficient/effective healthcare services.  
Similarly, in Overjysel, private diagnostic labs (biobanks) are located within the premises of local hospitals [75]. Medlon is one such example that is a member of Unilab (a prominent European tycoon in lab, imaging, and pathology diagnostics with labs majorly across Europe and in a few parts globally). Medlon is working with its sister firms in the pharmaceutical manufacturing industry as well. Biobanks deal with DSE (Data and Sample exchange) within and across healthcare providers and play a vital role in improving healthcare services and keeping track of treatments and respective patient responses. Biosamples and biodata are then kept safe for further research [13]. The Process Mining real-world data-oriented process analytics are used to explore the privacy assurance evaluation level.  
Datasets and Datasource: The Sepsis event-log is a \"Sepsis\" patients', publicly available healthcare event log, from an Electronic Health Record (EHR) of a local Dutch hospital [65]. The event log has up to 16 activities for 1050 cases and 15,214 events. 'Hospital billing' is another publicly available event log extracted from a regional hospital IS with the billing of healthcare packages of the patients with 18 activities, 100,000 cases, and 451,359 events [65]. Another publicly available (healthcare) event log (i.e., Urineweginfectie (UWI-casus) logbook) [65] was used. However, it gave the lowest utility because of its sensitive nature (presumably because of a biosample that requires exclusive privacy preservation). It, therefore, was not added below in the results of the experiments.
# Path Segmentation-Based Hybrid Caching in Information-Centric Networks
## 4 Performances Evaluations
### Simulation Result
#### 4.3.2 Impact of Cache Size Ratio  
We changed the cache size ratio to see the impact on cache hit ratio and average access latency in three network topologies. In this part of the experiment, skewness (\(\alpha\)) is set to 0.6. The results are shown in Figure 5.  
The results show that PSBC performs better than the other four caching strategies in both cache hit ratio and average access latency. The increased cache size means that more contents can be cached. As shown in Figure 5a-c, the cache hit ratio increases as the cache size ratio increases in the three network topologies. LCE has the lowest cache hit ratio due to cache redundancy. LCD reduces cache redundancy relative to LCE and potentially considers the content's popularity, with the cache hit ratio being second only to PSBC in three topologies. ProbCache makes caching decisions based on data transmission distance and the cache space available on the return path but ignores differences in content popularity. CLAM cannot effectively utilize network cache resources as the cache size is increased. The cache hit ratio of these two caching strategies is lower than PSBC. PSBC takes both network resource utilization and content popularity into account. With the increase of cache size ratio, PSBC has a more obvious advantage in cache hit ratio. In Figure 5d-f, we observe the cache size ratio's impact on the average access latency. To reduce user access latency, nodes near users should cache as much popular content as possible. In PSBC, nodes in the user-edge area mainly consider the popularity of content when making caching decisions. The non-edge area is used as a secondary cache of the user-edge area to improve cache resource utilization. This mechanism gives PSBC an advantage. Nodes near the user's edge can cache more popular content as the cache size increases, which greatly reduces the average access latency.  
Figure 5: Cache performance with different cache size values.
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 2 Frequency Control of Synchronous Generators
### Inertia Response  
If a large disturbance happens, the system frequency starts to decline. This is due to the imbalance between the mechanical input power (\(P_{mch}\)) of synchronous generators and the total load consumption (\(P_{load}\)). This relationship can be described as in Equation (1). The rate of change of system frequency, which can reflect how fast and deep the frequency deviates, is depending on the system inertia [14]. The additional power for counterbalancing the frequency variation is fed from the rotational energy of synchronous fleets for arresting the system frequency decline.  
\[2H_{sys}f_{sys}\frac{df_{sys}}{dt}=P_{mech}-P_{load}, \tag{1}\]  
where \(H_{sys}\) and \(f_{sys}\) are the system inertia constant and system frequency, respectively.  
As mentioned in the previous section, in DFIGs, the rotor speed is decoupled from the system frequency, and therefore the system inertia is reduced. As a result, the rate of change of system frequency becomes critical.
## 6 Conclusions  
In this study, the effects of geosynthetics reinforcement in the bridge abutment and the effects of geosynthetics reinforcement lengths on the deformation behavior of the integral bridge are presented. The main conclusions drawn from the series of numerical analyses using the Plaxis 2D program are as follows:  
* The lateral displacement behavior at wall facing was influenced by the bridge abutment construction method, the use of geosynthetics reinforcement, and the reinforcement length.
* The GRS-IB model showed the least lateral displacements at the wall facing compared with those of the IB model without geosynthetics reinforcement.
* The presence of the reinforcements in the bridge abutment has reduced the vertical displacement increments by 4.7 times and 1.3 times (maximum) after the applied general traffic loads and railway loads, respectively.
* Increasing the length-to-height ratio of reinforcements from 0.3H to 1.1H decreased the maximum lateral displacements at the wall facing by 29% at the end of construction.
* The increase in the length-to-height ratio of reinforcements from 0.3H to 1.1H has no visible effects at the base of the wall.
* The increase in the length-to-height ratio of reinforcements from 0.3H to 1.1H decreases the vertical displacements by 3% (at maximum) and 6% at the back (behind) and at the front of the wall facing, respectively, at the end of construction.
* The effect of the reinforcement length on the wall vertical displacements is minimal compared to the effect on the wall lateral displacements.  
The scope of the study is limited to the numerical analysis of the integral bridge with geosynthetic reinforced soil abutment and full height rigid facing (GRS-IB) in staged construction when one side of the bridge abutment is being modeled and simulated. Even though Plaxis 2D can simulate the actual behavior of the soil in the given condition, a field or laboratory experiment using the model is suggested for future study to validate the numerical analysis results.  
Conceptualization, M.-S.W. and C.P.L.; methodology, M.-S.W. and C.P.L.; software, C.P.L.; validation, M.-S.W. and C.P.L.; formal analysis, M.-S.W. and C.P.L.; investigation, C.P.L.; resources, M.-S.W.; data curation, C.P.L.; writing--original draft preparation, C.P.L.; writing--review and editing, M.-S.W. and C.P.L.; visualization, C.P.L.; supervision, M.-S.W.; project administration, M.-S.W.; funding acquisition, M.-S.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF2021R1A6A1A0304518511).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Experiments and Performance Measurements
### Performance Measurement  
#### 3.2.1 Experiment 1: Packet Loss Test  
Because the data packet issued by the GPRS module contains the sequence number, we can determine if there is packet loss through the statistics of the sequence number for the received data packets. To perform the test, we deployed a total of 10 GPRS modules to transmit the data with a transmission period of 30 s. When the server collects 2000 data packets, it reports the packet loss statistics. A total of 20 experiments were performed, 10 in clear weather and 10 in windy and rainy weather. The statistical results are shown in Figure 7.  
In Figure 7, the horizontal axis marks each of the 10 experiments: every experiment receives 2000 data packets. The vertical axis is the packet loss. The data show that in clear weather, when 20,000 data packets are received, a total of 15 packet loss events occur. The average packet loss rate over 10 experiments is only 0.75%.  
Figure 6: Topology diagram of the test environment.  
In bad weather, when 20,000 data packets are received, a total of 40 packet loss events occur. In this case, the average package loss rate over 10 experiments is 2%. The packet loss rate is relatively low, therefore satisfying the requirement. In Figure 7, the data show that in good weather, the communication is very good; however, when the weather is adverse, communication degrades. We should consider methods to ensure communication quality and reduce the packet loss rate in adverse weather.