esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result Analysis
### Analysis of the Document Collection
#### 4.1.5 Country Analysis  
Moreover, the documents were published by authors from 28 different countries based on the country of the corresponding author or the first author, in case no corresponding author was specified. The distribution of the documents in the three aforementioned categories and the number of published documents of each country are presented in Figure 7. Specifically, Germany and Greece were the countries which published the most documents with each having published nine documents (12.86%). Japan, Hong Kong, and the United States followed with seven published documents each (10.00%). Hence, Germany, Greece, Japan, Hong Kong, and the United States were the countries which published the most documents. When considering the different categories of documents, Germany contributed the most Theoretical and Review studies (n = 5), Greece and Japan contributed the most Proposal and Showcase studies (n = 3), and Hong Kong and the United States contributed the most Experimental and Case studies (n = 7). However, meaningful and impactful contributions have been made from several other countries as well. Based on the results presented in Figure 7, the significance of the topic is highlighted due to the global interest it receives and the fact that countries from different continents, predominantly Europe, Asia, and North America, contribute to developing both the theoretical and practical aspects of the field.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Source** & **Rank** & **Freq.** & **cumFreq.** & **Cluster** \\ \hline “IEEE International Conference on Advanced Learning & 1 & 6 & 6 & 1 \\ Technologies (ICALT)” & 2 & 5 & 11 & 1 \\ “CCUR Workshop Proceedings” & 3 & 4 & 15 & 1 \\ “IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)” & 4 & 3 & 18 & 1 \\ “Lecture Notes in Computer Science (including subseries & & & & \\ Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)” & 5 & 3 & 21 & 1 \\ “British Journal of Educational Technology” & 6 & 2 & 23 & 1 \\ “Communications in Computer and Information Science” & 7 & 2 & 25 & 1 \\ “British Journal of Educational Technology” & 6 & 2 & 23 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Most relevant sources of the document collection according to [PERSON]’s law.  
Figure 7: Distribution of studies per country and category.  
Moreover, among the 70 documents examined, 65 documents (92.86%) were authored by an average of 3.99 authors, while only five documents (7.14%) were single-authored. Despite this fact and the number of countries involved, there was a clear lack of global collaborations. Specifically, among the 70 documents examined, only 14 (20.00%) involved authors of multiple countries, while the vast majority of studies (n = 56, 80.00%) involved only authors from the same country and/or institute. Even among these 14 global collaborations, most involved the same set of authors. Hence, more efforts should be placed in laying the foundations for more global collaborations to occur.
## 2 Preliminaries
### Flowchart of MOFECO-MS  
The flowchart depicting the MOFECO-MS algorithm is presented in Figure 4. Notably, the parameter \(c_{max}\) employed in the initialization phase denotes the maximum number of objective function computations and serves as the termination criterion for the algorithm. From this flowchart, it is evident that the operators used in our proposed algorithm are genetic operators, such as crossover and mutation.  
While MOFECO-MS shares several similarities with traditional genetic algorithms (GAs) in terms of using these genetic operators and following an iterative process, there are key differences that distinguish it. In MOFECO-MS, the population is organized into cycles (or rings) as defined by the Five-element Cycle Model, where each element corresponds to an individual in a GA. The entire set of cycles represents the population, and genetic operators are applied based on the sorting of elements within each cycle, rather than through the selection mechanisms typical in GAs. This cyclic ordering, driven by the Five-element Cycle, governs how elements are updated, ensuring that the process aligns with the model's inherent dynamics. This approach not only maintains a balance between exploration and exploitation but also introduces a novel mechanism that differentiates MOFECO-MS from conventional GAs.
## 2 K-Sim Polaris Simulator Characteristic  
The Maritime University of Szczecin (MUS) has a state-of-the-art navigation bridge simulator, the K-Sim Polaris 7.5, manufactured by the Norwegian company Kongsberg Maritime AS. This system makes it possible to provide comprehensive training for students of the MUS and enhances the skills of navigation officers, captains, and pilots operating on various types of vessels. An advantage of the K-Sim Polaris simulator is the ability to customize the configuration to meet the needs of a wide range of various courses. This applies to both hardware and software. The highest level of the system is documented by the Det Norske Veritas-Germansicher Lloyd (DNV-GL) certificate, which expresses full compliance with the guidelines of the STCW Convention on requirements for simulators used during training on specialized courses. These include sections A-I/12, B-I/12, table A-II/1, A-II/2, and A-II/3. It is possible to create and execute exercises within 16 real coast areas and on the open sea. During these exercises, 22 available own ship models with a variety of characteristics can be used (Table 1). The available software allows the database to be extended with customized models of ships, targets, or coastlines according to individual needs and requirements. The system can also simulate (visually and acoustically) any weather conditions (fog, rain, snow, and storms) and hydrological conditions (sea state, currents, etc.) at different times of the day. The simulator includes an instructor console, a server, and four equally equipped navigation bridges (Figure 1). The instructor station (Figure 2) consists of the following components:  
* One PC;
* Two operational monitors;
* Three monitors with a video preview of the selected navigation bridge;
* A panel for switching the display of the image of the selected navigation bridge;
* A microphone for voice communication;
* Loudspeakers.
## 1 Introduction  
Goji berries, the fruit of the medicinal and edible plant _Lycium barbarum_, are rich in active ingredients [1] such as polysaccharides, flavonoids, alkaloids, and carotenoids. Goji berries offer various health benefits, including reducing cholesterol [2], alleviating atherosclerosis [3], lowering blood sugar [4], and boosting immunity [5]. Currently, the Chinese market offers a wide range of goji-processed products, including goji puree, juice, yogurt, fruit paste, and goji vinegar [6]. Among these, goji juice is particularly favoured by consumers for its unique flavour. The main types of goji juice products include fermented goji beverages [7], clear goji juice [8], and goji compound juice [9]. However, excessive processing often results in significant loss of nutrition and flavour, thereby affecting product quality. To address this issue, developing minimally processed goji juice products that retain both nutritional value and flavour will be a new trend. Not-from-concentrate (NFC) juice is 100% pure fresh juice obtained by directly squeezing fresh fruits. NFC juice retains the original colour, nutrition, and flavour of the fruit better than from-concentrate juice.  
NFC juice represents a new type of processed product with enhanced health, functional, and nutritional qualities, and holds broad market prospects [10]. Current research on NFC juice primarily focuses on the quality and flavour analysis of apple [11], orange [12], and pear juices [13] using various processing methods. However, there are no reports on the processing technology and quality changes of NFC goji juice.  
Fuzzy-mathematics-based comprehensive sensory evaluation is a method that applies fuzzy mathematics theory to product sensory evaluation [14], suitable for assessing subjective and hard-to-quantify characteristics. It has been used to evaluate the sensory characteristics of products such as food, beverages, and cosme [15; 16; 17]. Response surface optimisation is a statistical technique aimed at determining the optimal parameter combination in multivariable systems to maximise or minimise the target function (response value) [18]. While widely used in food research, it is prone to subjective intervention by evaluators. There have been no reports on using fuzzy mathematics sensory evaluation combined with response surface methodology to study the processing technology of NFC goji juice.  
In this study, fresh Xinjiang goji berries were used as experimental materials. The ratio of total soluble solids to tittatable acidity (TSS/TA) and sensory evaluation scores were used as evaluation criteria. The processing parameters of goji juice extraction, homogenisation, ultrasonic treatment, and sterilisation were optimised using fuzzy mathematics sensory evaluation combined with response surface methodology. The multidimensional effects of processing technology on the quality of NFC goji juice products, including physicochemical, microbiological, nutritional, and volatile substances, were evaluated. This study aims to provide technical guidance and a theoretical reference for the diversified development and production of processed goji products.
## 4 A Survey on Cognitive Radio Implementation Research
### GNU/USRP Radio Platform  
GNU/USRP radio is one of the largest scale open source software defined radio platform today [95, 96]. It consists of GNU software package and the USRP hardware platform, Figure 7 shows a typical block diagram for a the GNU/USRP radio. The GNU radio is a free/open-source software toolkit for building software radios, in which software defines the transmitted waveforms and demodulates the received waveforms. The USRP (Universal Software Radio Peripheral) is the associated hardware platform, which is completely open to the public, including the circuit schematic and FPGA source code [95, 96].  
With GNU radio's open architecture, open source code, available functionalities developed by developers from all over the world grows quickly. Because it is based on general purpose processor (GPP) architecture, it is flexible, extensible and portable. Currently, the GNU radio software capabilities support the development of various waveforms including AM and FM analog waveforms, narrowband digital waveforms of GMSK, BPSK, QPSK, and even multi-carrier waveforms [95]. GNU radio is written in both C++ and Python language, and programs can be compiled and run on most GPPs and operating systems including Linux, Mac OSX, and Windows XP. Typically GNU radio is used with a USRP radio front-end.  
The USRP, whose architecture and layout are as described in Figure 8, consists of a small motherboard containing up to four 12-bit 64M sample/s ADCs for receive functions, four 14-bit, 128M sample/s DACs for transmit functions, a million gate Altera Cyclone FPGA and a programmable USB 2.0 controller. Each fully populated USRP motherboard supports four daughterboards, two for receiving and two for transmission. RF front ends are implemented on the daughterboards. Depending on the specific daughterboards added, it can cover a variety of frequency bands. A list of current available daughterboards with specification is shown in Table 1[96].  
Hardware drivers for the USRP are included in the standard build of GNU radio software package by default, most of the USRP settings, such as center frequency, PGA gain, interpolation, decimation, and other transmission and receiving path options on the USRP can be controlled using GNU Radio. The drivers for the USRP in the GNU radio package are provided both at elementary C++ class level and Python API function level [96].  
Figure 7: Architecture of GNU software Radio.  
GNU/USRP radio provides great flexibility to support various independent cognitive radio developments through software. However, it cannot support high computational throughput for real-time processing and controlled physical and network layer integration.
# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 3 Methodology
### Data Embedding and Input Transformation  
The input sequence \(\mathbf{x}\in\mathbb{R}^{B\times L\times F}\), where \(B\) is the batch size, \(L\) is the sequence length, and \(F\) is the feature dimension, is first processed using an embedding layer.  
Let \(\mathbf{x}\) denote the multivariate feature vector representing the operational state of the water management system. Each vector includes continuous measurements and indicators of all physical devices serving as data sources across five stations (tank, refilling1, refilling2, purification, and dam) working in the drinking water process. These features include variables such as the Dam Level, Purification Level, Elevator Level, Consumer Level, and Tank Level, as well as the pump status and flow level of these stations. The full feature vector spans \(\mathbf{n}\) = 16 dimensions, and the data are collected at fixed intervals (e.g., 10 min), forming a multivariate time series used for anomaly detection.  
DataEmbeddingInverted refers to the inverse transformation of input features with the aim of dimensionality transformation for temporal encoding. In our case, it denotes the reconstruction of the original input vector from its compressed representation, which is essential for reconstruction-based anomaly detection. This step typically involves a linear projection layer that maps low-dimensional latent vectors back to the input feature space.  
\[\mathbf{z}=\text{DataEmbeddingInverted}(\mathbf{x}) \tag{1}\]  
where \(\mathbf{z}\in\mathbb{R}^{B\times F\times D}\) represents the transformed embedding. This transformation ensures that the model captures temporal dependencies before further processing in the encoder.
# Communication Protocols of an Industrial Internet of Things Environment: A Comparative Study
## 6 Latency and CPU Usage  
In order to support the results of Section 5, the performance of MQTT and the MODBUS TCP are compared in this section. This performance is based on the Round-Trip Time (RTT) measurements and the CPU usage. A Java-based MQTT publisher was developed and installed on a laptop that runs the operating system Linux Ubuntu 16.04. The specifications of the laptop were: Dell-Inspiron 3537, Intel(r) CoreTM i5-4200U CPU at 1.6 GHz \(\times\) 4, 5.85 GiB RAM. The server (broker) was the Apache ActiveMQ software, which was installed on a desktop PC that runs the operating system Microsoft Windows XP. The desktop PC featured Pentium Dual Core CPU at 3.2 GHz and 2 GB RAM. The consumer was the MQTT.FX software, which was installed on the desktop PC as well. The RTT values measured from the  
Figure 9: The overhead of an MQTT message compared to that of MODBUS.  
publisher and the CPU usage measured from the desktop PC are shown in Figure 10. The publisher that connected to the server (broker) started counting the RTT from the time of creating the socket to closing it, which includes the time needed for formatting the message in a publish message, connecting to the server without a logon process, publishing the message, and disconnecting from the server, in addition to the network latency. The CPU usage values are the maximum registered values, and the tests were conducted for payload sizes of 5, 10, 25, 50, and 100 bytes.  
The RTT measurements of the MODBUS TCP were also conducted using the same network. A MODBUS TCP client was developed and installed on the laptop, and a MODBUS TCP server was developed and installed on the desktop PC. Many tests of function codes were carried out for \"read holding registers\" (FC = 0x03), \"read input registers\" (FC = 0x04), and \"write multiple registers\" (FC = 0x10). The total transmitted bytes in each transaction were 27 bytes, 33 bytes, and 35 bytes respectively; the total was 95 bytes when all messages were transmitted within one transaction. The RTT values, which were measured on the client side, were between 6 ms and 8 ms, and the CPU usage, which was measured on the server side, had a maximum value of 3%.  
These results confirm the theoretical outcomes of Section 5, which show that the MODBUS TCP is a lightweight protocol suitable for SCADA-like systems and industrial applications. The MODBUS TCP client connects to the MODBUS TCP server using polling communications (direct one-to-one communications). However, the MQTT client connects to another client via a server, called a broker. This fact explains the high CPU consumption while the MQTT server was busy in receiving and redirecting the publish messages.
## 1 Introduction  
### Background  
The share of power electronics in transmission and distribution grids has been growing now for decades and will continue to expand effectively also in the future. The reasons for this are the accelerated transition and migration towards renewable energy systems, primarily driven by compliance with climate targets and thus the reduction of GHG emissions. There are a vast number of applications covered by power electronic circuits and at least as many different implementations and strategies for the realization and provision of the required energy transfer from the generator to the corresponding load. In general, the efficiency curve of such a power electronics inverter, active rectifier or converter shows a nonlinear characteristic which is depending on its instantaneous load. It typically peaks in the region of around 40%-60% (partial load operation) of the system's nominal load. Under the nominal load, the efficiency of such an inverter slightly diminishes (approximately 1%-7% lower compared to its absolute maximum), if an inverter or active rectifier reaches no-load or light-load conditions, its performance in terms of efficiency is vastly decreasing as the switching losses of semiconductors dominate the operating mode. Due to, for example, capacitive charging/discharging effects, circulating currents caused by parallelization of several power systems and occurring input/output current ripple with switching frequency that still must be processed. Thus, the efficiency of the system ramps down expeditiously (Figure 1a--red area) if no loss compensating control strategy is employed. Energy efficiency has an important societal impact as it directly affects CO\({}_{2}\) emissions. Due to a higher energy efficiency either less energy is required for a specific load (such as a motor drive) or a higher amount of generated energy can be fed to the grid and less additional power sources are required. Due to the environmental impact of energy efficiency international societies such as the International Energy Agency (IEA) are running several working groups which are elaborating on energy efficiency related topics.
# Proteomics Analysis in Dairy Products: Cheese, a Review
## 3 Graviera Kritis  
Graviera Kritis is a hard PDO cheese (EU L148/1996), produced in Crete, made from drained curds, ewe's milk or mixtures with caprime milk up to 20%, scaled up to 50 \({}^{\circ}\)C and pH 6.2-6.3, pressed, salted in brine and ripened for at least three months. The mean moisture content, fat-in-dry matter, protein, salt-in moisture, and pH of Graviera is about 33-35%, 53-58%, 25-27%, 4%, and 5.5-5.8, respectively [30; 33; 34; 35].  
As a way to keep Graviera authentic, the production process chosen is the traditional one, as described below. After milking, the milk is heated at a range from 33 to 36 \({}^{\circ}\)C and a quantity of rennet able to produce a coagul within 30-40 min is added. Then, it is stirred and cut into small cubes. Afterwards, the curd is heated at 50 \({}^{\circ}\)C and then removed from the heat. After one hour of stirring the coagul, in order to separate the whey from the cheese curds, salt approximately 1% is added and the cheese curd is left to settle and then collected using special cheesecloth. The curd is transferred to a mold and initially pressed for 24 h with low pressure (1-2 times the weight of the cheese), which is gradually increased (to 12 times the weight of the cheese). For the same reason, the cheese is turned upside down three or four times during its drainage in the mold. The cheesecloth is also changed three times. After the cheese has been sufficiently drained, it is put on shelves in the ripening cellar (14-16 \({}^{\circ}\)C) for 14-15 days and every morning is inverted and salted. The cheese ripens for 3-4 months. Enterobacteriaceaeae and coliforms concentration was reduced in the curd after cooking [25].  
In a study conducted by [PERSON] et al. [30] the Feta and Graviera Kritis' solubilized caseins were compared. In the paracasein matrix of Graviera high insoluble calcium, resulting from cheese's pH and low moisture, was found. The \(\alpha\)s1-casein and para-\(\kappa\)-casein contribution in the urea extractants of Feta were significantly higher than those of Graviera. Therefore \(\alpha\)s1-casein interactions with calcium have a key role in Graviera.
###### Abstract  
This paper presents a robustness benchmark evaluation and optimization for vehicle detection. Real-time vehicle detection has become an essential means of data perception in the transportation field, covering various aspects such as intelligent transportation systems, video surveillance, and autonomous driving. However, evaluating and optimizing the robustness of vehicle detection in real traffic scenarios remains challenging. When data distributions change, such as the impact of adverse weather or sensor damages, model reliability cannot be guaranteed. We first conducted a large-scale robustness benchmark evaluation for vehicle detection. Analysis revealed that adverse weather, motion, and occlusion are the most detrimental factors to vehicle detection performance. The impact of color changes and noise, while present, is relatively less pronounced. Moreover, the robustness of vehicle detection is closely linked to its baseline performance and model size. And as the severity of corruption intensifies, the performance of models experiences a sharp drop. When the data distribution of images changes, the features of the vehicles that the model focuses on are weakened, making the activation level of the targets significantly reduced. By evaluation, we provided guidance and direction for optimizing detection robustness. Based on these findings, we propose TDIRM, a traffic-degraded image restoration model based on stable diffusion, designed to efficiently restore degraded images in real traffic scenarios and thereby enhance the robustness of vehicle detection. The model introduces an image semantics encoder (ISE) module to extract features that align with the latent description of the real background while excluding degradation-related information. Additionally, a triple control embedding attention (TCE) module is proposed to fully integrate all condition controls. Through a triple condition control mechanism, TDIRM achieves restoration results with high fidelity and consistency. Experimental results demonstrate that TDIRM improves vehicle detection mAP by 6.92% on real dense fog data, especially for small distant vehicles that were severely obscured by fog. By enabling semantic-structural-content collaborative optimization within the diffusion framework, TDIRM establishes a novel paradigm for traffic scene image restoration.  
Keywords:intelligent transportation system; adverse environments; vehicle detection robustness; robustness optimization