esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services
## 5 Experimental Evaluation
### Results
#### 5.3.1 System Capacity  
The objective of the test is to verify the maximum number of containers that can be supported by the prototype of the 5 GT System.  
Figure 3 shows an example of the messages sent by the CTDs. The messages mainly contain information regarding the containers status such as temperature, humidity and the containers position in the ship, in addition to the sending time and the outcome of the operation (e.g., \"SEND OK\"). In Table 3, we detail the number of messages sent by each CTD in the testbed. To measure the quality of the connection, we also report the values of the Received Signal Strength Indicator (RSSI), which indicates the strength of the received signal or the \"Total Power\". However, RSSI allows only a rough estimate of the quality of the connection, for this reason, we also report the Reference Signal Received Power (RSRP) values, which are similar to RSSI but measure the signal power from the specific sector while excluding the noise and interference from other sectors. Therefore, RSRP is a more suitable indicator to be considered. When RSRP is greater or equal than \(-80\) dBm, the quality of the signal is considered as excellent and all data are transmitted. If RSRP is below \(-100\) dBm, the performance of the device drops sharply up to the disconnection. Results show that a total number of 3100 messages was delivered to the OneM2M platform, which confirms the high capacity of the system. Moreover, it is clear that there is a strict correlation between the quality of the signal and the number of transmitted messages. In fact, when the RSRP is below \(-100\) dBm, the number of delivered messages almost halved.  
\begin{table}
\begin{tabular}{l l l l} \hline
**CTD \#ID** & **RSSI [dBm]** & **RSRP [dBm]** & **\# of Messages** \\ \hline
7898 & \(-68.7\) & \(-74.7\) & \(405\) \\ \hline
6940 & \(-68.7\) & \(-74.7\) & \(405\) \\ \hline
2259 & \(-78.9\) & \(-84.9\) & \(400\) \\ \hline
4805 & \(-78.9\) & \(-84.9\) & \(400\) \\ \hline
5333 & \(-82.1\) & \(-89.4\) & \(398\) \\ \hline
7464 & \(-82.1\) & \(-89.4\) & \(398\) \\ \hline
6599 & \(-99.2\) & \(-105.3\) & \(200\) \\ \hline
3714 & \(-68.7\) & \(-74.7\) & \(404\) \\ \hline  & & & \\ \end{tabular}
\end{table}
Table 3: CTDs delivered messages.
# Amharic _Adhoc_ Information Retrieval System Based on Morphological Features
## 4 Amharic IR System Design and Text Representation  
The main objective of this work is to identify the optimal representations for documents and queries in Amharic IR. Our approach focuses on the selection of the term structures and stopwords, based on the morphological characteristics of the language. Taking these issues into consideration, we also proposed an Amharic retrieval system as shown in Figure 1, which is slightly different from the basic architecture of IR systems. In the architecture we propose, stopwords are removed after the application of morphological analysis on documents and queries. Both documents and queries pass through the same preprocessing and linguistic text processing modules.
## 3 LPBF Sensing Techniques
### Miscellaneous Techniques
[61] showed using EIS that the body fluid resistance (simulated body fluid resistance between the working and the reference electrode) of the LPBF TiNi printed sample is lower than that of the TiNi ingot. Still, the charge transfer resistance (resistance of the electrode reaction) is higher than that of the ingot, showing better corrosion resistance. [PERSON] et al. [147] investigated the effect of various passivation times on the structure and the properties of the passivation film of an LPBF-printed IN718 sample using EIS. The results imply that as the film formation time increases, the passive film becomes more stable, containing fewer internal defects and better corrosion resistance. Despite an expansive list of technologies and methods available for LPBF defect detection, there are a myriad of other sensing technologies used that are not elaborated on in this paper. Other sensing technologies used to detect, quantify and characterize LPBF defects include: surface characterization tools (surface profilometers [208], Vickers hardness testers [209] and Rockwell [210] hardness testers), optical imaging technologies (X-ray ptychography (XRP) [211]), thermal sensing technologies (pulsed infrared thermography (PIT) [212], thermoreflectance thermal imaging (TTI) [213] and thermal desorption spectroscopy (TDS) [214]), acoustic and ultrasonic sensing technologies (scanning acoustic microscopy (SAM) [215], spatially resolved acoustic spectroscopy (SRAS) [216], electromagnetic acoustic transducer (EMAT) [217], and advanced ultrasonic backscatter technique (AUBT) [218]) and other miscellaneous sensing techniques (Mott-Schottky (M-S) [219], Raman spectroscopy (RS) [220], electrical resistance tomography (ER) [221], secondary ion mass spectroscopy (SIMS), [222], atomic absorption spectroscopy (AAS) [223], replication metallography (RM) [224], electronic speckle pattern interferometry (ESPI) [225], magnetic force microscopy (MFM) [226], giant magneto resistive (GMR) sensors [227], alternating current potential drop (ACPD) [228], magnetic Barkhausen noise (MBN) [229], and dye penetrant testing (PT), [230]). Other sensing technologies proven to detect metal welding defects but have yet to be used in LPBF defect detection include X-ray computed laminography (CL), photocurrent spectroscopy (PS), frequency modulated thermography (FMT), micro-laser line thermography (\(\mu\)LLT), micro-laser spot thermography (\(\mu\)LST), Barker code laser infrared thermography (BCLIT), scanning thermal microscopy (SThM), Golay-coded thermal wave imaging (GCTWI), ultrasonic infrared thermography (UIT), magnetic flux leakage testing (MFL), magneto-acousto-electrical tomography (MAET), tunnel magneto resistive (TMR) sensors, lateral shearing interferometer (LSI), digital shearography (DS), alternating current field measurement (ACFM) method, magnetic particles testing (MT), metal magnetic memory testing (MMM), bacterial cells testing (BCT) and quantum dots fluorescent-penetrant testing (Qdots) [231].
## 1 Introduction
Using MODIS NDVI data to estimate wheat yields in Canada and corn and soybean yields in the United States, the results showed that there was a good correlation between estimated yield and statistical yield, which provided support for the establishment of empirical prediction models and dynamic monitoring systems for main crop yields [16; 17; 18]. In the past five years, the Google Earth Engine (GEE), a remote sensing cloud computing platform, has provided the possibility for agricultural remote sensing monitoring using high temporal and spatial resolution remote sensing images [19]. For cultivated land productivity, the distribution and yield of small-scale farmland have been mapped by using high-precision data, such as Sentinel-1 on the GEE platform, which provided the possibility for estimating cultivated land productivity in complex terrain areas and small-scale farmland [20; 21]. Meanwhile, some related studies have begun to focus on how to dynamically monitor cultivated land productivity by using remote sensing data under frequent extreme climates [22; 23]. Over the past 20 years, the Vegetation Photosynthesis Model (VPM) has been validated in studies of farmland ecosystems around the world. This includes winter wheat and summer maize rotation farmland at the Yucheng flux station, Yingke farmland (maize) station and other typical farmland ecosystems in China, and the simulated Gross Primary Production (GPP) data show good agreement with the site observation data [24; 25; 26]. By considering the type of cropland vegetation and multi-cropping, the Net Primary Production (NPP) data generated by VPM can effectively improve the apparent underestimation of C4 crops and the productivity of multi-cropping agricultural areas [27; 28; 29]. However, the current light-use efficiency (LUE) model is mainly driven by Moderate-Resolution Imaging Spectroradiometer (MODIS) data, and its field application is limited by low spatial resolution. With the development of multi-source remote sensing fusion technology, the integration of remote sensing data with high spatial and temporal resolution has become an important method for obtaining detailed farmland data, such as farmland productivity [30; 31]. In this study, the VPM that can accurately simulate farmland productivity in multiple cropping regions and high spatiotemporal resolution based on multi-source remote sensing fusion technology were applied to construct the remote sensing monitoring index of MLPF transformation and HSF construction effectiveness. Henan Province, China's greatest grain-producing province, was selected as a typical area, and the MLPF and HSF project regions were selected for remote sensing monitoring and the evaluation of construction effectiveness to evaluate the improvement of cropland productivity level (CPL), disaster resistance ability (DRA) and homogeneous yield degree (HYD).
# Controller-Pilot Data Link Communication Security
## 4 Threat Model  
We construct the threat model reflecting vulnerabilities of CPDLC technology. A cybersecurity threat model identifies all possible cyberthreats to ATM information systems, enabling the ATC authorities to be proactive and devote resources to addressing vulnerabilities ahead of time.  
CPDLC messages are transferred over the data link, which does not provide any authentication or encryption. As a result, any outsider possessing radio equipment can monitor data link-based communications and decode these messages.
## 3 Experimental Design and Setup
### Stimuli Preparation  
#### Recordings  
The helicopter noise samples originated from field recordings of helicopter landings at Genchen Airport, Switzerland (ICAO code: LSZG) [8]. Single-channel audio recordings were performed by means of a series of microphones (B&K 4188, Brul and Kjaer, Naerum, Denmark) and (Nor 1227, Norsonic AS, Tranby, Norway) placed 4 m above the ground in the eastern side of the airport, on the left and right sides of the extension of the runway [8]. The six recordings selected for this study were from helicopter types A109, A119, AS35, and EC20, all of which are categorized as light-weight civil helicopters (weight: 1.7-3.2 tons). Helicopter recordings were done at the sampling frequency of 44.1 kHz and with 16 bit depth.  
Single-channel recordings of ambient sound were done by means of a microphone (B&K 4006, Brul and Kjaer, Naerum, Denmark) placed 2 m above the ground in and around the city of Dubendorf, Switzerland. Background sound recordings were carried out at the sampling frequency of 48 kHz and with 24 bit depth.
## 3 Results and Discussion
### Phytochemical Profile of Bell Pepper Pulp and Bell Pepper Processing By-Products
#### 3.1.4 Antioxidant Activity
Results are expressed as the mean value of three determinations \(\pm\) standard deviation (SD) indicated by the error bars. According to the \(t\)-test, different letters (a–f) represent significant differences (\(p<0.05\)) between the values represented in the columns. for both fresh and dried samples, the FRAP values were higher for the fractions (pulp and by-product) derived from red bell pepper compared to those from yellow bell pepper. The trend recorded by the FRAP value is in line with that recorded by the DPPH radical scavenging activity and closely aligned with the TPC and TFC content. The results proved that the bioactive compounds present in the pulp and processing by-products of bell peppers show both ferric reducing capacity and free radical scavenging activity. These results, together with those on TPC, TFC, and DPPH radical scavenging activity, showed that a considerable part of bioactive compounds with antioxidant activity is contained in the by-products of bell pepper processing and justifies the recovery of secondary processing products and their reintegration into the food chain. Similar results of antioxidant activity using FRAP assays have been reported by [PERSON] et al. (2017). [PERSON] et al. (2017) have reported slightly lowered values for the red bell pepper (125 ug/g) and the yellow bell pepper (19.87 ug/g).
## 3 Materials and Methods
### Cyclic Voltammetry Experiments  
Cyclic voltammetry experiments were performed using a conventional glass electrochemical cell containing three electrodes and connected to a potentiostat/galvanostat, model PGSTAT 101 (Metrohm, Herisau, AR., Switzerland), which was controlled by computer using the software NOVA 2.1. A disk glassy carbon (0.071 cm\({}^{2}\)) was the working electrode, while two platinum plates, one with 1.0 cm\({}^{2}\) and the other with 0.5 cm\({}^{2}\), were the auxiliary and reference electrodes, respectively. Prior to the electrochemical measurements, the working electrode was manually polished with a 3 \(\upmu\)m diamond paste, followed by washing with ultrapure water. Next, the electrode was washed in ethyl alcohol and in water under ultrasound irradiation. The cyclic voltammograms of both chalcones were obtained in dichloromethane at 50 mV s\({}^{-1}\) and at room temperature (\(\pm\)25 \({}^{\circ}\)C). The working solutions contained 50 mmol L\({}^{-1}\) of tetrabutylammonium perchlorate and 0.5 mmol L\({}^{-1}\) of one of the investigated chalcones. All solutions were previously deaerated by nitrogen bubbling N\({}_{2}\) (g) for 5 min.
# Sand Production Characteristics of Hydrate Reservoirs in the South China Sea
## 4 Numerical Simulation of Sand Production Based on Logging Data
The combined modulus E\({}_{\text{C}}\) value of the hydrate layer (1899.3-1906.7 m) ranges between 1.5 \(\times\) 10\({}^{4}\)\(\sim\)2.05 \(\times\) 10\({}^{4}\) MPa, although belongs to the slight sand and no sand formation, this is mainly caused by the hydrate on the reservoir cementation and curing once the hydrate decomposition, the nature of the sand will be the same as the upper and lower layers; 2 W05A well hydrate reservoir sand index value between: 6.0 \(\times\) 10\({}^{3}\)\(\sim\)2.05 \(\times\) 10\({}^{4}\) MPa, moderate to severe sanding, and the hydrate upper and lower layers of the sand index value. The sand index value of the formation above and below the hydrate drops steeply to 3.8 \(\times\) 10\({}^{3}\)\(\sim\)5.4 \(\times\) 10\({}^{3}\) MPa, which is far lower than 1.4 \(\times\) 10\({}^{4}\) MPa (the interval of 1.4 \(\times\) 10\({}^{4}\)\(\sim\)2.0 \(\times\) 10\({}^{4}\) MPa, is moderate sanding. The value of sand index less than this range indicates serious sanding), indicating the risk of serious sanding; 3 the simulation results of well W10 have similar trends as W05A, but the value of the sanding parameter of the non-aqueous section of the simulated well is obviously low, and the simulation results of well W19 have the lowest, showing the state of serious sanding. The simulation result of the W10 well is similar to that of W05A, but its simulation parameter value of sand release in the non-aqueous material section is obviously low. Whereas the simulation result of the W19 well is the lowest, showing a serious sand release status. In summary, the simulation results show that the stability of hydrate reservoirs in the South China Sea is generally poor, and all of them are serious sand outburst formations. Figure 4: Schlumberger ratio of wells W05A (**a**), W10 (**b**) and W19 (**c**) simulated sand results.
## 3 Experimental Validation  
To validate the simulation results, a prototype of the proposed antenna was fabricated based on the optimized parameters listed in Table 1. Figure 14 displays the fabricated antenna, with dimensions of 40 \(\times\) 40 \(\times\) 1.6 mm\({}^{3}\), confirming its compact design. The reflection coefficient and other performance parameters were measured and evaluated using an Agilent N5247A network analyzer.  
Figure 12: Current distribution at 5 GHz: (**a**) top side; (**b**) bottom side.  
Figure 13: Three-dimensional radiation patterns of maximum gains of (**a**) 5.01 dBi at 2.4 GHz and (**b**) 5.27 dBi at 5 GHz.  
Figure 15 shows the simulated return loss compared with the measurements. It is clearly shown that there is a good agreement between the simulated and measured results.  
This correlation reinforces the accuracy of the simulation model and the effectiveness of the design approach. The simulated frequency bandwidths for \(\left|\text{S}_{11}\right|<-10\) dB were found to be 124 MHz (from 2.396 to 2.52 GHz) and 470 MHz (from 4.91 to 5.38 GHz), while the measured frequency bandwidths for \(\left|\text{S}_{11}\right|<-10\) dB were found to be 210 MHz (from  
Figure 14: Manufacture of the proposed antenna: (**a**) top side; (**b**) bottom side; (**c**) measurement setup.  
Figure 15: Simulated and measured S-parameter.  
2.32 to 2.53 GHz) and 450 MHz (from 4.8 to 5.25 GHz). The measured results validate the enhancements achieved through the design modifications, such as the incorporation of truncated corners and stubs, which contribute to improved bandwidth and gain.  
Figures 16 and 17 illustrate the simulation and measurement of AR and radiation patterns for the proposed antenna. The radiation characteristics of the fabricated antenna were evaluated in an RF anechoic chamber, which provides a controlled environment free from external interference, ensuring accurate measurement results. From Figure 16, it is found that the 3 dB ARBW were 13.80% (2.3 to 2.64 GHz) and 2.6% (5 to 5.08 GHz) in both simulations and measurements. Importantly, the 3 dB ARBW is fully encompassed within the \(-10\) dB reflection bandwidth, confirming the antenna's effective operation across the specified frequency ranges
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Network Function Virtualisation (NFV)
#### 3.2.4 Challenges of NFV  
One of the primary security challenges in virtualised environments, particularly in NFV, stems from vulnerabilities in hypervisors [47]. Hypervisors, responsible for creating and managing VMs within the infrastructure, present considerable security challenges for NFV. Their capability to monitor each VM's operating system introduces potential vulnerabilities related to the confidentiality, integrity, and availability (CIA) triad. Attackers can exploit vulnerabilities in hypervisors to gain unauthorised access to NFV resources, such as memory and storage, and manipulate operational state information [36]. Additionally, factors like multi-tenancy, application sharing, and the use of open-source software increase the risk of security threats, such as authentication issues, information leakage, and data corruption in cloud environments, including NFV [51; 52]. Compromised virtual network functions within the NFV infrastructure can further exacerbate security risks by monitoring the activities of other virtual functions or sending deceptive commands through the hypervisor to disrupt their operations [35]. Moreover, a crash of any virtualised function within the NFV infrastructure (NFVI) can lead to serious security vulnerabilities, potentially granting attackers access to sensitive information stored on that component [45]. Deploying NFV in programmable fixed and mobile internet infrastructure requires a thorough understanding of its capabilities and use cases to ensure effective deployment and management [24; 49].
# CGM: Copy Mechanism GPT with Mask for Ellipsis and Anaphora Resolution in Dialogue
## 4 Experimental Results
### Comparing Models  
We implemented and compared the following models to verify whether the model preserves the given keywords effectively in environments with limited training data. The detailed model structure is shown in the Figure 2.  
1. \(R^{-}P\): a general GPT-2 structure without a copying mechanism, using a prompt that sequentially concatenates the unresolved previous utterances, \(R^{-}\), and the current utterance, \(P\), without any delimiters.
2. \(RP\): a general GPT-2 structure without a copying mechanism, using a prompt that concatenates the resolved previous utterances, \(R\), and the current utterance, \(P\), in sequence without any delimiters.
3. Baseline: a version of \(RP\) that inserts the special character '@@' when there is no previous information and uses the delimiter ';' between each element.
4. PG-GPT: a model that applies the Pointer Generator structure to GPT, using a Mask based on the input tokens to copy the information instead of a context vector based on attention distribution.
5. CGM: a structure within PGM that does not use the Mask when generating \(p_{\text{gen}}\).
6. CGM + HardPrompt A: a modified model within the CGM structure that changes the prompt structure. Prompt A is 'Reference: \(R\) Prompt: \(P\) Generate:'.
7. CGM + HardPrompt B: prompt B excludes the 'Generate:' part from CGM + HardPrompt A.
8. CGM + 0/1 Mask only: a structure within CGM that does not use the last hidden state when generating the Mask.
9. CGM + SepTwice: a model that modifies the prompt structure within CGM, using the delimiter twice before the current utterance, \(P\), in the prompt.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Languages** & **Type** & **Train** & **Dev** & **Test** & **Total** \\ \hline \multirow{2}{*}{Korean} & IT & 2603 & 325 & 327 & 3255 \\  & Finance & 6371 & 354 & 354 & 7079 \\ \hline English & Restaurant reservation & 2210 & 267 & 267 & 2744 \\ \hline \multirow{2}{*}{} & Total & 11,184 & 946 & 948 & 13,079 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistics of experimental dataset.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Languages** & **Type** & **Ellipsis** & **Anaphora** & **Others** & **\# Dialogues** \\ \hline \multirow{2}{*}{Korean} & IT & 1364 & 365 & 1526 & 878 \\  & Finance & 3082 & 1139 & 2424 & 2225 \\ \hline English & Restaurant reservation & 651 & 661 & 1432 & 673 \\ \hline \hline  & Total & 5097 & 2165 & 5382 & 12,644 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of ellipsis and anaphora in experimental dataset.