esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurement of Frequency Characteristics
However, for the measured frequency characteristics, sound pressure below 500 Hz was very low; from this frequency to 10 kHz and higher frequencies, the sound pressure increased dramatically and then decreased, reflecting the frequency characteristics of a typical parametric audio system. Regardless of \(l\), various peaks were observed between 0.5 and 10 kHz, and the range of peak values was also larger than that of the BA receiver. A section in which the sound pressure measured at a distance greater than 3 mm from the radiator surface exceeded the sound pressure measured at 3 mm was also observed. Additionally, the average sound pressure in the range of 1 to 10 kHz was about 70 dB SPL. The BA receiver has a maximum sound pressure at 2-3 kHz and slope of \(-\)20 dB/decade, and the gain decreases as it goes to high frequencies. However, the proposed PU receiver has an increase in gain as it goes to high frequencies and exceeds that of the BA receiver at frequencies higher than 3 kHz, as predicted. This finding implies that the receiver can be used by people with impaired sensorineural hearing who require a large high-frequency gain above 2 kHz. It is necessary to add a pre-emphasis filter to enhance the low-frequency gain because the sensitivity is lower than that of a BA-type receiver at 3 kHz or less. To be satisfactory as a practical hearing aid output device, an output sound pressure level of 100 dB at 1 kHz is required. However, the experimental result reached only 60 dB SPL. The main reason for this shortcoming is likely the inability to conduct closed-field experiments, such as those in the external auditory canal, due to limitations in the experimental setup. Additionally, the failure to apply the required high voltage may have contributed to the lower SPL. Nevertheless, the frequency characteristics of the output device are expected to remain unchanged, regardless of the experimental setup.
# Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network
## 3 System Model
### Energy Model  
We assume that the multimedia sensor nodes are battery-powered, and the energy of the sensor nodes cannot be replenished. To model the energy consumption of the sensor nodes, we use the same model as in [42],which is diagrammatically shown in Figure 2. Let \(E(K,d)_{TX}\) be the energy consumed by a sensor node, \(v_{i}\in V\), to transmit K-bit data to another sensor node, \(v_{j}\in V\), that is \(d\) distance away in the WMSN. Every time a sensor node, \(v_{i}\in V\), transmits sensed data to another sensor node, \(v_{j}\in V\), in the WMSN, the sender node consumes a certain amount of energy. This energy depends on whether the transmission is over a single-hop (i.e., direct transmission) or is multi-hop. In the single-hop case, a free-space model (\(d^{2}\) power loss) is used, whereas in the multi-hop case a multi-path fading model (\(d^{4}\) power loss) is used. Therefore, \(E(K,d)_{TX}\) can be defined as shown in Equation (1):  
\[E(K,d)_{TX}=\ \left\{\begin{array}{l}K\cdot E_{\mathit{elec}}+\ K\cdot\varepsilon_ {\mathit{fs}}d^{2},\ d<d_{0}\\ K\cdot E_{\mathit{elec}}+\ K\cdot\varepsilon_{\mathit{mp}}d^{4},d\geq d_{0} \end{array}\right. \tag{1}\]  
where, \(E_{\mathit{elec}}\) is electronics energy, \(\varepsilon_{\mathit{mp}}\) is the multipath energy loss and \(\varepsilon_{\mathit{fs}}\) representsfree space energy loss; \(d_{0}\) is a threshold distance and is computed as \(d_{0}=\sqrt{\frac{\varepsilon_{\mathit{fs}}}{\varepsilon_{\mathit{mp}}}}\). The threshold is used to determine whether the transmission is a multi-hop (i.e., \(d\geq d_{0}\)) or a single-hop (i.e., direct transmission (\(d<d_{0}\))) and assign the appropriate energy consumption based on the transmission distance:  
\[E_{RX}(K)=K\cdot E_{\mathit{elec}} \tag{2}\]  
Similarly, the energy consumed by a sensor node, \(v_{i}\in V\), to receive K-bit data from another sensor node, \(v_{j}\in V\), that is \(d\) distance away, is computed as the product of the size of data and the energy dissipated by the receiver electronics (\(E_{\mathit{elec}}\)) as shown in Equation (2).
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 3 System Model  
This article considers a wireless powered multi-user MEC system, which consists of an multi-antenna AP and a set \(\mathbf{M}\triangleq\{1,...,M\}\) of single-antenna MDs. The AP is directly integrated in the MEC server and has \(\mathbf{N}\triangleq\{1,...,N\}\) time slots. The AP not only receives task data and transmits the data to the edge server, but also powers MDs through WPT. MDs can offload and execute computation tasks, and harvest energy from APs. However, since the MD is a single antenna, the MD cannot offload tasks and harvest energy at the same time. The system model is shown in Figure 1.  
Taking the communication requirements of MDs during task offloading into account, we adopt a slot-based time division multiplexing (TDMA) model. We assume that the length of each time block is T and the channel remains static in one time block but changes between blocks. The MD collects energy at the beginning of each time block and then offloads the computing task to the edge server during the remaining time. The computing task is divided into two parts and executed on the local and edge servers, respectively. As for the local computation part, the execution starts at the beginning and will not stop until use up the whole time block. In order to understand the AP's energy transmission and computing offloading better, we assume that the AP has perfect knowledge of the channels from AP to MDs and the computation requirement for each task, and it can coordinate the task partition, computation offloading and local computing by jointly allocating both communication and computation resources. The parameters are given in Table 1.  
Figure 1: System model.
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 2 Background
### A Primer on GNSS Datums
In another example, data captured on WGS 84 with a handheld receiver on 25 April 2023 is likely to have been observed using the latest WGS 84 revision and should  
Figure 1: Coordinate transformation between GDA2020, GDA94, ATRF, and WGS84. Note that transformations from the ensemble WGS84 will introduce a ca. 5 m uncertainty to the data. be labeled with the coordinate epoch (date of data collection) as WGS 84 (G1762)@2023.315. However, if an augmentation service (RTK, PPP) has been used, it is likely that the data will have been collected in another datum, even though the system software may indicate the use of WGS84. For example, the Australian CORS RTK outputs data in the GDA2020 datum. Transformation of these data to the latest realization of WGS84 will produce WGS 84 (G1762)@2020.000. For emphasis, geolocation data should be reported in terms of the reference datum and any transformation methods used for data records requiring sub-meter resolution. If a dynamic datum is used, the date of data capture (coordinate epoch) should also be recorded. Further, the uncertainty resulting from the transformation of data should be documented for applications requiring <1 m accuracy [31].
## Acknowledgement to Reviewers of _Applied Sciences_ in 2019
Sci._**2020**, _10_, 725  
Kim, Sun-Sook  
Kim, Sunwook  
Kim, Tae Hee  
Kim, Taehyoung  
Kim, Wonha  
Kim, Wonhee  
Kim, Wonjun  
Kim, Yeon Bok  
Kim, Yong Joo  
Kim, Yong-Guk  
Kim, Yoo-jae  
Kim, Yoog Ahn  
Kim, Young Sik  
Kim, Youngbok  
Kim, Young-Duk  
Kim, Young-Joon  
Kim, Young-Kee  
Kim, Yun Ho  
Kimber-Trojnar, Zaneta  
Kinjo, Hidekazu  
Kinzel, Carolina  
Kiourt, Chairi  
Kiran, Rage  
Kirchner, Robert  
Kirchner-Bossi, Nicolas  
Kirenko, Ihor O.  
Kirilov, Leoneed  
Kirilov, Plamen  
Kirkham, Sandra  
Kirsch, Therese  
Kirste, Ronny  
Kis, Zsolt  
Kiselev, Aleksei P.  
Kiselev, Andrey  
Kishimoto, Riwa  
Kishore, Ratna  
Kiss, Balint  
Kiss, Gabor  
Kist, Alexander  
Kita, Yutaku  
Kitahara, Tatsumi  
Kitamura, Akira  
Kitanovski, Vlado  
Kitazumi, Ai  
Kittisopikul, Mark  
Kityk, Iwan  
Klamerus-Iwan, Anna  
Kleijnen, Rob  
Klein, Marcus  
Klein, Susanne  
Kleiss, Jessica  
Kleizene, Rita  
Klemes, JIRI  
Klenk, Markus  
Klepacki, Dariusz  
Klimach, Harald  
Kliros, George S  
Klobucar, Hrvoje  
Kloker, Markus J.  
Klontzas, Michael  
Klose, Andrew  
Klosowski, Grzegorz  
Klotzkin, David  
Kluczka, Joanna  
Klump, Jens  
Klus, Lukas  
Klusman, Ronald W.  
Kluss, Joni  
Kluza, Krzysztof  
Knapp, Ken  
Knaus, Hermann  
Knez, Dariusz  
Knicky, Martin  
Kniesburges, Stefan  
Knight, Kristin  
Knockaert, Jose  
Knoll, Alois  
Knopf, G.K.  
Knowlton, Barbara J.  
Knyazeva, Marina  
Knyziak, Piotr  
Ko, ByoungChul  
Ko, Jae-Hyeon  
Ko, Steve Ko, Woong  
Kobashi, Syoji  
Kobayashi, Jun  
Kobayashi, Masaki  
Kobayashi, Shunsuke  
Kobayashi, Yoshikazu  
Kobayashi, Yutaro  
Kobler, James  
Kobtsev, Sergey  
Kobus-Ciskowska, Joanna  
Koch, Alexander  
Koch, Philipp  
Kocharian, Armen  
Kochetova, Nadezhda Alexandrovna  
Kocsis, Bela  
Koda, Eugeniusz  
Koda, Kazuhisa  
Kodali, Maheedhar  
Koduru, Janardhan  
Koehler, Bernd  
Koehler, Hartmut  
Koehler, Mihkel  
Koenders, E.A.B. Koenig, Andreas  
Koenigsberger, Erich  
Koerner, Joerg  
Koh, II-Seuk  
Koh, Jaehan  
Koh, Seok-Joo  
Koh, Sung-Cheol  
Kohno, Yoshiumi I.  
Koikawa, Masayuki  
Koike, Mari  
Koike, Takahiko  
Koike, Yoshikazu  
Koit, Mare  
Koji, Ichii  
Kokanyan, Ninel  
Kokkinos, Evgenios  
Kolar, Praveen  
Kolaric, Branko  
Kolarik, Andrew J.  
Kolarik, Martin  
Kolasinski, Piotr  
Kolb, Andreas  
Kolbeinsson, Ari  
Kolenda, Zygmunt  
Kolerski, Tomasz  
Kolivand, Hoshamg  
Kollar, Mojmir  
Kollaros, George  
Kolmas, Joanna  
Koloda, Jan  
Kolomvatsos, Kostas  
Koltermann, Jan  
Kolymbas, Dimitrios  
Komano, Yuichi  
Komine, Hideo  
Komives, Tamas  
Komiljenovic, Dragan  
Kominos, Neofytos  
Komodromos, Petros  
Komori, Mochimitsu  
Komorska, Iwona  
Kompil, Mert  
Komuro, Takashi  
Konar, Arkaprabha  
Konda Gokuldoss, Prashanth  
Kondracki, Marcin  
Kondratiuk, Miroslaw  
Konev, Anton  
Kong, Byeong Yong  
Kong, Lingyun  
Kong, Qingzhao  
Kong, Xiangrui  
Kong, Xiangxiong  
Kong, Elif  
Konb, Bardia  
Konidaris, Thomas  
Konieczny, Lukasz  
Koniorczyk, Marcin  
Konkol, Janusz  
Konorski, Jerzy  
Konsgen, Andreas _Appl.
# Daydirex: Translating Nighttime Scenes towards Daytime Driving Experience at Night
## 3 Motivation  
We investigate the limitation of an existing image-to-image translation model in our target scenario in terms of image quality. We use CycleGAN, a representative model for unsupervised image-to-image translation. We trained a model using 2467 daytime and 2376 nighttime images that we collected. We used the PyTorch code downloaded from the authors' website with the same parameters in CycleGAN [1]. Given nighttime images, the trained model outputs translated images at 256 \(\times\) 256 resolution. The experiment was  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Target Input** & \begin{tabular}{c} **Raw** \\ **Format Input** \\ \end{tabular} & **Target Output** & \begin{tabular}{c} **Hint Supplemented** \\ **Input Preprocessing** \\ \end{tabular} \\ \hline \begin{tabular}{c} [PERSON] et al. \\ (SID) [9] \\ \end{tabular} & Low-light image & Yes & Light-enhanced image & No \\ \hline \begin{tabular}{c} [PERSON] et al. \\ \end{tabular} & Low-light image & No & Light-enhanced image & No \\ \hline \begin{tabular}{c} [PERSON] et al. \\ (EnlightenGAN) [10] \\ \end{tabular} & Low-light image & No & Light-enhanced image & No \\ \hline \begin{tabular}{c} [PERSON] et al. \\ (HiDT) [18] \\ \end{tabular} & \begin{tabular}{c} Different times \\ of day image \\ \end{tabular} & No & \begin{tabular}{c} Different times \\ of day image \\ \end{tabular} & No \\ \hline \begin{tabular}{c} [PERSON] et al. \\ (ToDayGAN) [19] \\ \end{tabular} & Nighttime image & No & Daytime image & No \\ \hline \
\begin{tabular}{c} [PERSON] et al. \\ (CycleGAN) [1] \\ \end{tabular} & Not specific & No & Not specific & No \\ \hline
\begin{tabular}{c} Ours \\ (Daydirex) \\ \end{tabular} & Nighttime image & No & Daytime image & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with related works.  
conducted using a computer with Intel i7-7700 CPU, NVIDIA GTX 1080 Ti, and 32 GB RAM, running Windows 10 (see Table 2).  
Figure 2 shows three example nighttime images used as inputs and Figure 3 shows the corresponding outputs of translated images. As shown in the figure, the dark part of the input images mainly results in low quality translation. Such dark areas hardly have any information on geographical features, structures, and/or objects that actually exist in those areas. Thus, the translation model fails to provide high quality of translated images. As seen in the output images (Figure 3), the boundary between the sky and building or trees is not clear. A part of the building and trees are incorrectly translated to the blue sky in Figure 3c.
# Detecting Personally Identifiable Information Through Natural Language Processing: A Step Forward
## 3 Materials and Methods
### Pre-Trained Model Selection
For input data processing, words are represented as a sequence using three types of embeddings: (i) Token Embedding, where the token [CLS] is added at the beginning and [SEP] at the end of the sentence; (ii) Segment Embedding, to help the model differentiate between distinct sentences within a single input, assigning a different parameter to each sentence; and (iii) Position Embedding, to represent the position of each word in the sequence. Once these embeddings are summed, the resulting final embedding is used as input for the BERT model. During pre-training, 15% of the tokens in the input are randomly masked as [MASK]. The model's objective is to predict which word corresponds to the original word replaced by [MASK]. The masked input passes through the Encoder layers, where the self-attention mechanism is applied at each layer to enrich the representation of each token. It then \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Corpus** & **MNLI-(m/mm) 1** & **QQP** & **QNLI** & **SST-2** & **CoLA** & **STS-B** & **MRPC** & **RTE** & **Avg** \\
**Model** & **392k**2  & **363k** & **67k** & **8.5k** & **5.7k** & **3.5k** & **2.5k** & **-** \\ \hline Pre-OpenAI SOTA & 80.6/80.1 & 66.1 & 82.3 & 93.2 & 35.0 & 81.0 & 86.0 & 61.7 & 74.0 \\ BiLSTM + ELMo + Attn & 76.4/76.1 & 64.8 & 79.8 & 90.4 & 36.0 & 73.3 & 84.9 & 56.8 & 71.0 \\ OpenAI GPT & 82.1/81.4 & 70.3 & 87.4 & 91.3 & 45.4 & 80.0 & 82.3 & 56.0 & 75.1 \\ \hline BERTBASE & 84.6/83.4 & 71.2 & 90.5 & 93.5 & 52.1 & 85.8 & 88.9 & 66.4 & 79.6 \\ BERTLARGE & 86.7/85.9 & 72.1 & 92.7 & 94.9 & 60.5 & 86.5 & 89.3 & 70.1 & 82.1 \\ \hline \hline \end{tabular}  
* MNLI-m = MNLI-Matched (the corpus contains sentence pairs where the premise and hypothesis come from the same genre); MNLI-mm = MNLI-Mismatched (the corpus contains sentence pairs where the premise and hypothesis come from different genres). 2 Number of examples.  
\end{table} 
Table 1: GLUE benchmark results, as reported by [PERSON] et al. (2018). proceeds through the classification layers, generating a vector of values (logits), which represents the words that the model believes are most appropriate to replace the [MASK] token. The attention score for each token analyzed is associated with the logits. Finally, a softmax function is applied to convert the logits into probabilities. For the NSP task, a sample set is used containing 50% related sentences and 50% unrelated sentences. The corresponding embedding vectors are input into the Encoders and then passed to the classification layer, where a softmax function generates the probability that one sentence is related to the other.
## 1 Introduction
* What are the practical challenges involved in this watermarking scheme when applying it to real-world digital media applications, and how can these limitation be addressed?The motivation underlying this work is to contribute to the domain of image watermarking and to propose an invisible and fragile method using DTCWT along with PSO algorithms. The main theoretical contribution of this work is the exploitation of shift invariance, redundancy, and directionality selection properties of DTCWT, which when applied in image applications give better results than traditional transform techniques such as DWT, DCT or DFT. These features make the watermark more robust against a number of image processing attacks, while preserving exceptional image quality. In order to improve the method and reduce any distortion of the watermarked images, PSO is used as an optimization algorithm. The proposed application of PSO has been integrated very well in research, and in this work, it is used to determine better coefficient embedding with optimal performance that will lead to good robustness and less visual distortion in the watermarked image. Additionally, DTCWT has a parallel dual-tree structure that enables efficient computation and is perfectly reconstructable, and therefore, it is well suited for real-world applications requiring both quality and security [29]. However, the proposed method faces limitations in computational complexity, applicability to color images, and resistance to advanced attacks.
## 2 Literature Review
### Synthesis of Relevant Challenges in BIM-Enabled Asset Information Delivery  
Many studies have identified the lack of owner-defined information requirements as a critical barrier hindering the integration of BIM for FM [12; 24; 61]. This study therefore investigated a project with owner-defined information requirements to understand what the remaining challenges are and how they manifest in a project context. A review of the related literature provides a point of departure for the analysis of challenges in the case study presented in this paper.  
Based on the current literature, there are still significant challenges preventing owners and facility managers from seamlessly integrating asset information produced as part of a BIM process. These challenges are framed in many different ways across the literature. Table 2 categorizes some of the main challenges:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Challenge/Issue** & **Mentioned by** & **Short Description or Example** \\ \hline Limited interoperability (Lack of integration between BIM and FM systems) & [2; 6; 63; 60; 62; 64; 60; 62; 63] & “There are still many limitations with BIM integration into existing CAFM systems; this integration is necessary as not all FM related information is suitable for hosting in a BIM Environment.” [19] \\ \hline Unclear BIM requirements for FM at early project stages & [6; 62; 63; 64; 60
## 3 Results and Discussion
### The Chromatographic Characterization of BDs  
Table 2 shows the contents of the main bioactive compounds by their chromatographic fingerprints, which were obtainable under routine conditions and at low costs. The differences among the six investigated species can be summarized as follows, in accordance with previous similar studies [2; 15; 16]:  
* FE is very rich in phenolics and cinnamic acids (more than 40% of total phenolics, as shown in Figure 2) and in chlorogenic acid (489.94 \(\pm\) 0.79 mg/100 g FW), followed by ferrulic acid (295.28 \(\pm\) 0.68 mg/100 g FW); it is also rich in catechins, especially epicactin (270.51 \(\pm\) 0.89 mg/100 g FW), and flavonoids, especially hyperoside (242.26 \(\pm\) 0.89 mg/100 g FW), and benzoic acids, especially ellagic acid (214.49 \(\pm\) 0.69 mg/100 g FW), as shown in Figure 2. _Frazinus excelsior_ L. (FE) has been widely used in traditional medicine due to several claimed beneficial health effects including antioxidant, anti-inflammatory, anti-rheumatic and anti-pyretic activities [17]. Several findings from in vivo studies showed that the FE extract has hypoglycemic and anti-hyperlipidemic activities, providing anti-diabetic and anti-obesity effects [18; 19]. * QP is particularly rich in ellagic acid (231.56 \(\pm\) 0.65 mg/100 g FW), catechins (especially epicatechin, 199.92 \(\pm\) 0.46 mg/100 g FW) and quercetin (188.45 \(\pm\) 0.79 mg/100 g FW). The glyceric macrote of _Quercus petraea_ L. (QP) is employed to regularize the action of the intestinal system, to counteract the state of asthenia and to fight against oxidative stress, helping to protect and preserve the well-being of the human organism [20]. * CA is rich in ellagic acid (193.53 + 2.22 mg/100 g FW), quercetin (171.25 + 14.28 mg/100 g FW) and to a lesser extent, in catechins (about 100 mg/100 g FW), while cinnamic acids (less than 2%) are only represented by caffe acid in trace amounts (<3 mg/100 g FW), as shown in Figure 2. _Corylus avelman_ L. (CA), the common hazel plant, has been known since ancient times for its asringent and antiedema properties, vasoprotective activity and mild antimicrobial effect: in fact, it was used in traditional medicine for the treatment of edema, hemorrhoids, varicose veins and phlebitis [21]. Nowadays, its characteristic compounds, cyclic diarylheptanoids, are receiving increasing interest due to
# The Efficiency of Binaural Beats on Anxiety and Depression--A Systematic Review
## 4 Discussions
Patients who were stimulated with a beta band model showed a decrease in theta band power according to the EEG. The papers included in this review mostly focused on anxiety as a secondary disorder caused by another illness or stress. To develop a treatment that can be universally applied, further research into the long-term impacts is required. Concerning the assessment of depression, only one study [29] meeting the inclusion criteria was identified in the databases. The authors of this study concluded that exposure to binaural beats helped to reduce depression levels, as measured using the PHQ-9 scale. Similar to the effects of binaural beats on anxiety studies, the authors used alpha waves. Notably, the effects identified in this study persisted over a longer duration of binaural beat exposure, specifically 4 and 8 weeks, compared to the previously presented studies where the authors only used binaural beats for shorter sessions. This study has the advantage of promoting the use of BB for treating specific conditions like anxiety and depression. It can be a starting point for future studies to explore its potential as an additional therapy in other medical fields. However, this review has some limitations. There is a significant variation in the research, with anxiety being studied in different diseases, each affecting patients' quality of life differently. This means that patients may experience and describe anxiety in various ways. Additionally, there is a low number of studies in this area, and the review only includes studies written in English, potentially excluding significant research published in other languages.